Fuzzy Ants as a Clustering Concept

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Fuzzy Ants as a Clustering ConceptParag M. KanadeUniversity of South Florida

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Fuzzy Ants as a Clustering Concept

by

Parag M. Kanade

A thesis submitted in partial fulfillmentof the requirements for the degree of

Master of Science in Computer ScienceDepartment of Computer Science and Engineering

College of EngineeringUniversity of South Florida

Major Professor: Lawrence O. Hall, Ph.D.Dmitry B. Goldgof, Ph.D.

Sudeep Sarkar, Ph.D.

Date of Approval:June 17, 2004

Keywords: Cluster Analysis, Swarm Intelligence, Ant Colony Optimization, Fuzzy C MeansAlgorithm, Hard C Means Algorithm

c© Copyright 2004, Parag M. Kanade

DEDICATION

To my parents and my loving sister Pallavi.

ACKNOWLEDGEMENTS

I would like to thank Dr. Lawrence O. Hall for giving me the opportunity to work with him. His

guidance and support was invaluable. I would like to thank Dr. Dmitry Goldgof, and Dr. Sudeep

Sarkar for taking the time and effort to ensure that my work was of quality. I am grateful to my

family, friends and colleagues who gave me inspiration and helped me in every step.

TABLE OF CONTENTS

LIST OF TABLES iii

LIST OF FIGURES vi

ABSTRACT viii

CHAPTER 1 INTRODUCTION 11.1 Clustering 1

1.1.1 Hard Clustering 21.1.2 Fuzzy Clustering 3

1.2 Swarm Intelligence 31.2.1 Self Organization in Social Insects 41.2.2 Stigmergy 51.2.3 Cemetery Organization and Brood Sorting in Ants 5

CHAPTER 2 DATASETS 72.1 Iris Plant Dataset 82.2 Wine Recognition Dataset 82.3 Glass Identification Dataset 132.4 Multiple Sclerosis Dataset 142.5 MRI and British Towns’ Datasets 142.6 Artificial Datasets 14

CHAPTER 3 FUZZY ANTS ALGORITHM 153.1 Picking up an Object 203.2 Dropping an Object 203.3 The Second Stage 213.4 Results 23

3.4.1 Iris Dataset 233.4.2 Wine Recognition Dataset 243.4.3 Glass Identification Dataset 253.4.4 Artificial Datasets 26

3.5 Effect of Parameter Variation 293.6 Variations in the Algorithm 293.7 Discussion 36

CHAPTER 4 AUTOMATICALLY SETTING THE PARAMETERS 384.1 New Metric for Merging Heaps 384.2 Results Using the New Metric 384.3 Fuzzy Hypervolume 40

i

CHAPTER 5 FUZZY ANT CLUSTERING WITH CENTROIDS 445.1 Introduction 445.2 Reformulation of Clustering Criteria for FCM and HCM 445.3 Algorithm 455.4 Results 495.5 Hard C Means 515.6 Execution Time 535.7 Conclusions 55

CHAPTER 6 CONCLUSIONS AND FUTURE WORK 576.1 Combined Algorithm 576.2 Contributions 596.3 Future Work 59

REFERENCES 60

APPENDICES 63Appendix A Datasets 64A.1 Parameters Used to Generate the Gaussian Datasets 64A.2 Plots of the Different Datasets 66

ii

LIST OF TABLES

Table 2.1 Datasets 7

Table 3.1 Values of the Parameters Used in the Fuzzy Ants Experiments 23

Table 3.2 Results from FCM 24

Table 3.3 Results for Iris Dataset 24

Table 3.4 Results for Iris Dataset (2 Class) 25

Table 3.5 Results for Wine Recognition Dataset 25

Table 3.6 Results for Glass Identification Dataset 25

Table 3.7 Results for Glass Identification Dataset (2 Class) 26

Table 3.8 Results for Gauss-1 Dataset 27





Table 3.13 Results for Gauss500-1 Dataset 28





Table 3.18 Effect of Variation of Iterations on Iris Dataset 30

Table 3.19 Effect of Variation of Iterations on Wine Dataset 30

Table 3.20 Results for the Iris Dataset (3D Board) 31

Table 3.21 Results for the Iris Dataset (2 Class) (3D board) 31

Table 3.22 Results for the Wine Dataset (3D Board) 32

Table 3.23 Results for the Glass Dataset (3D board) 32

iii

Table 3.24 Results for the Glass (2 Class) Dataset (3D Board) 33

Table 3.25 Results for the Gauss500-1 Dataset (3D Board) 34





Table 3.30 Typical Example of Variance in Data Partitions Obtained with the Wine Datasetwith Tcreateforheap = 0.09 and 3000 Iterations 37

Table 4.1 Results from the New Metric for Iris Dataset 39

Table 4.2 Results from the New Metric for Wine Dataset 39

Table 4.3 Effect of Variation of Iterations on Iris Dataset 40

Table 4.4 Effect of Variation of Iterations on Wine Dataset 40

Table 4.5 Tcreateforheap and Fuzzy Hypervolume 42

Table 5.1 Parameter Values 49

Table 5.2 Results for Fuzzy C Means 50

Table 5.3 New Parameters 50

Table 5.4 Results for FCM Obtained from Modified Parameters 51

Table 5.5 Results for Hard C Means 52

Table 5.6 Results for the British Towns Dataset 52

Table 5.7 Results for the Wine Dataset 53

Table 5.8 Results for the MRI Dataset 53

Table 5.9 Variation of Rm with the Number of Ants for Slice # 35 of MRI Dataset 53

Table 5.10 Execution Time 54

Table 5.11 Frequency of Different Extrema from FCM, for British Towns and MRI Datasets 55

Table 5.12 Frequency of Different Extrema from HCM, for Glass (2 Class) and Iris Datasets 56

Table 5.13 Frequency of Different Extrema from HCM, MRI Dataset 56

Table 6.1 Results from the Combined Algorithm 58

Table 6.2 Results from the Combined Algorithm (Errors) 58

Table A.1 Parameters Used to Generate the Gauss Datasets 64

iv

Table A.2 Parameters Used to Generate the Gauss-500 Datasets 65

v

LIST OF FIGURES

Figure 1.1 Fuzzy C Means Algorithm 4

Figure 2.1 Iris Dataset (Normalized)- First 2 Principal Components 9

Figure 2.2 Iris Dataset (Normalized)- First 3 Principal Components 10

Figure 2.3 Iris Dataset: 2 Classes (Normalized)- First 2 Principal Components 11

Figure 2.4 Iris Dataset: 2 Classes (Normalized)- First 3 Principal Components 12

Figure 3.1 The Ant Based Algorithm 16

Figure 3.2 Algorithm 18

Figure 3.3 Algorithm (cont...) 19

Figure 3.4 Algorithm to Pick up an Object 20

Figure 3.5 Algorithm for Dropping an Object 21

Figure 3.6 The Two-stage Algorithm 22

Figure 4.1 Tcreateforheap vs Fuzzy Hypervolume 42

Figure 4.2 Tcreateforheap vs Fuzzy Hypervolume (Log) 43

Figure 5.1 Pictorial View of the Algorithm 47

Figure 5.2 Fuzzy Ant Clustering with Centroids Algorithm 48

Figure A.1 Wine Dataset (Normalized)- First 2 Principal Components 66

Figure A.2 Wine Dataset (Normalized)- First 3 Principal Components 66

Figure A.3 Glass Dataset (Normalized)- First 2 Principal Components 67

Figure A.4 Glass Dataset (Normalized)- First 3 Principal Components 67

Figure A.5 Glass Dataset: 2 classes (Normalized)- First 2 Principal Components 68

Figure A.6 Glass Dataset: 2 classes (Normalized)- First 3 Principal Components 68

Figure A.7 Multiple Sclerosis Dataset (Normalized)- First 2 Principal Components 69

Figure A.8 Multiple Sclerosis Dataset (Normalized)- First 3 Principal Components 69

Figure A.9 Gauss-1 Dataset (Normalized) 70

vi





Figure A.14 Gauss500-1 Dataset (Normalized) 72





vii

FUZZY ANTS AS A CLUSTERING CONCEPT

Parag M. Kanade

ABSTRACT

We present two Swarm Intelligence based approaches for data clustering. The first algorithm,

Fuzzy Ants, presented in this thesis clusters data without the initial knowledge of the number of

clusters. It is a two stage algorithm. In the first stage the ants cluster data to initially create raw

clusters which are refined using the Fuzzy C Means algorithm. Initially, the ants move the individual

objects to form heaps. The centroids of these heaps are refined by the Fuzzy C Means algorithm.

In the second stage the objects obtained from the Fuzzy C Means algorithm are hardened according

to the maximum membership criteria to form new heaps. These new heaps are then moved by the

ants. The final clusters formed are refined by using the Fuzzy C Means algorithm. Results from

experiments with 13 datasets show that the partitions produced are competitive with those from

FCM. The second algorithm, Fuzzy ant clustering with centroids, is also a two stage algorithm, it

requires an initial knowledge of the number of clusters in the data. In the first stage of the algorithm

ants move the cluster centers in feature space. The cluster centers found by the ants are evaluated

using a reformulated Fuzzy C Means criterion. In the second stage the best cluster centers found

are used as the initial cluster centers for the Fuzzy C Means algorithm. Results on 18 datasets show

that the partitions found by FCM using the ant initialization are better than those from randomly

initialized FCM. Hard C Means was also used in the second stage and the partitions from the ant

algorithm are better than from randomly initialized Hard C Means. The Fuzzy Ants algorithm is

a novel method to find the number of clusters in the data and also provides good initializations for

the FCM and HCM algorithms. We performed sensitivity analysis on the controlling parameters

and found the Fuzzy Ants algorithm to be very sensitive to the Tcreateforheap parameter. The FCM

and HCM algorithms, with random initializations can get stuck in a bad extrema, the Fuzzy ant

clustering with centroids algorithm successfully avoids these bad extremas.

viii

CHAPTER 1

INTRODUCTION

Modern technology provides us with efficient and low-cost techniques for data collection. Raw

data, however, is of limited use for decision making and intelligent analysis. Machine learning aims

to create automatic or semiautomatic tools for the analysis of raw data to discover useful patterns

and rules. Clustering is one of the most important unsupervised learning techniques.

1.1 Clustering

The aim of cluster analysis is to find groupings or structures within unlabeled data [19]. The

partitions found should in general have the following properties

Homogeneity: The data that are assigned to the same cluster should be similar.

Heterogeneity: The data that are assigned to different clusters should be different.

In most cases the data is in the form of real-valued vectors. The euclidean distance is a suitable

measure of similarity for these datasets. The partitions should then be such that the intra-cluster

distance is minimized and the inter-cluster distance is maximized.

The clustering techniques can be broadly classified as follows [16]:

• Incomplete/Heuristic: Clusters are determined by heuristic methods based on visualization

after dimensionality reduction of the data. The dimensionality is reduced by using geometrical

methods (PCA) or projection techniques.

• Deterministic crisp: Each datum is assigned to one and only one cluster.

• Overlapping crisp: Each datum can be simultaneously assigned to several clusters.

• Probabilistic: A probability distribution specifies the probability of assignment of the datum

to a cluster.

1

• Possibilistic/Fuzzy: These are pure fuzzy clustering techniques. Degrees of membership indi-

cate the extent to which the datum belongs to the cluster. The sum of memberships of each

datum across all the clusters may not be 1 for possibilistic clustering.

• Hierarchical: These techniques are based on either dividing the data into more fine-grained

classes or combining small classes to more coarse-grained classes.

• Objective function based: These techniques are based on an objective or evaluation function

that assigns a quality or error value to each possible partition or group of clusters. The

partition that obtains the best evaluation is the chosen solution.

• Cluster estimation: These techniques use heuristic functions to build partitions and estimate

the cluster parameters.

1.1.1 Hard Clustering

Hard C Means (HCM) is one of the simplest unsupervised clustering algorithms to cluster data

into a fixed number of clusters. The basic idea of the algorithm is to initially guess the centroids

of the clusters and then refine them. Cluster initialization is very crucial because the algorithm is

very sensitive to this initialization. A good choice for the initial cluster centers is to place them as

far away from each other as possible. The nearest neighbor algorithm is then used to assign each

feature vector to a cluster. Using the clusters obtained, new cluster centroids are calculated. The

above steps are repeated until there is no significant change in the centroids.

The objective function minimized by the hard C Means algorithm is given in 1.1

J =c∑

i=1

n∑k=1

Dik(xk, βi) (1.1)

where

• c≥ 2: Number of clusters

• n: Number of data points

• βi: The ith cluster prototype

• Dik(xk, βi): Distance of xk from ith cluster center

2

1.1.2 Fuzzy Clustering

Hard clustering algorithms assign each datum to one and only one cluster. This model is

inappropriate for real datasets in which the boundaries between the clusters may be fuzzy. Fuzzy

algorithms can assign data to multiple clusters. Fuzzy algorithms are based on fuzzy logic which

was developed by Lotfi Zadeh [38]. Deterministic membership functions assign each datum to a

particular cluster, that is, the membership functions map the membership of the datum in a cluster

to either 0 or 1. Fuzzy membership functions, on the other hand, map the memberships to the real

interval [0.0 1.0]. The degree of membership in the cluster depends on the closeness of the datum

to the cluster center. High membership values indicate less distance between the datum and the

cluster center.

The Fuzzy C Means algorithm (FCM), developed by Bezdek [3], allows the datum to be a

partial member of more than one cluster. The FCM algorithm is based on minimizing the objective

function 1.2

Jm(U, β) =c∑

i=1

n∑k=1

Umik Dik(xk, βi) (1.2)

where





• Uik: Membership of the kth object in the ith cluster

• m ≥ 1: The degree of fuzzification

The Fuzzy C Means algorithm is shown in Figure 1.1

The drawback of clustering algorithms like FCM and HCM, which are based on the hill climb-

ing heuristic is, prior knowledge of the number of clusters in the data is required and they have

significant sensitivity to cluster center initialization.

1.2 Swarm Intelligence

Research in using the social insect metaphor for solving problems is still in its infancy. The

systems developed using swarm intelligence principles emphasize distributiveness, direct or indirect

3

1. Initialize the initial cluster centers and calculate the U matrix U (0)

2. At the tth step, calculate the new cluster centers

βi =∑

n

k=1um

ik·xk∑n

k=1um

ik

3. Update the U matrix, U t, U (t−1)

uik = 1∑c

j=1

(Dik(xk,βi)Dij(xk,βj)

) 11−m

4.If |U t − U t−1| < ε then STOP; otherwise goto step 2

Figure 1.1. Fuzzy C Means Algorithm

interactions among relatively simple agents, flexibility and robustness [7]. Successful applications

have been developed in the communication networks, robotics and combinatorial optimization fields.

1.2.1 Self Organization in Social Insects

Self Organization is the mechanism of forming global structures from the interaction of lower-

level components. The rules specifying the interactions among lower-level components are based

on local information, without reference to global information. The global pattern is an emergent

property of the system. Self organization is based on four basic principles [7]:

• Positive feedback: It is the basis of morphogenesis. Examples of positive feedback are recruit-

ment and reinforcement.

• Negative feedback: It counterbalances positive feedback and is responsible for the stabilization

of the emergent pattern. Negative feedback may be in the form of saturation, exhaustion, or

competition.

• Amplification of fluctuations: Amplification of fluctuations is an important factor for self

organization. Randomness is crucial, since it enables discovery of new solutions. Fluctuations

can act as seeds for the growth of new structures.

• Multiple interactions: Self organization is a result of interactions within mutually tolerant

individuals. Individuals make use of their own activities as well as of others’ activities. For

4

examples the trails laid by ants appear to self-organize and be used collectively if individuals

use others’ pheromone.

The key characteristics of a self organized system are [7]:

• Structures: Self organized systems create spatiotemporal structures in an initially homoge-

neous medium. Nest architectures, foraging trails, and social organization are the examples

of spatiotemporal structures.

• Coexistence: Coexistence of several stable states is possible. The emergent system is the

result of amplification of random deviations, depending on the initial conditions the system

converges to one among several possible stable states.

• Bifurcations: Depending on the variation in the parameters, the system may bifurcate. The

behavior of the system changes drastically at bifurcations.

1.2.2 Stigmergy

Self-organization in social insects is the result of direct and indirect interactions. Examples of

direct interactions are visual contact, chemical contact, antennation, trophallaxis, and mandibular

contact [7]. Indirect interactions occur when one individual changes the environment and the others

respond to the new environment at a later time. This subtle interaction is an example of stigmergy.

Stigmergy helps in replacing coordination through direct communications by indirect interac-

tions, reducing the communication among agents. Stigmergy also allows the emergent system to

be flexible. The insects respond to an external perturbation as if it were a modification of the

environment caused by the colony’s activities. That is, the colony collectively responds to the per-

turbation [7]. Artificial agents designed using this principle can respond to a perturbation without

being reprogrammed to deal with that particular perturbation.

1.2.3 Cemetery Organization and Brood Sorting in Ants

Many species of ants cluster dead bodies to form cemeteries, and sort the larvae into several

piles. This behavior can be simulated using a simple model in which the agents move randomly in

space and pick up and deposit items on the basis of local information. The clustering and sorting

behavior of ants can be used as a metaphor for designing new algorithms for data analysis and

graph partitioning. The objects can be considered as items to be sorted. Objects placed next to

5

each other have similar attributes. This sorting takes place in two-dimensional space, offering a

low-dimensional representation of the objects.

The cemetery organization of the ant Lasius niger and Pheidole pallidula has been well studied

[7]. The ants form piles of corpses, cemeteries, to clean up their nests. It is observed that if corpses

are randomly distributed, the workers form cemetery clusters. If the area is not large or if there

are spatial heterogeneities, the clusters are formed along the edges or following the heterogeneities

[7]. This aggregation phenomenon is due to the attraction between the dead items mediated by the

worker ants. Small clusters of items grow by attracting workers to deposit more items, this positive

feedback leads to the formation of larger and larger clusters.

Brood sorting is also widespread in ants. It is observed in the ant Leptothorax unifasciatus

[7]. Worker ants gather larvae according to their size, all larvae of the same size tend to cluster

together. An item is dropped by the ant if it is surrounded by items similar to the item the ant is

carrying, an item is picked up by the ant when it perceives items in the neighborhood which are

dissimilar to the items to be picked up. In Leptothorax unifasciatus the small larvae are located in

the center and larger larvae are located toward the periphery. The amount of space allocated to a

brood item varies with the type of brood. Eggs and micro-larvae are clustered compactly at the

center, with each individual item given little individual space. Individual space tends to increase

toward the periphery. The largest larvae, which are located at the periphery, are allocated more

individual space [7].

6

CHAPTER 2

DATASETS

Six real datasets and ten artificial datasets were tested in this thesis. The datasets tested were

• Iris Plant Dataset

• Wine Recognition Dataset

• Glass Identif ication Dataset

• Multiple Sclerosis Dataset

• MRI Dataset

• British Towns’ Dataset

• Gauss 1-5 Datasets

• Gauss500 1-5 Datasets

The datasets are described in Table 2.1 and the following sections.

Table 2.1. Datasets

Number of Number of Number ofDataset

Examples Continuous Attributes Classes

Iris 150 4 3

Wine 178 13 3

Glass 214 9 6

MRI 65536 3 3

Multiple Sclerosis 98 5 2

British Towns 50 4 5

Gauss1-5 1000 2 5

Gauss500-1-5 500 2 5

7

2.1 Iris Plant Dataset

This is probably the most studied dataset in the field of pattern recognition. The dataset has

information about different types of Iris flowers [6].

The dataset contains 3 classes, Iris Setosa, Iris Versicolour, and Iris Virginica, with 50 instances

of each class. One class is linearly separable from the other two. The remaining classes are not

linearly separable from each other. There are four numeric predictive attributes for each instance,

they are

• Petal Length

• Petal Width

• Sepal Length

• Sepal Width

To visualize the dataset, the Principal Component Analysis (PCA) algorithm [11] is used to

project the data points to a 2D and 3D space. Figures 2.1 and 2.2 show the scatter of the points,

after PCA, in 2D and 3D respectively. One class is linearly separable from the other two. For

clustering purposes, the iris dataset can be considered having only 2 clusters. The dataset with 2

clusters is shown in Figures 2.3 and 2.4.

2.2 Wine Recognition Dataset

The data are the results of a chemical analysis of wines grown in the same region in Italy but

derived from three different cultivars. Quantities of 13 different constituents found in each of the

three types of wines was measured [6]. The constituents are:

• Alcohol

• Malic acid

• Ash

• Alcalinity of ash

• Magnesium

• Total phenols

8

−0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

1st Principal Component

2nd

Prin

cipa

l Com

pone

nt

Iris−setosaIris−versicolorIris−virginica

Figure 2.1. Iris Dataset (Normalized)- First 2 Principal Components

Note: All values are in generic units

9

−1

−0.5

0

0.5

1

−1

−0.5

0

0.5

1−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

1st Principal Component2nd Principal Component

3rd

Prin

cipa

l Com

pone

nt

Iris−setosaIris−versicolorIris−virginica

Figure 2.2. Iris Dataset (Normalized)- First 3 Principal Components


10

−0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

1st Principal Component

2nd

Prin

cipa

l Com

pone

nt

Iris−setosaIris−versicolor & Iris−virginica

Figure 2.3. Iris Dataset: 2 Classes (Normalized)- First 2 Principal Components


11

−1

−0.5

0

0.5

1

−1

−0.5

0

0.5

1−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

1st Principal Component2nd Principal Component

3rd

Prin

cipa

l Com

pone

nt

Iris−setosaIris−versicolor & Iris−virginica

Figure 2.4. Iris Dataset: 2 Classes (Normalized)- First 3 Principal Components


12

• Flavanoids

• Nonflavanoid phenols

• Proanthocyanins

• Color intensity

• Hue

• OD280/OD315 of diluted wines

• Proline

The class distribution is class 1: 59 class 2: 71 and class 3: 48. The projection of the data

points in 2D and 3D space is shown in appendix A, in Figures A.1 and A.2 respectively.

2.3 Glass Identif ication Dataset

The study of types of glass was motivated by its immense potential in criminology. The glass at

the scene of crime, if identified correctly, can be used as evidence. The dataset has 214 examples of

six different types of glass. Each example has 9 attributes, giving the quantity of different chemical

elements present in the glass [6]. The attributes are:

• Refractive Index

• Na: Sodium

• Mg: Magnesium

• Al: Aluminum

• Si: Silicon

• K: Potassium

• Ca: Calcium

• Ba: Barium

• Fe: Iron

13

The projection of the data points in 2D and 3D space is shown in Figures A.3 and A.4 respectively.

The glass dataset can be considered as having two classes window glass and non-window glass. The

projection of the data considering two classes is shown in Figures A.5 and A.6.

2.4 Multiple Sclerosis Dataset

The Multiple Sclerosis dataset has 98 instances each having 5 numeric continuous attributes.

The projection of the data points in 2D and 3D space is shown in Figures A.7 and A.8 respectively.

2.5 MRI and British Towns’ Datasets

The MRI dataset has 65,536 instances, each with 3 numeric continuous attributes, from 3 classes.

The British Towns’ dataset has 50 instances, each with 4 numeric continuous attributes, from 5

classes.

2.6 Artif icial Datasets

Ten artificial datasets were generated using Gaussian distributions. A mixture of five Gaussians

was used to generate the data. The probability distribution across all the datasets is the same but

the means and standard deviations of the Gaussians are different. Of the ten datasets, five datasets

had 500 instances each and the remaining five datasets had 1000 instances each. Each example had

two attributes. The datasets are shown in Figures A.9–A.18. The parameters used to generate the

datasets are shown in the Appendix A.1.

14

CHAPTER 3

FUZZY ANTS ALGORITHM

The Fuzzy Ants algorithm is based on Swarm Intelligence principles applied to cluster data.

Data is clustered without the initial knowledge of the number of clusters. We use ant based

clustering to initially create raw clusters and then these clusters are refined using the Fuzzy C

Means algorithm. Initially the ants move the individual objects to form heaps. The centroids of

these heaps are taken as the initial cluster centers and the Fuzzy C Means algorithm is used to

refine these clusters. In the second stage the objects obtained from the Fuzzy C Means algorithm

are hardened according to the maximum membership criterion to form new heaps. These new heaps

are then sometimes moved and merged by the ants. The final clusters formed are refined by using

the Fuzzy C Means algorithm.

In past research the K means clustering algorithm has been used on the centers obtained from

the ant based algorithm as introduced in [26]; here we study the effect of using the Fuzzy C means

approach on the cluster centers obtained from the ant based algorithm. In [26] the Fuzzy K-means

algorithm was used to refine the clusters found by the ants. In [4] the ant system and the K-means

algorithm were used for document clustering.

The general outline of the ant based algorithm used in this study was proposed in [26]. Initially

the objects are scattered randomly on a discrete 2D board. The board can be considered a matrix

of m × m cells. The matrix is toroidal which allows the ants to travel from one end to another

easily. The size of the board is dependent on the number of objects. We have used a board of

m × m such that m2 = 4n where n is the total number of objects to be clustered. Initially the ants

are randomly scattered throughout the board. There are n3 ants, where n is the total number of

objects to be clustered.

The ants cluster the objects to form heaps. A heap is defined as a collection of 2 or more

objects. A heap is spatially located in a single cell.

15

Consider a heap H with nH objects, then we define the following parameters:

• The maximum distance between two s-dimensional objects in the heap

Dmax(H) = maxXi,XjεHD(Xi, Xj)

Where D is the euclidean distance between the objects.

• The center of mass of all the objects in the heap

Ocenter(H) =1

nH

∑OiεH

Oi

• The most dissimilar object in the heap Odissim(H ) : It is the object which is the farthest from

the center of the heap.

• The mean distance between the objects of H and the center of the mass of the heap

Dmean(H) =1

nH

∑OiεH

D (Oi, Ocenter(H))

The main ant based clustering algorithm is presented in Figure 3.1 [26].

1. Randomly place the ants on the board. Randomly place objects on the board at

most one per cell

2. Repeat

3. For each ant Do

3.1 Move the ant

3.2 If the ant does not carry any object then if there is an object in the 8 neigh-

boring cells of the ant, the ant possibly picks up the object,

3.3 Else the ant possibly drops a carried object, by looking at the 8 neighboring

cells around it.

4. Until stopping criteria.

Figure 3.1. The Ant Based Algorithm

Initially the ants are scattered randomly on the 2D board. The ant moves on the board and

possibly picks up an object or drops an object. The movement of the ant is not completely random.

Initially the ant picks a direction randomly, then the ant continues in the same direction with a

16

probability Pdirection , otherwise it generates a new random direction. On reaching the new location

on the board the ant may possibly pick up an object or drop an object, if it is carrying one. The

heuristics and the exact mechanism for picking up or dropping an object are explained below. The

stopping criterion for the ants, here, is the upper limit on number of times through the repeat loop.

The different steps of the algorithm are shown in Figures 3.2 and 3.3.

17

Figure 3.2. Algorithm

18

Figure 3.3. Algorithm (cont...)

19

3.1 Picking up an Object

When the ant is not carrying any object, it searches for possible objects to pick up by examining

the eight neighboring cells around its current position. If an object or heap is found then the ant

possibly picks up an object. The heuristic for picking up an object depends on the number of

objects in the heap. Three cases are considered: only one object, a heap of two objects and a heap

of more than two objects. If a single object is present then the ant has a fixed probability of picking

it up. If there is a heap of two objects then with a probability Pdestroy the ant destroys the heap

by picking a random object from the heap. In the third case the ant picks up the most dissimilar

object from the heap if the dissimilarity is above a given threshold Tremove . The algorithm for

picking up an object is given in Figure 3.4 [26].

1. Mark the 8 neighboring cells around the ant as ‘unexplored’

2. Repeat

2.1 Consider the next unexplored cell around the ant

2.2 If the cell is not empty then

2.2.1 If the cell contains a single object X, then the object X is picked up with

a probability Pload , else

2.2.2 If the cell contains a heap of two objects, then the heap is destroyed by

picking up a random object with a probability Pdestroy else

2.2.3 If the cell contains a heap H of more than 2 objects, then the most

dissimilar object, Odissim (H ), of H is removed only ifD (Odissim(H), Ocenter(H))

Dmean(H)> Tremove

2.3 Label the cell as ‘explored’

3. Until all the neighboring cells have been explored or one object has been picked

up.

Figure 3.4. Algorithm to Pick up an Object

3.2 Dropping an Object

When the ant is carrying an object, it examines the 8 cells surrounding its current location.

Three cases are considered: the cell is empty, the cell contains one object only, and the cell contains

20

a heap. In the first case the ant has a constant probability of dropping the object. In the second

case a heap is created if the object carried is sufficiently similar to the one already in the cell. In

the third case the ant will add its object to the heap if the object is closer to H ’s center than the

most dissimilar object of H. The algorithm for dropping the object is given in Figure 3.5 [26].

1. Mark the 8 neighboring cells around the ant as ‘unexplored’

2. Repeat

2.1 Consider the next unexplored cell around the ant

2.1.1 If the cell is empty then the object carried by the ant, X, is dropped with

a probability Pdrop else

2.1.2 If the cell contains a single object X’ then a heap of two objects is created

by dropping X on X’ only if D(X,X′)Dmax

< Tcreate else

2.1.3 If the cell contains a heap H then X is dropped on H only if

D(X, Ocenter(H)) < D(Odissim(H), Ocenter(H))

2.2 Label c as ‘explored’

3. Until all the neighboring cells have been explored or the carried object has been

dropped.

Figure 3.5. Algorithm for Dropping an Object

3.3 The Second Stage

The ant based algorithm provides a partition of the data without any knowledge of the initial

cluster centers. In the ant based algorithm if an object is a poor fit to a heap then it can take a long

amount of time for it to be transported to a better heap/cluster. So in the past researchers have

used ant based algorithms which are based on stochastic principles in combination with the K-means

algorithm which is based on deterministic principles. We have used the Fuzzy C means algorithm

as the deterministic algorithm. The fuzzy C means algorithm requires good initializations, which

can be provided by the ant based algorithm.

One problem with the ant based algorithm not fixed by the FCM algorithm is that the number

of classes is always overestimated. Many small homogeneous heaps are formed. We use these heaps

as the building blocks to build large heaps.

21

In the second stage we consider the heaps formed by the first stage and move the entire heap

on the 2D board. The ants carry an entire heap of objects. The algorithm for picking up a heap

is the same as that for the objects. Ants will pick the heap with the same probability Pload . Ants

drop a heap H1 onto another heap H2 provided that:

D (Ocenter(H1), Ocenter(H2))Dmax

< Tcreateforheap

When two heaps H1 and H2 are clustered together, they form only one heap H3 . They cannot

be separated any more. The number of heaps either decrease or remain constant as the number of

iterations increase.

The Fuzzy C Means algorithm is then used to cluster the data using the cluster centers obtained

from the second stage of the ant based algorithm as an initialization. The algorithm used in the

study is given in Figure 3.6.

1. Scatter the objects randomly on the board

2. Initialize the ants with random position, and random direction

3. For Niterations iterations Do

3.1 For each ant Do

3.1.1 Move the ant

3.1.2 If the ant is carrying an object X then possibly drop the object X else

3.1.3 Possibly pick up an object X

4. Use the cluster centers obtained in step 3 to initialize cluster centers for the Fuzzy

C Means algorithm

5. Cluster the data using the Fuzzy C Means algorithm

6. Harden the data obtained from the Fuzzy C means algorithm, using the maximum

membership criterion, to form new heaps

7. Repeat steps 1-6 by considering each heap as a single object

Figure 3.6. The Two-stage Algorithm

The values of the various parameters as used here are shown in Table 3.1.

22

Table 3.1. Values of the Parameters Used in the Experiments

Parameter Value

Niterations 1000

Tcreate 0.5

Pdrop 0.2

Pdestroy 0.3

Pload 0.3

Tremove (Iris) 1.5

Tremove (Wine) 3.0

Tremove (Glass) 2.0

Tremove (Gauss) 1.5

3.4 Results

The reported results are averaged for 50 runs of the experiments. In each run, the ants and

the objects were initially randomly placed at different positions on the board. The movement of

ants and the picking and dropping of the objects also had a stochastic component. We performed

experiments using different parameter values. By varying the parameters we could control the

number of heaps obtained. In the experiments all but one parameter is fixed. The parameter

Tcreateforheap is varied. This parameter is the threshold for the maximum dissimilarity allowed

while merging the heaps. The original parameters are shown in Table 3.1, the results for different

parameter values are shown below. The average results for 50 runs of the FCM algorithm with

random initializations are in Table 3.2. We report errors which may not be the fairest measure

given the algorithm is optimizing a function which indirectly relates to errors.

3.4.1 Iris Dataset

Results for the Iris Data set are shown in Table 3.3. As Tcreateforheap is increased fewer clusters

are found. Every time two clusters are found, which is 3 times with the value of 0.12, 19 times

with the value of 0.14, and 41 times with the value of 0.18, at least 50 errors are observed. This is

because the separable class is almost always correctly split off with the other cluster consisting of

a mixture of the other two clusters.

In the Iris dataset feature space one class is linearly separable from the other two, so strictly

speaking one could come to the determination that there are only two clusters. Hence, the results

23

Table 3.2. Results for FCM Algorithm (Avg. from 50 random initializations)

Dataset Classes Errors

Iris 3 16

Iris 2 3

Wine 3 9

Glass 6 96

Glass 2 20

Gauss 1 5 0

Gauss 2 5 0

Gauss 3 5 0

Gauss 4 5 25.32

Gauss 5 5 7

Gauss 500-1 5 1

Gauss 500-2 5 0

Gauss 500-3 5 1

Gauss 500-4 5 1.94

Gauss 500-5 5 0

for the Iris dataset considering 2 classes are shown in Table 3.4. In this case if we allow up to four

classes to be found, while over clustering is done, we always get homogeneous clusters.

Table 3.3. Results for the Iris Dataset

Classes Errors after Errors afterTcreateforheap

found ant stage FCM

0.18 2.18 49.76 43.88

0.14 2.76 45.92 29.42

0.12 3.74 36.34 19.42

0.10 5.12 25.56 15.16

3.4.2 Wine Recognition Dataset

The results for the Wine Recognition dataset are shown in Table 3.5. We get more errors than

randomly initialized FCM because the ants sometimes find only 2 clusters and the errors increase

dramatically because of this under clustering. Also, when more than 4 clusters are found the errors

shoot up.

24

Table 3.4. Results for the Iris Dataset Considering 2 Classes

Classes Errors after Errors afterTcreateforheap found ant stage FCM

0.20 2.02 3.74 2.94

0.18 2.14 1.16 2.58

0.12 3.74 0 0.18

Table 3.5. Results for the Wine Recognition Dataset


0.085 4.46 13.94 9.36

0.09 3.9 14.3 9.02

0.1 3.76 23.08 10.4

3.4.3 Glass Identif ication Dataset

This is perhaps the most difficult dataset to cluster. The results for the Glass Identification

dataset are shown in Table 3.6. If we allow over clustering then the errors are a little better than

the randomly initialized FCM, but as we close in on the correct number of clusters, sometimes

under clustering occurs and the errors shoot up.

The glass dataset can be considered as a two cluster dataset, window glass and non-window

glass. The results for this modified dataset are shown in Table 3.7. For this dataset both over

clustering and under clustering increase the errors, for this reason the errors for the ant algorithm

are always greater than for randomly initialized FCM.

Table 3.6. Results for the Glass Identification Dataset


found ant stage FCM

0.075 9.36 102.38 88.2

0.085 8.08 103.76 93.34

0.105 5.04 111.52 99.72

0.12 4.54 115.72 102.9

0.16 2.56 136.82 118.94

25

Table 3.7. Results for the Glass Identification Dataset Considering 2 Classes


0.12 4.54 22.52 21.26

0.135 2.62 40.64 25.36

0.15 2.22 49.7 27.3

0.16 2.56 49.84 26.22

0.18 2.16 49.96 28.04

0.20 1.78 50.12 31.58

3.4.4 Artif icial Datasets

Ten artificial datasets, each with 2 attributes, were generated using a Gaussian distribution.

The results for the datasets are shown in Tables 3.8–3.17.

For the Gauss-1, Gauss-2 and Gauss-3 datasets, over clustering and exact clustering gives us

perfect clustering, that is there are no errors. But the errors increase dramatically on under clus-

tering.

The Gauss-4 dataset, with 5 clusters has two extrema one with 0 errors and one with 211 errors.

When over clustering occurs we sometimes get 211 errors, because of this we don’t get perfect

clustering for this dataset.

The Gauss-5 dataset, with 5 clusters has 7 errors. Over clustering results in comparable errors,

but under clustering dramatically increases the errors.

For the Gauss500-1 dataset over clustering and exact clustering results in 1 error, but because

of under clustering the errors shoot up.

For the Gauss500-2 dataset we get perfect clustering if we over cluster or get an exact clustering,

but under clustering increases the errors.

For the Gauss500-3 dataset, over clustering doesn’t dramatically increase the errors, but under

clustering does. Also, we don’t get perfect clustering for this dataset.

For the Gauss500-4 and Gauss500-5 datasets we get perfect clustering if we over cluster or get

an exact clustering, but because of under clustering the errors shoot up.

26

Table 3.8. Results for the Gauss-1 Dataset


0.19 8.26 130.26 0

0.22 7.44 181.14 9.5

0.235 6.56 268.16 2.64

0.25 5.84 348.58 25.34



found ant stage FCM

0.19 9.76 27.08 0

0.225 7.96 136.92 0

0.25 6.96 212.68 5.28

0.28 5.86 369.68 15.84

0.295 4.60 492.08 118.14

0.30 4.48 560.24 136.08



0.25 7.44 191.9 0

0.265 6.56 276.18 13.2

0.28 5.66 380.96 46.98



found ant stage FCM

0.19 6.32 318.3 32.18

0.225 6.1 327.08 40.62

0.25 6.42 328.04 27.96

0.27 6.24 327.42 33.76

0.28 5.86 327.96 37.98

27



found ant stage FCM

0.19 8.1 188.42 8.06

0.205 6.8 252.96 10.34

0.215 6.3 329.4 20.56

0.22 5.94 375.28 58.22

0.25 4.4 552 119.84

Table 3.13. Results for the Gauss500-1 Dataset


0.185 5.7 54.3 13.04

0.19 5.68 63.28 8.56

0.20 5 79.38 25



found ant stage FCM

0.17 6.1 9.76 0

0.19 5.64 37.36 7.6

0.21 5.04 75.68 30.4



found ant stage FCM

0.155 5.96 17.98 3.82

0.17 5.42 50.48 12.92

0.185 5.10 72.22 20.46

0.19 5.02 72.9 21.96

28



0.19 6.36 1.48 0

0.195 6.34 3.62 0

0.22 5.18 62.3 32.14

0.25 4.02 153.18 90.8

0.28 3.94 175.16 97.3



found ant stage FCM

0.19 5.86 11.84 3.04

0.195 5.78 20.96 6.08

0.20 5.96 23.32 4.56

3.5 Effect of Parameter Variation

The above results show the sensitivity of the algorithm to the Tcreateforheap parameter. The

algorithm is not very sensitive to the other parameters. We show the results for the sensitivity of

the algorithm to the number of iterations in Tables 3.18 – 3.19.

For the Iris dataset, the number of clusters found increases for two values of Tcreateforheap and

decreases for one value of Tcreateforheap. The number of clusters differ because the ants get more

time to move and create new heaps or dump objects to existing heaps in the first stage, and merge

heaps in the second stage. For the Wine dataset the number of clusters found decreases for all the

different values of Tcreateforheap .

3.6 Variations in the Algorithm

The merging and creation of new heaps depend on the number of neighbors of the ant. If the

number of neighbors increases then the ant has a higher probability of finding a heap among its

neighbors. If we consider a 3D board, instead of a 2D board, the number of neighbors increases

from 8 to 26, this gives the ant a higher probability of finding a heap.

29

Table 3.18. Effect of Variation of Iterations on Iris Dataset

Classes Errors after Errors afterTcreateforheap Iterations

found ant stage FCM

0.10 1000 5.12 25.56 15.16

0.10 2000 5.14 26.10 14.26

0.10 3000 5.30 26.56 13.68

0.12 1000 3.74 36.34 19.42

0.12 2000 3.64 35.76 17.68

0.12 3000 3.78 36.50 18.50

0.14 1000 2.76 45.92 29.42

0.14 2000 2.82 46.54 33.36

0.14 3000 2.92 45.14 27.76

0.18 1000 2.18 49.76 43.88

0.18 2000 2.12 49.90 46.24

0.18 3000 2.10 49.92 46.92

Table 3.19. Effect of Variation of Iterations on Wine Dataset

Classes Errors after Errors afterTcreateforheap Iterations

found ant stage FCM

0.085 1000 4.46 13.94 9.36

0.085 2000 4.38 13.88 9.52

0.085 3000 4.22 14.88 9.38

0.09 1000 3.9 14.3 9.02

0.09 2000 3.8 15.48 10.32

0.09 3000 3.54 15.84 8.86

0.10 1000 3.76 23.08 10.04

0.10 2000 3.56 25.52 11.52

0.10 3000 3.46 26.76 10.16

We tried two approaches for the 3D board, in the first approach, we considered a 3D board with

z=3, that is the 3D board can be considered as three separate 2D boards. The ants can move from

one 2D board to the other. In the second approach, all the three dimensions are equal. A point to

be noted is that as the z-dimension increases, the size of the corresponding 2D board decreases.

The results are shown in Tables 3.20 – 3.29. The values of all the parameters, except the board

dimension, are the same as the original 2D board. For all the datasets, except Iris (2 class), the

30

number of clusters found by using the 3D board is less than that found by using the 2D board.

This is to be expected, because as the dimension increases, the number of neighbors increase, which

in turn increases the probability of finding a heap. For the Gauss500 datasets, except Gauss500-3

dataset, the 3D board based algorithm always finds perfect clustering, which was not possible with

the 2D board.

For 5 out of the 10 datasets, the 3D board with z=3 found fewer clusters than the 3D board

with equal dimensions. For the remaining 5 datasets, most of the time the the 3D board with z=3

found fewer clusters. For the Gauss500-3 and the Gauss500-5 datasets the 3D board with equal

dimensions found fewer clusters than the 3D board with z=3.

Table 3.20. Results for the Iris Dataset (3D Board)

Board Classes Errors after Errors after

typeTcreateforheap

found ant stage FCM

2D 0.12 3.74 36.34 19.42

3D(z=3) 0.12 3.38 36.38 22.56

3D(x=y=z) 0.12 3.46 36.92 20.4

2D 0.14 2.76 45.92 29.42

3D(z=3) 0.14 2.72 45.78 30

3D(x=y=z) 0.14 2.72 46.92 30.08

3D(z=3) 0.13 2.84 43.14 31.32

3D(x=y=z) 0.13 3.06 43.16 25.28

Table 3.21. Results for the Iris Dataset (2 Class) (3D Board)


typeTcreateforheap found ant stage FCM

2D 0.12 3.74 0 0.18

3D(z=3) 0.12 3.38 0 0.42

3D(x=y=z) 0.12 3.46 0 0.24

2D 0.18 2.14 1.16 2.58

3D(z=3) 0.18 2.06 0.74 2.82

3D(x=y=z) 0.18 2.1 0.7 2.7

2D 0.2 2.02 3.74 2.94

3D(z=3) 0.2 2.02 4.44 3.82

3D(x=y=z) 0.2 2.12 3.78 2.64

31

Table 3.22. Results for the Wine Dataset (3D Board)


typeTcreateforheap

found ant stage FCM

2D 0.085 4.46 13.94 9.36

3D(z=3) 0.085 3.68 14.26 8.78

3D(x=y=z) 0.085 3.8 16.00 8.76

2D 0.09 3.9 14.30 9.02

3D(z=3) 0.09 3.2 15.26 8.86

3D(x=y=z) 0.09 3.24 16.44 8.76

2D 0.1 3.76 23.08 10.04

3D(z=3) 0.1 3.16 25.02 12.58

3D(x=y=z) 0.1 3.12 26.66 10.12

Table 3.23. Results for the Glass Dataset (3D Board)


typeTcreateforheap

found ant stage FCM

2D 0.075 9.36 102.38 88.2

3D(z=3) 0.075 8.7 104.22 90.98

3D(x=y=z) 0.075 8.92 103.82 89.58

2D 0.08 8.08 103.76 93.34

3D(z=3) 0.085 6.42 106.46 95.36

3D(x=y=z) 0.085 6.44 106.44 95.74

2D 0.105 5.04 111.52 99.72

3D(z=3) 0.105 3.9 112.6 107.42

3D(x=y=z) 0.105 3.96 112.56 107.12

32

Table 3.24. Results for the Glass (2 Class) Dataset (3D Board)


typeTcreateforheap

found ant stage FCM

2D 0.12 4.54 22.52 21.26

3D(z=3) 0.13 2.92 33.7 23.36

3D(x=y=z) 0.13 2.84 32.56 23.46

3D(z=3) 0.135 2.48 40.36 23.74

3D(x=y=z) 0.135 2.6 38.36 24.22

3D(z=3) 0.14 2.1 47.3 28.96

3D(x=y=z) 0.14 2.26 46.14 29.22

2D 0.15 2.22 49.7 27.3

3D(z=3) 0.15 1.64 50.5 33.76

3D(x=y=z) 0.15 1.86 50.28 31.7

2D 0.16 2.56 49.84 26.22

3D(z=3) 0.16 1.56 50.86 35

3D(x=y=z) 0.16 1.84 50.42 32.32

2D 0.18 2.16 49.96 28.04

3D(z=3) 0.18 1.24 50.88 44.92

3D(x=y=z) 0.18 1.3 50.62 44.42

33

Table 3.25. Results for the Gauss500-1 Dataset (3D Board)


typeTcreateforheap

found ant stage FCM

3D(z=3) 0.17 4.98 26.76 2.5

3D(x=y=z) 0.17 5 23.66 1

3D(z=3) 0.175 4.94 31.24 5.5

3D(x=y=z) 0.175 4.98 30.72 4

3D(z=3) 0.18 4.8 43.18 16

3D(x=y=z) 0.18 4.9 39.44 8.5

2D 0.185 5.7 54.3 13.04

3D(z=3) 0.185 4.66 51.92 28

3D(x=y=z) 0.185 4.86 45 11.5

2D 0.19 5.68 63.28 8.56

3D(z=3) 0.19 4.62 59.42 29.5

3D(x=y=z) 0.19 4.64 56.52 28

2D 0.2 5 79.38 25

3D(z=3) 0.2 4.26 76.94 56.5

3D(x=y=z) 0.2 4.24 77.84 58



typeTcreateforheap

found ant stage FCM

2D 0.17 6.1 9.76 0

3D(z=3) 0.17 5 9.58 0

3D(x=y=z) 0.17 5 8.18 0

2D 0.19 5.64 37.36 7.6

3D(z=3) 0.19 4.7 37.42 22.8

3D(x=y=z) 0.19 4.76 35.44 19.76

2D 0.21 5.04 75.68 30.4

3D(z=3) 0.21 4.1 74.72 70.36

3D(x=y=z) 0.21 4.18 74.46 63.84

34



typeTcreateforheap

found ant stage FCM

3D(z=3) 0.155 4.92 15.98 7

3D(x=y=z) 0.155 4.92 16.5 7

3D(z=3) 0.16 4.84 23.88 13

3D(x=y=z) 0.16 4.8 27.1 16

3D(z=3) 0.165 4.66 32.62 26.5

3D(x=y=z) 0.165 4.58 40.12 32.5

2D 0.17 5.42 50.48 12.92

3D(z=3) 0.17 4.48 46.08 40

3D(x=y=z) 0.17 4.48 48.9 40

2D 0.185 5.1 72.22 20.46

3D(z=3) 0.185 4.08 73.2 70

3D(x=y=z) 0.185 4.06 73.22 71.5

2D 0.19 5.02 72.9 21.96

3D(z=3) 0.19 4 76.08 76

3D(x=y=z) 0.19 4.06 75.82 71.5



typeTcreateforheap

found ant stage FCM

2D 0.195 6.34 3.62 0

3D(z=3) 0.195 5 4.1 0

3D(x=y=z) 0.195 5 3.66 0

2D 0.22 5.18 62.3 32.14

3D(z=3) 0.22 4.38 66.74 59.72

3D(x=y=z) 0.22 4.62 42.44 36.02

3D(z=3) 0.235 3.8 116.28 110.52

3D(x=y=z) 0.235 4 101.72 94.48

2D 0.25 4.02 153.18 90.8

35



typeTcreateforheap

found ant stage FCM

3D(z=3) 0.18 5.02 2.4 0

3D(x=y=z) 0.18 5 4.32 0

3D(z=3) 0.185 4.98 6.6 3.04

3D(x=y=z) 0.185 4.98 7.3 1.52

2D 0.19 5.86 11.84 3.04

3D(z=3) 0.19 4.96 10.46 4.56

3D(x=y=z) 0.19 4.92 11.88 7.6

2D 0.195 5.78 20.96 6.08

2D 0.2 5.96 23.32 4.56

3D(z=3) 0.2 4.7 29.64 22.8

3D(x=y=z) 0.2 4.78 23.84 16.72

3D(z=3) 0.215 4.1 79.3 70.5

3D(x=y=z) 0.215 4.1 85.08 70.5

3.7 Discussion

The use of ants for the clustering process is one way to determine the number of clusters.

However, the ants are clearly sensitive to the threshold for deciding when to merge heaps. The

original work in this area provided values, but no justification or way to set them. We have

explored a range of values (albeit for one parameter) and shown how the results differ. We have

not yet found a systematic way to set the values of the parameters. The final partition found with

3 classes for the Iris dataset is always equivalent to what we get with FCM.

Essentially, the ants are finding the number of clusters and an initialization for FCM. They

are not really producing a final partition. A difficulty with taking what they produce as a final

partition is that nothing can be removed from a heap when heaps are being combined. This can be

problematic.

For comparison purposes, Table 3.30 shows the number of times a partition of each class size

was found for particular setting of parameters for the wine data set. It can be seen that the second

application of FCM usually, but not always improves the partition.

36

The results for the Iris dataset, when considered as a two cluster dataset, were better than the

randomly initialized FCM algorithm because sometimes over clustering occurs and we get perfect

clustering. Also the ants find a better partition than FCM, because the ants are able to split off

the linearly separable clusters.

Also, we found that the clustering was highly sensitive to Tcreateforheap . Tcreateforheap clearly

has a strong influence on how many final clusters are obtained.

In [4], a simpler approach than the one we discuss is used to cluster documents. The authors

utilize k-means after the ants find the initial cluster centers and find the number of centers. It

was startling to us that, over 10 runs, the ants find on average a non-fractional number of cluster

centers. Utilizing the ant based initialization, the accuracy of the final cluster partition was better

than just using K-means. They indicated that their implementation of k-means was sensitive to

the order of data, which suggests a nonstandard implementation.

Another avenue we have pursued is to allow the ants to relocate clusters centroids in features

space. The formulation is the same at a very high-level as was done in [14], but ants are utilized

rather than a genetic approach.

Table 3.30. Typical Example of Variance in Data Partitions Obtained with the Wine Dataset withTcreateforheap = 0.09 and 3000 Iterations

Clusters Errors after Errors after

FoundFrequency

Ant stage FCM

3 13 8.9230 9.00

4 18 14.6111 8.00

5 15 17.3333 11.00

6 3 24.00 11.00

7 1 33.00 10.00

37

CHAPTER 4

AUTOMATICALLY SETTING THE PARAMETERS

The ant algorithm is very sensitive to the threshold for deciding when to merge heaps and

remove the items from the heap. Results from the previous chapter show the sensitivity of the

algorithm to the Tcreateforheap parameter. The algorithm is less sensitive to the other parameters.

4.1 New Metric for Merging Heaps

We tried different approaches to automatically set the Tcreateforheap parameter.

The original approach for merging heaps is

If the distance between the centroid of the heap carried by the ant and the centroid of the heap on

the board is less than a constant Tcreateforheap then the heaps are merged.

Ants drop a heap H1 onto another heap H2 provided that:

D (Ocenter(H1), Ocenter(H2))Dmax

< Tcreateforheap

The drawback of this metric is that there is no heuristic to set the value of Tcreateforheap, the

value is determined empirically.

The new metric for merging heaps is

If the distance between the centroid of the heap carried and the centroid of the heap on the board is

less than a f ixed percent of the mean distance of all the objects in the heap from the center of the

heap on the board then the heaps are merged.

Ants merge a heap H1 onto another heap H2 provided that:

D (Ocenter(H1), Ocenter(H2)) < percent × Dmean(H2)

The aim is to get a percentage value which is uniform across all the data sets.

4.2 Results Using the New Metric

The new metric was applied to the Iris plant dataset and the Wine recognition dataset. The

results are shown in Tables 4.1 and 4.2. The results are encouraging on individual datasets but

38

the same percentage value doesn’t work across the datasets. For the Iris dataset, 2.40 is the best

percentage value. But for the wine dataset the value of 2.40 is too high, it results in only one cluster.

The value of 1.25 is the best for the wine dataset.

Table 4.1. Results from the New Metric for Iris Dataset

Classes Errors after Errors afterPercent Iterations

found Ant Stage FCM

2.25 1000 4.18 47.16 21.14

2.30 1000 3.72 47.02 22.08

2.35 1000 3.56 47.64 21.68

2.40 1000 3.28 49.22 25.12

2.45 1000 2.88 49.70 29.90

Table 4.2. Results from the New Metric for Wine Dataset


found Ant Stage FCM

2.40 1000 1 107 107

2.00 1000 1 107 107

1.25 1000 3.44 31.18 22.06

1.00 1000 4.62 13.12 9.42

The effect of varying the number of iterations, using the new metric, is shown in Tables 4.3 and

4.4. For both the Iris and Wine datasets as the number of iterations increase, the clusters found

decrease. This is to be expected, because as the number of iterations increase the ants get more

time to search the board and merge the heaps.

39

Table 4.3. Effect of Variation of Iterations on Iris Dataset


found Ant Stage FCM

2.25 1000 4.18 47.16 21.14

2.25 4000 3.48 48.94 21.60

2.30 1000 3.72 47.02 22.08

2.30 4000 3.22 49.16 23.42

2.35 1000 3.56 47.64 21.68

2.35 4000 3.12 49.28 28.20

2.40 1000 3.28 49.22 25.12

2.40 3000 2.90 49.52 29.36

2.40 4000 2.94 49.40 29.40

2.45 1000 2.88 49.70 29.90

2.45 4000 2.60 51.00 33.42

Table 4.4. Effect of Variation of Iterations on Wine Dataset


found Ant Stage FCM

2.45 1000 1.00 107.00 107

2.45 3000 1.00 107.00 107

2.00 1000 1.00 107.00 107

2.00 3000 1.00 107.00 107

1.25 1000 3.44 31.18 22.06

1.25 3000 3.04 37.24 28.28

1.00 1000 4.62 13.12 9.42

1.00 3000 4.32 14.40 9.26

4.3 Fuzzy Hypervolume

Traditional algorithms for determining the number and structure of clusters incorporate some

form of Partition Entropy [9, 13, 25, 32, 21, 22, 24, 33]. The membership matrix U is used to

compute the entropy of the partition. The membership of the object in cluster uij is interpreted

as the probability for the object j to be in cluster i [25]. The number of clusters is varied and the

40

partition entropy is computed. The optimum number of clusters is the one corresponding to the

minimum partition entropy.

In [12] the authors have used the Fuzzy Hypervolume and density criteria to find the number of

clusters. The exponential distance measure, d2e (Xj ,Vi), based on the maximum likelihood estima-

tion is used. The exponential distance is used in calculation of the posterior probability of selecting

the ith cluster given the jth feature vector h(i |Xj ).

h(i|Xj) =1

d2e(Xj ,Vi)∑K

k=11

d2e(Xj ,Vk)

(4.1)

d2e(Xj , Vi) =

[det(Fi)]12

Piexp

[(Xj − Vi)T F−1

i (Xj − Vi)2

](4.2)

where

• Fi: Fuzzy covariance matrix of the ith cluster

• Pi:a prior probability of selecting the ith cluster

The fuzzy covariance matrix of the ith cluster is computed as:

Fi =

∑Nj=1 h(i|Xj)(Xj − Vi)(Xj − Vi)T∑N

j=1 h(i|Xj)(4.3)

The Fuzzy Hypervolume is defined as:

FHV =K∑

i=1

[det(Fi)]12 (4.4)

The fuzzy hypervolume of the datasets, assuming uniform prior probabilities, was computed to

find a basis of setting the Tcreateforheap parameter. The hypothesis is that there is a correlation

between Tcreateforheap and the fuzzy hypervolume. Tcreateforheap could be modeled as a function

of the hypervolume and could be automatically set at run time. The fuzzy hypervolume for the

datasets with the best value found for Tcreateforheap is shown in Table 4.5.

41

Table 4.5. Tcreateforheap and Fuzzy Hypervolume

Dataset Tcreateforheap Fuzzy Hypervolume Fuzzy Hypervolume(log)

Iris 0.12 3.5186E-04 -3.453633681

Wine 0.09 1.5980E-11 -10.79641912

Glass 0.085 5.7413E-10 -9.240991303

Gauss-1 0.235 9.4408E-02 -1.024993149

Gauss-2 0.25 1.0947E-01 -0.960688101

Gauss-3 0.265 1.0682E-01 0.971361128

Gauss-4 0.25 1.0775E-01 -0.967596587

Gauss-5 0.21 7.1804E-02 -1.143849094

Gauss500-1 0.2 7.8007E-02 -1.107865773

Gauss500-2 0.19 9.0652E-02 -1.0426242

Gauss500-3 0.185 8.5193E-02 -1.069593575

Gauss500-4 0.195 8.6286E-02 -1.064060967

Gauss500-5 0.195 7.9956E-02 -1.097149734

The plots of Tcreateforheap vs Fuzzy Hypervolume and Tcreateforheap vs Fuzzy Hypervolume

(log) are shown in Figure 4.1 and 4.2. From the above results we can conclude that the two

parameters are not correlated.

0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26 0.280

0.02

0.04

0.06

0.08

0.1

0.12

Tcreateforheap

Fuz

zy H

yper

volu

me

Figure 4.1. Tcreateforheap vs Fuzzy Hypervolume

42

0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26 0.28−12

−10

−8

−6

−4

−2

0

Tcreateforheap

Fuz

zy H

yper

volu

me

(log)

Figure 4.2. Tcreateforheap vs Fuzzy Hypervolume (Log)

43

CHAPTER 5

FUZZY ANT CLUSTERING WITH CENTROIDS

5.1 Introduction

Previous ant based clustering algorithms cluster data by moving the objects in a 2D space and

merging them to form clusters. Similar objects tend to merge together to form heaps. This merging

is controlled by a threshold which dictates the permissible dissimilarity between the objects in a

cluster. The ant based clustering algorithms are very sensitive to this threshold [19]. The algorithm

presented in Chapter 3 finds the number of cluster centers and good initial cluster centers for the

Fuzzy C Means algorithm. The algorithm is very sensitive to the Tcreateforheap parameter.

In the proposed algorithm the stochastic property of ants was simulated to obtain good cluster

centers. The ants move randomly in the feature space carrying a feature of a cluster center with

them. After a fixed number of iterations the cluster centers are evaluated using the reformulation

of FCM which leaves out the membership matrix [15]. After the ant stage the best cluster centers

obtained are used as the initial cluster centers for the Fuzzy C Means and Hard C Means algorithms.

5.2 Reformulation of Clustering Criteria for FCM and HCM

In [15] the authors have proposed a reformulation of the optimization criteria used in a couple

of common clustering objective functions. The original clustering functions minimize the objective

function 5.1 to find good clusters.

Jm(U, β) =c∑

i=1

n∑k=1

Umik Dik(xk, βi) (5.1)

where





44

• Uik: Membership of the kth object in the ith cluster

• m ≥ 1: The degree of fuzzification

The reformulation replaces the membership matrix U with the necessary conditions which are

satisfied by U. The reformulated version of Jm is denoted as Rm.

For the Hard clustering case the U optimization is over a crisp membership matrix. The

necessary condition for U is given in equation 5.2. Equation 5.3 gives the the necessary conditions

for U, for the fuzzy case. The distance Dik(xk, βi) is denoted as Dik.

Uik = 0 if Dik > min (D1k, D2k, D3k, · · · , Dck)

= 1 otherwise (5.2)

Uik =

(D

11−m

ik

)(∑c

j=1 D1

1−m

jk

) (5.3)

The reformulations for hard and fuzzy optimization functions are given in equations 5.4 and

5.5 respectively. The function R depends only on the cluster prototype and not on the U ma-

trix, whereas J depends on both the cluster prototype and the U matrix. The U matrix for the

reformulated criterion can be easily computed using equation 5.2 or 5.3.

R1(β) =n∑

k=1

min (D1k, D2k, · · · , Dck) (5.4)

Rm(β) =n∑

k=1

(c∑

i=1

D1

1−m

ik

)1−m

(5.5)

5.3 Algorithm

The ants co-ordinate to move cluster centers in feature space to search for optimal cluster centers.

Initially the feature values are normalized between 0 and 1. Each ant is assigned to a particular

feature of a cluster in a partition. The ants never change the feature, cluster or partition assigned

to them. A pictorial view is given in Figure 5.1 where each vertical line is a dimension in parallel

coordinates [17, 2]. After randomly moving the cluster centers for a fixed number of iterations,

45

called an epoch, the quality of the partition is evaluated by using the reformulated criterion 5.4

or 5.5. If the current partition is better than any of the previous partitions’ in the ant’s memory

then the ant remembers this partition else the ant, with a given probability goes back to a better

partition or continues from the current partition. This ensures that the ants do not remember a

bad partition and erase a previously known good partition. Even if the ants change good cluster

centers to unreasonable cluster centers, the ants can go back to the good cluster centers as the ants

have a finite memory in which they keep the currently best known cluster centers. There are two

directions for the random movement of the ant. The positive direction is when the ant is moving in

the feature space from 0 to 1, and the negative direction is when the ant is moving in the feature

space from 1 to 0. If during the random movement the ant reaches the end of the feature space the

ant reverses direction. After a fixed number of epochs the ants stop.

The data is partitioned using the centroids obtained from the best known Rm value. The nearest

neighbor algorithm is used for assignment to a cluster. The cluster centers so obtained are then

used as the initial cluster centers for the Fuzzy C Means or the Hard C Means algorithm. The ant

based algorithm is presented in Figure 5.2.

46

Figure 5.1. Pictorial View of the Algorithm

47

1. Normalize the feature values between 0 and 1. The normalization is linear. The minimum

value of a particular feature is mapped to 0 and the maximum value of the feature is

mapped to 1.

2. Initialize the ants with random initial values and with random direction. There are two

directions, positive and negative. The positive direction means the ant is moving in the

feature space from 0 to 1. The negative direction means the ant is moving in the feature

space from 1 to 0. Clear the initial memory. The ants are initially assigned to a particular

feature within a particular cluster of a particular partition. The ants never change the

feature, cluster or the partition assigned to them.

3. Repeat

3.1 For one epoch /* One epoch is n iterations of random ant movement */

3.1.1 For all ants

3.1.1.1 With a probability Prest the ant rests for this epoch

3.1.1.2 If the ant is not resting then with a probability Pcontinue the ant continues

in the same direction, else it changes direction

3.1.1.3 With a value between Dmin and Dmax the ant moves in the selected direction

3.2 The new Rm value is calculated using the new cluster centers

3.2.1 If the partition is better than any of the old partitions in memory then the worst

partition is removed from the memory and this new partition is copied to the

memories of the ants making up the partition

3.2.2 If the partition is not better than any of the old partitions in memory

Then

With a probability PContinueCurrent the ant continues with the current partition

Else

With a probability 0.6 the ant chooses to go back to the best known partition,

with a probability 0.2 the ant goes back to the second best known partition,

with a probability 0.1 the ant goes to the third best known partition, with a

probability 0.075 the ant goes to the fourth best known partition and with a

probability 0.025 the ant goes to the worst known partition

Until Stopping criteria

The stopping criterion is the number of epochs.

Figure 5.2. Fuzzy Ant Clustering with Centroids Algorithm

48

The values of the parameters used in the algorithm are shown in Table 5.1.

Table 5.1. Parameter Values

Parameter Value

Number of ants 30 Partitions 1

Memory per ant 5

Iterations per epoch 50

Epochs 1000

Prest 0.01

Pcontinue 0.75

PContinueCurrent 0.20

Dmin 0.001

Dmax 0.01

5.4 Results

The algorithm was applied to six real datasets and ten artificial datasets: the Iris Plant dataset,

Glass Identification dataset, Wine Recognition dataset, MRI dataset, Multiple Sclerosis dataset,

the British Towns dataset and the 10 Gaussian datasets.

The results obtained for the datasets are shown in Table 5.2. All results are an average from 50

random initializations. The results for the FCM and HCM are the average results from 50 random

initializations. The glass dataset has been simplified to have just 2 classes window glass and non-

window glass. The results for this modified dataset are also shown in Table 5.2. The age factor

plays an important role in the Multiple Sclerosis dataset; the results considering the age feature

and ignoring the age feature are also shown. Note, the Rm value is always less than or equal to that

from randomly initialized FCM except for Glass (6 classes).Thirteen datasets have a single extrema

for the FCM algorithm. They converge to the same extrema, for all initializations tried here. This

is reflected in Table 5.2 where we have the same values in columns 3 and 4 for the thirteen datasets.

The parameters Number of epochs, Dmin and Dmax play an important role in determining

the quality of the clusters found. By performing manual search, new parameters, which gave

better results, were found. The values of the new parameters are shown in Table 5.3 and the

results obtained by using these modified parameters are shown in Table 5.4. Clear improvements

were observed for 3 datasets. For the British Towns dataset the average value for Rm after FCM11 partition = number of clusters × number of features per cluster

49

Table 5.2. Results for Fuzzy C Means

Min Rm Rm from FCM, Rm from FCM,Dataset

found by Ants Ant Initialization Random Initialization

British Towns 1.6828 1.6033 1.6039

Iris 5.4271 5.2330 5.2330

Wine 33.0834 28.7158 28.7158

Glass (6 classes) 11.3827 7.2937 7.2917

Glass (2 classes) 25.8531 24.3932 24.3932

Multiple Sclerosis (with age) 6.9456 6.8538 6.8538

Multiple Sclerosis (ignoring age) 3.5704 3.5319 3.5319

MRI 311.2397 302.1302 303.289

Gauss-1 6.1588 5.5481 5.5481

Gauss-2 4.6786 4.0646 4.0646

Gauss-3 4.8764 4.2120 4.2120

Gauss-4 2.5536 1.9156 2.7458

Gauss-5 8.6553 8.2035 8.2035

Gauss500-1 4.4866 4.2559 4.2559

Gauss500-2 3.6328 3.3681 3.3681

Gauss500-3 2.3564 2.0560 2.0560

Gauss500-4 1.9937 1.6880 1.7834

Gauss500-5 1.9031 1.5848 1.5848

decreased to 1.5999 from 1.6033, similarly for the Glass (6 classes) dataset the average value for Rm

after FCM decreased to 7.2897 from 7.2937. This average value is better than that obtained from

randomly initialized FCM. The average value for Rm after FCM for the MRI dataset decreased to

301.9198 from 302.1302.

Table 5.3. New Parameters

Parameter Old Value New Value

Epochs 1000 2000

Dmin 0.001 0.0001

Dmax 0.01 0.001

50

Table 5.4. Results for FCM Obtained from Modified Parameters

Min Rm Rm from FCM, Rm from FCM,Dataset


British Towns 1.6051 1.5999 1.6039

Glass (6 classes) 9.2284 7.2897 7.2917

MRI 302.1188 301.9189 303.2894

5.5 Hard C Means

The ant algorithm was applied together with the Hard C Means algorithm. The ants find the

cluster centers and these centers are used as the initial centers for the Hard C Means algorithm.

The parameter values are those shown in Table 5.1.

From Table 5.5 we see that the algorithm gives better results than randomly initialized HCM for

15 of the 18 datasets tested. Changing the parameter values can improve the results. By performing

a search in the parameter space we got parameter values that resulted in better partitions. Tables

5.6 and 5.7 show the variation in the results obtained by changing the number of ants per partition

and epochs for the British Towns’ and Wine datasets. From the tables we see that as the number

of epochs increase, the minimum Rm found by the ants decreases, this is to be expected because

as the number of epochs increase, the ants get more time to refine the centroids found. Also as

the number of ants increase, better Rm values are found. Table 5.8 shows the results obtained for

different MRI slices, the parameter values used for the MRI dataset are tabulated in Table 5.3 and

the ants per partition were 50. We can see the partitions all have lower Rm values.

51

Table 5.5. Results for Hard C Means

Min Rm Rm from HCM, Rm from HCM,Dataset


British Towns 5.5202 3.6260 3.4339

Iris 7.0055 6.9981 8.2516

Wine 52.8098 50.4573 48.9792

Glass (6 classes) 28.1317 24.3770 21.1165

Glass (2 classes) 34.2488 34.1352 36.9132

Multiple Sclerosis (with age) 10.2201 10.2016 10.3548

Multiple Sclerosis (ignoring age) 4.6406 4.6381 4.7759

MRI 433.2752 432.7499 452.384

Gauss-1 7.1391 6.4856 11.0962

Gauss-2 5.1055 4.4725 11.0645

Gauss-3 5.2625 4.6624 12.7167

Gauss-4 2.7273 2.0386 12.4133

Gauss-5 11.6107 10.9422 11.9558

Gauss500-1 5.8333 5.4921 6.0718

Gauss500-2 4.3405 4.0029 7.7512

Gauss500-3 2.6558 2.3140 6.3784

Gauss500-4 2.1678 1.8465 7.3442

Gauss500-5 2.0758 1.7314 7.0953

Table 5.6. Results for the British Towns Dataset

Ants per Min Rm Rm from HCM, Rm from HCM,

partitionEpochs


50 2000 5.3658 3.5812

50 4000 4.5048 3.5701

75 2000 5.1691 3.61343.4339

100 2000 3.0835 3.0661

52

Table 5.7. Results for the Wine Dataset


partitionEpochs


50 2000 52.8003 49.2405

50 4000 51.0631 49.2604

75 2000 50.1879 49.2604

75 3000 49.9230 48.974848.9792

100 2000 49.6415 48.9716

100 4000 49.2076 48.9697

Table 5.8. Results for the MRI Dataset

Slice Min Rm Rm from HCM, Rm from HCM,

# found by Ants Ant Initialization Random Initialization

20 853.7991 851.8342 882.5732

35 919.8082 917.6636 927.6961

45 839.1622 838.0175 851.3756

46 842.5583 841.3414 847.0730

47 796.8415 795.6057 834.1028

5.6 Execution Time

The variation of the minimum Rm found by the ants by changing the ants per partition for MR

slice # 35 is shown in Table 5.9. As the number of ants increases the minimum Rm found decreases,

but at the cost of increased execution time. As the number of ants increase, more search space is

explored and we get better Rm values.

Table 5.9. Variation of Rm with the Number of Ants for Slice # 35 of MRI Dataset


partition found by Ants Ant Initialization Random Initialization

50 919.8082 917.6636

75 912.0365 909.9324 927.6961

100 910.0054 907.9996

53

The execution time for the British Towns and the MRI dataset is shown in Table 5.10. These

two datasets were chosen because British Towns’ dataset is the smallest (in terms of number of

examples) dataset and the MRI dataset is the largest dataset used in the study. The values are

an average from 20000 epochs and 5 experiments for the British Towns’ dataset and from 6000

epochs and 3 experiments for the MRI dataset. One experiment consists of the ant stage and the

following Fuzzy C Means or the Hard C Means stage. The time required for one epoch for the

British towns dataset was more than that for the MRI dataset as there are more classes, and hence

more ants, in the British towns dataset. The time required for the entire experiment was more

for the MRI dataset as there are more examples in the MRI dataset. The time required for the

FCM algorithm is also shown in Table 5.10. From Table 5.10 we see that, for the British Towns’

dataset, approximately 9100 random FCM initializations can be performed in the time required for

one ant experiment. Similarly approximately 225 random FCM initializations can be performed for

the MRI dataset.

We performed 11300 (≈ 225 × 50) random initalizations for the MRI dataset and averaged the

best 50 Rm values. The average Rm value was 301.9189 and the average Rm value found by ants

from 50 random initalizations was 301.9189. Similarly for the British Towns’, dataset the average

of best 50 Rm values over 20000 iterations was 1.5999 and the average Rm value found by ants,

from 50 random initalizations, was 1.5999.

The experiments were performed on an Intel Pentium 4 2.53 GHz processor with 512 KB cache

and 2 GB of main memory.

Table 5.10. Execution Time

Ants Time for Time for Time for

Dataset per one Epoch one Experiment FCM

Partition (milliseconds) (seconds) (seconds)

50 14.9645 97

British Towns 75 22.2210 108.8380 0.01318

100 29.7060 120.896

50 6.7333 540.72MRI

75 10.1117 811.01332.3929

54

5.7 Conclusions

The algorithm is based on relocating the cluster centroids in the feature space. The ants move

the cluster centers, not the objects, in feature space to find a good partition for the data. The

algorithm does not use the object merging criterion, which makes it independent of the threshold

for merging the objects. Also there are less controlling parameters than the previous ant based

clustering algorithms [19].

Results from 18 datasets show the superiority of our algorithm over the randomly initialized FCM

and HCM algorithms. For comparison purposes, Tables 5.11, 5.12 and 5.13 show the frequency of

occurrence of different extrema for the ant initialized FCM and HCM algorithms and the randomly

initialized FCM and HCM algorithms. The ant initialized FCM algorithm always finds the better

extrema for the MRI dataset and for the British Towns’ dataset the ant initialized algorithm finds

the better extrema 49 out of 50 times. The ant initialized HCM algorithm finds the better extrema

for the Iris dataset all the time and for the Glass (2 class) dataset majority of the time. For the

different MRI slices, the ant initialized HCM algorithm finds the better extrema most of the time.

In [14], a Genetic programing approach was used to optimize the clustering criteria, the genetic

approach for Hard C Means, found better extrema 64% of the time for the Iris dataset. The ant

initialized HCM finds better extrema all the time.

The number of ants per partition is an important parameter of the algorithm. The quality of

the partition improves as number of ants increase, but the improvement comes at the expense of

increased execution time. Future work should focus on automatically finding the number of ants

per partition and the number of clusters. In this direction the algorithm proposed in [19] can be

used to find the number of clusters.

Table 5.11. Frequency of Different Extrema from FCM, for British Towns and MRI Datasets

Frequency FCM, Frequency FCMDataset Extrema

Ant Initialization Random Initialization

1.5999 49 16British

1.6037 1 18Towns

1.6081 0 16

301.9195 50 37MRI

307.1898 0 13

55

Table 5.12. Frequency of Different Extrema from HCM, for Glass (2 Class) and Iris Datasets

Frequency HCM, Frequency HCMDataset Extrema


34.1320 19 3

34.1343 11 19Glass

34.1372 19 15(2 class)

34.1658 1 5

6.9981 50 23

7.1386 0 14Iris

10.9083 0 5

12.1437 0 8

Table 5.13. Frequency of Different Extrema from HCM, MRI Dataset

Frequency HCM, Frequency HCMSlice # Extrema


841.3414 50 4420

889.1043 0 6

930.1677 45 30

35 951.4871 5 15

1003.492 0 5

838.0175 50 4145

912.2289 0 9

841.3414 50 4546

889.1043 0 5

795.3043 35 27

47 796.2459 15 13

970.5483 0 10

56

CHAPTER 6

CONCLUSIONS AND FUTURE WORK

6.1 Combined Algorithm

The Fuzzy ants algorithm automatically finds the number of clusters and good initial cluster

centers. The drawback of the algorithm is the large number of controlling parameters and the

absence of good methods to set those parameters.

The Fuzzy ant clustering with centroids algorithms has less controlling parameters and less

sensitivity to the parameters. The drawback of the algorithm is it requires the initial knowledge of

the number of clusters in data. The results are generally better then with FCM/HCM. One would

need a large number of random initialization to be competitive. Also, a parallel version of the ants

algorithm could operate much faster than the current sequential implementation, thereby making

it a clear choice for minimizing the chance of finding a poor extrema.

The Fuzzy ants algorithm can be used to find the number of clusters in the data and the results

of the algorithm can be given to the Fuzzy ant clustering with centroids algorithm.

We implemented the above idea and the results are promising. Tables 6.1 and 6.2 show the

results from the combined algorithm. The analysis can be performed using two metrics: Number of

errors and minimum Rm . For comparison, the results from randomly initialized FCM algorithm,

with known number of clusters, are also given.

In the second ant stage the ants optimize the Rm criteria, this can be observed in Table 6.1 .

The Rm value for the Iris dataset after the 1st stage FCM is 4.5220 and after the final FCM stage

the value is 4.4738. But the minimum Rm value sometimes doesn’t correspond to the minimum

errors, this is observed in the Iris dataset, the errors after FCM 1st stage are 15.92 and those after

the final FCM stage are 18.52.

57

Table 6.1. Results from the Combined Algorithm

Classes Rm after Rm found by Rm from Rm fromDataset

found FCM 1ststage Ants (2nd stage) FCM (final) FCM (random)

Iris 3.64 4.5220 4.7658 4.4738 5.2330

Wine 4.46 20.5878 22.9632 20.5878 28.7158

Glass 5.5 8.4098 9.9975 8.4098 7.2917

British Towns 6.74 1.1452 1.2429 1.1453 1.6039

Multiple Sclerosis 2.76 4.6844 4.8632 4.6844 6.8538

Gauss-1 5.84 6.2395 6.6336 6.1234 5.5481

Gauss-2 5.86 4.4591 5.1016 4.3724 4.0646

Gauss-3 6.34 4.1108 4.7385 4.1313 4.2120

Gauss-4 6.16 2.0323 2.7307 2.0462 2.7458

Gauss-5 6.30 7.1248 7.5398 7.0827 8.2035

Gauss500-1 5.68 4.0874 4.2854 4.0554 4.2559

Gauss500-2 5.64 3.3677 3.7338 3.3395 3.3681

Gauss500-3 5.02 2.6648 2.8838 2.6754 2.05560

Gauss500-4 5.18 2.7631 3.0501 2.7611 1.7834

Gauss500-5 5.78 1.6849 1.9576 1.6513 1.5848

Table 6.2. Results from the Combined Algorithm (Errors)

Classes Errors after Errors after Errors after Errors afterDataset

found Ants 1ststage FCM (1st stage) FCM (final) FCM (Random)

Iris 3.64 35.76 15.92 18.52 16

Wine 4.46 13.94 9.36 9.36 9

Glass 5.5 109.8 98.02 98.02 96

Multiple Sclerosis 2.76 20.86 17.86 17.86 17

Gauss-1 5.84 348.58 25.34 29.08 0

Gauss-2 5.86 369.68 15.84 15.84 0

Gauss-3 6.34 318.44 10.56 18.24 0

Gauss-4 6.16 328.98 7.92 7.92 25.32

Gauss-5 6.30 329.40 19.64 20.12 7

Gauss500-1 5.68 63.28 8.5 16.22 1

Gauss500-2 5.64 37.36 7.6 11.48 0

Gauss500-3 5.02 72.9 19 18.98 1

Gauss500-4 5.18 62.3 22.12 29.88 1.94

Gauss500-5 5.78 20.96 6.08 6.08 0

58

6.2 Contributions

The Fuzzy Ants algorithm is a novel method to find the number of clusters in the data and

also provides good initializations for the FCM and HCM algorithms. We performed sensitivity

analysis on the controlling parameters and found the Fuzzy Ants algorithm to be very sensitive to

the Tcreateforheap parameter.

The Fuzzy ant clustering with centroids algorithms has less controlling parameters and less

sensitivity to the parameters. The drawback of the algorithm is it requires the initial knowledge of

the number of clusters in data. The results are generally better then with FCM/HCM. One would

need a large number of random initialization to be competitive. Also, a parallel version of the ants

algorithm could operate much faster than the current sequential implementation, thereby making it

a clear choice for minimizing the chance of finding a poor extrema. The combined algorithm finds

the number of clusters in data and good partition for the data.

6.3 Future Work

The future work should focus on setting the Tcreateforheap parameter automatically. The algo-

rithm is very sensitive to this parameter. We have tried a couple of approaches to set the parameter

automatically, but the results were not encouraging.

In the Fuzzy ant clustering with centroids algorithm, the number of ants is an important pa-

rameter, and also the initial number of clusters in the data is required. Future work should focus

on setting these values automatically. We compare the results from the algorithms with randomly

initialized FCM and HCM. In future the results can be compared with cleverly initialized FCM

and HCM algorithms. The initial placement of the ants is also random, future work should focus

on giving clever initializations to the ants. The stopping criteria is currently based on the number

of iterations, future work should concentrate on automatically stopping the ants based on their

progress. Trails can be used to guide the movement of the ants, future work should concentrate on

incorporating trails in the algorithm.

59

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62

APPENDICES

63

Appendix A Datasets

A.1 Parameters Used to Generate the Gaussian Datasets

Table A.1. Parameters Used to Generate the Gauss Datasets

Dataset Mean Std. Dev

2 2 1 1

8 8 1 1

Gauss-1 2 16 1 1

19 3 1 1

14 14 1 1

2 2 1 1

10 10 1 1

Gauss-2 3 20 1 1

22 5 2 2

18 18 1 1

2 4 1 1

11 11 1 1

Gauss-3 5 20 1 1

22 5 2 2

20 20 1 1

3 3 1 1

11 11 1 1

Gauss-4 10 30 1 1

32 5 1 1

20 33 1 1

2 2 1 1

6 6 1 1

Gauss-5 2 10 1 1

12 4 2 2

11 11 1 1

64

Parag Kanade/fuzzy ants/Gauss2/gauss02.data

Gauss Data

#

-0.46551197 1.3396181

-0.14154019 1.6467355

-0.06147374 1.910021

-0.00960664 1.6959237

0.13522751 2.3371545

0.15816343 1.74101

0.16902868 1.7145902

0.20321506 1.3230994

0.24094079 1.3069846

0.25245225 2.1839526

0.26993324 2.5483323

0.28680391 1.6052432

0.3192367 2.2276855

0.36114833 2.8009784

0.4244067 1.8634449

0.46037797 1.4532789

0.46098959 2.3555875

0.48936047 1.8126198

0.493417 0.52432961

0.5579586 2.0069093

0.59517099 0.74266104

0.601286 2.447746

0.64249205 0.22137058

0.64603926 2.098612

0.68936857 2.3436606

0.70125847 1.9881431

0.70954162 1.8340884

0.71271143 1.1911658

0.73828501 2.9157625

0.76121259 2.887774

0.76300537 3.36236

0.78715506 1.7998882

0.79603202 1.3837631

0.81552587 0.7659359

0.81671088 2.0235945

0.83646559 1.7292928

0.84905869 0.90959966

0.87280583 1.9185033

0.93911004 2.2377149

0.94658989 1.8099329

0.95071896 3.3593771

0.95477185 0.82422933

1.0475351 3.7358953

1.0483555 2.6222619

1.0663274 2.0772458

1.0719789 2.3051114

1.0841659 2.8982847

1.0917331 1.4965835

1.1268271 1.7541776

1.1308673 2.1353612

1.1428466 2.0972579

1.1493513 1.8227118

1.1819349 1.8738619

1.1900377 1.5835831

1.2101874 1.8796963

1.2181118 3.0111068

1.2277972 2.0808183

1.2335498 2.9573163

1.2448562 1.6017201

1.2555436 1.9472067

1.2597352 0.95874592

1.2601195 0.81798378

1.2715242 0.46185734

1.2749423 3.2592336

1.2888759 2.4162943

1.319464 2.1180977

1.3266557 1.9249356

1.3269461 2.8783508

1.3458998 1.6047048

1.3471548 2.0660325

1.3753515 2.4089999

1.3793406 2.1089793

1.3872928 3.3455047

1.3979966 2.3829163

1.4001717 3.0482038

1.4054732 1.6215221

1.4312392 0.79191521

1.4354818 3.0271297

1.4410139 2.7986203

1.4641202 0.58155858

1.4646153 3.9791516

1.5055329 1.0990125

1.5286615 2.6943944

1.5534019 3.741445

1.5735908 1.5571016

1.5856639 0.99127551

1.6083411 2.3154722

1.6167515 3.4641362

1.6315791 2.9447703

1.6485825 1.9459715

1.6558902 1.1154736

1.6603318 2.4801616

1.6657018 1.9762646

1.6740867 2.9751515

1.7109312 1.5716093

1.7172934 1.9542265

1.7245997 2.387059

1.7280016 3.2168608

1.7407356 1.6770587

1.7446611 3.8440441

1.7479167 1.3913499

1.7507312 2.769599

1.7567404 1.4447784

1.7591262 3.9952283

1.7610234 3.5877581

1.7736913 1.4399875

1.7826518 3.1827802

1.7882331 2.7558682

1.8048077 1.4894358

1.806578 2.2980184

1.8105734 3.0728863

1.8132452 0.11900227

1.8581162 2.3816176

1.8735926 2.1120087

1.8771577 1.9606585

1.8791867 0.81713304

1.8866894 1.5761363

1.8881009 0.12698201

1.8980516 2.4640572

1.911659 0.92031474

1.9163593 2.2659011

1.9217044 1.9429349

1.9240625 1.2683148

1.933498 0.99443935

1.9366692 3.0426166

1.9572064 3.2689621

1.987931 4.1287767

1.9907116 1.6489055

1.9930155 2.6846483

2.0011578 2.3236036

2.0107086 2.1802041

2.0650972 0.62687304

2.0760993 1.8564104

2.076595 3.141211

2.0797088 2.6444816

2.0828643 2.4026682

2.0948837 1.8648059

2.0985622 3.7026465

2.0994979 4.0382142

2.1023642 3.2814187

2.1102441 3.3805769

2.1522589 1.6625872

2.1613626 2.6416719

2.1760954 0.66759943

2.1805424 2.0442384

2.1842685 2.5954692

2.1891405 2.9582391

2.1963915 2.0430046

2.2020264 2.176707

2.204684 0.83693931

2.2052541 1.7499996

2.2066926 2.7208274

2.2109871 0.60017807

2.2146996 1.399128

2.2174554 0.72364346

2.2304159 0.82961734

2.2473506 1.2875368

2.2490397 2.1734422

2.2519844 1.2969987

2.2604619 0.7826934

2.2688021 2.0460717

2.3082186 3.4655713

2.337285 2.3586476

2.3616657 2.9431843

2.372691 1.7305975

2.3783937 1.3535819

2.3816021 1.9007384

2.3967371 1.1569486

2.409683 2.3595594

2.4193469 1.0856312

2.4234583 0.51782552

2.4392568 3.1463499

2.440037 3.2618835

2.4499319 0.4923003

2.4582683 1.3588849

2.4769531 3.5814207

2.4844316 1.9984511

2.4845173 1.8737064

2.4932187 1.66146

2.5161047 2.2298843

2.5216744 2.9607432

2.5229139 1.492989

2.5270772 -0.06387469

2.5353904 0.28615059

2.5781225 2.5085023

2.5789043 2.6564247

2.5913078 3.4678316

2.6017795 2.3329228

2.6083691 1.7219107

2.609068 2.4104897

2.6328854 1.4587599

2.6469687 1.7014709

2.6920039 1.295654

2.6979838 2.9314267

2.7126542 2.1619493

2.7176177 2.6184841

2.720342 2.7590754

2.7288016 1.8601606

2.7729759 2.3649024

2.8041777 1.8947211

2.8297566 2.1058471

2.8568165 3.2398304

2.8708006 2.5365312

2.8884565 2.4983996

2.8940135 0.80718637

2.9270099 1.4705192

2.933241 0.61062

2.9722306 1.9105811

2.9772854 0.05260017

2.9773797 2.4382292

2.980513 0.98434561

3.005612 2.8833924

3.0089706 0.48283347

3.0261818 2.8696189

3.0400688 2.2351968

3.0647613 1.3867858

3.0958156 4.7977043

3.0974361 0.61238889

3.123407 3.2756611

3.140026 1.9104996

3.1401996 3.0088845

3.1743328 2.9899578

3.1808794 2.408549

3.1832203 1.0703815

3.2276428 2.180782

3.2372055 1.4413745

3.2952069 2.9016304

3.3387983 3.2365316

3.3562441 3.3595853

3.4416423 4.7357497

3.521928 2.8651324

3.5249241 2.4056552

3.6396121 0.74341452

3.6525806 4.0596418

3.6773415 0.65264229

3.7089812 3.4749576

3.7570425 2.2789297

3.7990812 1.8031089

3.8079212 3.2604948

3.8635523 3.3065351

3.8826769 2.4434805

3.9105801 0.0730681

4.0275824 2.9110729

4.2610977 3.5150765

4.2654032 2.5862794

4.3772226 3.6939538

4.3862929 1.8272327

4.6736332 1.4853571

7.9435583 9.4739571

7.9739184 7.87866

8.187397 9.348852

8.2407109 8.5518535

8.2436521 9.4541242

8.2551838 7.5564442

8.285243 10.2071673

8.3299446 9.0000361

8.3319237 10.0936765

8.373557 9.8826912

8.464094 9.0581829

8.4906742 9.3292611

8.5115338 9.8535287

8.5659971 10.0486414

8.6023175 7.9585361

8.6234813 8.3907157

8.6445082 9.6998504

8.649045 9.8849591

8.6584125 9.0152807

8.6665508 9.4282592

8.6730605 10.5942431

8.763729 8.2835604

8.7755229 10.407463

8.8076254 9.658945

8.8276345 8.4644596

8.8479234 7.756862

8.856574 8.503814

8.8622591 9.9170009

8.9363758 7.2319598

8.943909 8.4875439

8.9889206 10.611449

9.0165059 9.5475354

9.0419705 10.8528666

9.102277 11.0164613

9.1288555 8.7255618

9.1296474 9.9023458

9.1553296 10.365873

9.1730311 8.742746

9.1820635 8.0384904

9.2560427 10.2301575

9.2981906 11.2181469

9.3011218 5.9657148

9.301961 9.6504424

9.3145947 8.2175059

9.3361955 11.0435696

9.3803912 9.9176344

9.4078596 9.5672229

9.40994 9.6915476

9.4850186 8.672043

9.4965347 10.4085449

9.5192183 9.1697117

9.5647357 10.0321895

9.5648727 10.5589759

9.6015189 6.0994906

9.603581 10.3790253

9.6099817 7.1638026

9.6206807 9.6087325

9.6345994 9.1531639

9.6429483 10.3542054

9.6724078 8.4637002

9.7092349 8.7151763

9.714601 7.8089865

9.7182087 7.8359403

9.7245 8.2597278

9.7385045 8.4247141

9.770265 9.7400903

9.8110738 8.4712292

9.8312662 7.5418477

9.8319838 8.9931978

9.8578799 9.0934209

9.8853276 9.0234515

9.913638 9.5415647

9.9267408 8.7133325

10.018074 9.6140025

10.0184744 9.045611

10.0412225 8.2239493

10.0485496 9.0632895

10.0518117 8.0080764

10.0664894 9.1414022

10.0949584 9.125697

10.101395 9.8750952

10.1235131 8.6977035

10.1394266 8.110125

10.1673507 8.731084

10.1958179 9.1986208

10.2270168 10.334217

10.2319251 9.5158861

10.2327407 9.0726365

10.2746275 8.693953

10.2802248 6.3492874

10.307973 8.5744575

10.4089124 8.1652054

10.4206061 7.1979654

10.4332237 9.4580566

10.4413669 8.0914636

10.4696749 8.8581128

10.4830634 11.2508174

10.4864111 7.7290216

10.4928201 10.746028

10.4952661 7.12129

10.4990939 10.0108213

10.5146056 9.6430751

10.5521972 8.328596

10.574167 9.0679398

10.5811656 7.0079888

10.5818258 9.1019273

10.6091367 8.5752111

10.6246849 8.5365028

10.6642143 8.4458003

10.6691018 10.7915455

10.7002922 10.6383879

10.7120615 8.794039

10.7644048 8.8808288

10.7934826 7.4950836

10.9684594 10.0053487

10.9714406 7.2756851

10.9788712 9.9465767

11.0029676 7.9771513

11.024769 6.9830776

11.108955 10.0022074

11.1667502 7.1966235

11.2008513 11.2806987

11.2237865 9.3287031

11.3157701 7.5950945

11.3606768 12.1086731

11.5844491 9.865713

11.6733525 7.6230164

11.682216 9.4233843

11.6856208 9.310157

11.6906946 7.328908

11.7297457 10.1547281

14.382204 9.6953397

-0.53802474 19.8670258

-0.50977026 19.5352805

-0.43584458 20.831295

-0.04882059 18.9503718

0.05835481 20.557332

0.10288324 19.5935174

0.17447523 18.0540215

0.19810974 19.12201

0.21934452 19.0784394

0.23439904 19.844791

0.26788561 20.209558

0.47250581 18.6123102

0.5142698 18.4614009

0.54996643 19.2768358

0.55418227 18.884939

0.60602417 20.936468

0.63375978 20.206995

0.63673117 18.7844843

0.7274187 19.0976652

0.78672127 18.6171725

0.84069897 18.7650607

0.885701 20.137431

0.91613686 20.233336

0.92942006 18.5713626

0.95596944 20.535309

0.96854857 19.8038184

1.0145628 18.2879164

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Parag Kanade/fuzzy ants/Gauss2/gauss.c

#include "headerStatic.h" /*******************************************************************/ /*** Global Variables ***/ irisData data[MAXDATA]; //To hold the data read from the file int count=0; //Count of the total number of data elements int heapCount=0; // Total number of heaps int heapRow=0; // Rows for the second iteration with heaps int newHeapCount=0; // Count of the heaps for the second iteration heap heaps[MAXDATA]; // Data structure to hold the heap information heap newHeaps[MAXDATA]; // Data Structure to hold the new heap information // For the second iteration int board[MAXROWS][MAXCOLS]; // Board on which the objects are located ant ants[MAXANTS]; // Data structure to hold the ants float Dmax=0; // Distance of the most dissimilar object pair int minHeapCount=100000; // Counter to hold the minimum heap count, used only for academic purpose // no use in the algorithm int minPosition; // the minimum heap iteration , only for academic purpose // no use in the algorithm float clsCen[MAXDATA][MAXDATA][s];//to hold the objects after hardening int cenCount[MAXDATA];//to hold the number of objects after hardening /********************************************************************/ /*** Varaibles to normalize the features between 0 and 1 */ float minInput[features], maxInput[features],scale[features],minForHeaps[features],maxForHeaps[features],scaleForHeaps[features]; /********************************************************************/ /** Variables for the FCM algorithm */ int temp[200]; /* for square matrix tests */ float eps,m; matrix U, U1; BOOLEAN stop; center V; objects X; char fname[80]; FILE *fp; int run; float inv_cov[s+1][s+1]; /* inverse of covariance matrix Y */ float Y[s][s]; long rand_index, Random_seed; long state1[52] = { 9347, 3835, 5194, 8310, 346, 535, 5297, 6711, 77, 3834, 668, 4175, 6868, 5890, 9304, 8462, 5269, 920, 65392, 41600, 70119, 91032, 76220, 26245, 4746, 73608, 32823, 63264, 75641, 99104, 36534, 24704, 98255, 72266, 75336, 65152, 7269, 63163, 88471, 27271, 43641, 76649, 47773, 23777, 27491, 35926, 16651, 48652, 89766, 90921, 6056 }; int z; int mins=0; int aveit =0; float mintab[200]; int mincount[200]; long start,end,total; int lCount;//added by parag /********************************************************************/ /********************************************************************//*int lrand48(){return(rand());}void srand48(int a){return;}*//********************************************************************/ /********************************************************************/ /* Function to read the input data set */ /* The file name is assumed to be itest.data */ /* Status: Tested */ /* Output: Data from the file into the "data" (Global) data structure */ /* Output: Count (Global), the count of the number of objects */ /********************************************************************/ void readInput() { FILE *fp; char fname[80]; int i=0,j=0; char ch; sprintf(fname,"%s.data",filename); /* Check if the input file is opened */ if((fp=fopen(fname,"r"))==NULL) { printf("\n Unable to open input file \n"); exit(0); } /*initialise the minimum maximum and the scale */ for(i=0;i<features;i++) { minInput[i]=1000; maxInput[i]=0; minForHeaps[i]=1000; maxForHeaps[i]=0; scale[i]=0; scaleForHeaps[i]=0; } /* Discard leading comments */ ch = 'c'; while (ch != '#') fscanf (fp, "%c", &ch); i=0; /* Parse the file and populate the data structure */ while(!feof(fp)) { for(j=0;j<features;j++) { fscanf(fp,"%f ",&data[i][j]); } fscanf(fp,"\n"); for(j=0;j<features;j++) { if(data[i][j]<minInput[j]) minInput[j]=data[i][j]; if(data[i][j]>maxInput[j]) maxInput[j]=data[i][j]; } i++; } count=i; /* Normailze the feature values in between 0 and 1 */ for(j=0;j<features;j++) { if(minInput[j]!=maxInput[j]) scale[j]=1.0/(maxInput[j]-minInput[j]); else scale[j]=1.0; } for(i=0;i<count;i++) { for(j=0;j<features;j++) data[i][j]=(data[i][j]-minInput[j])*scale[j]; } fclose(fp); return; }//end of function /********************************************************************/ /*Function to compute the eucldiean distance for all input data set */ /* Output : an array conatining the distance of each object with the other */ /********************************************************************/ float computeDistance() { float distance,dist=0; int i=0,j=0,k=0; distance=0; for(j=0;j<count;j++) for(i=0;i<count;i++) { for(k=0;k<features;k++) dist+=sqrt((data[j][k]-data[i][k])*(data[j][k]-data[i][k])); if(distance<dist)distance=dist; dist=0; } return(distance); } /********************************************************************/ /* Function to compute the distance between two objects */ /* Output : Euclidean d istance between the two input objects */ /********************************************************************/ float computeDistanceBetween2(irisData a,irisData b) { float temp=0; int i=0; for(i=0;i<features;i++) temp+=(a[i]-b[i])*(a[i]-b[i]); temp=sqrt(temp); return(temp); } /********************************************************************/ /* Function to find the maximum distance between the input objects*/ /* Output: the maximum distance */ /* Input : The array of distances between objects */ /********************************************************************/ /*float findMax(float *distance) { int i=0,j=0; float max=distance[0]; for(i=0;i<count;i++) for(j=0;j<count;j++) { if(max<distance[i*count+j]) max=distance[i*count+j]; } return(max); }*/ /********************************************************************/ /* Function to compute the value of an object */ /********************************************************************/ float value(irisData a) { int i=0; float temp=0; for(i=0;i<features;i++) temp+=a[i]*a[i]; return(sqrt(temp)); } /********************************************************************/ /* Function to find the object in the data structure */ /* Output : The position of the object in the data */ /* Input : The object */ /********************************************************************/ int findInData(irisData object,int iFlag) { int i=0,lastCount=0,j,position=-1,flag[features],flg;if(iFlag==1)lastCount=MAXDATA;else{}for(i=0;i<lastCount;i++){ for(j=0;j<features;j++) if(fabs(object[j]-data[i][j])<0.00001) flag[j]=1; else { flag[j]=0; break;} flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /* Function to find whether there is an object on the board at a,b */ /* Output : a flag indicating whether an object is found or not */ /* Input : The row and column indicating the board position */ /********************************************************************/ int found(int a , int b) { if(board[a][b]==9) return 1; else return 0; } /********************************************************************/ /* Function to initialize the initial board */ /* Output : None */ /* Global Structures modified : heaps */ /* Input : Flag indicating the iteration 1: First 0: Second */ /********************************************************************/ void initialise(int flag) { int a,b,i=0,k=0; int loopCount,rowCount,colCount; /* Initialize the row, column and number of heaps */ if(flag==1) { loopCount=count; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=lCount; rowCount=heapRow; colCount=heapRow; } /* Initialize the heaps */ for(i=0;i<loopCount;i++) { heaps[i].count=0; heaps[i].x=0; heaps[i].y=0; heaps[i].disPosition=0; heaps[i].Dmax=0; heaps[i].Dmean=0; } for(i=0;i<loopCount;i++) { /*find a board position which is empty*/ do { a=lrand48()%rowCount; b=lrand48()%colCount; } while(found(a,b)); /*Allocate the object to that place and assign a heap to it*/ heaps[i].x=a; heaps[i].y=b; heaps[i].count=1; heaps[i].Dmax=0; heaps[i].Dmean=0; if(flag==1) { heaps[i].objects[0]=i; for(k=0;k<features;k++)heaps[i].Ocenter[k]=data[i][k]; } else { for(k=0;k<features;k++) heaps[i].Ocenter[k]=newHeaps[i].Ocenter[k]; } heaps[i].disPosition=0; board[a][b]=9; if((debugLevel>0)&&(flag==1))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],data[heaps[i].objects[0]][0]); if((debugLevel>0)&&(flag==0))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],newHeaps[i].Ocenter[0]); } /*initialise the initial heap count*/ heapCount=loopCount; currentRow=rowCount; } /********************************************************************/ /* Function to find whether an heap is present at the location a b */ /* Output : Flag indicating the presencs of the heap */ /* Input : Roe and column indicating the board position */ /********************************************************************/ int findObject(int a,int b) { int i; int position=-1; for(i=0;i<heapCount;i++) { if((heaps[i].x==a)&&(heaps[i].y==b)) { position=i; break; } } return(position); } /********************************************************************/ /* Function initialize the ants */ /* Output : None */ /* Input : Flag indicating the iteration */ /* Global Variables Modified : ant, heapCount, heaps, board */ /********************************************************************/ void initialiseAnts(int flag) { int i=0,a=0,b=0,position,j=0,loopCount=0,rowCount,colCount,k=0; if(flag==1) { loopCount=MAXANTS; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=MAXANTSFORHEAPS; rowCount=heapRow; colCount=heapRow; } for(i=0;i<loopCount;i++) { /*generate random positions for the ants*/ a=lrand48()%rowCount; b=lrand48()%colCount; ants[i].x=a; ants[i].y=b; /*Find if an object is present at that position or not*/ if(board[a][b]==9) { ants[i].carrying=1; /*find the heap at that location*/ position=findObject(a,b); if(position==-1) { printf("\n Error in logic: Ant init \n"); //getchar(); break; } else { /*Assign the heap data to the ant data and decrement the heap count*/ if(flag==1){ for(k=0;k<features;k++)ants[i].data[k]=data[heaps[position].objects[0]][k]; }else{for(k=0;k<features;k++)ants[i].data[k]=heaps[position].Ocenter[k];} for(j=position;j<heapCount-1;j++) heaps[j]=heaps[j+1]; heapCount--; } /*Clear the board position */ board[a][b]=-9; } else ants[i].carrying=-1; ants[i].speed=0; ants[i].dir=lrand48()%8; printf("\n Ant %2d X: %2d Y: %2d Carying : %2d ",i,ants[i].x,ants[i].y,ants[i].carrying); } currentAntCount=loopCount; } /********************************************************************/ /* Function to move the ant */ /* Output: None */ /* Input: Ant to be moved and the flag indicating the iteration */ /********************************************************************/ void moveNext(ant* a,int flag) { int rowCount,colCount; /*if(flag==1) { rowCount=MAXROWS; colCount=MAXCOLS; } else { rowCount=heapRow; colCount=heapRow; }*/rowCount=currentRow;colCount=currentRow; /*Depending upon the direction move the ant*/ switch (a->dir) { case 0: a->y++; if(a->y>=colCount)a->y=0; break; case 1: a->x--; a->y++;; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 2: a->x--; if(a->x<0)a->x=rowCount-1; break; case 3: a->x--; a->y--; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 4: a->y--; if(a->y<0)a->y=colCount-1; break; case 5: a->x++; a->y--; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 6: a->x++; if(a->x>=rowCount) a->x=0; break; case 7: a->x++; a->y++; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break; } }//end of function /********************************************************************/ /* Function to compute the parameters of the heap */ /* Output: None */ /* Input: The heap whose parameters are to be computed iteration flag*/ /********************************************************************/ void computeHeapParameters(heap* heapData,int flag) { int i=0,j=0,k=0,position=0; float distance=0; float Dmean=0.0; float *dis; float featureSum[features]; float max=0; float sum=0; /* init*/ for(k=0;k<features;k++) featureSum[k]=0; /*Compute the distance of each object and find the maximum distance*/ max=0; if(flag==1){ for(j=0;j<heapData->count;j++) for(i=0;i<heapData->count;i++) { for(k=0;k<features;k++) sum+= (data[heapData->objects[j]][k]-data[heapData->objects[i]][k])*(data[heapData->objects[j]][k]-data[heapData->objects[i]][k]); distance=sqrt(sum); if(max<distance) max=distance; sum=0; } /* Assign the maximum distance to the heap */ heapData->Dmax=max;/*Compute the centre object*/for(i=0;i<heapData->count;i++)for(k=0;k<features;k++) featureSum[k]+=data[heapData->objects[i]][k];for(k=0;k<features;k++)heapData->Ocenter[k]=featureSum[k]*1.0/heapData->count; /* Find the mean distance */if((dis=(float *) calloc(heapData->count,sizeof(float)))==NULL){printf("\nUnable to allocate memory in the compute heap parameters procedure\n");exit(0);} for(i=0;i<heapData->count;i++) { Dmean+=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); dis[i]=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); } heapData->Dmean=Dmean*1.0/heapData->count; /*Compute the most dissimilar object*/ max=dis[0]; for(i=1;i<heapData->count;i++) if(max<dis[i]) { max=dis[i]; position=i; } /*Assign the most dissimilar object to the heap variable*/ for(k=0;k<features;k++) heapData->Odissim[k]=data[heapData->objects[position]][k]; heapData->disPosition=position;free(dis); } else{/* Heap Parameters for the second stage */for(k=0;k<features;k++){ heapData->Odissim[k]=heapData->Ocenter[k];}heapData->disPosition=0;heapData->Dmean=0;heapData->Dmax=0;} }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a single object*/ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject1(ant *a,int position,int flag) { int i,k=0; /*Clear the board*/ board[heaps[position].x][heaps[position].y]=-9; /* Assign the data to the ant*/if(flag==1){ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[0]][k];}else {for(k=0;k<features;k++) a->data[k]=heaps[position].Ocenter[k];} a->carrying=1; /*Copy all the heaps remaining heaps in the structure*/ for(i=position;i<heapCount-1;i++) heaps[i]=heaps[i+1]; /*Decrement the heap count*/ heapCount--; }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a two objects */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject2(ant *a , int position,int flag) { float Pdestroy1=-1; int i=0,k=0; /*Compute the probability to destroy the heap*/ Pdestroy1=lrand48()%13*1.0/12.0; /*If the probability is more than Pdestroy then destroy the heap */ if(Pdestroy1<Pdestroy) { /*pick the object randomly*/ i=lrand48()%2; /* Assign the data to the ant */ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[i]][k]; a->carrying=1; /*Correct the heap parameters*/ heaps[position].count=1; heaps[position].objects[0]=heaps[position].objects[1-i]; computeHeapParameters(&heaps[position],flag); } }//end of function /********************************************************************/ /*Function to pick a object from a heap consisting of more than two object */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /********************************************************************/ void pickObjectn(ant *a,int position,int flag) { int i=0,k=0; /*If the criteria is satsfied then pick the object*/ if((computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)*1.0/heaps[position].Dmean)>Tremove) { a->carrying=1; for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[heaps[position].disPosition]][k]; for(i=heaps[position].disPosition;i<heaps[position].count-1;i++) heaps[position].objects[i]=heaps[position].objects[i+1]; heaps[position].count--; computeHeapParameters(&heaps[position],1); } }//end of function /********************************************************************/ /* Function to pick a object */ /********************************************************************/ void pick(ant *a,int flag) { int position=-1,i=0; float Pload1=-1.0; for(i=0;i<heapCount;i++) { /*Check if a heap is present in the eight neighbouring cells*/ if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if Heap is found*/ if(position!=-1) { /* heap or object found.*/ /*Depending upon the number present in the heap call the corresponding routine*/ switch(heaps[i].count) { case 1: Pload1 = lrand48()%151*1.0/150.0; if(Pload1<Pload) { pickObject1(a,position,flag); } break; case 2: pickObject2(a,position,flag); break; default: pickObjectn(a,position,flag); break; } } } /********************************************************************/ /* Function to drop a object to form a new heap */ /* Output: None */ /* Input: Ant, iteration flag */ /* Global Structure modified : heap, heapCount */ /********************************************************************/ void dropObject(ant* a,int flag) { /*Create a new heap*/ int k=0; heaps[heapCount].count=1; heaps[heapCount].x=a->x; heaps[heapCount].y=a->y; heaps[heapCount].Dmax=0; heaps[heapCount].Dmean=0; heaps[heapCount].disPosition=0; for(k=0;k<features;k++) { heaps[heapCount].Odissim[k]=a->data[k]; heaps[heapCount].Ocenter[k]=a->data[k]; } if(flag==1) {heaps[heapCount].objects[0]=findInData(a->data,flag); /*computeHeapParameters(&heaps[heapCount],flag);*/ } computeHeapParameters(&heaps[heapCount],flag); heapCount++; a->carrying=-1; board[a->x][a->y]=9; }//end of function /********************************************************************/ /* Function to find the heap position during the second iteration */ /* Output: position of the heap */ /* Input: object center of the heap that the ant is carrying */ /* Global Structure modified : none */ /********************************************************************/ int findInNewheaps(irisData object) { int position=-1,i,j=0,flag[features],flg; for(i=0;i<newHeapCount;i++) { for(j=0;j<features;j++) { if(fabs(object[j]-newHeaps[i].Ocenter[j])<0.00001) flag[j]=1; else {flag[j]=0;break;} } flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /*Function to drop a object to a heap consisting of a one or more object */ /* Output: none */ /* Input: Ant, position of the heap , iteration flag */ /* Global Structure modified: heaps,newHeaps */ /********************************************************************/ void dropObject1(ant* a, int position,int flag) { int heapPosition=-1,heapPosition1=-1,i,j,k=0; irisData newData,temp; if(flag==1) { /*Add the object to the heap and compute the heap parameters*/ /*if(heaps[position].count==heaps[position].objCount) {if((junk=(int *)realloc(heaps[position].objects,((heaps[position].objCount+OBJINC)*sizeof(int))))==NULL){printf("\n Cannot Allocate memory to the new object for %d heap",position);exit(0);}heaps[position].objects=junk;heaps[position].objCount+=OBJINC;free(junk); }*/ heaps[position].objects[heaps[position].count]=findInData(a->data,flag); heaps[position].count++; computeHeapParameters(&heaps[position],flag); } else { for(k=0;k<features;k++)temp[k]=heaps[position].Ocenter[k]; heapPosition=findInNewheaps(a->data); heapPosition1=findInNewheaps(heaps[position].Ocenter); if(heapPosition==-1||heapPosition1==-1) { printf("\n error in logic:new heaps\n"); //getchar(); return; } for(k=0;k<features;k++) {newData[k]=(newHeaps[heapPosition].Ocenter[k]*newHeaps[heapPosition].count+newHeaps[heapPosition1].Ocenter[k]*newHeaps[heapPosition1].count)*1.0/(newHeaps[heapPosition].count+newHeaps[heapPosition1].count);newHeaps[heapPosition].Ocenter[k]=newData[k];heaps[position].Ocenter[k]=newData[k]; } computeHeapParameters(&heaps[position],flag); for(i=newHeaps[heapPosition].count,j=0;j<newHeaps[heapPosition1].count;i++,j++) { newHeaps[heapPosition].objects[i]=newHeaps[heapPosition1].objects[j]; } newHeaps[heapPosition].count+=newHeaps[heapPosition1].count; for(i=heapPosition1;i<newHeapCount-1;i++) newHeaps[i]=newHeaps[i+1]; newHeapCount--; } a->carrying=-1; }//end of function /********************************************************************/ /* Function to drop a object */ /* Output: None */ /* Input: Ant, iteration flag */ /********************************************************************/ void drop(ant* a,int flag) { int i, position=-1; float Pdrop1=0.0; /*Find whether we have a heap in the neighbourhood of the cell*/ for(i=0;i<heapCount;i++) { if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if a heap is found*/ if(position!=-1) { /*heap or object found.*/ /*Depending on the number of objects present take action*/ switch(heaps[position].count) { /*If criteria is satisfied then drop the object*/ case 1: if(flag==1) { if((computeDistanceBetween2(a->data,data[heaps[position].objects[0]])*1.0/Dmax)<Tcreate) dropObject1(a,position,flag); } else { if((computeDistanceBetween2(a->data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) dropObject1(a,position,flag); } break; default: if(computeDistanceBetween2(a->data,heaps[position].Ocenter)<computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)) dropObject1(a,position,flag); break; } } else { /*Compute the probability to drop the object*/ Pdrop1 = lrand48()%151*1.0/150; if(Pdrop1<Pdrop) dropObject(a,flag); } }//end of function /********************************************************************/ /* Function to move the ant */ /* Output: none */ /* Input: Ant, iteration flag */ /********************************************************************/ void move(ant* a,int flag) { float pDir; /*Check the speed */ if (a->speed!=0) { /*move in the same direction*/ a->speed--; moveNext(a,flag); } else { /*generate the new direction*/ pDir=lrand48()%17*1.0/16.0; if(pDir<.85) { a->dir=lrand48()%8; } else if(pDir>.95) { a->speed=2; } moveNext(a,flag); } } /********************************************************************/ /* Function to simulate the movement of the ants */ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified: minHeapCount, minHeapPosition */ /********************************************************************/ void iterate(int flag) { int i=0,j=0,k=0,loopCount=0; if (flag==1) loopCount=MAXANTS; else loopCount=MAXANTSFORHEAPS; while (i<MAXITERATIONS) { i++; for(j=0;j<loopCount;j++) { move(&ants[j],flag); if(ants[j].carrying==1) { drop(&ants[j],flag); } else { pick(&ants[j],flag); } } if(debugLevel>0){ for(k=0;k<heapCount;k++) for(j=0;j<heapCount;j++) if((k!=j)&&(heaps[k].x==heaps[j].x)&&(heaps[k].y==heaps[j].y)) { printf("\nerror two heaps with same co ordinates %d %d ",k,j); //getchar(); } k=0; for(j=0;j<heapCount;j++) k+=heaps[j].count; } if(heapCount<minHeapCount) { minHeapCount=heapCount; minPosition=i; } if(debugLevel>0) printf("\n i:%d Heap count %d sum %d",i,heapCount,k); else printf("\n i:%d Heap count %d",i,heapCount); heapSum+=heapCount; /*if(i%updateAfter==0) {adjustBoard(flag); writeIncremental(i,flag);heapSum=0;loopCount=currentAntCount; }*/ } }//end of function /********************************************************************/ /*Function to drop the objects carried by the ants after all the iterations*/ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified:none */ /********************************************************************/ void dropFinal(int flag) { int i=0,j=0,position=-1,loopCount=0; /*if(flag==1) loopCount=MAXANTS; else loopCount=MAXANTSFORHEAPS;*/ loopCount=currentAntCount; for(i=0;i<loopCount;i++) { if(ants[i].carrying==1) { if(flag==1) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position,flag); } } else { /*Find if a heap is present */ do { position=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position,flag); position=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position!=-1); } } } }//end of function /********************************************************************/ /* Function for the implementation of QuickSort */ /* Output: none */ /* Input: data structure, low and high indices */ /* Global structure modified:none */ /********************************************************************/ void quickSort( heap A[], int low,int high ) { int mid=0; if(low<high) { mid=partition(A,low,high); quickSort(A,low,mid); quickSort(A,mid+1,high); } } /********************************************************************/ /* Partition routine used by quick sort */ /* Output: partition location */ /* Input: data , low, high */ /* Global structure modified:none */ /********************************************************************/ int partition(heap a[], int low,int high) { heap temp; int x=0,i=0,j=0; i=low; j=high; x=a[low].count; while (1) { while (a[j].count<x) j=j-1; while (a[i].count>x) i=i+1; if(i<j) { temp=a[i]; a[i]=a[j]; a[j--]=temp; } else return(j); } } /********************************************************************/ /* Function used to write the cluster information */ /* Output: none */ /* Input:iteration flag */ /* Global structure modified:none */ /********************************************************************/ void writeClusterInformation(int flag) { FILE *fp=fopen("center.clt","w"); FILE *fp1; FILE *fp2; FILE *fp3; char temp[80]; int i=0,j=0,k=0; int lastCount=0; int rowCount,colCount,antCount; strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp3=fopen(temp,"a"); if(dataDump) {strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp1=fopen(temp,"a");strcpy(temp,"");sprintf(temp,"%sObj.out",filename);fp2=fopen(temp,"a");if(fp1==NULL||fp2==NULL)printf("\n Error in opening file in the writer Cluster Information routine\n"); } /*if(flag==1) { rowCount=MAXROWS; colCount=MAXCOLS; antCount=MAXANTS; } else { rowCount=heapRow; colCount=heapRow; antCount=MAXANTSFORHEAPS; }*/ rowCount=currentRow; colCount=currentRow; antCount=currentAntCount; if(fp==NULL) { printf("\n Unable to open cluster center output file \n"); /*return;*/ exit(0); } /*sort the array according to the count value and write the value of the top three elements*/ if((flag==1)&&(!EntireHeaps)) quickSort(heaps,0,heapCount); if(EntireHeaps) {if(flag==1) lastCount=heapCount;else lastCount=newHeapCount; } else lastCount=c; for(i=0;i<lastCount;i++) { if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { /*fprintf(fp,"%5.2f \t ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]);*/ fprintf(fp,"%5.2f \t ",newHeaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else { for(k=0;k<features;k++) { /*fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]/scale[k]+minInput[k]);*/ fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",newHeaps[i].Ocenter[k]); fprintf(fp,"\n"); } else { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); fprintf(fp,"\n"); } } if(dataDump) { fprintf(fp1,"\n Information from the Ant Based Algorithm \n"); fprintf(fp1,"\n Important Parameters : Board %d X %d Ants : %d Iterations %d Total Heaps %d ",rowCount,colCount,antCount,MAXITERATIONS,lastCount); } if(flag==0) { if(dataDump) { fprintf(fp1," TCreateForHeaps %f \n",TcreateForHeaps); fprintf(fp2,"\nTotal Heaps : %d\n",heapCount); fprintf(fp2,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); } fprintf(fp3,"\n@ %d\n",lastCount); } else if(dataDump) fprintf(fp1,"\n");if(dataDump) { fprintf(fp1,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); for(i=0;i<lastCount;i++) if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp2,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); } fprintf(fp1," %5d\n",newHeaps[i].count); fprintf(fp2," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp1," %5d\n",heaps[i].count); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]); fprintf(fp1," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]); fprintf(fp1," %5d\n",heaps[i].count); }} if(flag==0) { if(dataDump) fprintf(fp1,"\n Objects in the heaps \n");for(i=0;i<newHeapCount;i++) { if(dataDump) fprintf(fp1,"\n\n Heap %d\n \n",i+1);fprintf(fp3,"# %d %d\n",i ,newHeaps[i].count); for(j=0;j<newHeaps[i].count;j++){ for(k=0;k<features;k++){ fprintf(fp3,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]); if(dataDump) fprintf(fp1,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]);} if(dataDump) fprintf(fp1,"\n"); fprintf(fp3,"\n"); } } } fclose(fp); fclose(fp3); if(dataDump) { fclose(fp1);fclose(fp2); }}//end of flie /********************************************************************/ /* Function to initialise the new heaps */ /* Output: none */ /* Input:new Heap structure */ /* Global structure modified:none */ /********************************************************************/ void initialiseNewHeaps(heap newHeaps[]) { int i=0,j=0; for(i=0;i<lCount;i++) { newHeaps[i].count=0; for(j=0;j<features;j++) newHeaps[i].Ocenter[j]=V[i][j]; newHeaps[i].x=heaps[i].x; newHeaps[i].y=heaps[i].y; } }//end of function /********************************************************************/ /* Function to initialise the new heaps */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ int findPosition(int i) { int j,position=0; for(j=1;j<lCount;j++) { if(U[j][i]>U[position][i]) position=j; } printf("\n Position %d ",position); return(position); }//end of function find position /********************************************************************/ /* Function to add object */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ void addObject(int object,heap * heapPosition) { heapPosition->objects[heapPosition->count]=object; heapPosition->count++; }//end of function add object /********************************************************************/ /* Function to harden the outputs obtained from fcm */ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/ void harden(heap newHeaps[]) { int i,heapPosition=-1,sum=0,j=0; /*initialise the new heaps*/ initialiseNewHeaps(newHeaps); for(i=0;i<count;i++) { heapPosition=findPosition(i); addObject(i,&newHeaps[heapPosition]); } /* To check for degenerate partitions*/ for(i=0;i<lCount;i++) { if(newHeaps[i].count==0){for(j=i;j<lCount-1;j++){newHeaps[j]=newHeaps[j+1];}lCount--;i--;} } newHeapCount=lCount; for(i=0;i<lCount;i++) computeHeapParameters(&newHeaps[i],1); if(debugLevel>0) { for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\n sum = %d",sum);} }//end of function/********************************************************************/ /* Function to initialize the initial parameters for the ant algorithm*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void init() { int i=0,j=0; for(i=0;i<MAXROWS;i++) for(j=0;j<MAXCOLS;j++) board[i][j]=-9; heapCount=0; newHeapCount=0; heapRow=0; minPosition=-1; minHeapCount=100000; for(i=0;i<MAXANTS;i++) { ants[i].carrying=-1; ants[i].x=-1; ants[i].y=-1; ants[i].speed=-1; ants[i].dir=-1; } } /********************************************************************/ /* Function to initialize the initial board */ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void initialiseBoard() { int i,j; for(i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; }/********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void harden1() { int i=0,heapPosition=-1; for(i=0;i<heapCount;i++) cenCount[i]=0; for(i=0;i<n;i++) { heapPosition=findPosition1(i); addObject1(i,heapPosition); } writeInfo(); }//end of function/********************************************************************/ /* Function to find to which class the object belongs */ /* Output: Class number */ /* Input: object position */ /* Global Structure Modified: None */ /********************************************************************/int findPosition1(int i) { int j,position=0; for(j=1;j<heapCount;j++) { if(U[j][i]>U[position][i]) position=j; } return(position); }//end of function find position/********************************************************************/ /* Function to Write the output of the hardened objects to file*/ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/void writeInfo(){int i=0,j=0,k=0;FILE *fp=fopen("fcm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}fprintf(fp,"\n@ %d\n",heapCount);for(i=0;i<heapCount;i++){fprintf(fp,"# %d %d\n",i,cenCount[i]);for(j=0;j<cenCount[i];j++){for(k=0;k<s;k++)fprintf(fp,"%8.2f ",clsCen[i][j][k]/scale[k]+minInput[k]);fprintf(fp,"\n");}}fclose(fp);return;}/********************************************************************/ /* Function to add a object to a cluster (2nd stage) */ /* Output: none */ /* Input: object poistion , cluster position */ /* Global Structure Modified: Cluster object and cluster count*/ /*******************************************************************/void addObject1(int object,int heapPosition) { int k=0; for(k=0;k<features;k++) clsCen[heapPosition][cenCount[heapPosition]][k]=X[object][k]; cenCount[heapPosition]++; return; }//end of function add object/********************************************************************/ /* Function to read the original classification of objects */ /* Output: none */ /* Input: none */ /* Global Structure Modified: None */ /*******************************************************************/void readOrg(){int i=0,k=0;char temp[80];FILE *fp;//= fopen(dataname,"r");strcpy(temp,"");sprintf(temp,"%sFull.data",filename);fp=fopen(temp,"r");if(fp==NULL){printf("\n Unable to open full data input file ");exit(0);}for(i=0;i<n;i++){fscanf(fp,"%f ",&orgData[i][0]);for(k=1;k<s+1;k++)fscanf(fp,"%f ",&orgData[i][k]);}return;}// end of function/********************************************************************/ /* Function to classify the objects in different clusters */ /* Output: none */ /* Input: the cluster objects, the original classification, */ /* number of objects in the cluster,file to write the data */ /* Global Structure Modified: None *//********************************************************************/int analysisData(float data[][s],float orgData[n][s+1],int count,FILE * fp){int i=0,j=0,k=0,type=1,max=0;heapInfo info;for(i=0;i<c;i++){info.classObj[i]=0;}info.total=0;info.total=count;for(i=0;i<count;i++)for(j=0;j<n;j++){type=1;for(k=0;k<s;k++)if(fabs(data[i][k]-orgData[j][k+1])>errThresh){type=-1;break;}if(type!=-1){info.classObj[(int)orgData[j][0]-1]++;break;}}max=0;for(k=0;k<c;k++){if(info.classObj[k]>info.classObj[max])max=k;fprintf(fp,"\nClass %d Objects % d ",k+1,info.classObj[k]);}j=0;for(k=0;k<c;k++){if(k!=max)j+=info.classObj[k];}fprintf(fp,"\n Total errors : %d \n",j);return(j);}//end of analyse/********************************************************************/ /* Function to read the cluster information */ /* Output: none */ /* Input: input file pointer , output file pointer */ /* Global Structure Modified: None *//********************************************************************/void readFCM(FILE* fp,FILE *fp1){int i=0,j=0,k=0,l=0;int totalRuns=0,heapCount=0,junk=0,sum=0;char ch;FILE *fp2=fopen("errors.out","a");ch = 'c'; while (ch != '$') fscanf (fp, "%c", &ch);fscanf(fp,"%d\n",&totalRuns);printf("\n total runs %d ",totalRuns);for(l=0;l<totalRuns;l++){fscanf(fp,"%c",&ch);fscanf(fp,"%d\n",&heapCount);fprintf(fp1,"\n \nExperiment %d \n ",l+1);sum=0;for(i=0;i<heapCount;i++){fscanf(fp,"%c",&ch);fscanf(fp," %d %d\n",&junk,&clsCenCount[i]);for(j=0;j<clsCenCount[i];j++)for(k=0;k<s;k++)fscanf(fp,"%f",&fcmData[i][j][k]);fscanf(fp,"\n");fprintf(fp1,"\n\n Heap %d Total Objects %d \n",i+1,clsCenCount[i]);sum+=analysisData(fcmData[i],orgData,clsCenCount[i],fp1);}fprintf(fp2," %3d %3d %3d \n",l,sum,heapCount);}fprintf(fp2,"\n --------------------------\n");fclose(fp2);return;}//end of function/********************************************************************/ /* Function to update the board parameters */ /* Output: none */ /* Input: none */ /* Global Structure Modified: board size, ant count *//********************************************************************/void adjustBoard(int flag){int newRow=0,newAnt=0,antDiff=0,antsKilled=0,i=0,position[MAXANTS],position1=-1,j=0,k=0,a,b; newRow= (int)ceil(sqrt(4.0*heapSum/updateAfter)); newAnt= (int)ceil((heapSum*1.0/updateAfter)*1.0/3.0); if(newAnt<minAnts)newAnt=minAnts; if(newRow<rowMin)newRow=rowMin;antDiff=currentAntCount-newAnt; if((antDiff>0)&&(newAnt>=minAnts)) {for(i=0;i<currentAntCount;i++){if(ants[i].carrying==-1){position[antsKilled++]=i;}} if(antsKilled>=antDiff){for(i=0;i<antDiff;i++){for(j=position[i];j<currentAntCount-1;j++)ants[j]=ants[j+1];for(k=i+1;k<antsKilled;k++)position[k]--; } currentAntCount=newAnt; }else {for(i=0;i<antsKilled;i++){for(j=position[i];j<currentAntCount-1;j++)ants[j]=ants[j+1]; for(k=i+1;k<antsKilled;k++)position[k]--; }if(flag==1) { for(i=currentAntCount-1,k=0;k<antDiff-antsKilled;k++,i--) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position1=i; break; } } if(position1!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position1,flag); }else { dropObject(&ants[i],flag); } } } else { //flag is zero 2nd stage /*Find if a heap is present */ do { position1=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position1=i; break; } } if(position1!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position1].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position1,flag); position1=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position1!=-1); }//end else currentAntCount=newAnt;}//end elsecurrentAntCount=newAnt;}//end iffor(i=0;i<newAnt;i++){if((ants[i].x>=newRow)||(ants[i].y>=newRow)) { a=lrand48()%newRow; b=lrand48()%newRow; ants[i].x=a; ants[i].y=b; }} //Done with ants, now adjust the boardif((currentRow>newRow)&&(newRow>=rowMin)){for(i=0;i<heapCount;i++){if((heaps[i].x>=newRow)||(heaps[i].y>=newRow)) {do { a=lrand48()%newRow; b=lrand48()%newRow; } while(found(a,b));//adjust the heap poistionheaps[i].x=a;heaps[i].y=b;board[a][b]=9;}//end if}//end for for(i=0;i<currentAntCount;i++){if((ants[i].carrying==1)&&((ants[i].x>=newRow)||(ants[i].y>=newRow))){//adjust the ant locationdo { a=lrand48()%newRow; b=lrand48()%newRow; } while(found(a,b));//adjust the heap poistionants[i].x=a;ants[i].y=b;}//end if}//end for currentRow=newRow;}//end if }//end of function void writeIncremental(int iteration,int flag){FILE *fp=fopen("dump.txt","w");int i=0,j=0,k=0;fprintf(fp,"Stage %d\n",flag);fprintf(fp,"Board %d\n",currentRow);fprintf(fp,"Ants %d\n",currentAntCount);fprintf(fp,"Iteration %d\n",iteration);fprintf(fp,"Heaps %d\n",heapCount); if(flag==0)fprintf(fp,"NewHeapCount %d\n",newHeapCount); fprintf(fp,"Ants\n");for(i=0;i<currentAntCount;i++){fprintf(fp,"%d %d %d %d %d ",ants[i].x,ants[i].y,ants[i].carrying,ants[i].speed,ants[i].dir);for(k=0;k<features;k++)fprintf(fp,"%8.4f ",ants[i].data[k]);fprintf(fp,"\n");}fprintf(fp,"Heaps\n");for(i=0;i<heapCount;i++){fprintf(fp,"%d %d %d %d %8.2f %8.2f \n",heaps[i].x,heaps[i].y,heaps[i].count,heaps[i].disPosition,heaps[i].Dmax,heaps[i].Dmean);for(k=0;k<features;k++)fprintf(fp,"%8.4f ",heaps[i].Ocenter[k]);fprintf(fp,"\n");for(k=0;k<features;k++)fprintf(fp,"%8.4f ",heaps[i].Odissim[k]);fprintf(fp,"\n");if(flag==1){for(j=0;j<heaps[i].count;j++)fprintf(fp,"%d ",heaps[i].objects[j]);fprintf(fp,"\n");}}if(flag==0){fprintf(fp,"newHeaps\n");for(i=0;i<newHeapCount;i++){fprintf(fp,"%d\n",newHeaps[i].count);for(j=0;j<newHeaps[i].count;j++)fprintf(fp,"%d ",newHeaps[i].objects[j]);fprintf(fp,"\n");}}fclose(fp);}//end of function/********************************************************************/ /* Function Main */ /********************************************************************/ int main(int argc ,char** argv) { int sum=0,k=0,i=0,j=0; char temp[80]; FILE *fp=fopen("fcm.out","w"); FILE *fp1; FILE *fp2=fopen("antAnalysis.out","a"); FILE *fp3=fopen("fcmAnalysis.out","a"); FILE *fp4=fopen("errors.out","w"); FILE *fp5; fclose(fp4); sprintf(temp,"%sObj.out",filename); fp5=fopen(temp,"w"); fclose(fp5); strcpy(temp,""); sprintf(temp,"%sTot.out",filename); fp5=fopen(temp,"w"); fclose(fp5); strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp1=fopen(temp,"w"); fprintf(fp,"\n$%d",EXPRUN); fprintf(fp1,"\n$%d",EXPRUN); fclose(fp); fclose(fp1); if(argc==2)sscanf(argv[1],"%d",&debugLevel); printf("\n Debug level %d\n",debugLevel); for( i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; //Read from the file readInput(); //Compute the distance of all the points if(maxDist) { Dmax=computeDistance(); //Compute the maximum distance fp5=fopen("maxData.txt","w");fprintf(fp5,"%f",Dmax);fclose(fp5); } else {fp5=fopen("maxData.txt","r");fscanf(fp5,"%f",&Dmax);fclose(fp5); } printf("\n %f \n",Dmax); //Initialise the board for(k=0;k<EXPRUN;k++) { init(); sum=0; srand48(state1[k]); initialiseBoard(); initialise(1); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(1); printf("\n %d \n",heapCount); //Simulate the working of ants iterate(1); //drop the objects after all the iterations dropFinal(1); //to verify the correctness for(i=0;i<heapCount;i++) sum+=heaps[i].count; //write the cluster centers to the file writeClusterInformation(1); printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); //getchar(); //Run FCM // heapCount=2973;/* To be commented */ printf("\n\n k %d \n\n",k); Fcm(data); //compute new board size. lCount=heapCount; heapRow=(int)ceil(sqrt(4*heapCount)); newHeapCount=lCount; printf("\n LCount %d newHeapCount %d heap Row %d \n",lCount,newHeapCount,heapRow); //harden the objects harden(newHeaps); //iterate with the heaps as the objects. initialiseBoard(); initialise(0); for(i=0;i<lCount;i++) computeHeapParameters(&heaps[i],0); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(0); printf("\n %d \n",heapCount); //getchar(); //Simulate the working of ants iterate(0); dropFinal(0); writeClusterInformation(0); sum=0; for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); // getchar(); Fcm(data); harden1(); } fp=fopen("fcm.out","a"); fp1=fopen(temp,"a"); fprintf(fp,"\n"); fprintf(fp1,"\n"); fclose(fp); fclose(fp1); fp=fopen("fcm.out","r"); fp1=fopen(temp,"r");printf("\n Before read org\n");readOrg();printf("\nafter read org\n");readFCM(fp1,fp2);printf("\n After 1st read fcm\n");readFCM(fp,fp3);printf("\n After 2 fcm read \n");return(1); }//end of main /****************************************************************/ float NORM(feature , feature); void find_covar(); void Fcm (irisData Data[MAXDATA]) { int i,j,l; FILE *ofile; char temp[80]; int flag=0;//added by parag FILE *iniClsCen=fopen("center.clt","a+"); if(iniClsCen==NULL) { printf("\n Unable to open the initial cluster center file\n"); printf("\n choosing random cluster centers....\n"); flag=1; } strcpy(temp,""); sprintf(temp,"%s.data",filename); strcpy (fname,temp); /* Read from this file */ m=2.0; /* The exponent */ /*GET_INPUTS1(fname);*/ /* read in feature vectors */ for (j=0; j<n ; j++) /* Read File in row-major */ for (i=0; i<s ; i++) /* format */ X[j][i]=Data[j][i]; Random_seed=state1[run];/*randseed*/ srand48(Random_seed); for (run=0;run<TOTRUNS;run++) { //flag=1;//for testing random values if (EntireHeaps) lCount=heapCount; else lCount=c; for(i=0;i<lCount;i++) for(j=0;j<s;j++) V[i][j]=0; for(i=0;i<lCount;i++) { for(l=0;l<s;l++) { if(flag==1) { rand_index = lrand48() % n; V[i][l] = X[rand_index][l]; } else { fscanf(iniClsCen,"%f ",&V[i][l]); } }//end for } eps=0.001; stop = FALSE; /* This loop finds the final cluster center and the corresponding U values */ z = 0; while (stop == FALSE) { z++; UMAT(U,V,X); /** Update the U matrix **/ stop = FINISHED(U1,eps); CLUSTER(U,X); /** Get new cluster center **/ printf("Iteration: %d\n\n", z); } /* end while */ printf("Number of iterations: %d\n\n", z);strcpy(temp,""); sprintf(temp,"%sFCM.out",filename);if(dataDump) Output(temp); /* output the results */ }/*end for*/ if(dataDump) { ofile=fopen(temp,"a"); for (i=0; i <= mins; i++) fprintf(ofile, "Min %d of %10.3f occurred %d times\n", i, mintab[i], mincount[i]); fprintf(ofile, "Average iterations %f\n", (float)aveit/(float)TOTRUNS); fclose(ofile); } }/* end Main */ /* The GET_INPUTS1 procedure opens an input file, reads the number of input vectors, the number of features per vector, the number of clusters, and epsilon, then it reads the input values */ void GET_INPUTS1(char fname[]) //char fname[]; { /* Parameters: fname -- name of file containing input info. */ FILE *infile ;//*fopen(); //char junk[10][80]; char realfname[80]; char ch; char fileName1[80]; //int temp[10], i, j, inputs, outputs, count; int i,j; sprintf (realfname, "%s", fname); /* open input file */ if(normalize==1) strcpy(fileName1,"gtest1.data"); else strcpy(fileName1,fname); if((infile = fopen (fileName1, "r")) == NULL) printf("\n CAN'T OPEN %s\n",realfname); else { ch = 'c'; /* Discard leading comments */ while (ch != '#') fscanf (infile, "%c", &ch); for (j=0; j<n ; j++) /* Read File in row-major */ { /* format */ for (i=0; i<s ; i++) fscanf (infile, "%f", &X[j][i]); /* fscanf (infile, "%d", &temp[1]); */ /* end for */ } /* end for */ fclose(infile); /* close input file */ }/* end else */ } /* end GET_INPUTS1 */ /* The NORM function computes the norm of two vectors, x and v, of dimension s. This gives an indication of the distance between them */ float NORM( feature x, feature v) //feature x, v; { /* Parameters : x -- a local copy of an input vector v -- a local copy of a cluster center */ int i,j; /* loop variable */ double sum; /* the square root of sum is the norm; sum is the sum of the differences squared */ float mat1[s]; float mat2[s]; if (maha) { for (i=0; i<s; i++) mat1[i]=x[i]-v[i]; for(i=0;i<s;i++) { sum =0; for(j=0;j<s;j++) sum += mat1[j] * inv_cov[j][i]; mat2[i] = sum; } sum =0; for(j=0;j<s;j++) sum += mat1[j]*mat2[j]; } else { sum = 0; for(j=0;j<s;j++) sum += (x[j] - v[j])* (x[j] - v[j]); } return (float)(sqrt(sum)); } /* end NORM */ /* The UMAT procedure calculates the updated U matrix according to Bezdek's formula. Its side-effect is the update of the global array U1 */ void UMAT( matrix U,center V, objects X) /*matrix U; center V; objects X;*/ { /* Parameters: U, V, and X: same as in main program */ int i, j, k,l ;/* loop variables *///, class /* class whose cluster center //is closest to pattern k */; float d1,d2,d; float sum; int exact; for (k=0;k<n;k++) { /*printf("\n k %d ",k);*/ exact=false; for (i=0;i<lCount;i++) { sum=0; for (j=0;j<lCount;j++) { d1=NORM(X[k],V[i]); d2=NORM(X[k],V[j]); if(d2!=0) { d= d1/d2; d= d * d;/* pow(d1/d2,2.0/(m-1.0)); */ sum+=d; } else { exact = true; for(l=0;l<lCount;l++) if (l==j) U1[l][k]=1.0; else U1[l][k]=0.0; break; } } if (exact!=true) U1[i][k]=1.0/sum; } } } /* end UMAT */ /* The function FINISHED determines if a termination occurs. If not it updates U with U1, so its side-effect is the potential modification of U */ BOOLEAN FINISHED( matrix U1,float eps) //matrix U1; float eps; { /* Parameters: U1, eps-- same as in main */ float sum; /* sum of squared differences between corresponding elements of U and U1 */ int i,k; /* loop control variabes */ BOOLEAN finished; /* set to true if all corresponding elements are within epsilon */ finished = TRUE; sum = 0; for (i=0; i<lCount; i++) for (k=0; k<n; k++) sum += (U[i][k] - U1[i][k])*(U[i][k]-U1[i][k]); if (sqrt(sum) > eps) { finished = FALSE; for (i=0; i<lCount; i++) for (k=0; k<n; k++) U[i][k] = U1[i][k]; } /* end if */ return finished; } /* end FINISHED */ /* The CLUSTER procedure determines cluster centers in V for the c classes being searched for. The global array V is updated */ void CLUSTER( matrix U, objects X) //matrix U; objects X; { /* Parameters: U, X -- as in main */ int i, j, k; /* loop control variables */ feature num; /* numerator in the cluster center computation formula presented by Bezdek */ float denum; /* denumenator in the obove formula */ for (i=0; i<lCount; i++) { denum = 0; for (j=0; j<s; j++) { num[j] = 0; for (k=0; k<n; k++) num[j] += U[i][k] * U[i][k] * X[k][j]; /* pow(U[i][k],m) * X[k][j]; */ } /* end for */ for (k=0; k<n; k++) denum += U[i][k] * U[i][k]; /* pow(U[i][k],m); */ for (j=0; j<s; j++) V[i][j] = num[j] / denum; } /* end for */ } /* end CLUSTER */ float Compute_Jm(center V, objects X) //center V; //objects X; { int i, k,found; double Jm_value; float u,dik; Jm_value = 0; for (k=0; k<n; k++) { found = FALSE; for (i=0; i<lCount; i++) { dik=NORM(X[k],V[i]); dik=dik*dik; if (dik==0.0) { if (found == FALSE) { u=1.0; found = TRUE; printf("found the case with dik=0\n"); } else { printf("\n cant proceed with this initialization"); exit(1); } } else { if (found != TRUE) u=U1[i][k]; /* get_Uvalue(V,X,dik,k); */ else u=0.0; } Jm_value += u * u * dik;/* pow(u, m) * dik; */ } } return (Jm_value); } /* end Compute_Jm */ /* The Output procedure outputs the U matrix which gives the fuzzy measure of each feature belonging to a cluster center. It also outputs the cluster center for each class */ void Output(char fname[]) //char fname[80]; /* Parameters: U, V -- same as in main */ { int i, j, found; /* loop control variables */ float J2c; if ((fp=fopen(fname,"a"))==NULL) printf("\n CAN'T OPEN %s\n",fname); else { fprintf(fp,"\n The cluster centers are: \n"); fprintf(fp,"\n \n"); for (i=0; i<lCount; i++) { fprintf(fp,"\nCluster center %d: ",i+1); fprintf(fp,"\n"); for (j=0; j<s; j++) fprintf(fp,"%8.7f ",V[i][j]); } /* end for */ fprintf(fp,"\nThe membership grades for the feature vectors are:\n"); for (j=0; j<n; j++) { fprintf(fp,"\n"); for (i=0; i<lCount; i++) fprintf(fp," %8.7f",U[i][j]); } /* end for */ } /* end if */ J2c= Compute_Jm(V,X); found =0; for (i=0;i<= mins; i++){ if ((found == 0) && ((J2c - mintab[i]) < .001) && ((J2c - mintab[i]) > -.001)){ mincount[i]++; found = 1; } } if (found == 0){ mins++; mintab[mins]=J2c; mincount[mins]=1; } fprintf(fp,"\nJ%3.1f = %f", m,J2c); fprintf(fp, "Number of iterations: %d\n\n", z); aveit=aveit + z; fclose(fp); } /***************************************************************/ void find_covar(int m) //int m; { int i,j,k; float mean[s],sum; for (i=0;i<s;i++) mean[i]=0; for (i=0;i<s;i++) for (j=0;j<m;j++) mean[i]+=X[j][i]; for (i=0;i<s;i++) mean[i]/=m; for (i=0;i<s;i++) for (j=0;j<s;j++) { sum=0; for (k=0;k<m;k++) sum+=(X[k][i]-mean[i])*(X[k][j]-mean[j]); Y[i][j]=sum/m; } }

Parag Kanade/fuzzy ants/Gauss2/headerStatic.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include<stdlib.h>#include <ctype.h>#include <time.h>#define MAXANTS 334#define MAXDATA 1000#define MAXANTSFORHEAPS 90#define MAXITERATIONS 1000#define MAXROWS 64#define MAXCOLS 64#define EXPRUN 10#define Tremove 1.50#define Tcreate 0.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.19#define features 2#define filename "gauss02"#define errThresh 0.1#define dataDump 1#define maxDist 1#define INITIALOBJECTS 75#define OBJINC 10/* some type definitions to make C code a little more readable */#define TRUE 1#define FALSE 0#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define c 5 /* number of clusters */#define TOTRUNS 1 /* number of V's to write out */#define false 0#define true 1#define maha 0#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap {float Dmax;//maximum dissimilar distanceirisData Ocenter;//center objectfloat Dmean;//mean of the distance between the center and all the objectsirisData Odissim;//most dissimilar objectint x;//x position on the boardint y;//y position on the boardint count;//number of objects in the heapsint disPosition;//most dissimilar objectint objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int carrying;int speed;int dir;irisData data;}ant;float orgData[n][s+1];float fcmData[MAXDATA][n][s];int clsCenCount[MAXDATA];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int a , int b);void initialise(int flag);int findObject(int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);void pickObject2(ant *a , int position,int flag);void pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);/********************************************************************//***For FCM algorithm*/void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects);void Output(char[]);void Fcm();/********************************************************************/

Parag Kanade/fuzzy ants/Gauss2/maxData.txt

1.801062

Parag Kanade/fuzzy ants/Iris/iris.c


Parag Kanade/fuzzy ants/Iris/iris.data

Iris Data # 5.1 3.5 1.4 0.2 4.9 3.0 1.4 0.2 4.7 3.2 1.3 0.2 4.6 3.1 1.5 0.2 5.0 3.6 1.4 0.2 5.4 3.9 1.7 0.4 4.6 3.4 1.4 0.3 5.0 3.4 1.5 0.2 4.4 2.9 1.4 0.2 4.9 3.1 1.5 0.1 5.4 3.7 1.5 0.2 4.8 3.4 1.6 0.2 4.8 3.0 1.4 0.1 4.3 3.0 1.1 0.1 5.8 4.0 1.2 0.2 5.7 4.4 1.5 0.4 5.4 3.9 1.3 0.4 5.1 3.5 1.4 0.3 5.7 3.8 1.7 0.3 5.1 3.8 1.5 0.3 5.4 3.4 1.7 0.2 5.1 3.7 1.5 0.4 4.6 3.6 1.0 0.2 5.1 3.3 1.7 0.5 4.8 3.4 1.9 0.2 5.0 3.0 1.6 0.2 5.0 3.4 1.6 0.4 5.2 3.5 1.5 0.2 5.2 3.4 1.4 0.2 4.7 3.2 1.6 0.2 4.8 3.1 1.6 0.2 5.4 3.4 1.5 0.4 5.2 4.1 1.5 0.1 5.5 4.2 1.4 0.2 4.9 3.1 1.5 0.1 5.0 3.2 1.2 0.2 5.5 3.5 1.3 0.2 4.9 3.1 1.5 0.1 4.4 3.0 1.3 0.2 5.1 3.4 1.5 0.2 5.0 3.5 1.3 0.3 4.5 2.3 1.3 0.3 4.4 3.2 1.3 0.2 5.0 3.5 1.6 0.6 5.1 3.8 1.9 0.4 4.8 3.0 1.4 0.3 5.1 3.8 1.6 0.2 4.6 3.2 1.4 0.2 5.3 3.7 1.5 0.2 5.0 3.3 1.4 0.2 7.0 3.2 4.7 1.4 6.4 3.2 4.5 1.5 6.9 3.1 4.9 1.5 5.5 2.3 4.0 1.3 6.5 2.8 4.6 1.5 5.7 2.8 4.5 1.3 6.3 3.3 4.7 1.6 4.9 2.4 3.3 1.0 6.6 2.9 4.6 1.3 5.2 2.7 3.9 1.4 5.0 2.0 3.5 1.0 5.9 3.0 4.2 1.5 6.0 2.2 4.0 1.0 6.1 2.9 4.7 1.4 5.6 2.9 3.6 1.3 6.7 3.1 4.4 1.4 5.6 3.0 4.5 1.5 5.8 2.7 4.1 1.0 6.2 2.2 4.5 1.5 5.6 2.5 3.9 1.1 5.9 3.2 4.8 1.8 6.1 2.8 4.0 1.3 6.3 2.5 4.9 1.5 6.1 2.8 4.7 1.2 6.4 2.9 4.3 1.3 6.6 3.0 4.4 1.4 6.8 2.8 4.8 1.4 6.7 3.0 5.0 1.7 6.0 2.9 4.5 1.5 5.7 2.6 3.5 1.0 5.5 2.4 3.8 1.1 5.5 2.4 3.7 1.0 5.8 2.7 3.9 1.2 6.0 2.7 5.1 1.6 5.4 3.0 4.5 1.5 6.0 3.4 4.5 1.6 6.7 3.1 4.7 1.5 6.3 2.3 4.4 1.3 5.6 3.0 4.1 1.3 5.5 2.5 4.0 1.3 5.5 2.6 4.4 1.2 6.1 3.0 4.6 1.4 5.8 2.6 4.0 1.2 5.0 2.3 3.3 1.0 5.6 2.7 4.2 1.3 5.7 3.0 4.2 1.2 5.7 2.9 4.2 1.3 6.2 2.9 4.3 1.3 5.1 2.5 3.0 1.1 5.7 2.8 4.1 1.3 6.3 3.3 6.0 2.5 5.8 2.7 5.1 1.9 7.1 3.0 5.9 2.1 6.3 2.9 5.6 1.8 6.5 3.0 5.8 2.2 7.6 3.0 6.6 2.1 4.9 2.5 4.5 1.7 7.3 2.9 6.3 1.8 6.7 2.5 5.8 1.8 7.2 3.6 6.1 2.5 6.5 3.2 5.1 2.0 6.4 2.7 5.3 1.9 6.8 3.0 5.5 2.1 5.7 2.5 5.0 2.0 5.8 2.8 5.1 2.4 6.4 3.2 5.3 2.3 6.5 3.0 5.5 1.8 7.7 3.8 6.7 2.2 7.7 2.6 6.9 2.3 6.0 2.2 5.0 1.5 6.9 3.2 5.7 2.3 5.6 2.8 4.9 2.0 7.7 2.8 6.7 2.0 6.3 2.7 4.9 1.8 6.7 3.3 5.7 2.1 7.2 3.2 6.0 1.8 6.2 2.8 4.8 1.8 6.1 3.0 4.9 1.8 6.4 2.8 5.6 2.1 7.2 3.0 5.8 1.6 7.4 2.8 6.1 1.9 7.9 3.8 6.4 2.0 6.4 2.8 5.6 2.2 6.3 2.8 5.1 1.5 6.1 2.6 5.6 1.4 7.7 3.0 6.1 2.3 6.3 3.4 5.6 2.4 6.4 3.1 5.5 1.8 6.0 3.0 4.8 1.8 6.9 3.1 5.4 2.1 6.7 3.1 5.6 2.4 6.9 3.1 5.1 2.3 5.8 2.7 5.1 1.9 6.8 3.2 5.9 2.3 6.7 3.3 5.7 2.5 6.7 3.0 5.2 2.3 6.3 2.5 5.0 1.9 6.5 3.0 5.2 2.0 6.2 3.4 5.4 2.3 5.9 3.0 5.1 1.8

Parag Kanade/fuzzy ants/Iris/irisFull.data

1 5.1 3.5 1.4 0.2

1 4.9 3 1.4 0.2

1 4.7 3.2 1.3 0.2

1 4.6 3.1 1.5 0.2

1 5 3.6 1.4 0.2

1 5.4 3.9 1.7 0.4

1 4.6 3.4 1.4 0.3

1 5 3.4 1.5 0.2

1 4.4 2.9 1.4 0.2

1 4.9 3.1 1.5 0.1

1 5.4 3.7 1.5 0.2

1 4.8 3.4 1.6 0.2

1 4.8 3 1.4 0.1

1 4.3 3 1.1 0.1

1 5.8 4 1.2 0.2

1 5.7 4.4 1.5 0.4

1 5.4 3.9 1.3 0.4

1 5.1 3.5 1.4 0.3

1 5.7 3.8 1.7 0.3

1 5.1 3.8 1.5 0.3

1 5.4 3.4 1.7 0.2

1 5.1 3.7 1.5 0.4

1 4.6 3.6 1 0.2

1 5.1 3.3 1.7 0.5

1 4.8 3.4 1.9 0.2

1 5 3 1.6 0.2

1 5 3.4 1.6 0.4

1 5.2 3.5 1.5 0.2

1 5.2 3.4 1.4 0.2

1 4.7 3.2 1.6 0.2

1 4.8 3.1 1.6 0.2

1 5.4 3.4 1.5 0.4

1 5.2 4.1 1.5 0.1

1 5.5 4.2 1.4 0.2

1 4.9 3.1 1.5 0.1

1 5 3.2 1.2 0.2

1 5.5 3.5 1.3 0.2

1 4.9 3.1 1.5 0.1

1 4.4 3 1.3 0.2

1 5.1 3.4 1.5 0.2

1 5 3.5 1.3 0.3

1 4.5 2.3 1.3 0.3

1 4.4 3.2 1.3 0.2

1 5 3.5 1.6 0.6

1 5.1 3.8 1.9 0.4

1 4.8 3 1.4 0.3

1 5.1 3.8 1.6 0.2

1 4.6 3.2 1.4 0.2

1 5.3 3.7 1.5 0.2

1 5 3.3 1.4 0.2

2 7 3.2 4.7 1.4

2 6.4 3.2 4.5 1.5

2 6.9 3.1 4.9 1.5

2 5.5 2.3 4 1.3

2 6.5 2.8 4.6 1.5

2 5.7 2.8 4.5 1.3

2 6.3 3.3 4.7 1.6

2 4.9 2.4 3.3 1

2 6.6 2.9 4.6 1.3

2 5.2 2.7 3.9 1.4

2 5 2 3.5 1

2 5.9 3 4.2 1.5

2 6 2.2 4 1

2 6.1 2.9 4.7 1.4

2 5.6 2.9 3.6 1.3

2 6.7 3.1 4.4 1.4

2 5.6 3 4.5 1.5

2 5.8 2.7 4.1 1

2 6.2 2.2 4.5 1.5

2 5.6 2.5 3.9 1.1

2 5.9 3.2 4.8 1.8

2 6.1 2.8 4 1.3

2 6.3 2.5 4.9 1.5

2 6.1 2.8 4.7 1.2

2 6.4 2.9 4.3 1.3

2 6.6 3 4.4 1.4

2 6.8 2.8 4.8 1.4

2 6.7 3 5 1.7

2 6 2.9 4.5 1.5

2 5.7 2.6 3.5 1

2 5.5 2.4 3.8 1.1

2 5.5 2.4 3.7 1

2 5.8 2.7 3.9 1.2

2 6 2.7 5.1 1.6

2 5.4 3 4.5 1.5

2 6 3.4 4.5 1.6

2 6.7 3.1 4.7 1.5

2 6.3 2.3 4.4 1.3

2 5.6 3 4.1 1.3

2 5.5 2.5 4 1.3

2 5.5 2.6 4.4 1.2

2 6.1 3 4.6 1.4

2 5.8 2.6 4 1.2

2 5 2.3 3.3 1

2 5.6 2.7 4.2 1.3

2 5.7 3 4.2 1.2

2 5.7 2.9 4.2 1.3

2 6.2 2.9 4.3 1.3

2 5.1 2.5 3 1.1

2 5.7 2.8 4.1 1.3

3 6.3 3.3 6 2.5

3 5.8 2.7 5.1 1.9

3 7.1 3 5.9 2.1

3 6.3 2.9 5.6 1.8

3 6.5 3 5.8 2.2

3 7.6 3 6.6 2.1

3 4.9 2.5 4.5 1.7

3 7.3 2.9 6.3 1.8

3 6.7 2.5 5.8 1.8

3 7.2 3.6 6.1 2.5

3 6.5 3.2 5.1 2

3 6.4 2.7 5.3 1.9

3 6.8 3 5.5 2.1

3 5.7 2.5 5 2

3 5.8 2.8 5.1 2.4

3 6.4 3.2 5.3 2.3

3 6.5 3 5.5 1.8

3 7.7 3.8 6.7 2.2

3 7.7 2.6 6.9 2.3

3 6 2.2 5 1.5

3 6.9 3.2 5.7 2.3

3 5.6 2.8 4.9 2

3 7.7 2.8 6.7 2

3 6.3 2.7 4.9 1.8

3 6.7 3.3 5.7 2.1

3 7.2 3.2 6 1.8

3 6.2 2.8 4.8 1.8

3 6.1 3 4.9 1.8

3 6.4 2.8 5.6 2.1

3 7.2 3 5.8 1.6

3 7.4 2.8 6.1 1.9

3 7.9 3.8 6.4 2

3 6.4 2.8 5.6 2.2

3 6.3 2.8 5.1 1.5

3 6.1 2.6 5.6 1.4

3 7.7 3 6.1 2.3

3 6.3 3.4 5.6 2.4

3 6.4 3.1 5.5 1.8

3 6 3 4.8 1.8

3 6.9 3.1 5.4 2.1

3 6.7 3.1 5.6 2.4

3 6.9 3.1 5.1 2.3

3 5.8 2.7 5.1 1.9

3 6.8 3.2 5.9 2.3

3 6.7 3.3 5.7 2.5

3 6.7 3 5.2 2.3

3 6.3 2.5 5 1.9

3 6.5 3 5.2 2

3 6.2 3.4 5.4 2.3

3 5.9 3 5.1 1.8

Parag Kanade/fuzzy ants/Iris/2class/iris.c


Parag Kanade/fuzzy ants/Iris/2class/maxData.txt

3.220810

Parag Kanade/fuzzy ants/Iris/2class/iris.data

Iris Data # 5.1 3.5 1.4 0.2 4.9 3.0 1.4 0.2 4.7 3.2 1.3 0.2 4.6 3.1 1.5 0.2 5.0 3.6 1.4 0.2 5.4 3.9 1.7 0.4 4.6 3.4 1.4 0.3 5.0 3.4 1.5 0.2 4.4 2.9 1.4 0.2 4.9 3.1 1.5 0.1 5.4 3.7 1.5 0.2 4.8 3.4 1.6 0.2 4.8 3.0 1.4 0.1 4.3 3.0 1.1 0.1 5.8 4.0 1.2 0.2 5.7 4.4 1.5 0.4 5.4 3.9 1.3 0.4 5.1 3.5 1.4 0.3 5.7 3.8 1.7 0.3 5.1 3.8 1.5 0.3 5.4 3.4 1.7 0.2 5.1 3.7 1.5 0.4 4.6 3.6 1.0 0.2 5.1 3.3 1.7 0.5 4.8 3.4 1.9 0.2 5.0 3.0 1.6 0.2 5.0 3.4 1.6 0.4 5.2 3.5 1.5 0.2 5.2 3.4 1.4 0.2 4.7 3.2 1.6 0.2 4.8 3.1 1.6 0.2 5.4 3.4 1.5 0.4 5.2 4.1 1.5 0.1 5.5 4.2 1.4 0.2 4.9 3.1 1.5 0.1 5.0 3.2 1.2 0.2 5.5 3.5 1.3 0.2 4.9 3.1 1.5 0.1 4.4 3.0 1.3 0.2 5.1 3.4 1.5 0.2 5.0 3.5 1.3 0.3 4.5 2.3 1.3 0.3 4.4 3.2 1.3 0.2 5.0 3.5 1.6 0.6 5.1 3.8 1.9 0.4 4.8 3.0 1.4 0.3 5.1 3.8 1.6 0.2 4.6 3.2 1.4 0.2 5.3 3.7 1.5 0.2 5.0 3.3 1.4 0.2 7.0 3.2 4.7 1.4 6.4 3.2 4.5 1.5 6.9 3.1 4.9 1.5 5.5 2.3 4.0 1.3 6.5 2.8 4.6 1.5 5.7 2.8 4.5 1.3 6.3 3.3 4.7 1.6 4.9 2.4 3.3 1.0 6.6 2.9 4.6 1.3 5.2 2.7 3.9 1.4 5.0 2.0 3.5 1.0 5.9 3.0 4.2 1.5 6.0 2.2 4.0 1.0 6.1 2.9 4.7 1.4 5.6 2.9 3.6 1.3 6.7 3.1 4.4 1.4 5.6 3.0 4.5 1.5 5.8 2.7 4.1 1.0 6.2 2.2 4.5 1.5 5.6 2.5 3.9 1.1 5.9 3.2 4.8 1.8 6.1 2.8 4.0 1.3 6.3 2.5 4.9 1.5 6.1 2.8 4.7 1.2 6.4 2.9 4.3 1.3 6.6 3.0 4.4 1.4 6.8 2.8 4.8 1.4 6.7 3.0 5.0 1.7 6.0 2.9 4.5 1.5 5.7 2.6 3.5 1.0 5.5 2.4 3.8 1.1 5.5 2.4 3.7 1.0 5.8 2.7 3.9 1.2 6.0 2.7 5.1 1.6 5.4 3.0 4.5 1.5 6.0 3.4 4.5 1.6 6.7 3.1 4.7 1.5 6.3 2.3 4.4 1.3 5.6 3.0 4.1 1.3 5.5 2.5 4.0 1.3 5.5 2.6 4.4 1.2 6.1 3.0 4.6 1.4 5.8 2.6 4.0 1.2 5.0 2.3 3.3 1.0 5.6 2.7 4.2 1.3 5.7 3.0 4.2 1.2 5.7 2.9 4.2 1.3 6.2 2.9 4.3 1.3 5.1 2.5 3.0 1.1 5.7 2.8 4.1 1.3 6.3 3.3 6.0 2.5 5.8 2.7 5.1 1.9 7.1 3.0 5.9 2.1 6.3 2.9 5.6 1.8 6.5 3.0 5.8 2.2 7.6 3.0 6.6 2.1 4.9 2.5 4.5 1.7 7.3 2.9 6.3 1.8 6.7 2.5 5.8 1.8 7.2 3.6 6.1 2.5 6.5 3.2 5.1 2.0 6.4 2.7 5.3 1.9 6.8 3.0 5.5 2.1 5.7 2.5 5.0 2.0 5.8 2.8 5.1 2.4 6.4 3.2 5.3 2.3 6.5 3.0 5.5 1.8 7.7 3.8 6.7 2.2 7.7 2.6 6.9 2.3 6.0 2.2 5.0 1.5 6.9 3.2 5.7 2.3 5.6 2.8 4.9 2.0 7.7 2.8 6.7 2.0 6.3 2.7 4.9 1.8 6.7 3.3 5.7 2.1 7.2 3.2 6.0 1.8 6.2 2.8 4.8 1.8 6.1 3.0 4.9 1.8 6.4 2.8 5.6 2.1 7.2 3.0 5.8 1.6 7.4 2.8 6.1 1.9 7.9 3.8 6.4 2.0 6.4 2.8 5.6 2.2 6.3 2.8 5.1 1.5 6.1 2.6 5.6 1.4 7.7 3.0 6.1 2.3 6.3 3.4 5.6 2.4 6.4 3.1 5.5 1.8 6.0 3.0 4.8 1.8 6.9 3.1 5.4 2.1 6.7 3.1 5.6 2.4 6.9 3.1 5.1 2.3 5.8 2.7 5.1 1.9 6.8 3.2 5.9 2.3 6.7 3.3 5.7 2.5 6.7 3.0 5.2 2.3 6.3 2.5 5.0 1.9 6.5 3.0 5.2 2.0 6.2 3.4 5.4 2.3 5.9 3.0 5.1 1.8

Parag Kanade/fuzzy ants/Iris/2class/irisFull.data

1 5.1 3.5 1.4 0.2

1 4.9 3 1.4 0.2

1 4.7 3.2 1.3 0.2

1 4.6 3.1 1.5 0.2

1 5 3.6 1.4 0.2

1 5.4 3.9 1.7 0.4

1 4.6 3.4 1.4 0.3

1 5 3.4 1.5 0.2

1 4.4 2.9 1.4 0.2

1 4.9 3.1 1.5 0.1

1 5.4 3.7 1.5 0.2

1 4.8 3.4 1.6 0.2

1 4.8 3 1.4 0.1

1 4.3 3 1.1 0.1

1 5.8 4 1.2 0.2

1 5.7 4.4 1.5 0.4

1 5.4 3.9 1.3 0.4

1 5.1 3.5 1.4 0.3

1 5.7 3.8 1.7 0.3

1 5.1 3.8 1.5 0.3

1 5.4 3.4 1.7 0.2

1 5.1 3.7 1.5 0.4

1 4.6 3.6 1 0.2

1 5.1 3.3 1.7 0.5

1 4.8 3.4 1.9 0.2

1 5 3 1.6 0.2

1 5 3.4 1.6 0.4

1 5.2 3.5 1.5 0.2

1 5.2 3.4 1.4 0.2

1 4.7 3.2 1.6 0.2

1 4.8 3.1 1.6 0.2

1 5.4 3.4 1.5 0.4

1 5.2 4.1 1.5 0.1

1 5.5 4.2 1.4 0.2

1 4.9 3.1 1.5 0.1

1 5 3.2 1.2 0.2

1 5.5 3.5 1.3 0.2

1 4.9 3.1 1.5 0.1

1 4.4 3 1.3 0.2

1 5.1 3.4 1.5 0.2

1 5 3.5 1.3 0.3

1 4.5 2.3 1.3 0.3

1 4.4 3.2 1.3 0.2

1 5 3.5 1.6 0.6

1 5.1 3.8 1.9 0.4

1 4.8 3 1.4 0.3

1 5.1 3.8 1.6 0.2

1 4.6 3.2 1.4 0.2

1 5.3 3.7 1.5 0.2

1 5 3.3 1.4 0.2

2 7 3.2 4.7 1.4

2 6.4 3.2 4.5 1.5

2 6.9 3.1 4.9 1.5

2 5.5 2.3 4 1.3

2 6.5 2.8 4.6 1.5

2 5.7 2.8 4.5 1.3

2 6.3 3.3 4.7 1.6

2 4.9 2.4 3.3 1

2 6.6 2.9 4.6 1.3

2 5.2 2.7 3.9 1.4

2 5 2 3.5 1

2 5.9 3 4.2 1.5

2 6 2.2 4 1

2 6.1 2.9 4.7 1.4

2 5.6 2.9 3.6 1.3

2 6.7 3.1 4.4 1.4

2 5.6 3 4.5 1.5

2 5.8 2.7 4.1 1

2 6.2 2.2 4.5 1.5

2 5.6 2.5 3.9 1.1

2 5.9 3.2 4.8 1.8

2 6.1 2.8 4 1.3

2 6.3 2.5 4.9 1.5

2 6.1 2.8 4.7 1.2

2 6.4 2.9 4.3 1.3

2 6.6 3 4.4 1.4

2 6.8 2.8 4.8 1.4

2 6.7 3 5 1.7

2 6 2.9 4.5 1.5

2 5.7 2.6 3.5 1

2 5.5 2.4 3.8 1.1

2 5.5 2.4 3.7 1

2 5.8 2.7 3.9 1.2

2 6 2.7 5.1 1.6

2 5.4 3 4.5 1.5

2 6 3.4 4.5 1.6

2 6.7 3.1 4.7 1.5

2 6.3 2.3 4.4 1.3

2 5.6 3 4.1 1.3

2 5.5 2.5 4 1.3

2 5.5 2.6 4.4 1.2

2 6.1 3 4.6 1.4

2 5.8 2.6 4 1.2

2 5 2.3 3.3 1

2 5.6 2.7 4.2 1.3

2 5.7 3 4.2 1.2

2 5.7 2.9 4.2 1.3

2 6.2 2.9 4.3 1.3

2 5.1 2.5 3 1.1

2 5.7 2.8 4.1 1.3

2 6.3 3.3 6 2.5

2 5.8 2.7 5.1 1.9

2 7.1 3 5.9 2.1

2 6.3 2.9 5.6 1.8

2 6.5 3 5.8 2.2

2 7.6 3 6.6 2.1

2 4.9 2.5 4.5 1.7

2 7.3 2.9 6.3 1.8

2 6.7 2.5 5.8 1.8

2 7.2 3.6 6.1 2.5

2 6.5 3.2 5.1 2

2 6.4 2.7 5.3 1.9

2 6.8 3 5.5 2.1

2 5.7 2.5 5 2

2 5.8 2.8 5.1 2.4

2 6.4 3.2 5.3 2.3

2 6.5 3 5.5 1.8

2 7.7 3.8 6.7 2.2

2 7.7 2.6 6.9 2.3

2 6 2.2 5 1.5

2 6.9 3.2 5.7 2.3

2 5.6 2.8 4.9 2

2 7.7 2.8 6.7 2

2 6.3 2.7 4.9 1.8

2 6.7 3.3 5.7 2.1

2 7.2 3.2 6 1.8

2 6.2 2.8 4.8 1.8

2 6.1 3 4.9 1.8

2 6.4 2.8 5.6 2.1

2 7.2 3 5.8 1.6

2 7.4 2.8 6.1 1.9

2 7.9 3.8 6.4 2

2 6.4 2.8 5.6 2.2

2 6.3 2.8 5.1 1.5

2 6.1 2.6 5.6 1.4

2 7.7 3 6.1 2.3

2 6.3 3.4 5.6 2.4

2 6.4 3.1 5.5 1.8

2 6 3 4.8 1.8

2 6.9 3.1 5.4 2.1

2 6.7 3.1 5.6 2.4

2 6.9 3.1 5.1 2.3

2 5.8 2.7 5.1 1.9

2 6.8 3.2 5.9 2.3

2 6.7 3.3 5.7 2.5

2 6.7 3 5.2 2.3

2 6.3 2.5 5 1.9

2 6.5 3 5.2 2

2 6.2 3.4 5.4 2.3

2 5.9 3 5.1 1.8

Parag Kanade/fuzzy ants/Iris/2class/headerStatic.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include<stdlib.h>#include <ctype.h>#include <time.h>#define MAXANTS 50#define MAXDATA 150#define MAXANTSFORHEAPS 10#define MAXITERATIONS 1000#define MAXROWS 30#define MAXCOLS 30#define EXPRUN 50#define Tremove 1.50#define Tcreate 0.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.12#define features 4#define filename "iris"#define errThresh 0.1#define dataDump 1#define maxDist 1#define INITIALOBJECTS 75#define OBJINC 10/* some type definitions to make C code a little more readable */#define TRUE 1#define FALSE 0#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define c 2 /* number of clusters */#define TOTRUNS 1 /* number of V's to write out */#define false 0#define true 1#define maha 0#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap {float Dmax;//maximum dissimilar distanceirisData Ocenter;//center objectfloat Dmean;//mean of the distance between the center and all the objectsirisData Odissim;//most dissimilar objectint x;//x position on the boardint y;//y position on the boardint count;//number of objects in the heapsint disPosition;//most dissimilar objectint objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int carrying;int speed;int dir;irisData data;}ant;float orgData[n][s+1];float fcmData[260][n][s];int clsCenCount[260];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int a , int b);void initialise(int flag);int findObject(int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);void pickObject2(ant *a , int position,int flag);void pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);/********************************************************************//***For FCM algorithm*/void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects);void Output(char[]);void Fcm();/********************************************************************/

Parag Kanade/fuzzy ants/Iris/headerStatic.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include<stdlib.h>#include <ctype.h>#include <time.h>#define MAXANTS 50#define MAXDATA 150#define MAXANTSFORHEAPS 10#define MAXITERATIONS 1000#define MAXROWS 30#define MAXCOLS 30#define EXPRUN 50#define Tremove 1.50#define Tcreate 0.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.14#define features 4#define filename "iris"#define errThresh 0.1#define dataDump 1#define maxDist 0#define INITIALOBJECTS 75#define OBJINC 10/* some type definitions to make C code a little more readable */#define TRUE 1#define FALSE 0#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define c 3 /* number of clusters */#define TOTRUNS 1 /* number of V's to write out */#define false 0#define true 1#define maha 0#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap {float Dmax;//maximum dissimilar distanceirisData Ocenter;//center objectfloat Dmean;//mean of the distance between the center and all the objectsirisData Odissim;//most dissimilar objectint x;//x position on the boardint y;//y position on the boardint count;//number of objects in the heapsint disPosition;//most dissimilar objectint objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int carrying;int speed;int dir;irisData data;}ant;float orgData[n][s+1];float fcmData[260][n][s];int clsCenCount[260];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int a , int b);void initialise(int flag);int findObject(int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);void pickObject2(ant *a , int position,int flag);void pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);/********************************************************************//***For FCM algorithm*/void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects);void Output(char[]);void Fcm();/********************************************************************/

Parag Kanade/fuzzy ants/Iris/maxData.txt

3.220810

Parag Kanade/fuzzy ants/Glass/headerStatic.h

/********************************************************************/ /* Ant Class parameters */ /* SPEED # amplitude of moves [1,10] */ /* P direction Probability to move in the same direction [.5,1] */ /* Max carry : Maximum object carrying time [20,200] */ /* P load Probability to pick up a single object [0.4,0.8] */ /* P destroy probability to destroy a heap of two objects [0,0.6] */ /* T remove min. dissimilarity necessary for removing an object */ /* from a heap [.1,.2] */ /* T create max dissimilarity permitted for creating a heap[.05,.2]*/ /********************************************************************/ #include <stdio.h> #include <math.h> #include <string.h> #include<stdlib.h> #include <ctype.h> #include <time.h> #define MAXANTS 72 #define MAXDATA 214 #define MAXANTSFORHEAPS 10 #define MAXITERATIONS 1000 #define MAXROWS 30 #define MAXCOLS 30 #define EXPRUN 50 #define Tremove 2.0 #define Tcreate 0.5 #define Pdrop 0.2 #define Pdestroy 0.3 #define Pload 0.3 #define normalize 0 #define EntireHeaps 1 #define TcreateForHeaps 0.105 #define features 9 #define filename "glass" #define errThresh 0.01 #define dataDump 1 #define maxDist 0 #define INITIALOBJECTS 75 #define OBJINC 10 /* some type definitions to make C code a little more readable */ #define TRUE 1 #define FALSE 0 #define n MAXDATA /* number of input vectors */ #define s features /* number of features */ #define c 6 /* number of clusters */ #define TOTRUNS 1 /* number of V's to write out */ #define false 0 #define true 1 #define maha 0 #define square 0 extern int count; int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/ int currHeapCount=0; typedef int BOOLEAN; typedef short TYPE_IN_DATA; /* Type of input binary data */ typedef float feature[s]; /* Type of each input; made up of input features */ typedef float irisData[s]; typedef feature objects[MAXDATA]; /* Type of array holding all inputs */ typedef feature center[MAXDATA]; /* Type of array holding cluster centers */ typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */ typedef struct Heap { float Dmax;//maximum dissimilar distance irisData Ocenter;//center object float Dmean;//mean of the distance between the center and all the objects irisData Odissim;//most dissimilar object int x;//x position on the board int y;//y position on the board int count;//number of objects in the heaps int disPosition;//most dissimilar object int objects[MAXDATA]; int objCount; }heap; typedef struct Ant { int x; int y; int carrying; int speed; int dir; irisData data; }ant; float orgData[n][s+1]; float fcmData[260][n][s]; int clsCenCount[260]; int currentRow=0; int currentAntCount=0; int updateAfter=50; int heapSum=0; int minAnts=3; int rowMin=5; typedef struct HeapInfo { int total; int classObj[c]; } heapInfo; /*heapInfo info[MAXDATA];*/ /********************************************************************/ /***Function decelarations*/ /********************************************************************/ /***For ant based algorithm*/ void readInput(); float computeDistance(); float computeDistanceBetween2(irisData a,irisData b); float findMax(float *distance); float value(irisData a); int found(int a , int b); void initialise(int flag); int findObject(int a,int b); void initialiseAnts(int flag); void moveNext(ant* a,int flag); void computeHeapParameters(heap* data,int flag); void pickObject1(ant *a,int position,int flag); void pickObject2(ant *a , int position,int flag); void pickObjectn(ant *a,int position,int flag); void pick(ant *a,int flag); void dropObject(ant* a,int flag); int findInNewheaps(irisData object); void dropObject1(ant* a, int position,int flag); void drop(ant* a,int flag); void move(ant* a,int flag); void iterate(int flag); void dropFinal(int flag); int partition(heap a[], int low,int high); void quickSort( heap A[], int low,int high ) ; void writeClusterInformation(int flag); void initialiseNewHeaps(heap newHeaps[]); int findPosition(int i); void harden(heap newHeaps[]); void harden1(); int findPosition1(int ); void writeInfo(); void addObject1(int ,int); void readFCM(FILE *,FILE *); void readOrg(); int findInData(irisData input,int iflag); void adjustBoard(int flag); void writeIncremental(int iteration,int flag); /********************************************************************/ /***For FCM algorithm*/ void GET_INPUTS1(char []); void UMAT( matrix,center,objects); BOOLEAN FINISHED(matrix, float); void CLUSTER( matrix, objects); void Output(char[]); void Fcm(); /********************************************************************/

Parag Kanade/fuzzy ants/Glass/maxData.txt

5.691089

Parag Kanade/fuzzy ants/Glass/glass.data

Glass Data # 1.52101 13.64 4.49 1.1 71.78 0.06 8.75 0 0 1.51761 13.89 3.6 1.36 72.73 0.48 7.83 0 0 1.51618 13.53 3.55 1.54 72.99 0.39 7.78 0 0 1.51766 13.21 3.69 1.29 72.61 0.57 8.22 0 0 1.51742 13.27 3.62 1.24 73.08 0.55 8.07 0 0 1.51596 12.79 3.61 1.62 72.97 0.64 8.07 0 0.26 1.51743 13.3 3.6 1.14 73.09 0.58 8.17 0 0 1.51756 13.15 3.61 1.05 73.24 0.57 8.24 0 0 1.51918 14.04 3.58 1.37 72.08 0.56 8.3 0 0 1.51755 13 3.6 1.36 72.99 0.57 8.4 0 0.11 1.51571 12.72 3.46 1.56 73.2 0.67 8.09 0 0.24 1.51763 12.8 3.66 1.27 73.01 0.6 8.56 0 0 1.51589 12.88 3.43 1.4 73.28 0.69 8.05 0 0.24 1.51748 12.86 3.56 1.27 73.21 0.54 8.38 0 0.17 1.51763 12.61 3.59 1.31 73.29 0.58 8.5 0 0 1.51761 12.81 3.54 1.23 73.24 0.58 8.39 0 0 1.51784 12.68 3.67 1.16 73.11 0.61 8.7 0 0 1.52196 14.36 3.85 0.89 71.36 0.15 9.15 0 0 1.51911 13.9 3.73 1.18 72.12 0.06 8.89 0 0 1.51735 13.02 3.54 1.69 72.73 0.54 8.44 0 0.07 1.5175 12.82 3.55 1.49 72.75 0.54 8.52 0 0.19 1.51966 14.77 3.75 0.29 72.02 0.03 9 0 0 1.51736 12.78 3.62 1.29 72.79 0.59 8.7 0 0 1.51751 12.81 3.57 1.35 73.02 0.62 8.59 0 0 1.5172 13.38 3.5 1.15 72.85 0.5 8.43 0 0 1.51764 12.98 3.54 1.21 73 0.65 8.53 0 0 1.51793 13.21 3.48 1.41 72.64 0.59 8.43 0 0 1.51721 12.87 3.48 1.33 73.04 0.56 8.43 0 0 1.51768 12.56 3.52 1.43 73.15 0.57 8.54 0 0 1.51784 13.08 3.49 1.28 72.86 0.6 8.49 0 0 1.51768 12.65 3.56 1.3 73.08 0.61 8.69 0 0.14 1.51747 12.84 3.5 1.14 73.27 0.56 8.55 0 0 1.51775 12.85 3.48 1.23 72.97 0.61 8.56 0.09 0.22 1.51753 12.57 3.47 1.38 73.39 0.6 8.55 0 0.06 1.51783 12.69 3.54 1.34 72.95 0.57 8.75 0 0 1.51567 13.29 3.45 1.21 72.74 0.56 8.57 0 0 1.51909 13.89 3.53 1.32 71.81 0.51 8.78 0.11 0 1.51797 12.74 3.48 1.35 72.96 0.64 8.68 0 0 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0 0 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0 0 1.51793 12.79 3.5 1.12 73.03 0.64 8.77 0 0 1.51755 12.71 3.42 1.2 73.2 0.59 8.64 0 0 1.51779 13.21 3.39 1.33 72.76 0.59 8.59 0 0 1.5221 13.73 3.84 0.72 71.76 0.17 9.74 0 0 1.51786 12.73 3.43 1.19 72.95 0.62 8.76 0 0.3 1.519 13.49 3.48 1.35 71.95 0.55 9 0 0 1.51869 13.19 3.37 1.18 72.72 0.57 8.83 0 0.16 1.52667 13.99 3.7 0.71 71.57 0.02 9.82 0 0.1 1.52223 13.21 3.77 0.79 71.99 0.13 10.02 0 0 1.51898 13.58 3.35 1.23 72.08 0.59 8.91 0 0 1.5232 13.72 3.72 0.51 71.75 0.09 10.06 0 0.16 1.51926 13.2 3.33 1.28 72.36 0.6 9.14 0 0.11 1.51808 13.43 2.87 1.19 72.84 0.55 9.03 0 0 1.51837 13.14 2.84 1.28 72.85 0.55 9.07 0 0 1.51778 13.21 2.81 1.29 72.98 0.51 9.02 0 0.09 1.51769 12.45 2.71 1.29 73.7 0.56 9.06 0 0.24 1.51215 12.99 3.47 1.12 72.98 0.62 8.35 0 0.31 1.51824 12.87 3.48 1.29 72.95 0.6 8.43 0 0 1.51754 13.48 3.74 1.17 72.99 0.59 8.03 0 0 1.51754 13.39 3.66 1.19 72.79 0.57 8.27 0 0.11 1.51905 13.6 3.62 1.11 72.64 0.14 8.76 0 0 1.51977 13.81 3.58 1.32 71.72 0.12 8.67 0.69 0 1.52172 13.51 3.86 0.88 71.79 0.23 9.54 0 0.11 1.52227 14.17 3.81 0.78 71.35 0 9.69 0 0 1.52172 13.48 3.74 0.9 72.01 0.18 9.61 0 0.07 1.52099 13.69 3.59 1.12 71.96 0.09 9.4 0 0 1.52152 13.05 3.65 0.87 72.22 0.19 9.85 0 0.17 1.52152 13.05 3.65 0.87 72.32 0.19 9.85 0 0.17 1.52152 13.12 3.58 0.9 72.2 0.23 9.82 0 0.16 1.523 13.31 3.58 0.82 71.99 0.12 10.17 0 0.03 1.51574 14.86 3.67 1.74 71.87 0.16 7.36 0 0.12 1.51848 13.64 3.87 1.27 71.96 0.54 8.32 0 0.32 1.51593 13.09 3.59 1.52 73.1 0.67 7.83 0 0 1.51631 13.34 3.57 1.57 72.87 0.61 7.89 0 0 1.51596 13.02 3.56 1.54 73.11 0.72 7.9 0 0 1.5159 13.02 3.58 1.51 73.12 0.69 7.96 0 0 1.51645 13.44 3.61 1.54 72.39 0.66 8.03 0 0 1.51627 13 3.58 1.54 72.83 0.61 8.04 0 0 1.51613 13.92 3.52 1.25 72.88 0.37 7.94 0 0.14 1.5159 12.82 3.52 1.9 72.86 0.69 7.97 0 0 1.51592 12.86 3.52 2.12 72.66 0.69 7.97 0 0 1.51593 13.25 3.45 1.43 73.17 0.61 7.86 0 0 1.51646 13.41 3.55 1.25 72.81 0.68 8.1 0 0 1.51594 13.09 3.52 1.55 72.87 0.68 8.05 0 0.09 1.51409 14.25 3.09 2.08 72.28 1.1 7.08 0 0 1.51625 13.36 3.58 1.49 72.72 0.45 8.21 0 0 1.51569 13.24 3.49 1.47 73.25 0.38 8.03 0 0 1.51645 13.4 3.49 1.52 72.65 0.67 8.08 0 0.1 1.51618 13.01 3.5 1.48 72.89 0.6 8.12 0 0 1.5164 12.55 3.48 1.87 73.23 0.63 8.08 0 0.09 1.51841 12.93 3.74 1.11 72.28 0.64 8.96 0 0.22 1.51605 12.9 3.44 1.45 73.06 0.44 8.27 0 0 1.51588 13.12 3.41 1.58 73.26 0.07 8.39 0 0.19 1.5159 13.24 3.34 1.47 73.1 0.39 8.22 0 0 1.51629 12.71 3.33 1.49 73.28 0.67 8.24 0 0 1.5186 13.36 3.43 1.43 72.26 0.51 8.6 0 0 1.51841 13.02 3.62 1.06 72.34 0.64 9.13 0 0.15 1.51743 12.2 3.25 1.16 73.55 0.62 8.9 0 0.24 1.51689 12.67 2.88 1.71 73.21 0.73 8.54 0 0 1.51811 12.96 2.96 1.43 72.92 0.6 8.79 0.14 0 1.51655 12.75 2.85 1.44 73.27 0.57 8.79 0.11 0.22 1.5173 12.35 2.72 1.63 72.87 0.7 9.23 0 0 1.5182 12.62 2.76 0.83 73.81 0.35 9.42 0 0.2 1.52725 13.8 3.15 0.66 70.57 0.08 11.64 0 0 1.5241 13.83 2.9 1.17 71.15 0.08 10.79 0 0 1.52475 11.45 0 1.88 72.19 0.81 13.24 0 0.34 1.53125 10.73 0 2.1 69.81 0.58 13.3 3.15 0.28 1.53393 12.3 0 1 70.16 0.12 16.19 0 0.24 1.52222 14.43 0 1 72.67 0.1 11.52 0 0.08 1.51818 13.72 0 0.56 74.45 0 10.99 0 0 1.52664 11.23 0 0.77 73.21 0 14.68 0 0 1.52739 11.02 0 0.75 73.08 0 14.96 0 0 1.52777 12.64 0 0.67 72.02 0.06 14.4 0 0 1.51892 13.46 3.83 1.26 72.55 0.57 8.21 0 0.14 1.51847 13.1 3.97 1.19 72.44 0.6 8.43 0 0 1.51846 13.41 3.89 1.33 72.38 0.51 8.28 0 0 1.51829 13.24 3.9 1.41 72.33 0.55 8.31 0 0.1 1.51708 13.72 3.68 1.81 72.06 0.64 7.88 0 0 1.51673 13.3 3.64 1.53 72.53 0.65 8.03 0 0.29 1.51652 13.56 3.57 1.47 72.45 0.64 7.96 0 0 1.51844 13.25 3.76 1.32 72.4 0.58 8.42 0 0 1.51663 12.93 3.54 1.62 72.96 0.64 8.03 0 0.21 1.51687 13.23 3.54 1.48 72.84 0.56 8.1 0 0 1.51707 13.48 3.48 1.71 72.52 0.62 7.99 0 0 1.52177 13.2 3.68 1.15 72.75 0.54 8.52 0 0 1.51872 12.93 3.66 1.56 72.51 0.58 8.55 0 0.12 1.51667 12.94 3.61 1.26 72.75 0.56 8.6 0 0 1.52081 13.78 2.28 1.43 71.99 0.49 9.85 0 0.17 1.52068 13.55 2.09 1.67 72.18 0.53 9.57 0.27 0.17 1.5202 13.98 1.35 1.63 71.76 0.39 10.56 0 0.18 1.52177 13.75 1.01 1.36 72.19 0.33 11.14 0 0 1.52614 13.7 0 1.36 71.24 0.19 13.44 0 0.1 1.51813 13.43 3.98 1.18 72.49 0.58 8.15 0 0 1.518 13.71 3.93 1.54 71.81 0.54 8.21 0 0.15 1.51811 13.33 3.85 1.25 72.78 0.52 8.12 0 0 1.51789 13.19 3.9 1.3 72.33 0.55 8.44 0 0.28 1.51806 13 3.8 1.08 73.07 0.56 8.38 0 0.12 1.51711 12.89 3.62 1.57 72.96 0.61 8.11 0 0 1.51674 12.79 3.52 1.54 73.36 0.66 7.9 0 0 1.51674 12.87 3.56 1.64 73.14 0.65 7.99 0 0 1.5169 13.33 3.54 1.61 72.54 0.68 8.11 0 0 1.51851 13.2 3.63 1.07 72.83 0.57 8.41 0.09 0.17 1.51662 12.85 3.51 1.44 73.01 0.68 8.23 0.06 0.25 1.51709 13 3.47 1.79 72.72 0.66 8.18 0 0 1.5166 12.99 3.18 1.23 72.97 0.58 8.81 0 0.24 1.51839 12.85 3.67 1.24 72.57 0.62 8.68 0 0.35 1.51769 13.65 3.66 1.11 72.77 0.11 8.6 0 0 1.5161 13.33 3.53 1.34 72.67 0.56 8.33 0 0 1.5167 13.24 3.57 1.38 72.7 0.56 8.44 0 0.1 1.51643 12.16 3.52 1.35 72.89 0.57 8.53 0 0 1.51665 13.14 3.45 1.76 72.48 0.6 8.38 0 0.17 1.52127 14.32 3.9 0.83 71.5 0 9.49 0 0 1.51779 13.64 3.65 0.65 73 0.06 8.93 0 0 1.5161 13.42 3.4 1.22 72.69 0.59 8.32 0 0 1.51694 12.86 3.58 1.31 72.61 0.61 8.79 0 0 1.51646 13.04 3.4 1.26 73.01 0.52 8.58 0 0 1.51655 13.41 3.39 1.28 72.64 0.52 8.65 0 0 1.52121 14.03 3.76 0.58 71.79 0.11 9.65 0 0 1.51776 13.53 3.41 1.52 72.04 0.58 8.79 0 0 1.51796 13.5 3.36 1.63 71.94 0.57 8.81 0 0.09 1.51832 13.33 3.34 1.54 72.14 0.56 8.99 0 0 1.51934 13.64 3.54 0.75 72.65 0.16 8.89 0.15 0.24 1.52211 14.19 3.78 0.91 71.36 0.23 9.14 0 0.37 1.51514 14.01 2.68 3.5 69.89 1.68 5.87 2.2 0 1.51915 12.73 1.85 1.86 72.69 0.6 10.09 0 0 1.52171 11.56 1.88 1.56 72.86 0.47 11.41 0 0 1.52151 11.03 1.71 1.56 73.44 0.58 11.62 0 0 1.51969 12.64 0 1.65 73.75 0.38 11.53 0 0 1.51666 12.86 0 1.83 73.88 0.97 10.17 0 0 1.51994 13.27 0 1.76 73.03 0.47 11.32 0 0 1.52369 13.44 0 1.58 72.22 0.32 12.24 0 0 1.51316 13.02 0 3.04 70.48 6.21 6.96 0 0 1.51321 13 0 3.02 70.7 6.21 6.93 0 0 1.52043 13.38 0 1.4 72.25 0.33 12.5 0 0 1.52058 12.85 1.61 2.17 72.18 0.76 9.7 0.24 0.51 1.52119 12.97 0.33 1.51 73.39 0.13 11.27 0 0.28 1.51905 14 2.39 1.56 72.37 0 9.57 0 0 1.51937 13.79 2.41 1.19 72.76 0 9.77 0 0 1.51829 14.46 2.24 1.62 72.38 0 9.26 0 0 1.51852 14.09 2.19 1.66 72.67 0 9.32 0 0 1.51299 14.4 1.74 1.54 74.55 0 7.59 0 0 1.51888 14.99 0.78 1.74 72.5 0 9.95 0 0 1.51916 14.15 0 2.09 72.74 0 10.88 0 0 1.51969 14.56 0 0.56 73.48 0 11.22 0 0 1.51115 17.38 0 0.34 75.41 0 6.65 0 0 1.51131 13.69 3.2 1.81 72.81 1.76 5.43 1.19 0 1.51838 14.32 3.26 2.22 71.25 1.46 5.79 1.63 0 1.52315 13.44 3.34 1.23 72.38 0.6 8.83 0 0 1.52247 14.86 2.2 2.06 70.26 0.76 9.76 0 0 1.52365 15.79 1.83 1.31 70.43 0.31 8.61 1.68 0 1.51613 13.88 1.78 1.79 73.1 0 8.67 0.76 0 1.51602 14.85 0 2.38 73.28 0 8.76 0.64 0.09 1.51623 14.2 0 2.79 73.46 0.04 9.04 0.4 0.09 1.51719 14.75 0 2 73.02 0 8.53 1.59 0.08 1.51683 14.56 0 1.98 73.29 0 8.52 1.57 0.07 1.51545 14.14 0 2.68 73.39 0.08 9.07 0.61 0.05 1.51556 13.87 0 2.54 73.23 0.14 9.41 0.81 0.01 1.51727 14.7 0 2.34 73.28 0 8.95 0.66 0 1.51531 14.38 0 2.66 73.1 0.04 9.08 0.64 0 1.51609 15.01 0 2.51 73.05 0.05 8.83 0.53 0 1.51508 15.15 0 2.25 73.5 0 8.34 0.63 0 1.51653 11.95 0 1.19 75.18 2.7 8.93 0 0 1.51514 14.85 0 2.42 73.72 0 8.39 0.56 0 1.51658 14.8 0 1.99 73.11 0 8.28 1.71 0 1.51617 14.95 0 2.27 73.3 0 8.71 0.67 0 1.51732 14.95 0 1.8 72.99 0 8.61 1.55 0 1.51645 14.94 0 1.87 73.11 0 8.67 1.38 0 1.51831 14.39 0 1.82 72.86 1.41 6.47 2.88 0 1.5164 14.37 0 2.74 72.85 0 9.45 0.54 0 1.51623 14.14 0 2.88 72.61 0.08 9.18 1.06 0 1.51685 14.92 0 1.99 73.06 0 8.4 1.59 0 1.52065 14.36 0 2.02 73.42 0 8.44 1.64 0 1.51651 14.38 0 1.94 73.61 0 8.48 1.57 0 1.51711 14.23 0 2.08 73.36 0 8.62 1.67 0

Parag Kanade/fuzzy ants/Glass/glassFull.data

1 1.52101 13.64 4.49 1.1 71.78 0.06 8.75 0 0

1 1.51761 13.89 3.6 1.36 72.73 0.48 7.83 0 0

1 1.51618 13.53 3.55 1.54 72.99 0.39 7.78 0 0

1 1.51766 13.21 3.69 1.29 72.61 0.57 8.22 0 0

1 1.51742 13.27 3.62 1.24 73.08 0.55 8.07 0 0

1 1.51596 12.79 3.61 1.62 72.97 0.64 8.07 0 0.26

1 1.51743 13.3 3.6 1.14 73.09 0.58 8.17 0 0

1 1.51756 13.15 3.61 1.05 73.24 0.57 8.24 0 0

1 1.51918 14.04 3.58 1.37 72.08 0.56 8.3 0 0

1 1.51755 13 3.6 1.36 72.99 0.57 8.4 0 0.11

1 1.51571 12.72 3.46 1.56 73.2 0.67 8.09 0 0.24

1 1.51763 12.8 3.66 1.27 73.01 0.6 8.56 0 0

1 1.51589 12.88 3.43 1.4 73.28 0.69 8.05 0 0.24

1 1.51748 12.86 3.56 1.27 73.21 0.54 8.38 0 0.17

1 1.51763 12.61 3.59 1.31 73.29 0.58 8.5 0 0

1 1.51761 12.81 3.54 1.23 73.24 0.58 8.39 0 0

1 1.51784 12.68 3.67 1.16 73.11 0.61 8.7 0 0

1 1.52196 14.36 3.85 0.89 71.36 0.15 9.15 0 0

1 1.51911 13.9 3.73 1.18 72.12 0.06 8.89 0 0

1 1.51735 13.02 3.54 1.69 72.73 0.54 8.44 0 0.07

1 1.5175 12.82 3.55 1.49 72.75 0.54 8.52 0 0.19

1 1.51966 14.77 3.75 0.29 72.02 0.03 9 0 0

1 1.51736 12.78 3.62 1.29 72.79 0.59 8.7 0 0

1 1.51751 12.81 3.57 1.35 73.02 0.62 8.59 0 0

1 1.5172 13.38 3.5 1.15 72.85 0.5 8.43 0 0

1 1.51764 12.98 3.54 1.21 73 0.65 8.53 0 0

1 1.51793 13.21 3.48 1.41 72.64 0.59 8.43 0 0

1 1.51721 12.87 3.48 1.33 73.04 0.56 8.43 0 0

1 1.51768 12.56 3.52 1.43 73.15 0.57 8.54 0 0

1 1.51784 13.08 3.49 1.28 72.86 0.6 8.49 0 0

1 1.51768 12.65 3.56 1.3 73.08 0.61 8.69 0 0.14

1 1.51747 12.84 3.5 1.14 73.27 0.56 8.55 0 0

1 1.51775 12.85 3.48 1.23 72.97 0.61 8.56 0.09 0.22

1 1.51753 12.57 3.47 1.38 73.39 0.6 8.55 0 0.06

1 1.51783 12.69 3.54 1.34 72.95 0.57 8.75 0 0

1 1.51567 13.29 3.45 1.21 72.74 0.56 8.57 0 0

1 1.51909 13.89 3.53 1.32 71.81 0.51 8.78 0.11 0

1 1.51797 12.74 3.48 1.35 72.96 0.64 8.68 0 0

1 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0 0

1 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0 0

1 1.51793 12.79 3.5 1.12 73.03 0.64 8.77 0 0

1 1.51755 12.71 3.42 1.2 73.2 0.59 8.64 0 0

1 1.51779 13.21 3.39 1.33 72.76 0.59 8.59 0 0

1 1.5221 13.73 3.84 0.72 71.76 0.17 9.74 0 0

1 1.51786 12.73 3.43 1.19 72.95 0.62 8.76 0 0.3

1 1.519 13.49 3.48 1.35 71.95 0.55 9 0 0

1 1.51869 13.19 3.37 1.18 72.72 0.57 8.83 0 0.16

1 1.52667 13.99 3.7 0.71 71.57 0.02 9.82 0 0.1

1 1.52223 13.21 3.77 0.79 71.99 0.13 10.02 0 0

1 1.51898 13.58 3.35 1.23 72.08 0.59 8.91 0 0

1 1.5232 13.72 3.72 0.51 71.75 0.09 10.06 0 0.16

1 1.51926 13.2 3.33 1.28 72.36 0.6 9.14 0 0.11

1 1.51808 13.43 2.87 1.19 72.84 0.55 9.03 0 0

1 1.51837 13.14 2.84 1.28 72.85 0.55 9.07 0 0

1 1.51778 13.21 2.81 1.29 72.98 0.51 9.02 0 0.09

1 1.51769 12.45 2.71 1.29 73.7 0.56 9.06 0 0.24

1 1.51215 12.99 3.47 1.12 72.98 0.62 8.35 0 0.31

1 1.51824 12.87 3.48 1.29 72.95 0.6 8.43 0 0

1 1.51754 13.48 3.74 1.17 72.99 0.59 8.03 0 0

1 1.51754 13.39 3.66 1.19 72.79 0.57 8.27 0 0.11

1 1.51905 13.6 3.62 1.11 72.64 0.14 8.76 0 0

1 1.51977 13.81 3.58 1.32 71.72 0.12 8.67 0.69 0

1 1.52172 13.51 3.86 0.88 71.79 0.23 9.54 0 0.11

1 1.52227 14.17 3.81 0.78 71.35 0 9.69 0 0

1 1.52172 13.48 3.74 0.9 72.01 0.18 9.61 0 0.07

1 1.52099 13.69 3.59 1.12 71.96 0.09 9.4 0 0

1 1.52152 13.05 3.65 0.87 72.22 0.19 9.85 0 0.17

1 1.52152 13.05 3.65 0.87 72.32 0.19 9.85 0 0.17

1 1.52152 13.12 3.58 0.9 72.2 0.23 9.82 0 0.16

1 1.523 13.31 3.58 0.82 71.99 0.12 10.17 0 0.03

2 1.51574 14.86 3.67 1.74 71.87 0.16 7.36 0 0.12

2 1.51848 13.64 3.87 1.27 71.96 0.54 8.32 0 0.32

2 1.51593 13.09 3.59 1.52 73.1 0.67 7.83 0 0

2 1.51631 13.34 3.57 1.57 72.87 0.61 7.89 0 0

2 1.51596 13.02 3.56 1.54 73.11 0.72 7.9 0 0

2 1.5159 13.02 3.58 1.51 73.12 0.69 7.96 0 0

2 1.51645 13.44 3.61 1.54 72.39 0.66 8.03 0 0

2 1.51627 13 3.58 1.54 72.83 0.61 8.04 0 0

2 1.51613 13.92 3.52 1.25 72.88 0.37 7.94 0 0.14

2 1.5159 12.82 3.52 1.9 72.86 0.69 7.97 0 0

2 1.51592 12.86 3.52 2.12 72.66 0.69 7.97 0 0

2 1.51593 13.25 3.45 1.43 73.17 0.61 7.86 0 0

2 1.51646 13.41 3.55 1.25 72.81 0.68 8.1 0 0

2 1.51594 13.09 3.52 1.55 72.87 0.68 8.05 0 0.09

2 1.51409 14.25 3.09 2.08 72.28 1.1 7.08 0 0

2 1.51625 13.36 3.58 1.49 72.72 0.45 8.21 0 0

2 1.51569 13.24 3.49 1.47 73.25 0.38 8.03 0 0

2 1.51645 13.4 3.49 1.52 72.65 0.67 8.08 0 0.1

2 1.51618 13.01 3.5 1.48 72.89 0.6 8.12 0 0

2 1.5164 12.55 3.48 1.87 73.23 0.63 8.08 0 0.09

2 1.51841 12.93 3.74 1.11 72.28 0.64 8.96 0 0.22

2 1.51605 12.9 3.44 1.45 73.06 0.44 8.27 0 0

2 1.51588 13.12 3.41 1.58 73.26 0.07 8.39 0 0.19

2 1.5159 13.24 3.34 1.47 73.1 0.39 8.22 0 0

2 1.51629 12.71 3.33 1.49 73.28 0.67 8.24 0 0

2 1.5186 13.36 3.43 1.43 72.26 0.51 8.6 0 0

2 1.51841 13.02 3.62 1.06 72.34 0.64 9.13 0 0.15

2 1.51743 12.2 3.25 1.16 73.55 0.62 8.9 0 0.24

2 1.51689 12.67 2.88 1.71 73.21 0.73 8.54 0 0

2 1.51811 12.96 2.96 1.43 72.92 0.6 8.79 0.14 0

2 1.51655 12.75 2.85 1.44 73.27 0.57 8.79 0.11 0.22

2 1.5173 12.35 2.72 1.63 72.87 0.7 9.23 0 0

2 1.5182 12.62 2.76 0.83 73.81 0.35 9.42 0 0.2

2 1.52725 13.8 3.15 0.66 70.57 0.08 11.64 0 0

2 1.5241 13.83 2.9 1.17 71.15 0.08 10.79 0 0

2 1.52475 11.45 0 1.88 72.19 0.81 13.24 0 0.34

2 1.53125 10.73 0 2.1 69.81 0.58 13.3 3.15 0.28

2 1.53393 12.3 0 1 70.16 0.12 16.19 0 0.24

2 1.52222 14.43 0 1 72.67 0.1 11.52 0 0.08

2 1.51818 13.72 0 0.56 74.45 0 10.99 0 0

2 1.52664 11.23 0 0.77 73.21 0 14.68 0 0

2 1.52739 11.02 0 0.75 73.08 0 14.96 0 0

2 1.52777 12.64 0 0.67 72.02 0.06 14.4 0 0

2 1.51892 13.46 3.83 1.26 72.55 0.57 8.21 0 0.14

2 1.51847 13.1 3.97 1.19 72.44 0.6 8.43 0 0

2 1.51846 13.41 3.89 1.33 72.38 0.51 8.28 0 0

2 1.51829 13.24 3.9 1.41 72.33 0.55 8.31 0 0.1

2 1.51708 13.72 3.68 1.81 72.06 0.64 7.88 0 0

2 1.51673 13.3 3.64 1.53 72.53 0.65 8.03 0 0.29

2 1.51652 13.56 3.57 1.47 72.45 0.64 7.96 0 0

2 1.51844 13.25 3.76 1.32 72.4 0.58 8.42 0 0

2 1.51663 12.93 3.54 1.62 72.96 0.64 8.03 0 0.21

2 1.51687 13.23 3.54 1.48 72.84 0.56 8.1 0 0

2 1.51707 13.48 3.48 1.71 72.52 0.62 7.99 0 0

2 1.52177 13.2 3.68 1.15 72.75 0.54 8.52 0 0

2 1.51872 12.93 3.66 1.56 72.51 0.58 8.55 0 0.12

2 1.51667 12.94 3.61 1.26 72.75 0.56 8.6 0 0

2 1.52081 13.78 2.28 1.43 71.99 0.49 9.85 0 0.17

2 1.52068 13.55 2.09 1.67 72.18 0.53 9.57 0.27 0.17

2 1.5202 13.98 1.35 1.63 71.76 0.39 10.56 0 0.18

2 1.52177 13.75 1.01 1.36 72.19 0.33 11.14 0 0

2 1.52614 13.7 0 1.36 71.24 0.19 13.44 0 0.1

2 1.51813 13.43 3.98 1.18 72.49 0.58 8.15 0 0

2 1.518 13.71 3.93 1.54 71.81 0.54 8.21 0 0.15

2 1.51811 13.33 3.85 1.25 72.78 0.52 8.12 0 0

2 1.51789 13.19 3.9 1.3 72.33 0.55 8.44 0 0.28

2 1.51806 13 3.8 1.08 73.07 0.56 8.38 0 0.12

2 1.51711 12.89 3.62 1.57 72.96 0.61 8.11 0 0

2 1.51674 12.79 3.52 1.54 73.36 0.66 7.9 0 0

2 1.51674 12.87 3.56 1.64 73.14 0.65 7.99 0 0

2 1.5169 13.33 3.54 1.61 72.54 0.68 8.11 0 0

2 1.51851 13.2 3.63 1.07 72.83 0.57 8.41 0.09 0.17

2 1.51662 12.85 3.51 1.44 73.01 0.68 8.23 0.06 0.25

2 1.51709 13 3.47 1.79 72.72 0.66 8.18 0 0

2 1.5166 12.99 3.18 1.23 72.97 0.58 8.81 0 0.24

2 1.51839 12.85 3.67 1.24 72.57 0.62 8.68 0 0.35

3 1.51769 13.65 3.66 1.11 72.77 0.11 8.6 0 0

3 1.5161 13.33 3.53 1.34 72.67 0.56 8.33 0 0

3 1.5167 13.24 3.57 1.38 72.7 0.56 8.44 0 0.1

3 1.51643 12.16 3.52 1.35 72.89 0.57 8.53 0 0

3 1.51665 13.14 3.45 1.76 72.48 0.6 8.38 0 0.17

3 1.52127 14.32 3.9 0.83 71.5 0 9.49 0 0

3 1.51779 13.64 3.65 0.65 73 0.06 8.93 0 0

3 1.5161 13.42 3.4 1.22 72.69 0.59 8.32 0 0

3 1.51694 12.86 3.58 1.31 72.61 0.61 8.79 0 0

3 1.51646 13.04 3.4 1.26 73.01 0.52 8.58 0 0

3 1.51655 13.41 3.39 1.28 72.64 0.52 8.65 0 0

3 1.52121 14.03 3.76 0.58 71.79 0.11 9.65 0 0

3 1.51776 13.53 3.41 1.52 72.04 0.58 8.79 0 0

3 1.51796 13.5 3.36 1.63 71.94 0.57 8.81 0 0.09

3 1.51832 13.33 3.34 1.54 72.14 0.56 8.99 0 0

3 1.51934 13.64 3.54 0.75 72.65 0.16 8.89 0.15 0.24

3 1.52211 14.19 3.78 0.91 71.36 0.23 9.14 0 0.37

4 1.51514 14.01 2.68 3.5 69.89 1.68 5.87 2.2 0

4 1.51915 12.73 1.85 1.86 72.69 0.6 10.09 0 0

4 1.52171 11.56 1.88 1.56 72.86 0.47 11.41 0 0

4 1.52151 11.03 1.71 1.56 73.44 0.58 11.62 0 0

4 1.51969 12.64 0 1.65 73.75 0.38 11.53 0 0

4 1.51666 12.86 0 1.83 73.88 0.97 10.17 0 0

4 1.51994 13.27 0 1.76 73.03 0.47 11.32 0 0

4 1.52369 13.44 0 1.58 72.22 0.32 12.24 0 0

4 1.51316 13.02 0 3.04 70.48 6.21 6.96 0 0

4 1.51321 13 0 3.02 70.7 6.21 6.93 0 0

4 1.52043 13.38 0 1.4 72.25 0.33 12.5 0 0

4 1.52058 12.85 1.61 2.17 72.18 0.76 9.7 0.24 0.51

4 1.52119 12.97 0.33 1.51 73.39 0.13 11.27 0 0.28

5 1.51905 14 2.39 1.56 72.37 0 9.57 0 0

5 1.51937 13.79 2.41 1.19 72.76 0 9.77 0 0

5 1.51829 14.46 2.24 1.62 72.38 0 9.26 0 0

5 1.51852 14.09 2.19 1.66 72.67 0 9.32 0 0

5 1.51299 14.4 1.74 1.54 74.55 0 7.59 0 0

5 1.51888 14.99 0.78 1.74 72.5 0 9.95 0 0

5 1.51916 14.15 0 2.09 72.74 0 10.88 0 0

5 1.51969 14.56 0 0.56 73.48 0 11.22 0 0

5 1.51115 17.38 0 0.34 75.41 0 6.65 0 0

6 1.51131 13.69 3.2 1.81 72.81 1.76 5.43 1.19 0

6 1.51838 14.32 3.26 2.22 71.25 1.46 5.79 1.63 0

6 1.52315 13.44 3.34 1.23 72.38 0.6 8.83 0 0

6 1.52247 14.86 2.2 2.06 70.26 0.76 9.76 0 0

6 1.52365 15.79 1.83 1.31 70.43 0.31 8.61 1.68 0

6 1.51613 13.88 1.78 1.79 73.1 0 8.67 0.76 0

6 1.51602 14.85 0 2.38 73.28 0 8.76 0.64 0.09

6 1.51623 14.2 0 2.79 73.46 0.04 9.04 0.4 0.09

6 1.51719 14.75 0 2 73.02 0 8.53 1.59 0.08

6 1.51683 14.56 0 1.98 73.29 0 8.52 1.57 0.07

6 1.51545 14.14 0 2.68 73.39 0.08 9.07 0.61 0.05

6 1.51556 13.87 0 2.54 73.23 0.14 9.41 0.81 0.01

6 1.51727 14.7 0 2.34 73.28 0 8.95 0.66 0

6 1.51531 14.38 0 2.66 73.1 0.04 9.08 0.64 0

6 1.51609 15.01 0 2.51 73.05 0.05 8.83 0.53 0

6 1.51508 15.15 0 2.25 73.5 0 8.34 0.63 0

6 1.51653 11.95 0 1.19 75.18 2.7 8.93 0 0

6 1.51514 14.85 0 2.42 73.72 0 8.39 0.56 0

6 1.51658 14.8 0 1.99 73.11 0 8.28 1.71 0

6 1.51617 14.95 0 2.27 73.3 0 8.71 0.67 0

6 1.51732 14.95 0 1.8 72.99 0 8.61 1.55 0

6 1.51645 14.94 0 1.87 73.11 0 8.67 1.38 0

6 1.51831 14.39 0 1.82 72.86 1.41 6.47 2.88 0

6 1.5164 14.37 0 2.74 72.85 0 9.45 0.54 0

6 1.51623 14.14 0 2.88 72.61 0.08 9.18 1.06 0

6 1.51685 14.92 0 1.99 73.06 0 8.4 1.59 0

6 1.52065 14.36 0 2.02 73.42 0 8.44 1.64 0

6 1.51651 14.38 0 1.94 73.61 0 8.48 1.57 0

6 1.51711 14.23 0 2.08 73.36 0 8.62 1.67 0

Parag Kanade/fuzzy ants/Glass/2class/maxData.txt

5.691089

Parag Kanade/fuzzy ants/Glass/2class/glassFull.data

1 1.52101 13.64 4.49 1.1 71.78 0.06 8.75 0 01 1.51761 13.89 3.6 1.36 72.73 0.48 7.83 0 01 1.51618 13.53 3.55 1.54 72.99 0.39 7.78 0 01 1.51766 13.21 3.69 1.29 72.61 0.57 8.22 0 01 1.51742 13.27 3.62 1.24 73.08 0.55 8.07 0 01 1.51596 12.79 3.61 1.62 72.97 0.64 8.07 0 0.261 1.51743 13.3 3.6 1.14 73.09 0.58 8.17 0 01 1.51756 13.15 3.61 1.05 73.24 0.57 8.24 0 01 1.51918 14.04 3.58 1.37 72.08 0.56 8.3 0 01 1.51755 13 3.6 1.36 72.99 0.57 8.4 0 0.111 1.51571 12.72 3.46 1.56 73.2 0.67 8.09 0 0.241 1.51763 12.8 3.66 1.27 73.01 0.6 8.56 0 01 1.51589 12.88 3.43 1.4 73.28 0.69 8.05 0 0.241 1.51748 12.86 3.56 1.27 73.21 0.54 8.38 0 0.171 1.51763 12.61 3.59 1.31 73.29 0.58 8.5 0 01 1.51761 12.81 3.54 1.23 73.24 0.58 8.39 0 01 1.51784 12.68 3.67 1.16 73.11 0.61 8.7 0 01 1.52196 14.36 3.85 0.89 71.36 0.15 9.15 0 01 1.51911 13.9 3.73 1.18 72.12 0.06 8.89 0 01 1.51735 13.02 3.54 1.69 72.73 0.54 8.44 0 0.071 1.5175 12.82 3.55 1.49 72.75 0.54 8.52 0 0.191 1.51966 14.77 3.75 0.29 72.02 0.03 9 0 01 1.51736 12.78 3.62 1.29 72.79 0.59 8.7 0 01 1.51751 12.81 3.57 1.35 73.02 0.62 8.59 0 01 1.5172 13.38 3.5 1.15 72.85 0.5 8.43 0 01 1.51764 12.98 3.54 1.21 73 0.65 8.53 0 01 1.51793 13.21 3.48 1.41 72.64 0.59 8.43 0 01 1.51721 12.87 3.48 1.33 73.04 0.56 8.43 0 01 1.51768 12.56 3.52 1.43 73.15 0.57 8.54 0 01 1.51784 13.08 3.49 1.28 72.86 0.6 8.49 0 01 1.51768 12.65 3.56 1.3 73.08 0.61 8.69 0 0.141 1.51747 12.84 3.5 1.14 73.27 0.56 8.55 0 01 1.51775 12.85 3.48 1.23 72.97 0.61 8.56 0.09 0.221 1.51753 12.57 3.47 1.38 73.39 0.6 8.55 0 0.061 1.51783 12.69 3.54 1.34 72.95 0.57 8.75 0 01 1.51567 13.29 3.45 1.21 72.74 0.56 8.57 0 01 1.51909 13.89 3.53 1.32 71.81 0.51 8.78 0.11 01 1.51797 12.74 3.48 1.35 72.96 0.64 8.68 0 01 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0 01 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0 01 1.51793 12.79 3.5 1.12 73.03 0.64 8.77 0 01 1.51755 12.71 3.42 1.2 73.2 0.59 8.64 0 01 1.51779 13.21 3.39 1.33 72.76 0.59 8.59 0 01 1.5221 13.73 3.84 0.72 71.76 0.17 9.74 0 01 1.51786 12.73 3.43 1.19 72.95 0.62 8.76 0 0.31 1.519 13.49 3.48 1.35 71.95 0.55 9 0 01 1.51869 13.19 3.37 1.18 72.72 0.57 8.83 0 0.161 1.52667 13.99 3.7 0.71 71.57 0.02 9.82 0 0.11 1.52223 13.21 3.77 0.79 71.99 0.13 10.02 0 01 1.51898 13.58 3.35 1.23 72.08 0.59 8.91 0 01 1.5232 13.72 3.72 0.51 71.75 0.09 10.06 0 0.161 1.51926 13.2 3.33 1.28 72.36 0.6 9.14 0 0.111 1.51808 13.43 2.87 1.19 72.84 0.55 9.03 0 01 1.51837 13.14 2.84 1.28 72.85 0.55 9.07 0 01 1.51778 13.21 2.81 1.29 72.98 0.51 9.02 0 0.091 1.51769 12.45 2.71 1.29 73.7 0.56 9.06 0 0.241 1.51215 12.99 3.47 1.12 72.98 0.62 8.35 0 0.311 1.51824 12.87 3.48 1.29 72.95 0.6 8.43 0 01 1.51754 13.48 3.74 1.17 72.99 0.59 8.03 0 01 1.51754 13.39 3.66 1.19 72.79 0.57 8.27 0 0.111 1.51905 13.6 3.62 1.11 72.64 0.14 8.76 0 01 1.51977 13.81 3.58 1.32 71.72 0.12 8.67 0.69 01 1.52172 13.51 3.86 0.88 71.79 0.23 9.54 0 0.111 1.52227 14.17 3.81 0.78 71.35 0 9.69 0 01 1.52172 13.48 3.74 0.9 72.01 0.18 9.61 0 0.071 1.52099 13.69 3.59 1.12 71.96 0.09 9.4 0 01 1.52152 13.05 3.65 0.87 72.22 0.19 9.85 0 0.171 1.52152 13.05 3.65 0.87 72.32 0.19 9.85 0 0.171 1.52152 13.12 3.58 0.9 72.2 0.23 9.82 0 0.161 1.523 13.31 3.58 0.82 71.99 0.12 10.17 0 0.031 1.51574 14.86 3.67 1.74 71.87 0.16 7.36 0 0.121 1.51848 13.64 3.87 1.27 71.96 0.54 8.32 0 0.321 1.51593 13.09 3.59 1.52 73.1 0.67 7.83 0 01 1.51631 13.34 3.57 1.57 72.87 0.61 7.89 0 01 1.51596 13.02 3.56 1.54 73.11 0.72 7.9 0 01 1.5159 13.02 3.58 1.51 73.12 0.69 7.96 0 01 1.51645 13.44 3.61 1.54 72.39 0.66 8.03 0 01 1.51627 13 3.58 1.54 72.83 0.61 8.04 0 01 1.51613 13.92 3.52 1.25 72.88 0.37 7.94 0 0.141 1.5159 12.82 3.52 1.9 72.86 0.69 7.97 0 01 1.51592 12.86 3.52 2.12 72.66 0.69 7.97 0 01 1.51593 13.25 3.45 1.43 73.17 0.61 7.86 0 01 1.51646 13.41 3.55 1.25 72.81 0.68 8.1 0 01 1.51594 13.09 3.52 1.55 72.87 0.68 8.05 0 0.091 1.51409 14.25 3.09 2.08 72.28 1.1 7.08 0 01 1.51625 13.36 3.58 1.49 72.72 0.45 8.21 0 01 1.51569 13.24 3.49 1.47 73.25 0.38 8.03 0 01 1.51645 13.4 3.49 1.52 72.65 0.67 8.08 0 0.11 1.51618 13.01 3.5 1.48 72.89 0.6 8.12 0 01 1.5164 12.55 3.48 1.87 73.23 0.63 8.08 0 0.091 1.51841 12.93 3.74 1.11 72.28 0.64 8.96 0 0.221 1.51605 12.9 3.44 1.45 73.06 0.44 8.27 0 01 1.51588 13.12 3.41 1.58 73.26 0.07 8.39 0 0.191 1.5159 13.24 3.34 1.47 73.1 0.39 8.22 0 01 1.51629 12.71 3.33 1.49 73.28 0.67 8.24 0 01 1.5186 13.36 3.43 1.43 72.26 0.51 8.6 0 01 1.51841 13.02 3.62 1.06 72.34 0.64 9.13 0 0.151 1.51743 12.2 3.25 1.16 73.55 0.62 8.9 0 0.241 1.51689 12.67 2.88 1.71 73.21 0.73 8.54 0 01 1.51811 12.96 2.96 1.43 72.92 0.6 8.79 0.14 01 1.51655 12.75 2.85 1.44 73.27 0.57 8.79 0.11 0.221 1.5173 12.35 2.72 1.63 72.87 0.7 9.23 0 01 1.5182 12.62 2.76 0.83 73.81 0.35 9.42 0 0.21 1.52725 13.8 3.15 0.66 70.57 0.08 11.64 0 01 1.5241 13.83 2.9 1.17 71.15 0.08 10.79 0 01 1.52475 11.45 0 1.88 72.19 0.81 13.24 0 0.341 1.53125 10.73 0 2.1 69.81 0.58 13.3 3.15 0.281 1.53393 12.3 0 1 70.16 0.12 16.19 0 0.241 1.52222 14.43 0 1 72.67 0.1 11.52 0 0.081 1.51818 13.72 0 0.56 74.45 0 10.99 0 01 1.52664 11.23 0 0.77 73.21 0 14.68 0 01 1.52739 11.02 0 0.75 73.08 0 14.96 0 01 1.52777 12.64 0 0.67 72.02 0.06 14.4 0 01 1.51892 13.46 3.83 1.26 72.55 0.57 8.21 0 0.141 1.51847 13.1 3.97 1.19 72.44 0.6 8.43 0 01 1.51846 13.41 3.89 1.33 72.38 0.51 8.28 0 01 1.51829 13.24 3.9 1.41 72.33 0.55 8.31 0 0.11 1.51708 13.72 3.68 1.81 72.06 0.64 7.88 0 01 1.51673 13.3 3.64 1.53 72.53 0.65 8.03 0 0.291 1.51652 13.56 3.57 1.47 72.45 0.64 7.96 0 01 1.51844 13.25 3.76 1.32 72.4 0.58 8.42 0 01 1.51663 12.93 3.54 1.62 72.96 0.64 8.03 0 0.211 1.51687 13.23 3.54 1.48 72.84 0.56 8.1 0 01 1.51707 13.48 3.48 1.71 72.52 0.62 7.99 0 01 1.52177 13.2 3.68 1.15 72.75 0.54 8.52 0 01 1.51872 12.93 3.66 1.56 72.51 0.58 8.55 0 0.121 1.51667 12.94 3.61 1.26 72.75 0.56 8.6 0 01 1.52081 13.78 2.28 1.43 71.99 0.49 9.85 0 0.171 1.52068 13.55 2.09 1.67 72.18 0.53 9.57 0.27 0.171 1.5202 13.98 1.35 1.63 71.76 0.39 10.56 0 0.181 1.52177 13.75 1.01 1.36 72.19 0.33 11.14 0 01 1.52614 13.7 0 1.36 71.24 0.19 13.44 0 0.11 1.51813 13.43 3.98 1.18 72.49 0.58 8.15 0 01 1.518 13.71 3.93 1.54 71.81 0.54 8.21 0 0.151 1.51811 13.33 3.85 1.25 72.78 0.52 8.12 0 01 1.51789 13.19 3.9 1.3 72.33 0.55 8.44 0 0.281 1.51806 13 3.8 1.08 73.07 0.56 8.38 0 0.121 1.51711 12.89 3.62 1.57 72.96 0.61 8.11 0 01 1.51674 12.79 3.52 1.54 73.36 0.66 7.9 0 01 1.51674 12.87 3.56 1.64 73.14 0.65 7.99 0 01 1.5169 13.33 3.54 1.61 72.54 0.68 8.11 0 01 1.51851 13.2 3.63 1.07 72.83 0.57 8.41 0.09 0.171 1.51662 12.85 3.51 1.44 73.01 0.68 8.23 0.06 0.251 1.51709 13 3.47 1.79 72.72 0.66 8.18 0 01 1.5166 12.99 3.18 1.23 72.97 0.58 8.81 0 0.241 1.51839 12.85 3.67 1.24 72.57 0.62 8.68 0 0.351 1.51769 13.65 3.66 1.11 72.77 0.11 8.6 0 01 1.5161 13.33 3.53 1.34 72.67 0.56 8.33 0 01 1.5167 13.24 3.57 1.38 72.7 0.56 8.44 0 0.11 1.51643 12.16 3.52 1.35 72.89 0.57 8.53 0 01 1.51665 13.14 3.45 1.76 72.48 0.6 8.38 0 0.171 1.52127 14.32 3.9 0.83 71.5 0 9.49 0 01 1.51779 13.64 3.65 0.65 73 0.06 8.93 0 01 1.5161 13.42 3.4 1.22 72.69 0.59 8.32 0 01 1.51694 12.86 3.58 1.31 72.61 0.61 8.79 0 01 1.51646 13.04 3.4 1.26 73.01 0.52 8.58 0 01 1.51655 13.41 3.39 1.28 72.64 0.52 8.65 0 01 1.52121 14.03 3.76 0.58 71.79 0.11 9.65 0 01 1.51776 13.53 3.41 1.52 72.04 0.58 8.79 0 01 1.51796 13.5 3.36 1.63 71.94 0.57 8.81 0 0.091 1.51832 13.33 3.34 1.54 72.14 0.56 8.99 0 01 1.51934 13.64 3.54 0.75 72.65 0.16 8.89 0.15 0.241 1.52211 14.19 3.78 0.91 71.36 0.23 9.14 0 0.372 1.51514 14.01 2.68 3.5 69.89 1.68 5.87 2.2 02 1.51915 12.73 1.85 1.86 72.69 0.6 10.09 0 02 1.52171 11.56 1.88 1.56 72.86 0.47 11.41 0 02 1.52151 11.03 1.71 1.56 73.44 0.58 11.62 0 02 1.51969 12.64 0 1.65 73.75 0.38 11.53 0 02 1.51666 12.86 0 1.83 73.88 0.97 10.17 0 02 1.51994 13.27 0 1.76 73.03 0.47 11.32 0 02 1.52369 13.44 0 1.58 72.22 0.32 12.24 0 02 1.51316 13.02 0 3.04 70.48 6.21 6.96 0 02 1.51321 13 0 3.02 70.7 6.21 6.93 0 02 1.52043 13.38 0 1.4 72.25 0.33 12.5 0 02 1.52058 12.85 1.61 2.17 72.18 0.76 9.7 0.24 0.512 1.52119 12.97 0.33 1.51 73.39 0.13 11.27 0 0.282 1.51905 14 2.39 1.56 72.37 0 9.57 0 02 1.51937 13.79 2.41 1.19 72.76 0 9.77 0 02 1.51829 14.46 2.24 1.62 72.38 0 9.26 0 02 1.51852 14.09 2.19 1.66 72.67 0 9.32 0 02 1.51299 14.4 1.74 1.54 74.55 0 7.59 0 02 1.51888 14.99 0.78 1.74 72.5 0 9.95 0 02 1.51916 14.15 0 2.09 72.74 0 10.88 0 02 1.51969 14.56 0 0.56 73.48 0 11.22 0 02 1.51115 17.38 0 0.34 75.41 0 6.65 0 02 1.51131 13.69 3.2 1.81 72.81 1.76 5.43 1.19 02 1.51838 14.32 3.26 2.22 71.25 1.46 5.79 1.63 02 1.52315 13.44 3.34 1.23 72.38 0.6 8.83 0 02 1.52247 14.86 2.2 2.06 70.26 0.76 9.76 0 02 1.52365 15.79 1.83 1.31 70.43 0.31 8.61 1.68 02 1.51613 13.88 1.78 1.79 73.1 0 8.67 0.76 02 1.51602 14.85 0 2.38 73.28 0 8.76 0.64 0.092 1.51623 14.2 0 2.79 73.46 0.04 9.04 0.4 0.092 1.51719 14.75 0 2 73.02 0 8.53 1.59 0.082 1.51683 14.56 0 1.98 73.29 0 8.52 1.57 0.072 1.51545 14.14 0 2.68 73.39 0.08 9.07 0.61 0.052 1.51556 13.87 0 2.54 73.23 0.14 9.41 0.81 0.012 1.51727 14.7 0 2.34 73.28 0 8.95 0.66 02 1.51531 14.38 0 2.66 73.1 0.04 9.08 0.64 02 1.51609 15.01 0 2.51 73.05 0.05 8.83 0.53 02 1.51508 15.15 0 2.25 73.5 0 8.34 0.63 02 1.51653 11.95 0 1.19 75.18 2.7 8.93 0 02 1.51514 14.85 0 2.42 73.72 0 8.39 0.56 02 1.51658 14.8 0 1.99 73.11 0 8.28 1.71 02 1.51617 14.95 0 2.27 73.3 0 8.71 0.67 02 1.51732 14.95 0 1.8 72.99 0 8.61 1.55 02 1.51645 14.94 0 1.87 73.11 0 8.67 1.38 02 1.51831 14.39 0 1.82 72.86 1.41 6.47 2.88 02 1.5164 14.37 0 2.74 72.85 0 9.45 0.54 02 1.51623 14.14 0 2.88 72.61 0.08 9.18 1.06 02 1.51685 14.92 0 1.99 73.06 0 8.4 1.59 02 1.52065 14.36 0 2.02 73.42 0 8.44 1.64 02 1.51651 14.38 0 1.94 73.61 0 8.48 1.57 02 1.51711 14.23 0 2.08 73.36 0 8.62 1.67 0

Parag Kanade/fuzzy ants/Glass/2class/headerStatic.h

/********************************************************************/ /* Ant Class parameters */ /* SPEED # amplitude of moves [1,10] */ /* P direction Probability to move in the same direction [.5,1] */ /* Max carry : Maximum object carrying time [20,200] */ /* P load Probability to pick up a single object [0.4,0.8] */ /* P destroy probability to destroy a heap of two objects [0,0.6] */ /* T remove min. dissimilarity necessary for removing an object */ /* from a heap [.1,.2] */ /* T create max dissimilarity permitted for creating a heap[.05,.2]*/ /********************************************************************/ #include <stdio.h> #include <math.h> #include <string.h> #include<stdlib.h> #include <ctype.h> #include <time.h> #define MAXANTS 72 #define MAXDATA 214 #define MAXANTSFORHEAPS 10 #define MAXITERATIONS 1000 #define MAXROWS 30 #define MAXCOLS 30 #define EXPRUN 50 #define Tremove 4.0 #define Tcreate 0.5 #define Pdrop 0.2 #define Pdestroy 0.3 #define Pload 0.3 #define normalize 0 #define EntireHeaps 1 #define TcreateForHeaps 0.135 #define features 9 #define filename "glass" #define errThresh 0.01 #define dataDump 1 #define maxDist 0 #define INITIALOBJECTS 75 #define OBJINC 10 /* some type definitions to make C code a little more readable */ #define TRUE 1 #define FALSE 0 #define n MAXDATA /* number of input vectors */ #define s features /* number of features */ #define c 2 /* number of clusters */ #define TOTRUNS 1 /* number of V's to write out */ #define false 0 #define true 1 #define maha 0 #define square 0 extern int count; int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/ int currHeapCount=0; typedef int BOOLEAN; typedef short TYPE_IN_DATA; /* Type of input binary data */ typedef float feature[s]; /* Type of each input; made up of input features */ typedef float irisData[s]; typedef feature objects[MAXDATA]; /* Type of array holding all inputs */ typedef feature center[MAXDATA]; /* Type of array holding cluster centers */ typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */ typedef struct Heap { float Dmax;//maximum dissimilar distance irisData Ocenter;//center object float Dmean;//mean of the distance between the center and all the objects irisData Odissim;//most dissimilar object int x;//x position on the board int y;//y position on the board int count;//number of objects in the heaps int disPosition;//most dissimilar object int objects[MAXDATA]; int objCount; }heap; typedef struct Ant { int x; int y; int carrying; int speed; int dir; irisData data; }ant; float orgData[n][s+1]; float fcmData[260][n][s]; int clsCenCount[260]; int currentRow=0; int currentAntCount=0; int updateAfter=50; int heapSum=0; int minAnts=3; int rowMin=5; typedef struct HeapInfo { int total; int classObj[c]; } heapInfo; /*heapInfo info[MAXDATA];*/ /********************************************************************/ /***Function decelarations*/ /********************************************************************/ /***For ant based algorithm*/ void readInput(); float computeDistance(); float computeDistanceBetween2(irisData a,irisData b); float findMax(float *distance); float value(irisData a); int found(int a , int b); void initialise(int flag); int findObject(int a,int b); void initialiseAnts(int flag); void moveNext(ant* a,int flag); void computeHeapParameters(heap* data,int flag); void pickObject1(ant *a,int position,int flag); void pickObject2(ant *a , int position,int flag); void pickObjectn(ant *a,int position,int flag); void pick(ant *a,int flag); void dropObject(ant* a,int flag); int findInNewheaps(irisData object); void dropObject1(ant* a, int position,int flag); void drop(ant* a,int flag); void move(ant* a,int flag); void iterate(int flag); void dropFinal(int flag); int partition(heap a[], int low,int high); void quickSort( heap A[], int low,int high ) ; void writeClusterInformation(int flag); void initialiseNewHeaps(heap newHeaps[]); int findPosition(int i); void harden(heap newHeaps[]); void harden1(); int findPosition1(int ); void writeInfo(); void addObject1(int ,int); void readFCM(FILE *,FILE *); void readOrg(); int findInData(irisData input,int iflag); void adjustBoard(int flag); void writeIncremental(int iteration,int flag); /********************************************************************/ /***For FCM algorithm*/ void GET_INPUTS1(char []); void UMAT( matrix,center,objects); BOOLEAN FINISHED(matrix, float); void CLUSTER( matrix, objects); void Output(char[]); void Fcm(); /********************************************************************/

Parag Kanade/fuzzy ants/Glass/2class/glass.data

Glass Data # 1.52101 13.64 4.49 1.1 71.78 0.06 8.75 0 0 1.51761 13.89 3.6 1.36 72.73 0.48 7.83 0 0 1.51618 13.53 3.55 1.54 72.99 0.39 7.78 0 0 1.51766 13.21 3.69 1.29 72.61 0.57 8.22 0 0 1.51742 13.27 3.62 1.24 73.08 0.55 8.07 0 0 1.51596 12.79 3.61 1.62 72.97 0.64 8.07 0 0.26 1.51743 13.3 3.6 1.14 73.09 0.58 8.17 0 0 1.51756 13.15 3.61 1.05 73.24 0.57 8.24 0 0 1.51918 14.04 3.58 1.37 72.08 0.56 8.3 0 0 1.51755 13 3.6 1.36 72.99 0.57 8.4 0 0.11 1.51571 12.72 3.46 1.56 73.2 0.67 8.09 0 0.24 1.51763 12.8 3.66 1.27 73.01 0.6 8.56 0 0 1.51589 12.88 3.43 1.4 73.28 0.69 8.05 0 0.24 1.51748 12.86 3.56 1.27 73.21 0.54 8.38 0 0.17 1.51763 12.61 3.59 1.31 73.29 0.58 8.5 0 0 1.51761 12.81 3.54 1.23 73.24 0.58 8.39 0 0 1.51784 12.68 3.67 1.16 73.11 0.61 8.7 0 0 1.52196 14.36 3.85 0.89 71.36 0.15 9.15 0 0 1.51911 13.9 3.73 1.18 72.12 0.06 8.89 0 0 1.51735 13.02 3.54 1.69 72.73 0.54 8.44 0 0.07 1.5175 12.82 3.55 1.49 72.75 0.54 8.52 0 0.19 1.51966 14.77 3.75 0.29 72.02 0.03 9 0 0 1.51736 12.78 3.62 1.29 72.79 0.59 8.7 0 0 1.51751 12.81 3.57 1.35 73.02 0.62 8.59 0 0 1.5172 13.38 3.5 1.15 72.85 0.5 8.43 0 0 1.51764 12.98 3.54 1.21 73 0.65 8.53 0 0 1.51793 13.21 3.48 1.41 72.64 0.59 8.43 0 0 1.51721 12.87 3.48 1.33 73.04 0.56 8.43 0 0 1.51768 12.56 3.52 1.43 73.15 0.57 8.54 0 0 1.51784 13.08 3.49 1.28 72.86 0.6 8.49 0 0 1.51768 12.65 3.56 1.3 73.08 0.61 8.69 0 0.14 1.51747 12.84 3.5 1.14 73.27 0.56 8.55 0 0 1.51775 12.85 3.48 1.23 72.97 0.61 8.56 0.09 0.22 1.51753 12.57 3.47 1.38 73.39 0.6 8.55 0 0.06 1.51783 12.69 3.54 1.34 72.95 0.57 8.75 0 0 1.51567 13.29 3.45 1.21 72.74 0.56 8.57 0 0 1.51909 13.89 3.53 1.32 71.81 0.51 8.78 0.11 0 1.51797 12.74 3.48 1.35 72.96 0.64 8.68 0 0 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0 0 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0 0 1.51793 12.79 3.5 1.12 73.03 0.64 8.77 0 0 1.51755 12.71 3.42 1.2 73.2 0.59 8.64 0 0 1.51779 13.21 3.39 1.33 72.76 0.59 8.59 0 0 1.5221 13.73 3.84 0.72 71.76 0.17 9.74 0 0 1.51786 12.73 3.43 1.19 72.95 0.62 8.76 0 0.3 1.519 13.49 3.48 1.35 71.95 0.55 9 0 0 1.51869 13.19 3.37 1.18 72.72 0.57 8.83 0 0.16 1.52667 13.99 3.7 0.71 71.57 0.02 9.82 0 0.1 1.52223 13.21 3.77 0.79 71.99 0.13 10.02 0 0 1.51898 13.58 3.35 1.23 72.08 0.59 8.91 0 0 1.5232 13.72 3.72 0.51 71.75 0.09 10.06 0 0.16 1.51926 13.2 3.33 1.28 72.36 0.6 9.14 0 0.11 1.51808 13.43 2.87 1.19 72.84 0.55 9.03 0 0 1.51837 13.14 2.84 1.28 72.85 0.55 9.07 0 0 1.51778 13.21 2.81 1.29 72.98 0.51 9.02 0 0.09 1.51769 12.45 2.71 1.29 73.7 0.56 9.06 0 0.24 1.51215 12.99 3.47 1.12 72.98 0.62 8.35 0 0.31 1.51824 12.87 3.48 1.29 72.95 0.6 8.43 0 0 1.51754 13.48 3.74 1.17 72.99 0.59 8.03 0 0 1.51754 13.39 3.66 1.19 72.79 0.57 8.27 0 0.11 1.51905 13.6 3.62 1.11 72.64 0.14 8.76 0 0 1.51977 13.81 3.58 1.32 71.72 0.12 8.67 0.69 0 1.52172 13.51 3.86 0.88 71.79 0.23 9.54 0 0.11 1.52227 14.17 3.81 0.78 71.35 0 9.69 0 0 1.52172 13.48 3.74 0.9 72.01 0.18 9.61 0 0.07 1.52099 13.69 3.59 1.12 71.96 0.09 9.4 0 0 1.52152 13.05 3.65 0.87 72.22 0.19 9.85 0 0.17 1.52152 13.05 3.65 0.87 72.32 0.19 9.85 0 0.17 1.52152 13.12 3.58 0.9 72.2 0.23 9.82 0 0.16 1.523 13.31 3.58 0.82 71.99 0.12 10.17 0 0.03 1.51574 14.86 3.67 1.74 71.87 0.16 7.36 0 0.12 1.51848 13.64 3.87 1.27 71.96 0.54 8.32 0 0.32 1.51593 13.09 3.59 1.52 73.1 0.67 7.83 0 0 1.51631 13.34 3.57 1.57 72.87 0.61 7.89 0 0 1.51596 13.02 3.56 1.54 73.11 0.72 7.9 0 0 1.5159 13.02 3.58 1.51 73.12 0.69 7.96 0 0 1.51645 13.44 3.61 1.54 72.39 0.66 8.03 0 0 1.51627 13 3.58 1.54 72.83 0.61 8.04 0 0 1.51613 13.92 3.52 1.25 72.88 0.37 7.94 0 0.14 1.5159 12.82 3.52 1.9 72.86 0.69 7.97 0 0 1.51592 12.86 3.52 2.12 72.66 0.69 7.97 0 0 1.51593 13.25 3.45 1.43 73.17 0.61 7.86 0 0 1.51646 13.41 3.55 1.25 72.81 0.68 8.1 0 0 1.51594 13.09 3.52 1.55 72.87 0.68 8.05 0 0.09 1.51409 14.25 3.09 2.08 72.28 1.1 7.08 0 0 1.51625 13.36 3.58 1.49 72.72 0.45 8.21 0 0 1.51569 13.24 3.49 1.47 73.25 0.38 8.03 0 0 1.51645 13.4 3.49 1.52 72.65 0.67 8.08 0 0.1 1.51618 13.01 3.5 1.48 72.89 0.6 8.12 0 0 1.5164 12.55 3.48 1.87 73.23 0.63 8.08 0 0.09 1.51841 12.93 3.74 1.11 72.28 0.64 8.96 0 0.22 1.51605 12.9 3.44 1.45 73.06 0.44 8.27 0 0 1.51588 13.12 3.41 1.58 73.26 0.07 8.39 0 0.19 1.5159 13.24 3.34 1.47 73.1 0.39 8.22 0 0 1.51629 12.71 3.33 1.49 73.28 0.67 8.24 0 0 1.5186 13.36 3.43 1.43 72.26 0.51 8.6 0 0 1.51841 13.02 3.62 1.06 72.34 0.64 9.13 0 0.15 1.51743 12.2 3.25 1.16 73.55 0.62 8.9 0 0.24 1.51689 12.67 2.88 1.71 73.21 0.73 8.54 0 0 1.51811 12.96 2.96 1.43 72.92 0.6 8.79 0.14 0 1.51655 12.75 2.85 1.44 73.27 0.57 8.79 0.11 0.22 1.5173 12.35 2.72 1.63 72.87 0.7 9.23 0 0 1.5182 12.62 2.76 0.83 73.81 0.35 9.42 0 0.2 1.52725 13.8 3.15 0.66 70.57 0.08 11.64 0 0 1.5241 13.83 2.9 1.17 71.15 0.08 10.79 0 0 1.52475 11.45 0 1.88 72.19 0.81 13.24 0 0.34 1.53125 10.73 0 2.1 69.81 0.58 13.3 3.15 0.28 1.53393 12.3 0 1 70.16 0.12 16.19 0 0.24 1.52222 14.43 0 1 72.67 0.1 11.52 0 0.08 1.51818 13.72 0 0.56 74.45 0 10.99 0 0 1.52664 11.23 0 0.77 73.21 0 14.68 0 0 1.52739 11.02 0 0.75 73.08 0 14.96 0 0 1.52777 12.64 0 0.67 72.02 0.06 14.4 0 0 1.51892 13.46 3.83 1.26 72.55 0.57 8.21 0 0.14 1.51847 13.1 3.97 1.19 72.44 0.6 8.43 0 0 1.51846 13.41 3.89 1.33 72.38 0.51 8.28 0 0 1.51829 13.24 3.9 1.41 72.33 0.55 8.31 0 0.1 1.51708 13.72 3.68 1.81 72.06 0.64 7.88 0 0 1.51673 13.3 3.64 1.53 72.53 0.65 8.03 0 0.29 1.51652 13.56 3.57 1.47 72.45 0.64 7.96 0 0 1.51844 13.25 3.76 1.32 72.4 0.58 8.42 0 0 1.51663 12.93 3.54 1.62 72.96 0.64 8.03 0 0.21 1.51687 13.23 3.54 1.48 72.84 0.56 8.1 0 0 1.51707 13.48 3.48 1.71 72.52 0.62 7.99 0 0 1.52177 13.2 3.68 1.15 72.75 0.54 8.52 0 0 1.51872 12.93 3.66 1.56 72.51 0.58 8.55 0 0.12 1.51667 12.94 3.61 1.26 72.75 0.56 8.6 0 0 1.52081 13.78 2.28 1.43 71.99 0.49 9.85 0 0.17 1.52068 13.55 2.09 1.67 72.18 0.53 9.57 0.27 0.17 1.5202 13.98 1.35 1.63 71.76 0.39 10.56 0 0.18 1.52177 13.75 1.01 1.36 72.19 0.33 11.14 0 0 1.52614 13.7 0 1.36 71.24 0.19 13.44 0 0.1 1.51813 13.43 3.98 1.18 72.49 0.58 8.15 0 0 1.518 13.71 3.93 1.54 71.81 0.54 8.21 0 0.15 1.51811 13.33 3.85 1.25 72.78 0.52 8.12 0 0 1.51789 13.19 3.9 1.3 72.33 0.55 8.44 0 0.28 1.51806 13 3.8 1.08 73.07 0.56 8.38 0 0.12 1.51711 12.89 3.62 1.57 72.96 0.61 8.11 0 0 1.51674 12.79 3.52 1.54 73.36 0.66 7.9 0 0 1.51674 12.87 3.56 1.64 73.14 0.65 7.99 0 0 1.5169 13.33 3.54 1.61 72.54 0.68 8.11 0 0 1.51851 13.2 3.63 1.07 72.83 0.57 8.41 0.09 0.17 1.51662 12.85 3.51 1.44 73.01 0.68 8.23 0.06 0.25 1.51709 13 3.47 1.79 72.72 0.66 8.18 0 0 1.5166 12.99 3.18 1.23 72.97 0.58 8.81 0 0.24 1.51839 12.85 3.67 1.24 72.57 0.62 8.68 0 0.35 1.51769 13.65 3.66 1.11 72.77 0.11 8.6 0 0 1.5161 13.33 3.53 1.34 72.67 0.56 8.33 0 0 1.5167 13.24 3.57 1.38 72.7 0.56 8.44 0 0.1 1.51643 12.16 3.52 1.35 72.89 0.57 8.53 0 0 1.51665 13.14 3.45 1.76 72.48 0.6 8.38 0 0.17 1.52127 14.32 3.9 0.83 71.5 0 9.49 0 0 1.51779 13.64 3.65 0.65 73 0.06 8.93 0 0 1.5161 13.42 3.4 1.22 72.69 0.59 8.32 0 0 1.51694 12.86 3.58 1.31 72.61 0.61 8.79 0 0 1.51646 13.04 3.4 1.26 73.01 0.52 8.58 0 0 1.51655 13.41 3.39 1.28 72.64 0.52 8.65 0 0 1.52121 14.03 3.76 0.58 71.79 0.11 9.65 0 0 1.51776 13.53 3.41 1.52 72.04 0.58 8.79 0 0 1.51796 13.5 3.36 1.63 71.94 0.57 8.81 0 0.09 1.51832 13.33 3.34 1.54 72.14 0.56 8.99 0 0 1.51934 13.64 3.54 0.75 72.65 0.16 8.89 0.15 0.24 1.52211 14.19 3.78 0.91 71.36 0.23 9.14 0 0.37 1.51514 14.01 2.68 3.5 69.89 1.68 5.87 2.2 0 1.51915 12.73 1.85 1.86 72.69 0.6 10.09 0 0 1.52171 11.56 1.88 1.56 72.86 0.47 11.41 0 0 1.52151 11.03 1.71 1.56 73.44 0.58 11.62 0 0 1.51969 12.64 0 1.65 73.75 0.38 11.53 0 0 1.51666 12.86 0 1.83 73.88 0.97 10.17 0 0 1.51994 13.27 0 1.76 73.03 0.47 11.32 0 0 1.52369 13.44 0 1.58 72.22 0.32 12.24 0 0 1.51316 13.02 0 3.04 70.48 6.21 6.96 0 0 1.51321 13 0 3.02 70.7 6.21 6.93 0 0 1.52043 13.38 0 1.4 72.25 0.33 12.5 0 0 1.52058 12.85 1.61 2.17 72.18 0.76 9.7 0.24 0.51 1.52119 12.97 0.33 1.51 73.39 0.13 11.27 0 0.28 1.51905 14 2.39 1.56 72.37 0 9.57 0 0 1.51937 13.79 2.41 1.19 72.76 0 9.77 0 0 1.51829 14.46 2.24 1.62 72.38 0 9.26 0 0 1.51852 14.09 2.19 1.66 72.67 0 9.32 0 0 1.51299 14.4 1.74 1.54 74.55 0 7.59 0 0 1.51888 14.99 0.78 1.74 72.5 0 9.95 0 0 1.51916 14.15 0 2.09 72.74 0 10.88 0 0 1.51969 14.56 0 0.56 73.48 0 11.22 0 0 1.51115 17.38 0 0.34 75.41 0 6.65 0 0 1.51131 13.69 3.2 1.81 72.81 1.76 5.43 1.19 0 1.51838 14.32 3.26 2.22 71.25 1.46 5.79 1.63 0 1.52315 13.44 3.34 1.23 72.38 0.6 8.83 0 0 1.52247 14.86 2.2 2.06 70.26 0.76 9.76 0 0 1.52365 15.79 1.83 1.31 70.43 0.31 8.61 1.68 0 1.51613 13.88 1.78 1.79 73.1 0 8.67 0.76 0 1.51602 14.85 0 2.38 73.28 0 8.76 0.64 0.09 1.51623 14.2 0 2.79 73.46 0.04 9.04 0.4 0.09 1.51719 14.75 0 2 73.02 0 8.53 1.59 0.08 1.51683 14.56 0 1.98 73.29 0 8.52 1.57 0.07 1.51545 14.14 0 2.68 73.39 0.08 9.07 0.61 0.05 1.51556 13.87 0 2.54 73.23 0.14 9.41 0.81 0.01 1.51727 14.7 0 2.34 73.28 0 8.95 0.66 0 1.51531 14.38 0 2.66 73.1 0.04 9.08 0.64 0 1.51609 15.01 0 2.51 73.05 0.05 8.83 0.53 0 1.51508 15.15 0 2.25 73.5 0 8.34 0.63 0 1.51653 11.95 0 1.19 75.18 2.7 8.93 0 0 1.51514 14.85 0 2.42 73.72 0 8.39 0.56 0 1.51658 14.8 0 1.99 73.11 0 8.28 1.71 0 1.51617 14.95 0 2.27 73.3 0 8.71 0.67 0 1.51732 14.95 0 1.8 72.99 0 8.61 1.55 0 1.51645 14.94 0 1.87 73.11 0 8.67 1.38 0 1.51831 14.39 0 1.82 72.86 1.41 6.47 2.88 0 1.5164 14.37 0 2.74 72.85 0 9.45 0.54 0 1.51623 14.14 0 2.88 72.61 0.08 9.18 1.06 0 1.51685 14.92 0 1.99 73.06 0 8.4 1.59 0 1.52065 14.36 0 2.02 73.42 0 8.44 1.64 0 1.51651 14.38 0 1.94 73.61 0 8.48 1.57 0 1.51711 14.23 0 2.08 73.36 0 8.62 1.67 0

Parag Kanade/fuzzy ants/Glass/2class/glass.c


Parag Kanade/fuzzy ants/Glass/glass.c


Parag Kanade/fuzzy ants/wine/maxData.txt

6.282280

Parag Kanade/fuzzy ants/wine/headerStatic.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include<stdlib.h>#include <ctype.h>#include <time.h>#define MAXANTS 59#define MAXDATA 178#define MAXANTSFORHEAPS 15#define MAXITERATIONS 3000#define MAXROWS 30#define MAXCOLS 30#define EXPRUN 50#define Tremove 3.0#define Tcreate 1.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.09#define features 13#define filename "wine"#define errThresh 0.1#define dataDump 1#define maxDist 0#define INITIALOBJECTS 75#define OBJINC 10/* some type definitions to make C code a little more readable */#define TRUE 1#define FALSE 0#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define c 3 /* number of clusters */#define TOTRUNS 1 /* number of V's to write out */#define false 0#define true 1#define maha 0#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap {float Dmax;//maximum dissimilar distanceirisData Ocenter;//center objectfloat Dmean;//mean of the distance between the center and all the objectsirisData Odissim;//most dissimilar objectint x;//x position on the boardint y;//y position on the boardint count;//number of objects in the heapsint disPosition;//most dissimilar objectint objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int carrying;int speed;int dir;irisData data;}ant;float orgData[n][s+1];float fcmData[260][n][s];int clsCenCount[260];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int a , int b);void initialise(int flag);int findObject(int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);void pickObject2(ant *a , int position,int flag);void pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);/********************************************************************//***For FCM algorithm*/void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects);void Output(char[]);void Fcm(irisData data[]);/********************************************************************/

Parag Kanade/fuzzy ants/wine/wineFull.data

1 14.23 1.71 2.43 15.6 127 2.8 3.06 0.28 2.29 5.64 1.04 3.92 1065

1 13.2 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.4 1050

1 13.16 2.36 2.67 18.6 101 2.8 3.24 0.3 2.81 5.68 1.03 3.17 1185

1 14.37 1.95 2.5 16.8 113 3.85 3.49 0.24 2.18 7.8 0.86 3.45 1480

1 13.24 2.59 2.87 21 118 2.8 2.69 0.39 1.82 4.32 1.04 2.93 735

1 14.2 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 1450

1 14.39 1.87 2.45 14.6 96 2.5 2.52 0.3 1.98 5.25 1.02 3.58 1290

1 14.06 2.15 2.61 17.6 121 2.6 2.51 0.31 1.25 5.05 1.06 3.58 1295

1 14.83 1.64 2.17 14 97 2.8 2.98 0.29 1.98 5.2 1.08 2.85 1045

1 13.86 1.35 2.27 16 98 2.98 3.15 0.22 1.85 7.22 1.01 3.55 1045

1 14.1 2.16 2.3 18 105 2.95 3.32 0.22 2.38 5.75 1.25 3.17 1510

1 14.12 1.48 2.32 16.8 95 2.2 2.43 0.26 1.57 5 1.17 2.82 1280

1 13.75 1.73 2.41 16 89 2.6 2.76 0.29 1.81 5.6 1.15 2.9 1320

1 14.75 1.73 2.39 11.4 91 3.1 3.69 0.43 2.81 5.4 1.25 2.73 1150

1 14.38 1.87 2.38 12 102 3.3 3.64 0.29 2.96 7.5 1.2 3 1547

1 13.63 1.81 2.7 17.2 112 2.85 2.91 0.3 1.46 7.3 1.28 2.88 1310

1 14.3 1.92 2.72 20 120 2.8 3.14 0.33 1.97 6.2 1.07 2.65 1280

1 13.83 1.57 2.62 20 115 2.95 3.4 0.4 1.72 6.6 1.13 2.57 1130

1 14.19 1.59 2.48 16.5 108 3.3 3.93 0.32 1.86 8.7 1.23 2.82 1680

1 13.64 3.1 2.56 15.2 116 2.7 3.03 0.17 1.66 5.1 0.96 3.36 845

1 14.06 1.63 2.28 16 126 3 3.17 0.24 2.1 5.65 1.09 3.71 780

1 12.93 3.8 2.65 18.6 102 2.41 2.41 0.25 1.98 4.5 1.03 3.52 770

1 13.71 1.86 2.36 16.6 101 2.61 2.88 0.27 1.69 3.8 1.11 4 1035

1 12.85 1.6 2.52 17.8 95 2.48 2.37 0.26 1.46 3.93 1.09 3.63 1015

1 13.5 1.81 2.61 20 96 2.53 2.61 0.28 1.66 3.52 1.12 3.82 845

1 13.05 2.05 3.22 25 124 2.63 2.68 0.47 1.92 3.58 1.13 3.2 830

1 13.39 1.77 2.62 16.1 93 2.85 2.94 0.34 1.45 4.8 0.92 3.22 1195

1 13.3 1.72 2.14 17 94 2.4 2.19 0.27 1.35 3.95 1.02 2.77 1285

1 13.87 1.9 2.8 19.4 107 2.95 2.97 0.37 1.76 4.5 1.25 3.4 915

1 14.02 1.68 2.21 16 96 2.65 2.33 0.26 1.98 4.7 1.04 3.59 1035

1 13.73 1.5 2.7 22.5 101 3 3.25 0.29 2.38 5.7 1.19 2.71 1285

1 13.58 1.66 2.36 19.1 106 2.86 3.19 0.22 1.95 6.9 1.09 2.88 1515

1 13.68 1.83 2.36 17.2 104 2.42 2.69 0.42 1.97 3.84 1.23 2.87 990

1 13.76 1.53 2.7 19.5 132 2.95 2.74 0.5 1.35 5.4 1.25 3 1235

1 13.51 1.8 2.65 19 110 2.35 2.53 0.29 1.54 4.2 1.1 2.87 1095

1 13.48 1.81 2.41 20.5 100 2.7 2.98 0.26 1.86 5.1 1.04 3.47 920

1 13.28 1.64 2.84 15.5 110 2.6 2.68 0.34 1.36 4.6 1.09 2.78 880

1 13.05 1.65 2.55 18 98 2.45 2.43 0.29 1.44 4.25 1.12 2.51 1105

1 13.07 1.5 2.1 15.5 98 2.4 2.64 0.28 1.37 3.7 1.18 2.69 1020

1 14.22 3.99 2.51 13.2 128 3 3.04 0.2 2.08 5.1 0.89 3.53 760

1 13.56 1.71 2.31 16.2 117 3.15 3.29 0.34 2.34 6.13 0.95 3.38 795

1 13.41 3.84 2.12 18.8 90 2.45 2.68 0.27 1.48 4.28 0.91 3 1035

1 13.88 1.89 2.59 15 101 3.25 3.56 0.17 1.7 5.43 0.88 3.56 1095

1 13.24 3.98 2.29 17.5 103 2.64 2.63 0.32 1.66 4.36 0.82 3 680

1 13.05 1.77 2.1 17 107 3 3 0.28 2.03 5.04 0.88 3.35 885

1 14.21 4.04 2.44 18.9 111 2.85 2.65 0.3 1.25 5.24 0.87 3.33 1080

1 14.38 3.59 2.28 16 102 3.25 3.17 0.27 2.19 4.9 1.04 3.44 1065

1 13.9 1.68 2.12 16 101 3.1 3.39 0.21 2.14 6.1 0.91 3.33 985

1 14.1 2.02 2.4 18.8 103 2.75 2.92 0.32 2.38 6.2 1.07 2.75 1060

1 13.94 1.73 2.27 17.4 108 2.88 3.54 0.32 2.08 8.9 1.12 3.1 1260

1 13.05 1.73 2.04 12.4 92 2.72 3.27 0.17 2.91 7.2 1.12 2.91 1150

1 13.83 1.65 2.6 17.2 94 2.45 2.99 0.22 2.29 5.6 1.24 3.37 1265

1 13.82 1.75 2.42 14 111 3.88 3.74 0.32 1.87 7.05 1.01 3.26 1190

1 13.77 1.9 2.68 17.1 115 3 2.79 0.39 1.68 6.3 1.13 2.93 1375

1 13.74 1.67 2.25 16.4 118 2.6 2.9 0.21 1.62 5.85 0.92 3.2 1060

1 13.56 1.73 2.46 20.5 116 2.96 2.78 0.2 2.45 6.25 0.98 3.03 1120

1 14.22 1.7 2.3 16.3 118 3.2 3 0.26 2.03 6.38 0.94 3.31 970

1 13.29 1.97 2.68 16.8 102 3 3.23 0.31 1.66 6 1.07 2.84 1270

1 13.72 1.43 2.5 16.7 108 3.4 3.67 0.19 2.04 6.8 0.89 2.87 1285

2 12.37 0.94 1.36 10.6 88 1.98 0.57 0.28 0.42 1.95 1.05 1.82 520

2 12.33 1.1 2.28 16 101 2.05 1.09 0.63 0.41 3.27 1.25 1.67 680

2 12.64 1.36 2.02 16.8 100 2.02 1.41 0.53 0.62 5.75 0.98 1.59 450

2 13.67 1.25 1.92 18 94 2.1 1.79 0.32 0.73 3.8 1.23 2.46 630

2 12.37 1.13 2.16 19 87 3.5 3.1 0.19 1.87 4.45 1.22 2.87 420

2 12.17 1.45 2.53 19 104 1.89 1.75 0.45 1.03 2.95 1.45 2.23 355

2 12.37 1.21 2.56 18.1 98 2.42 2.65 0.37 2.08 4.6 1.19 2.3 678

2 13.11 1.01 1.7 15 78 2.98 3.18 0.26 2.28 5.3 1.12 3.18 502

2 12.37 1.17 1.92 19.6 78 2.11 2 0.27 1.04 4.68 1.12 3.48 510

2 13.34 0.94 2.36 17 110 2.53 1.3 0.55 0.42 3.17 1.02 1.93 750

2 12.21 1.19 1.75 16.8 151 1.85 1.28 0.14 2.5 2.85 1.28 3.07 718

2 12.29 1.61 2.21 20.4 103 1.1 1.02 0.37 1.46 3.05 0.906 1.82 870

2 13.86 1.51 2.67 25 86 2.95 2.86 0.21 1.87 3.38 1.36 3.16 410

2 13.49 1.66 2.24 24 87 1.88 1.84 0.27 1.03 3.74 0.98 2.78 472

2 12.99 1.67 2.6 30 139 3.3 2.89 0.21 1.96 3.35 1.31 3.5 985

2 11.96 1.09 2.3 21 101 3.38 2.14 0.13 1.65 3.21 0.99 3.13 886

2 11.66 1.88 1.92 16 97 1.61 1.57 0.34 1.15 3.8 1.23 2.14 428

2 13.03 0.9 1.71 16 86 1.95 2.03 0.24 1.46 4.6 1.19 2.48 392

2 11.84 2.89 2.23 18 112 1.72 1.32 0.43 0.95 2.65 0.96 2.52 500

2 12.33 0.99 1.95 14.8 136 1.9 1.85 0.35 2.76 3.4 1.06 2.31 750

2 12.7 3.87 2.4 23 101 2.83 2.55 0.43 1.95 2.57 1.19 3.13 463

2 12 0.92 2 19 86 2.42 2.26 0.3 1.43 2.5 1.38 3.12 278

2 12.72 1.81 2.2 18.8 86 2.2 2.53 0.26 1.77 3.9 1.16 3.14 714

2 12.08 1.13 2.51 24 78 2 1.58 0.4 1.4 2.2 1.31 2.72 630

2 13.05 3.86 2.32 22.5 85 1.65 1.59 0.61 1.62 4.8 0.84 2.01 515

2 11.84 0.89 2.58 18 94 2.2 2.21 0.22 2.35 3.05 0.79 3.08 520

2 12.67 0.98 2.24 18 99 2.2 1.94 0.3 1.46 2.62 1.23 3.16 450

2 12.16 1.61 2.31 22.8 90 1.78 1.69 0.43 1.56 2.45 1.33 2.26 495

2 11.65 1.67 2.62 26 88 1.92 1.61 0.4 1.34 2.6 1.36 3.21 562

2 11.64 2.06 2.46 21.6 84 1.95 1.69 0.48 1.35 2.8 1 2.75 680

2 12.08 1.33 2.3 23.6 70 2.2 1.59 0.42 1.38 1.74 1.07 3.21 625

2 12.08 1.83 2.32 18.5 81 1.6 1.5 0.52 1.64 2.4 1.08 2.27 480

2 12 1.51 2.42 22 86 1.45 1.25 0.5 1.63 3.6 1.05 2.65 450

2 12.69 1.53 2.26 20.7 80 1.38 1.46 0.58 1.62 3.05 0.96 2.06 495

2 12.29 2.83 2.22 18 88 2.45 2.25 0.25 1.99 2.15 1.15 3.3 290

2 11.62 1.99 2.28 18 98 3.02 2.26 0.17 1.35 3.25 1.16 2.96 345

2 12.47 1.52 2.2 19 162 2.5 2.27 0.32 3.28 2.6 1.16 2.63 937

2 11.81 2.12 2.74 21.5 134 1.6 0.99 0.14 1.56 2.5 0.95 2.26 625

2 12.29 1.41 1.98 16 85 2.55 2.5 0.29 1.77 2.9 1.23 2.74 428

2 12.37 1.07 2.1 18.5 88 3.52 3.75 0.24 1.95 4.5 1.04 2.77 660

2 12.29 3.17 2.21 18 88 2.85 2.99 0.45 2.81 2.3 1.42 2.83 406

2 12.08 2.08 1.7 17.5 97 2.23 2.17 0.26 1.4 3.3 1.27 2.96 710

2 12.6 1.34 1.9 18.5 88 1.45 1.36 0.29 1.35 2.45 1.04 2.77 562

2 12.34 2.45 2.46 21 98 2.56 2.11 0.34 1.31 2.8 0.8 3.38 438

2 11.82 1.72 1.88 19.5 86 2.5 1.64 0.37 1.42 2.06 0.94 2.44 415

2 12.51 1.73 1.98 20.5 85 2.2 1.92 0.32 1.48 2.94 1.04 3.57 672

2 12.42 2.55 2.27 22 90 1.68 1.84 0.66 1.42 2.7 0.86 3.3 315

2 12.25 1.73 2.12 19 80 1.65 2.03 0.37 1.63 3.4 1 3.17 510

2 12.72 1.75 2.28 22.5 84 1.38 1.76 0.48 1.63 3.3 0.88 2.42 488

2 12.22 1.29 1.94 19 92 2.36 2.04 0.39 2.08 2.7 0.86 3.02 312

2 11.61 1.35 2.7 20 94 2.74 2.92 0.29 2.49 2.65 0.96 3.26 680

2 11.46 3.74 1.82 19.5 107 3.18 2.58 0.24 3.58 2.9 0.75 2.81 562

2 12.52 2.43 2.17 21 88 2.55 2.27 0.26 1.22 2 0.9 2.78 325

2 11.76 2.68 2.92 20 103 1.75 2.03 0.6 1.05 3.8 1.23 2.5 607

2 11.41 0.74 2.5 21 88 2.48 2.01 0.42 1.44 3.08 1.1 2.31 434

2 12.08 1.39 2.5 22.5 84 2.56 2.29 0.43 1.04 2.9 0.93 3.19 385

2 11.03 1.51 2.2 21.5 85 2.46 2.17 0.52 2.01 1.9 1.71 2.87 407

2 11.82 1.47 1.99 20.8 86 1.98 1.6 0.3 1.53 1.95 0.95 3.33 495

2 12.42 1.61 2.19 22.5 108 2 2.09 0.34 1.61 2.06 1.06 2.96 345

2 12.77 3.43 1.98 16 80 1.63 1.25 0.43 0.83 3.4 0.7 2.12 372

2 12 3.43 2 19 87 2 1.64 0.37 1.87 1.28 0.93 3.05 564

2 11.45 2.4 2.42 20 96 2.9 2.79 0.32 1.83 3.25 0.8 3.39 625

2 11.56 2.05 3.23 28.5 119 3.18 5.08 0.47 1.87 6 0.93 3.69 465

2 12.42 4.43 2.73 26.5 102 2.2 2.13 0.43 1.71 2.08 0.92 3.12 365

2 13.05 5.8 2.13 21.5 86 2.62 2.65 0.3 2.01 2.6 0.73 3.1 380

2 11.87 4.31 2.39 21 82 2.86 3.03 0.21 2.91 2.8 0.75 3.64 380

2 12.07 2.16 2.17 21 85 2.6 2.65 0.37 1.35 2.76 0.86 3.28 378

2 12.43 1.53 2.29 21.5 86 2.74 3.15 0.39 1.77 3.94 0.69 2.84 352

2 11.79 2.13 2.78 28.5 92 2.13 2.24 0.58 1.76 3 0.97 2.44 466

2 12.37 1.63 2.3 24.5 88 2.22 2.45 0.4 1.9 2.12 0.89 2.78 342

2 12.04 4.3 2.38 22 80 2.1 1.75 0.42 1.35 2.6 0.79 2.57 580

3 12.86 1.35 2.32 18 122 1.51 1.25 0.21 0.94 4.1 0.76 1.29 630

3 12.88 2.99 2.4 20 104 1.3 1.22 0.24 0.83 5.4 0.74 1.42 530

3 12.81 2.31 2.4 24 98 1.15 1.09 0.27 0.83 5.7 0.66 1.36 560

3 12.7 3.55 2.36 21.5 106 1.7 1.2 0.17 0.84 5 0.78 1.29 600

3 12.51 1.24 2.25 17.5 85 2 0.58 0.6 1.25 5.45 0.75 1.51 650

3 12.6 2.46 2.2 18.5 94 1.62 0.66 0.63 0.94 7.1 0.73 1.58 695

3 12.25 4.72 2.54 21 89 1.38 0.47 0.53 0.8 3.85 0.75 1.27 720

3 12.53 5.51 2.64 25 96 1.79 0.6 0.63 1.1 5 0.82 1.69 515

3 13.49 3.59 2.19 19.5 88 1.62 0.48 0.58 0.88 5.7 0.81 1.82 580

3 12.84 2.96 2.61 24 101 2.32 0.6 0.53 0.81 4.92 0.89 2.15 590

3 12.93 2.81 2.7 21 96 1.54 0.5 0.53 0.75 4.6 0.77 2.31 600

3 13.36 2.56 2.35 20 89 1.4 0.5 0.37 0.64 5.6 0.7 2.47 780

3 13.52 3.17 2.72 23.5 97 1.55 0.52 0.5 0.55 4.35 0.89 2.06 520

3 13.62 4.95 2.35 20 92 2 0.8 0.47 1.02 4.4 0.91 2.05 550

3 12.25 3.88 2.2 18.5 112 1.38 0.78 0.29 1.14 8.21 0.65 2 855

3 13.16 3.57 2.15 21 102 1.5 0.55 0.43 1.3 4 0.6 1.68 830

3 13.88 5.04 2.23 20 80 0.98 0.34 0.4 0.68 4.9 0.58 1.33 415

3 12.87 4.61 2.48 21.5 86 1.7 0.65 0.47 0.86 7.65 0.54 1.86 625

3 13.32 3.24 2.38 21.5 92 1.93 0.76 0.45 1.25 8.42 0.55 1.62 650

3 13.08 3.9 2.36 21.5 113 1.41 1.39 0.34 1.14 9.4 0.57 1.33 550

3 13.5 3.12 2.62 24 123 1.4 1.57 0.22 1.25 8.6 0.59 1.3 500

3 12.79 2.67 2.48 22 112 1.48 1.36 0.24 1.26 10.8 0.48 1.47 480

3 13.11 1.9 2.75 25.5 116 2.2 1.28 0.26 1.56 7.1 0.61 1.33 425

3 13.23 3.3 2.28 18.5 98 1.8 0.83 0.61 1.87 10.52 0.56 1.51 675

3 12.58 1.29 2.1 20 103 1.48 0.58 0.53 1.4 7.6 0.58 1.55 640

3 13.17 5.19 2.32 22 93 1.74 0.63 0.61 1.55 7.9 0.6 1.48 725

3 13.84 4.12 2.38 19.5 89 1.8 0.83 0.48 1.56 9.01 0.57 1.64 480

3 12.45 3.03 2.64 27 97 1.9 0.58 0.63 1.14 7.5 0.67 1.73 880

3 14.34 1.68 2.7 25 98 2.8 1.31 0.53 2.7 13 0.57 1.96 660

3 13.48 1.67 2.64 22.5 89 2.6 1.1 0.52 2.29 11.75 0.57 1.78 620

3 12.36 3.83 2.38 21 88 2.3 0.92 0.5 1.04 7.65 0.56 1.58 520

3 13.69 3.26 2.54 20 107 1.83 0.56 0.5 0.8 5.88 0.96 1.82 680

3 12.85 3.27 2.58 22 106 1.65 0.6 0.6 0.96 5.58 0.87 2.11 570

3 12.96 3.45 2.35 18.5 106 1.39 0.7 0.4 0.94 5.28 0.68 1.75 675

3 13.78 2.76 2.3 22 90 1.35 0.68 0.41 1.03 9.58 0.7 1.68 615

3 13.73 4.36 2.26 22.5 88 1.28 0.47 0.52 1.15 6.62 0.78 1.75 520

3 13.45 3.7 2.6 23 111 1.7 0.92 0.43 1.46 10.68 0.85 1.56 695

3 12.82 3.37 2.3 19.5 88 1.48 0.66 0.4 0.97 10.26 0.72 1.75 685

3 13.58 2.58 2.69 24.5 105 1.55 0.84 0.39 1.54 8.66 0.74 1.8 750

3 13.4 4.6 2.86 25 112 1.98 0.96 0.27 1.11 8.5 0.67 1.92 630

3 12.2 3.03 2.32 19 96 1.25 0.49 0.4 0.73 5.5 0.66 1.83 510

3 12.77 2.39 2.28 19.5 86 1.39 0.51 0.48 0.64 9.899999 0.57 1.63 470

3 14.16 2.51 2.48 20 91 1.68 0.7 0.44 1.24 9.7 0.62 1.71 660

3 13.71 5.65 2.45 20.5 95 1.68 0.61 0.52 1.06 7.7 0.64 1.74 740

3 13.4 3.91 2.48 23 102 1.8 0.75 0.43 1.41 7.3 0.7 1.56 750

3 13.27 4.28 2.26 20 120 1.59 0.69 0.43 1.35 10.2 0.59 1.56 835

3 13.17 2.59 2.37 20 120 1.65 0.68 0.53 1.46 9.3 0.6 1.62 840

3 14.13 4.1 2.74 24.5 96 2.05 0.76 0.56 1.35 9.2 0.61 1.6 560

Parag Kanade/fuzzy ants/wine/wine.data

Wine data # 14.23 1.71 2.43 15.6 127 2.8 3.06 0.28 2.29 5.64 1.04 3.92 1065 13.2 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.4 1050 13.16 2.36 2.67 18.6 101 2.8 3.24 0.3 2.81 5.68 1.03 3.17 1185 14.37 1.95 2.5 16.8 113 3.85 3.49 0.24 2.18 7.8 0.86 3.45 1480 13.24 2.59 2.87 21 118 2.8 2.69 0.39 1.82 4.32 1.04 2.93 735 14.2 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 1450 14.39 1.87 2.45 14.6 96 2.5 2.52 0.3 1.98 5.25 1.02 3.58 1290 14.06 2.15 2.61 17.6 121 2.6 2.51 0.31 1.25 5.05 1.06 3.58 1295 14.83 1.64 2.17 14 97 2.8 2.98 0.29 1.98 5.2 1.08 2.85 1045 13.86 1.35 2.27 16 98 2.98 3.15 0.22 1.85 7.22 1.01 3.55 1045 14.1 2.16 2.3 18 105 2.95 3.32 0.22 2.38 5.75 1.25 3.17 1510 14.12 1.48 2.32 16.8 95 2.2 2.43 0.26 1.57 5 1.17 2.82 1280 13.75 1.73 2.41 16 89 2.6 2.76 0.29 1.81 5.6 1.15 2.9 1320 14.75 1.73 2.39 11.4 91 3.1 3.69 0.43 2.81 5.4 1.25 2.73 1150 14.38 1.87 2.38 12 102 3.3 3.64 0.29 2.96 7.5 1.2 3 1547 13.63 1.81 2.7 17.2 112 2.85 2.91 0.3 1.46 7.3 1.28 2.88 1310 14.3 1.92 2.72 20 120 2.8 3.14 0.33 1.97 6.2 1.07 2.65 1280 13.83 1.57 2.62 20 115 2.95 3.4 0.4 1.72 6.6 1.13 2.57 1130 14.19 1.59 2.48 16.5 108 3.3 3.93 0.32 1.86 8.7 1.23 2.82 1680 13.64 3.1 2.56 15.2 116 2.7 3.03 0.17 1.66 5.1 0.96 3.36 845 14.06 1.63 2.28 16 126 3 3.17 0.24 2.1 5.65 1.09 3.71 780 12.93 3.8 2.65 18.6 102 2.41 2.41 0.25 1.98 4.5 1.03 3.52 770 13.71 1.86 2.36 16.6 101 2.61 2.88 0.27 1.69 3.8 1.11 4 1035 12.85 1.6 2.52 17.8 95 2.48 2.37 0.26 1.46 3.93 1.09 3.63 1015 13.5 1.81 2.61 20 96 2.53 2.61 0.28 1.66 3.52 1.12 3.82 845 13.05 2.05 3.22 25 124 2.63 2.68 0.47 1.92 3.58 1.13 3.2 830 13.39 1.77 2.62 16.1 93 2.85 2.94 0.34 1.45 4.8 0.92 3.22 1195 13.3 1.72 2.14 17 94 2.4 2.19 0.27 1.35 3.95 1.02 2.77 1285 13.87 1.9 2.8 19.4 107 2.95 2.97 0.37 1.76 4.5 1.25 3.4 915 14.02 1.68 2.21 16 96 2.65 2.33 0.26 1.98 4.7 1.04 3.59 1035 13.73 1.5 2.7 22.5 101 3 3.25 0.29 2.38 5.7 1.19 2.71 1285 13.58 1.66 2.36 19.1 106 2.86 3.19 0.22 1.95 6.9 1.09 2.88 1515 13.68 1.83 2.36 17.2 104 2.42 2.69 0.42 1.97 3.84 1.23 2.87 990 13.76 1.53 2.7 19.5 132 2.95 2.74 0.5 1.35 5.4 1.25 3 1235 13.51 1.8 2.65 19 110 2.35 2.53 0.29 1.54 4.2 1.1 2.87 1095 13.48 1.81 2.41 20.5 100 2.7 2.98 0.26 1.86 5.1 1.04 3.47 920 13.28 1.64 2.84 15.5 110 2.6 2.68 0.34 1.36 4.6 1.09 2.78 880 13.05 1.65 2.55 18 98 2.45 2.43 0.29 1.44 4.25 1.12 2.51 1105 13.07 1.5 2.1 15.5 98 2.4 2.64 0.28 1.37 3.7 1.18 2.69 1020 14.22 3.99 2.51 13.2 128 3 3.04 0.2 2.08 5.1 0.89 3.53 760 13.56 1.71 2.31 16.2 117 3.15 3.29 0.34 2.34 6.13 0.95 3.38 795 13.41 3.84 2.12 18.8 90 2.45 2.68 0.27 1.48 4.28 0.91 3 1035 13.88 1.89 2.59 15 101 3.25 3.56 0.17 1.7 5.43 0.88 3.56 1095 13.24 3.98 2.29 17.5 103 2.64 2.63 0.32 1.66 4.36 0.82 3 680 13.05 1.77 2.1 17 107 3 3 0.28 2.03 5.04 0.88 3.35 885 14.21 4.04 2.44 18.9 111 2.85 2.65 0.3 1.25 5.24 0.87 3.33 1080 14.38 3.59 2.28 16 102 3.25 3.17 0.27 2.19 4.9 1.04 3.44 1065 13.9 1.68 2.12 16 101 3.1 3.39 0.21 2.14 6.1 0.91 3.33 985 14.1 2.02 2.4 18.8 103 2.75 2.92 0.32 2.38 6.2 1.07 2.75 1060 13.94 1.73 2.27 17.4 108 2.88 3.54 0.32 2.08 8.9 1.12 3.1 1260 13.05 1.73 2.04 12.4 92 2.72 3.27 0.17 2.91 7.2 1.12 2.91 1150 13.83 1.65 2.6 17.2 94 2.45 2.99 0.22 2.29 5.6 1.24 3.37 1265 13.82 1.75 2.42 14 111 3.88 3.74 0.32 1.87 7.05 1.01 3.26 1190 13.77 1.9 2.68 17.1 115 3 2.79 0.39 1.68 6.3 1.13 2.93 1375 13.74 1.67 2.25 16.4 118 2.6 2.9 0.21 1.62 5.85 0.92 3.2 1060 13.56 1.73 2.46 20.5 116 2.96 2.78 0.2 2.45 6.25 0.98 3.03 1120 14.22 1.7 2.3 16.3 118 3.2 3 0.26 2.03 6.38 0.94 3.31 970 13.29 1.97 2.68 16.8 102 3 3.23 0.31 1.66 6 1.07 2.84 1270 13.72 1.43 2.5 16.7 108 3.4 3.67 0.19 2.04 6.8 0.89 2.87 1285 12.37 0.94 1.36 10.6 88 1.98 0.57 0.28 0.42 1.95 1.05 1.82 520 12.33 1.1 2.28 16 101 2.05 1.09 0.63 0.41 3.27 1.25 1.67 680 12.64 1.36 2.02 16.8 100 2.02 1.41 0.53 0.62 5.75 0.98 1.59 450 13.67 1.25 1.92 18 94 2.1 1.79 0.32 0.73 3.8 1.23 2.46 630 12.37 1.13 2.16 19 87 3.5 3.1 0.19 1.87 4.45 1.22 2.87 420 12.17 1.45 2.53 19 104 1.89 1.75 0.45 1.03 2.95 1.45 2.23 355 12.37 1.21 2.56 18.1 98 2.42 2.65 0.37 2.08 4.6 1.19 2.3 678 13.11 1.01 1.7 15 78 2.98 3.18 0.26 2.28 5.3 1.12 3.18 502 12.37 1.17 1.92 19.6 78 2.11 2 0.27 1.04 4.68 1.12 3.48 510 13.34 0.94 2.36 17 110 2.53 1.3 0.55 0.42 3.17 1.02 1.93 750 12.21 1.19 1.75 16.8 151 1.85 1.28 0.14 2.5 2.85 1.28 3.07 718 12.29 1.61 2.21 20.4 103 1.1 1.02 0.37 1.46 3.05 0.906 1.82 870 13.86 1.51 2.67 25 86 2.95 2.86 0.21 1.87 3.38 1.36 3.16 410 13.49 1.66 2.24 24 87 1.88 1.84 0.27 1.03 3.74 0.98 2.78 472 12.99 1.67 2.6 30 139 3.3 2.89 0.21 1.96 3.35 1.31 3.5 985 11.96 1.09 2.3 21 101 3.38 2.14 0.13 1.65 3.21 0.99 3.13 886 11.66 1.88 1.92 16 97 1.61 1.57 0.34 1.15 3.8 1.23 2.14 428 13.03 0.9 1.71 16 86 1.95 2.03 0.24 1.46 4.6 1.19 2.48 392 11.84 2.89 2.23 18 112 1.72 1.32 0.43 0.95 2.65 0.96 2.52 500 12.33 0.99 1.95 14.8 136 1.9 1.85 0.35 2.76 3.4 1.06 2.31 750 12.7 3.87 2.4 23 101 2.83 2.55 0.43 1.95 2.57 1.19 3.13 463 12 0.92 2 19 86 2.42 2.26 0.3 1.43 2.5 1.38 3.12 278 12.72 1.81 2.2 18.8 86 2.2 2.53 0.26 1.77 3.9 1.16 3.14 714 12.08 1.13 2.51 24 78 2 1.58 0.4 1.4 2.2 1.31 2.72 630 13.05 3.86 2.32 22.5 85 1.65 1.59 0.61 1.62 4.8 0.84 2.01 515 11.84 0.89 2.58 18 94 2.2 2.21 0.22 2.35 3.05 0.79 3.08 520 12.67 0.98 2.24 18 99 2.2 1.94 0.3 1.46 2.62 1.23 3.16 450 12.16 1.61 2.31 22.8 90 1.78 1.69 0.43 1.56 2.45 1.33 2.26 495 11.65 1.67 2.62 26 88 1.92 1.61 0.4 1.34 2.6 1.36 3.21 562 11.64 2.06 2.46 21.6 84 1.95 1.69 0.48 1.35 2.8 1 2.75 680 12.08 1.33 2.3 23.6 70 2.2 1.59 0.42 1.38 1.74 1.07 3.21 625 12.08 1.83 2.32 18.5 81 1.6 1.5 0.52 1.64 2.4 1.08 2.27 480 12 1.51 2.42 22 86 1.45 1.25 0.5 1.63 3.6 1.05 2.65 450 12.69 1.53 2.26 20.7 80 1.38 1.46 0.58 1.62 3.05 0.96 2.06 495 12.29 2.83 2.22 18 88 2.45 2.25 0.25 1.99 2.15 1.15 3.3 290 11.62 1.99 2.28 18 98 3.02 2.26 0.17 1.35 3.25 1.16 2.96 345 12.47 1.52 2.2 19 162 2.5 2.27 0.32 3.28 2.6 1.16 2.63 937 11.81 2.12 2.74 21.5 134 1.6 0.99 0.14 1.56 2.5 0.95 2.26 625 12.29 1.41 1.98 16 85 2.55 2.5 0.29 1.77 2.9 1.23 2.74 428 12.37 1.07 2.1 18.5 88 3.52 3.75 0.24 1.95 4.5 1.04 2.77 660 12.29 3.17 2.21 18 88 2.85 2.99 0.45 2.81 2.3 1.42 2.83 406 12.08 2.08 1.7 17.5 97 2.23 2.17 0.26 1.4 3.3 1.27 2.96 710 12.6 1.34 1.9 18.5 88 1.45 1.36 0.29 1.35 2.45 1.04 2.77 562 12.34 2.45 2.46 21 98 2.56 2.11 0.34 1.31 2.8 0.8 3.38 438 11.82 1.72 1.88 19.5 86 2.5 1.64 0.37 1.42 2.06 0.94 2.44 415 12.51 1.73 1.98 20.5 85 2.2 1.92 0.32 1.48 2.94 1.04 3.57 672 12.42 2.55 2.27 22 90 1.68 1.84 0.66 1.42 2.7 0.86 3.3 315 12.25 1.73 2.12 19 80 1.65 2.03 0.37 1.63 3.4 1 3.17 510 12.72 1.75 2.28 22.5 84 1.38 1.76 0.48 1.63 3.3 0.88 2.42 488 12.22 1.29 1.94 19 92 2.36 2.04 0.39 2.08 2.7 0.86 3.02 312 11.61 1.35 2.7 20 94 2.74 2.92 0.29 2.49 2.65 0.96 3.26 680 11.46 3.74 1.82 19.5 107 3.18 2.58 0.24 3.58 2.9 0.75 2.81 562 12.52 2.43 2.17 21 88 2.55 2.27 0.26 1.22 2 0.9 2.78 325 11.76 2.68 2.92 20 103 1.75 2.03 0.6 1.05 3.8 1.23 2.5 607 11.41 0.74 2.5 21 88 2.48 2.01 0.42 1.44 3.08 1.1 2.31 434 12.08 1.39 2.5 22.5 84 2.56 2.29 0.43 1.04 2.9 0.93 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5 13.643215 12.5278489

5 13.650616 14.021068

5 13.659338 15.160969

5 13.664585 13.421214

5 13.67864 9.6651535

5 13.706178 15.788597

5 13.743804 14.332319

5 13.754485 13.600224

5 13.776854 14.299507

5 13.788332 14.461131

5 13.797314 10.9900939

5 13.812277 12.7877808

5 13.818708 13.767132

5 13.825362 13.998126

5 13.827257 13.411798

5 13.855049 16.06313

5 13.895247 11.6882882

5 13.89677 14.241082

5 13.90694 12.3827126

5 13.931778 15.185287

5 13.935962 13.947167

5 13.936954 15.25114

5 13.958115 14.59757

5 14.026628 15.205066

5 14.037261 13.780271

5 14.061187 15.016911

5 14.097614 14.740769

5 14.099027 14.797805

5 14.1145 13.767203

5 14.131498 13.562132

5 14.170495 14.460691

5 14.188678 15.321407

5 14.191673 11.5556509

5 14.201821 14.442701

5 14.237079 11.6778069

5 14.25179 14.030541

5 14.270079 15.044006

5 14.333913 11.3620963

5 14.35918 13.033823

5 14.361986 13.354937

5 14.365055 15.033464

5 14.365386 12.7474302

5 14.367864 14.965754

5 14.432841 15.920374

5 14.455325 13.293297

5 14.457648 14.584263

5 14.482073 12.4698072

5 14.495794 11.2869317

5 14.520868 13.981305

5 14.601644 11.5475595

5 14.626402 13.237819

5 14.639048 13.63884

5 14.664319 14.12114

5 14.678355 14.234852

5 14.706041 15.347441

5 14.946172 14.768173

5 14.954228 13.480191

5 15.016013 16.799353

5 15.025227 14.322595

5 15.065571 15.06146

5 15.080158 13.622466

5 15.260129 13.286994

5 15.385613 15.526589

5 15.591116 16.438186

5 15.774989 15.963467

5 15.973478 14.539765

5 16.657494 11.7015363




Gauss data

#

-0.46551197 1.3396181

-0.14154019 1.6467355

-0.06147374 1.910021

-0.00960664 1.6959237

0.13522751 2.3371545

0.15816343 1.74101

0.16902868 1.7145902

0.20321506 1.3230994

0.24094079 1.3069846

0.25245225 2.1839526

0.26993324 2.5483323

0.28680391 1.6052432

0.3192367 2.2276855

0.36114833 2.8009784

0.4244067 1.8634449

0.46037797 1.4532789

0.46098959 2.3555875

0.48936047 1.8126198

0.493417 0.52432961

0.5579586 2.0069093

0.59517099 0.74266104

0.601286 2.447746

0.64249205 0.22137058

0.64603926 2.098612

0.68936857 2.3436606

0.70125847 1.9881431

0.70954162 1.8340884

0.71271143 1.1911658

0.73828501 2.9157625

0.76121259 2.887774

0.76300537 3.36236

0.78715506 1.7998882

0.79603202 1.3837631

0.81552587 0.7659359

0.81671088 2.0235945

0.83646559 1.7292928

0.84905869 0.90959966

0.87280583 1.9185033

0.93911004 2.2377149

0.94658989 1.8099329

0.95071896 3.3593771

0.95477185 0.82422933

1.0475351 3.7358953

1.0483555 2.6222619

1.0663274 2.0772458

1.0719789 2.3051114

1.0841659 2.8982847

1.0917331 1.4965835

1.1268271 1.7541776

1.1308673 2.1353612

1.1428466 2.0972579

1.1493513 1.8227118

1.1819349 1.8738619

1.1900377 1.5835831

1.2101874 1.8796963

1.2181118 3.0111068

1.2277972 2.0808183

1.2335498 2.9573163

1.2448562 1.6017201

1.2555436 1.9472067

1.2597352 0.95874592

1.2601195 0.81798378

1.2715242 0.46185734

1.2749423 3.2592336

1.2888759 2.4162943

1.319464 2.1180977

1.3266557 1.9249356

1.3269461 2.8783508

1.3458998 1.6047048

1.3471548 2.0660325

1.3753515 2.4089999

1.3793406 2.1089793

1.3872928 3.3455047

1.3979966 2.3829163

1.4001717 3.0482038

1.4054732 1.6215221

1.4312392 0.79191521

1.4354818 3.0271297

1.4410139 2.7986203

1.4641202 0.58155858

1.4646153 3.9791516

1.5055329 1.0990125

1.5286615 2.6943944

1.5534019 3.741445

1.5735908 1.5571016

1.5856639 0.99127551

1.6083411 2.3154722

1.6167515 3.4641362

1.6315791 2.9447703

1.6485825 1.9459715

1.6558902 1.1154736

1.6603318 2.4801616

1.6657018 1.9762646

1.6740867 2.9751515

1.7109312 1.5716093

1.7172934 1.9542265

1.7245997 2.387059

1.7280016 3.2168608

1.7407356 1.6770587

1.7446611 3.8440441

1.7479167 1.3913499

1.7507312 2.769599

1.7567404 1.4447784

1.7591262 3.9952283

1.7610234 3.5877581

1.7736913 1.4399875

1.7826518 3.1827802

1.7882331 2.7558682

1.8048077 1.4894358

1.806578 2.2980184

1.8105734 3.0728863

1.8132452 0.11900227

1.8581162 2.3816176

1.8735926 2.1120087

1.8771577 1.9606585

1.8791867 0.81713304

1.8866894 1.5761363

1.8881009 0.12698201

1.8980516 2.4640572

1.911659 0.92031474

1.9163593 2.2659011

1.9217044 1.9429349

1.9240625 1.2683148

1.933498 0.99443935

1.9366692 3.0426166

1.9572064 3.2689621

1.987931 4.1287767

1.9907116 1.6489055

1.9930155 2.6846483

2.0011578 2.3236036

2.0107086 2.1802041

2.0650972 0.62687304

2.0760993 1.8564104

2.076595 3.141211

2.0797088 2.6444816

2.0828643 2.4026682

2.0948837 1.8648059

2.0985622 3.7026465

2.0994979 4.0382142

2.1023642 3.2814187

2.1102441 3.3805769

2.1522589 1.6625872

2.1613626 2.6416719

2.1760954 0.66759943

2.1805424 2.0442384

2.1842685 2.5954692

2.1891405 2.9582391

2.1963915 2.0430046

2.2020264 2.176707

2.204684 0.83693931

2.2052541 1.7499996

2.2066926 2.7208274

2.2109871 0.60017807

2.2146996 1.399128

2.2174554 0.72364346

2.2304159 0.82961734

2.2473506 1.2875368

2.2490397 2.1734422

2.2519844 1.2969987

2.2604619 0.7826934

2.2688021 2.0460717

2.3082186 3.4655713

2.337285 2.3586476

2.3616657 2.9431843

2.372691 1.7305975

2.3783937 1.3535819

2.3816021 1.9007384

2.3967371 1.1569486

2.409683 2.3595594

2.4193469 1.0856312

2.4234583 0.51782552

2.4392568 3.1463499

2.440037 3.2618835

2.4499319 0.4923003

2.4582683 1.3588849

2.4769531 3.5814207

2.4844316 1.9984511

2.4845173 1.8737064

2.4932187 1.66146

2.5161047 2.2298843

2.5216744 2.9607432

2.5229139 1.492989

2.5270772 -0.06387469

2.5353904 0.28615059

2.5781225 2.5085023

2.5789043 2.6564247

2.5913078 3.4678316

2.6017795 2.3329228

2.6083691 1.7219107

2.609068 2.4104897

2.6328854 1.4587599

2.6469687 1.7014709

2.6920039 1.295654

2.6979838 2.9314267

2.7126542 2.1619493

2.7176177 2.6184841

2.720342 2.7590754

2.7288016 1.8601606

2.7729759 2.3649024

2.8041777 1.8947211

2.8297566 2.1058471

2.8568165 3.2398304

2.8708006 2.5365312

2.8884565 2.4983996

2.8940135 0.80718637

2.9270099 1.4705192

2.933241 0.61062

2.9722306 1.9105811

2.9772854 0.05260017

2.9773797 2.4382292

2.980513 0.98434561

3.005612 2.8833924

3.0089706 0.48283347

3.0261818 2.8696189

3.0400688 2.2351968

3.0647613 1.3867858

3.0958156 4.7977043

3.0974361 0.61238889

3.123407 3.2756611

3.140026 1.9104996

3.1401996 3.0088845

3.1743328 2.9899578

3.1808794 2.408549

3.1832203 1.0703815

3.2276428 2.180782

3.2372055 1.4413745

3.2952069 2.9016304

3.3387983 3.2365316

3.3562441 3.3595853

3.4416423 4.7357497

3.521928 2.8651324

3.5249241 2.4056552

3.6396121 0.74341452

3.6525806 4.0596418

3.6773415 0.65264229

3.7089812 3.4749576

3.7570425 2.2789297

3.7990812 1.8031089

3.8079212 3.2604948

3.8635523 3.3065351

3.8826769 2.4434805

3.9105801 0.0730681

4.0275824 2.9110729

4.2610977 3.5150765

4.2654032 2.5862794

4.3772226 3.6939538

4.3862929 1.8272327

4.6736332 1.4853571

5.9435583 7.4739571

5.9739184 5.87866

6.187397 7.348852

6.2407109 6.5518535

6.2436521 7.4541242

6.2551838 5.5564442

6.285243 8.2071673

6.3299446 7.0000361

6.3319237 8.0936765

6.373557 7.8826912

6.464094 7.0581829

6.4906742 7.3292611

6.5115338 7.8535287

6.5659971 8.0486414

6.6023175 5.9585361

6.6234813 6.3907157

6.6445082 7.6998504

6.649045 7.8849591

6.6584125 7.0152807

6.6665508 7.4282592

6.6730605 8.5942431

6.763729 6.2835604

6.7755229 8.407463

6.8076254 7.658945

6.8276345 6.4644596

6.8479234 5.756862

6.856574 6.503814

6.8622591 7.9170009

6.9363758 5.2319598

6.943909 6.4875439

6.9889206 8.611449

7.0165059 7.5475354

7.0419705 8.8528666

7.102277 9.0164613

7.1288555 6.7255618

7.1296474 7.9023458

7.1553296 8.365873

7.1730311 6.742746

7.1820635 6.0384904

7.2560427 8.2301575

7.2981906 9.2181469

7.3011218 3.9657148

7.301961 7.6504424

7.3145947 6.2175059

7.3361955 9.0435696

7.3803912 7.9176344

7.4078596 7.5672229

7.40994 7.6915476

7.4850186 6.672043

7.4965347 8.4085449

7.5192183 7.1697117

7.5647357 8.0321895

7.5648727 8.5589759

7.6015189 4.0994906

7.603581 8.3790253

7.6099817 5.1638026

7.6206807 7.6087325

7.6345994 7.1531639

7.6429483 8.3542054

7.6724078 6.4637002

7.7092349 6.7151763

7.714601 5.8089865

7.7182087 5.8359403

7.7245 6.2597278

7.7385045 6.4247141

7.770265 7.7400903

7.8110738 6.4712292

7.8312662 5.5418477

7.8319838 6.9931978

7.8578799 7.0934209

7.8853276 7.0234515

7.913638 7.5415647

7.9267408 6.7133325

8.018074 7.6140025

8.0184744 7.045611

8.0412225 6.2239493

8.0485496 7.0632895

8.0518117 6.0080764

8.0664894 7.1414022

8.0949584 7.125697

8.101395 7.8750952

8.1235131 6.6977035

8.1394266 6.110125

8.1673507 6.731084

8.1958179 7.1986208

8.2270168 8.334217

8.2319251 7.5158861

8.2327407 7.0726365

8.2746275 6.693953

8.2802248 4.3492874

8.307973 6.5744575

8.4089124 6.1652054

8.4206061 5.1979654

8.4332237 7.4580566

8.4413669 6.0914636

8.4696749 6.8581128

8.4830634 9.2508174

8.4864111 5.7290216

8.4928201 8.746028

8.4952661 5.12129

8.4990939 8.0108213

8.5146056 7.6430751

8.5521972 6.328596

8.574167 7.0679398

8.5811656 5.0079888

8.5818258 7.1019273

8.6091367 6.5752111

8.6246849 6.5365028

8.6642143 6.4458003

8.6691018 8.7915455

8.7002922 8.6383879

8.7120615 6.794039

8.7644048 6.8808288

8.7934826 5.4950836

8.9684594 8.0053487

8.9714406 5.2756851

8.9788712 7.9465767

9.0029676 5.9771513

9.024769 4.9830776

9.108955 8.0022074

9.1667502 5.1966235

9.2008513 9.2806987

9.2237865 7.3287031

9.3157701 5.5950945

9.3606768 10.1086731

9.5844491 7.865713

9.6733525 5.6230164

9.682216 7.4233843

9.6856208 7.310157

9.6906946 5.328908

9.7297457 8.1547281

12.382204 7.6953397

-0.53802474 14.8670258

-0.50977026 14.5352805

-0.43584458 15.831295

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0.05835481 15.557332

0.10288324 14.5935174

0.17447523 13.0540215

0.19810974 14.12201

0.21934452 14.0784394

0.23439904 14.844791

0.26788561 15.209558

0.47250581 13.6123102

0.5142698 13.4614009

0.54996643 14.2768358

0.55418227 13.884939

0.60602417 15.936468

0.63375978 15.206995

0.63673117 13.7844843

0.7274187 14.0976652

0.78672127 13.6171725

0.84069897 13.7650607

0.885701 15.137431

0.91613686 15.233336

0.92942006 13.5713626

0.95596944 15.535309

0.96854857 14.8038184

1.0145628 13.2879164

1.0177927 15.84433

1.0215634 15.765331

1.0319656 14.6683734

1.0682963 14.1762061

1.0797411 15.497518

1.0890363 14.7297415

1.1020139 14.1104753

1.1111965 15.974068

1.1118762 15.730051

1.1190241 15.626021

1.1290053 15.708113

1.1567038 13.9859388

1.1642717 15.86776

1.1877287 14.0277225

1.2417761 14.9239422

1.2697233 15.58267

1.2805359 15.839587

1.315033 12.800678

1.3374848 14.8847305

1.3436787 16.160946

1.3601605 14.9220547

1.3804341 14.5464206

1.419576 13.5611838

1.44766 14.7976419

1.4497856 15.992422

1.4515913 14.246366

1.462917 14.0683491

1.469379 16.277371

1.476635 14.7049207

1.4789768 13.7587035

1.4864838 12.8367824

1.494347 13.4188009

1.4977578 15.235316

1.5150408 16.469438

1.5465376 15.102797

1.5481756 15.500124

1.5623819 16.11233

1.5657523 14.8653065

1.5680518 14.7992902

1.5787906 14.4795982

1.580711 15.634115

1.5882776 16.154661

1.6154553 14.5561227

1.6240277 13.1513374

1.6271007 14.9594745

1.6423654 14.5207591

1.6532214 13.9213972

1.6708971 14.0647823

1.6816109 15.307332

1.6831495 15.753881

1.6909234 13.3056743

1.694486 13.4031309

1.7029568 13.7852089

1.7209245 16.160009

1.7554475 14.5635625

1.7561105 14.1486694

1.7591644 16.053899

1.7816041 16.754704

1.7876631 15.692215

1.8234422 14.7212479

1.8262062 15.727142

1.8288627 15.673864

1.8297793 15.458916

1.832603 16.074711

1.8353628 14.0803144

1.8556235 14.6185811

1.8563045 15.950317

1.8567533 15.230949

1.8755811 14.7954

1.8808773 16.261579

1.8832988 13.9781784

1.8843075 14.8027541

1.8905587 17.469766

1.8975282 15.28635

1.9109833 15.301225

1.9144448 13.974059

1.9165083 16.676352

1.9209176 14.7131755

1.9781659 16.664901

1.9788063 12.9605311

1.9795792 15.604285

1.9988977 14.3873

2.0085747 15.703644

2.0196867 17.969208

2.025619 15.081437

2.0534619 15.705229

2.0547586 15.305228

2.0547586 15.816871

2.0755392 13.3552001

2.075569 14.5328423

2.1066715 13.9310227

2.1219421 13.0786952

2.1325375 15.278667

2.1346469 14.7929784

2.1377228 14.3141014

2.1424518 14.0621782

2.163728 13.0126414

2.1789909 16.545179

2.1910496 17.74184

2.192648 16.567828

2.2120042 14.5149355

2.2236802 14.7026045

2.235359 16.291386

2.2504888 15.255275

2.2849944 14.6670767

2.2936236 15.04094

2.2943962 13.6098342

2.2972952 16.044405

2.3159775 13.8025205

2.3183388 14.7124052

2.3312717 14.1945021

2.3332788 14.858556

2.3436751 15.278825

2.3510198 14.4443378

2.3536828 14.6918611

2.3762487 15.001178

2.3873331 14.944966

2.4018079 16.336344

2.4049931 15.354974

2.4069312 15.229606

2.4145946 16.865367

2.4298175 15.177013

2.4304013 16.295831

2.4350526 14.2814343

2.4365224 15.607422

2.4385972 15.113629

2.4395695 15.287594

2.4433867 14.5672236

2.4581051 14.3005269

2.484958 14.6122817

2.4855137 14.7430907

2.4961756 14.0852748

2.4961815 14.9774792

2.5060367 15.626502

2.5111277 15.048125

2.5353022 16.006037

2.5477455 14.5218191

2.5646723 13.5753288

2.6069566 14.4928334

2.610661 14.2333245

2.6110635 16.161409

2.6156599 17.24306

2.6157363 14.6247648

2.6231143 16.467851

2.6303499 16.166585

2.6479363 14.4927011

2.649848 14.9999997

2.6726846 16.196404

2.6956556 16.338031

2.7507975 16.35678

2.8039113 15.381728

2.8669884 16.926908

2.8952958 14.2367016

2.9323323 14.425336

2.9469818 15.622926

2.9524754 14.5783952

2.9781864 15.542842

2.9871194 13.4396185

3.0319823 15.890154

3.0549366 14.0793781

3.0848657 15.441317

3.0908691 15.080454

3.1299154 14.4593151

3.1662752 16.241106

3.1816155 13.5673625

3.2062795 14.3081385

3.2424753 15.28896

3.2425295 15.158123

3.2906467 16.072758

3.297312 14.8472714

3.2975145 15.757499

3.3062086 15.421346

3.3323506 15.513092

3.3448124 14.1347046

3.3474952 15.536695

3.4494709 16.900892

3.4986571 15.600885

3.5130076 16.136178

3.5275875 16.167535

3.5458203 15.827132

3.6357217 16.777843

3.6418671 15.280765

3.642627 14.750893

3.766496 15.256047

3.8495488 13.0793218

4.1037454 15.818065

4.2250822 17.607042

4.3813155 15.092317

4.634051 16.15266

5.0207123 15.895823

14.2951741 4.950383

14.6426889 3.026425

15.3783284 1.6677586

15.6610826 1.2858431

15.7658667 1.2502372

15.8735189 3.0070031

15.9202878 2.3794615

16.2157376 4.0456349

16.2828371 4.7614267

16.3416173 4.7342967

16.372867 4.071573

16.3793344 3.4288482

16.4471513 1.0290971

16.5507429 3.1520108

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Parag Kanade/fuzzy ants/3D Board/header_z3.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include<stdlib.h>#include <ctype.h>#include <time.h>#define MAXANTS 50#define MAXDATA 150#define MAXANTSFORHEAPS 10#define MAXITERATIONS 1000#define MAXROWS 17#define MAXCOLS 17#define zDim 3#define EXPRUN 50#define Tremove 1.50#define Tcreate 0.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.12#define features 4#define filename "iris"#define errThresh 0.1#define dataDump 1#define maxDist 0#define INITIALOBJECTS 75#define OBJINC 10/* some type definitions to make C code a little more readable */#define TRUE 1#define FALSE 0#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define c 3 /* number of clusters */#define TOTRUNS 1 /* number of V's to write out */#define false 0#define true 1#define maha 0#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap {float Dmax;//maximum dissimilar distanceirisData Ocenter;//center objectfloat Dmean;//mean of the distance between the center and all the objectsirisData Odissim;//most dissimilar objectint x;//x position on the boardint y;//y position on the boardint z;//z position on the boardint count;//number of objects in the heapsint disPosition;//most dissimilar objectint objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int z;int carrying;int speed;int dir;irisData data;}ant;float orgData[n][s+1];float fcmData[260][n][s];int clsCenCount[260];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int z,int a , int b);void initialise(int flag);int findObject(int z,int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);int pickObject2(ant *a , int position,int flag);int pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);/********************************************************************//***For FCM algorithm*/void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects);void Output(char[]);void Fcm(irisData data[],int stage);float Compute_Jm(center V, objects X);/********************************************************************/

Parag Kanade/fuzzy ants/3D Board/ant_z3.c

#include "header_z3.h" /*******************************************************************/ /*** Global Variables ***/ irisData data[MAXDATA]; //To hold the data read from the file int count=0; //Count of the total number of data elements int heapCount=0; // Total number of heaps int heapRow=0; // Rows for the second iteration with heaps int newHeapCount=0; // Count of the heaps for the second iteration heap heaps[MAXDATA]; // Data structure to hold the heap information heap newHeaps[MAXDATA]; // Data Structure to hold the new heap information // For the second iteration int board[zDim][MAXROWS][MAXCOLS]; // Board on which the objects are located ant ants[MAXANTS]; // Data structure to hold the ants float Dmax=0; // Distance of the most dissimilar object pair int minHeapCount=100000; // Counter to hold the minimum heap count, used only for academic purpose // no use in the algorithm int minPosition; // the minimum heap iteration , only for academic purpose // no use in the algorithm float clsCen[MAXDATA][MAXDATA][s];//to hold the objects after hardening int cenCount[MAXDATA];//to hold the number of objects after hardening /********************************************************************/ /*** Varaibles to normalize the features between 0 and 1 */ float minInput[features], maxInput[features],scale[features],minForHeaps[features],maxForHeaps[features],scaleForHeaps[features]; /********************************************************************/ /** Variables for the FCM algorithm */ int temp[200]; /* for square matrix tests */ float eps,m; matrix U, U1; BOOLEAN stop; center V; objects X; char fname[80]; FILE *fp; int run; float inv_cov[s+1][s+1]; /* inverse of covariance matrix Y */ float Y[s][s]; long rand_index, Random_seed; long state1[52] = { 9347, 3835, 5194, 8310, 346, 535, 5297, 6711, 77, 3834, 668, 4175, 6868, 5890, 9304, 8462, 5269, 920, 65392, 41600, 70119, 91032, 76220, 26245, 4746, 73608, 32823, 63264, 75641, 99104, 36534, 24704, 98255, 72266, 75336, 65152, 7269, 63163, 88471, 27271, 43641, 76649, 47773, 23777, 27491, 35926, 16651, 48652, 89766, 90921, 6056 }; int z; int mins=0; int aveit =0; float mintab[200]; int mincount[200]; long start,end,total; int lCount;//added by parag /********************************************************************/ /********************************************************************/ /* Function to read the input data set */ /* The file name is assumed to be itest.data */ /* Status: Tested */ /* Output: Data from the file into the "data" (Global) data structure */ /* Output: Count (Global), the count of the number of objects */ /********************************************************************/ void readInput() { FILE *fp; char fname[80]; int i=0,j=0; char ch; sprintf(fname,"%s.data",filename); /* Check if the input file is opened */ if((fp=fopen(fname,"r"))==NULL) { printf("\n Unable to open input file \n"); exit(0); } /*initialise the minimum maximum and the scale */ for(i=0;i<features;i++) { minInput[i]=1000; maxInput[i]=0; minForHeaps[i]=1000; maxForHeaps[i]=0; scale[i]=0; scaleForHeaps[i]=0; } /* Discard leading comments */ ch = 'c'; while (ch != '#') fscanf (fp, "%c", &ch); i=0; /* Parse the file and populate the data structure */ while(!feof(fp)) { for(j=0;j<features;j++) { fscanf(fp,"%f ",&data[i][j]); } fscanf(fp,"\n"); for(j=0;j<features;j++) { if(data[i][j]<minInput[j]) minInput[j]=data[i][j]; if(data[i][j]>maxInput[j]) maxInput[j]=data[i][j]; } i++; } count=i; /* Normailze the feature values in between 0 and 1 */ for(j=0;j<features;j++) { if(minInput[j]!=maxInput[j]) scale[j]=1.0/(maxInput[j]-minInput[j]); else scale[j]=1.0; } for(i=0;i<count;i++) { for(j=0;j<features;j++) data[i][j]=(data[i][j]-minInput[j])*scale[j]; } fclose(fp); return; }//end of function /********************************************************************/ /*Function to compute the eucldiean distance for all input data set */ /* Output : an array conatining the distance of each object with the other */ /********************************************************************/ float computeDistance() { float distance,dist=0; int i=0,j=0,k=0; distance=0; for(j=0;j<count;j++) for(i=0;i<count;i++) { for(k=0;k<features;k++) dist+=sqrt((data[j][k]-data[i][k])*(data[j][k]-data[i][k])); if(distance<dist)distance=dist; dist=0; } return(distance); } /********************************************************************/ /* Function to compute the distance between two objects */ /* Output : Euclidean d istance between the two input objects */ /********************************************************************/ float computeDistanceBetween2(irisData a,irisData b) { float temp=0; int i=0; for(i=0;i<features;i++) temp+=(a[i]-b[i])*(a[i]-b[i]); temp=sqrt(temp); return(temp); } /********************************************************************/ /* Function to compute the value of an object */ /********************************************************************/ float value(irisData a) { int i=0; float temp=0; for(i=0;i<features;i++) temp+=a[i]*a[i]; return(sqrt(temp)); } /********************************************************************/ /* Function to find the object in the data structure */ /* Output : The position of the object in the data */ /* Input : The object */ /********************************************************************/ int findInData(irisData object,int iFlag) { int i=0,lastCount=0,j,position=-1,flag[features],flg;if(iFlag==1)lastCount=MAXDATA;else{}for(i=0;i<lastCount;i++){ for(j=0;j<features;j++) if(fabs(object[j]-data[i][j])<0.00001) flag[j]=1; else { flag[j]=0; break;} flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /* Function to find whether there is an object on the board at a,b */ /* Output : a flag indicating whether an object is found or not */ /* Input : The row and column indicating the board position */ /********************************************************************/ int found(int z,int a , int b) { if(board[z][a][b]==9) return 1; else return 0; } /********************************************************************/ /* Function to initialize the initial board */ /* Output : None */ /* Global Structures modified : heaps */ /* Input : Flag indicating the iteration 1: First 0: Second */ /********************************************************************/ void initialise(int flag) { int a,b,i=0,k=0,z; int loopCount,rowCount,colCount,zCount; /* Initialize the row, column and number of heaps */ if(flag==1) { loopCount=count; rowCount=MAXROWS; colCount=MAXCOLS; /*zCount=zDim;*/ } else { loopCount=lCount; rowCount=heapRow; colCount=heapRow; /*zCount=heapRow;*/ } zCount=zDim; /* Initialize the heaps */ for(i=0;i<loopCount;i++) { heaps[i].count=0; heaps[i].x=0; heaps[i].y=0; heaps[i].z=0; heaps[i].disPosition=0; heaps[i].Dmax=0; heaps[i].Dmean=0; } for(i=0;i<loopCount;i++) { /*find a board position which is empty*/ do { a=lrand48()%rowCount; b=lrand48()%colCount; z=lrand48()%zCount; } while(found(z,a,b)); /*Allocate the object to that place and assign a heap to it*/ heaps[i].x=a; heaps[i].y=b; heaps[i].z=z; heaps[i].count=1; heaps[i].Dmax=0; heaps[i].Dmean=0; if(flag==1) { heaps[i].objects[0]=i; for(k=0;k<features;k++)heaps[i].Ocenter[k]=data[i][k]; } else { for(k=0;k<features;k++) heaps[i].Ocenter[k]=newHeaps[i].Ocenter[k]; } heaps[i].disPosition=0; board[z][a][b]=9; if((debugLevel>0)&&(flag==1))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],data[heaps[i].objects[0]][0]); if((debugLevel>0)&&(flag==0))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],newHeaps[i].Ocenter[0]); } /*initialise the initial heap count*/ heapCount=loopCount; currentRow=rowCount; } /********************************************************************/ /* Function to find whether an heap is present at the location a b */ /* Output : Flag indicating the presencs of the heap */ /* Input : Roe and column indicating the board position */ /********************************************************************/ int findObject(int z,int a,int b) { int i; int position=-1; for(i=0;i<heapCount;i++) { if((heaps[i].z==z)&&(heaps[i].x==a)&&(heaps[i].y==b)) { position=i; break; } } return(position); } /********************************************************************/ /* Function initialize the ants */ /* Output : None */ /* Input : Flag indicating the iteration */ /* Global Variables Modified : ant, heapCount, heaps, board */ /********************************************************************/ void initialiseAnts(int flag) { int i=0,a=0,b=0,position,j=0,loopCount=0,rowCount,colCount,k=0,z=0,zCount=0; if(flag==1) { loopCount=MAXANTS; rowCount=MAXROWS; colCount=MAXCOLS; /*zCount=zDim;*/ } else { loopCount=MAXANTSFORHEAPS; rowCount=heapRow; colCount=heapRow; /*zCount=heapRow;*/ } zCount=zDim; for(i=0;i<loopCount;i++) { /*generate random positions for the ants*/ a=lrand48()%rowCount; b=lrand48()%colCount; z=lrand48()%zCount; ants[i].x=a; ants[i].y=b; ants[i].z=z; /*Find if an object is present at that position or not*/ if(board[z][a][b]==9) { ants[i].carrying=1; /*find the heap at that location*/ position=findObject(z,a,b); if(position==-1) { printf("\n Error in logic: Ant init \n"); //getchar(); break; } else { /*Assign the heap data to the ant data and decrement the heap count*/ if(flag==1){ for(k=0;k<features;k++)ants[i].data[k]=data[heaps[position].objects[0]][k]; }else{for(k=0;k<features;k++)ants[i].data[k]=heaps[position].Ocenter[k];} for(j=position;j<heapCount-1;j++) heaps[j]=heaps[j+1]; heapCount--; } /*Clear the board position */ board[z][a][b]=-9; } else ants[i].carrying=-1; ants[i].speed=0; ants[i].dir=lrand48()%26; printf("\n Ant %2d X: %2d Y: %2d Z:%2d Carying : %2d ",i,ants[i].x,ants[i].y,ants[i].z,ants[i].carrying); } currentAntCount=loopCount; } /********************************************************************/ /* Function to move the ant */ /* Output: None */ /* Input: Ant to be moved and the flag indicating the iteration */ /********************************************************************/ void moveNext(ant* a,int flag) { int rowCount,colCount,zCount; rowCount=currentRow; colCount=currentRow; /*if(flag==1)zCount=zDim;else zCount=heapRow;*/ zCount=zDim; /*Depending upon the direction move the ant*/ switch (a->dir) { case 0: a->y++; if(a->y>=colCount)a->y=0; break; case 1: a->x--; a->y++; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 2: a->x--; if(a->x<0)a->x=rowCount-1; break; case 3: a->x--; a->y--; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 4: a->y--; if(a->y<0)a->y=colCount-1; break; case 5: a->x++; a->y--; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 6: a->x++; if(a->x>=rowCount) a->x=0; break; case 7: a->x++; a->y++; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break; case 8: a->y++; if(a->y>=colCount)a->y=0; a->z--; if(a->z<0)a->z=zCount-1; break; case 9: a->x--; a->y++; a->z--; if(a->z<0)a->z=zCount-1; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 10: a->x--; a->z--; if(a->z<0)a->z=zCount-1; if(a->x<0)a->x=rowCount-1; break; case 11: a->x--; a->y--; a->z--; if(a->z<0)a->z=zCount-1; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 12: a->y--; a->z--; if(a->z<0)a->z=zCount-1; if(a->y<0)a->y=colCount-1; break; case 13: a->x++; a->y--; a->z--; if(a->z<0)a->z=zCount-1; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 14: a->x++; a->z--; if(a->z<0)a->z=zCount-1; if(a->x>=rowCount) a->x=0; break; case 15: a->x++; a->y++; a->z--; if(a->z<0)a->z=zCount-1; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break; case 16: a->z--; if(a->z<0)a->z=zCount-1; break; case 17: a->y++; a->z++; if(a->z>=zCount)a->z=0; if(a->y>=colCount)a->y=0; break; case 18: a->x--; a->y++; a->z++; if(a->z>=zCount)a->z=0; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 19: a->x--; a->z++; if(a->z>=zCount)a->z=0; if(a->x<0)a->x=rowCount-1; break; case 20: a->x--; a->y--; a->z++; if(a->z>=zCount)a->z=0; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 21: a->y--; a->z++; if(a->z>=zCount)a->z=0; if(a->y<0)a->y=colCount-1; break; case 22: a->x++; a->y--; a->z++; if(a->z>=zCount)a->z=0; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 23: a->x++; a->z++; if(a->z>=zCount)a->z=0; if(a->x>=rowCount) a->x=0; break; case 24: a->x++; a->y++; a->z++; if(a->z>=zCount)a->z=0; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break;case 25: a->z++; if(a->z>=zCount)a->z=0; break;default: break; } }//end of function /********************************************************************/ /* Function to compute the parameters of the heap */ /* Output: None */ /* Input: The heap whose parameters are to be computed iteration flag*/ /********************************************************************/ void computeHeapParameters(heap* heapData,int flag) { int i=0,j=0,k=0,position=0; float distance=0; float Dmean=0.0; float *dis; float featureSum[features]; float max=0; float sum=0; /* init*/ for(k=0;k<features;k++) featureSum[k]=0; /*Compute the distance of each object and find the maximum distance*/ max=0; if(flag==1){ for(j=0;j<heapData->count;j++) for(i=0;i<heapData->count;i++) { for(k=0;k<features;k++) sum+= (data[heapData->objects[j]][k]-data[heapData->objects[i]][k])*(data[heapData->objects[j]][k]-data[heapData->objects[i]][k]); distance=sqrt(sum); if(max<distance) max=distance; sum=0; } /* Assign the maximum distance to the heap */ heapData->Dmax=max;/*Compute the centre object*/for(i=0;i<heapData->count;i++)for(k=0;k<features;k++) featureSum[k]+=data[heapData->objects[i]][k];for(k=0;k<features;k++)heapData->Ocenter[k]=featureSum[k]*1.0/heapData->count; /* Find the mean distance */if((dis=(float *) calloc(heapData->count,sizeof(float)))==NULL){printf("\nUnable to allocate memory in the compute heap parameters procedure\n");exit(0);} for(i=0;i<heapData->count;i++) { Dmean+=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); dis[i]=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); } heapData->Dmean=Dmean*1.0/heapData->count; /*Compute the most dissimilar object*/ max=dis[0]; for(i=1;i<heapData->count;i++) if(max<dis[i]) { max=dis[i]; position=i; } /*Assign the most dissimilar object to the heap variable*/ for(k=0;k<features;k++) heapData->Odissim[k]=data[heapData->objects[position]][k]; heapData->disPosition=position;free(dis); } else{/* Heap Parameters for the second stage */for(k=0;k<features;k++){ heapData->Odissim[k]=heapData->Ocenter[k];}heapData->disPosition=0;heapData->Dmean=0;heapData->Dmax=0;} }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a single object*/ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject1(ant *a,int position,int flag) { int i,k=0; /*Clear the board*/ board[heaps[position].z][heaps[position].x][heaps[position].y]=-9; /* Assign the data to the ant*/if(flag==1){ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[0]][k];}else {for(k=0;k<features;k++) a->data[k]=heaps[position].Ocenter[k];} a->carrying=1; /*Copy all the heaps remaining heaps in the structure*/ for(i=position;i<heapCount-1;i++) heaps[i]=heaps[i+1]; /*Decrement the heap count*/ heapCount--; }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a two objects */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ int pickObject2(ant *a , int position,int flag) { float Pdestroy1=-1; int i=0,k=0; /*Compute the probability to destroy the heap*/ Pdestroy1=lrand48()%13*1.0/12.0; /*If the probability is more than Pdestroy then destroy the heap */ if(Pdestroy1<Pdestroy) { /*pick the object randomly*/ i=lrand48()%2; /* Assign the data to the ant */ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[i]][k]; a->carrying=1; /*Correct the heap parameters*/ heaps[position].count=1; heaps[position].objects[0]=heaps[position].objects[1-i]; computeHeapParameters(&heaps[position],flag); return(1); } else return(0); }//end of function /********************************************************************/ /*Function to pick a object from a heap consisting of more than two object */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /********************************************************************/ int pickObjectn(ant *a,int position,int flag) { int i=0,k=0; /*If the criteria is satsfied then pick the object*/ if((computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)*1.0/heaps[position].Dmean)>Tremove) { a->carrying=1; for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[heaps[position].disPosition]][k]; for(i=heaps[position].disPosition;i<heaps[position].count-1;i++) heaps[position].objects[i]=heaps[position].objects[i+1]; heaps[position].count--; computeHeapParameters(&heaps[position],1); return(1);}elsereturn(0); }//end of function /********************************************************************/ /* Function to pick a object */ /********************************************************************/ void pick(ant *a,int flag) { int position=-1,i=0,j=0; float Pload1=-1.0; int hCount=0,hPos[26],picked=0; for(i=0;i<heapCount;i++) { /*Check if a heap is present in the eight neighbouring cells*/ if(abs(heaps[i].z-a->z)<=1&&abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; hPos[hCount++]=i; /*break;*/ } } /*Check if Heap is found*/ /*if(position!=-1)*/ if(hCount>0) { /* heap or object found.*/ /*Depending upon the number present in the heap call the corresponding routine*/ j=0; while(j<hCount && !picked) { switch(heaps[hPos[j]].count) { case 1: Pload1 = lrand48()%151*1.0/150.0; if(Pload1<Pload) { pickObject1(a,hPos[j],flag);picked=1; }else picked=0; break; case 2: picked=pickObject2(a,hPos[j],flag); break; default: picked=pickObjectn(a,hPos[j],flag); break; }j++;} } } /********************************************************************/ /* Function to drop a object to form a new heap */ /* Output: None */ /* Input: Ant, iteration flag */ /* Global Structure modified : heap, heapCount */ /********************************************************************/ void dropObject(ant* a,int flag) { /*Create a new heap*/ int k=0; heaps[heapCount].count=1; heaps[heapCount].x=a->x; heaps[heapCount].y=a->y; heaps[heapCount].z=a->z; heaps[heapCount].Dmax=0; heaps[heapCount].Dmean=0; heaps[heapCount].disPosition=0; for(k=0;k<features;k++) { heaps[heapCount].Odissim[k]=a->data[k]; heaps[heapCount].Ocenter[k]=a->data[k]; } if(flag==1) {heaps[heapCount].objects[0]=findInData(a->data,flag); /*computeHeapParameters(&heaps[heapCount],flag);*/ } computeHeapParameters(&heaps[heapCount],flag); heapCount++; a->carrying=-1; board[a->z][a->x][a->y]=9; }//end of function /********************************************************************/ /* Function to find the heap position during the second iteration */ /* Output: position of the heap */ /* Input: object center of the heap that the ant is carrying */ /* Global Structure modified : none */ /********************************************************************/ int findInNewheaps(irisData object) { int position=-1,i,j=0,flag[features],flg; for(i=0;i<newHeapCount;i++) { for(j=0;j<features;j++) { if(fabs(object[j]-newHeaps[i].Ocenter[j])<0.00001) flag[j]=1; else {flag[j]=0;break;} } flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /*Function to drop a object to a heap consisting of a one or more object */ /* Output: none */ /* Input: Ant, position of the heap , iteration flag */ /* Global Structure modified: heaps,newHeaps */ /********************************************************************/ void dropObject1(ant* a, int position,int flag) { int heapPosition=-1,heapPosition1=-1,i,j,k=0; irisData newData,temp; if(flag==1) { heaps[position].objects[heaps[position].count]=findInData(a->data,flag); heaps[position].count++; computeHeapParameters(&heaps[position],flag); } else { for(k=0;k<features;k++)temp[k]=heaps[position].Ocenter[k]; heapPosition=findInNewheaps(a->data); heapPosition1=findInNewheaps(heaps[position].Ocenter); if(heapPosition==-1||heapPosition1==-1) { printf("\n error in logic:new heaps\n"); //getchar(); return; } for(k=0;k<features;k++) {newData[k]=(newHeaps[heapPosition].Ocenter[k]*newHeaps[heapPosition].count+newHeaps[heapPosition1].Ocenter[k]*newHeaps[heapPosition1].count)*1.0/(newHeaps[heapPosition].count+newHeaps[heapPosition1].count);newHeaps[heapPosition].Ocenter[k]=newData[k];heaps[position].Ocenter[k]=newData[k]; } computeHeapParameters(&heaps[position],flag); for(i=newHeaps[heapPosition].count,j=0;j<newHeaps[heapPosition1].count;i++,j++) { newHeaps[heapPosition].objects[i]=newHeaps[heapPosition1].objects[j]; } newHeaps[heapPosition].count+=newHeaps[heapPosition1].count; for(i=heapPosition1;i<newHeapCount-1;i++) newHeaps[i]=newHeaps[i+1]; newHeapCount--; } a->carrying=-1; }//end of function /********************************************************************/ /* Function to drop a object */ /* Output: None */ /* Input: Ant, iteration flag */ /********************************************************************/ void drop(ant* a,int flag) { float Pdrop1=0.0; int position=-1,i=0,j=0; int hCount=0,hPos[26],dropped=0; for(i=0;i<heapCount;i++) { /*Check if a heap is present in the eight neighbouring cells*/ if(abs(heaps[i].z-a->z)<=1&&abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; hPos[hCount++]=i; /*break;*/ } } /*Check if Heap is found*/ /*if(position!=-1)*/ if(hCount>0) { /*heap or object found.*/ /*Depending on the number of objects present take action*/j=0;while(j<hCount && !dropped){ switch(heaps[hPos[j]].count) { /*If criteria is satisfied then drop the object*/ case 1: if(flag==1) { if((computeDistanceBetween2(a->data,data[heaps[position].objects[0]])*1.0/Dmax)<Tcreate) { dropObject1(a,position,flag);dropped=1; } else dropped =0; } else { if((computeDistanceBetween2(a->data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(a,position,flag); dropped=1; } else dropped=0; } break; default: if(computeDistanceBetween2(a->data,heaps[position].Ocenter)<computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)) { dropObject1(a,position,flag); dropped=1; } else dropped=0; break; }j++;} } else { /*Compute the probability to drop the object*/ Pdrop1 = lrand48()%151*1.0/150; if(Pdrop1<Pdrop) dropObject(a,flag); } }//end of function /********************************************************************/ /* Function to move the ant */ /* Output: none */ /* Input: Ant, iteration flag */ /********************************************************************/ void move(ant* a,int flag) { float pDir; /*Check the speed */ if (a->speed!=0) { /*move in the same direction*/ a->speed--; moveNext(a,flag); } else { /*generate the new direction*/ pDir=lrand48()%17*1.0/16.0; if(pDir<.85) { a->dir=lrand48()%26; } else if(pDir>.95) { a->speed=2; } moveNext(a,flag); } } /********************************************************************/ /* Function to simulate the movement of the ants */ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified: minHeapCount, minHeapPosition */ /********************************************************************/ void iterate(int flag) { int i=0,j=0,k=0,loopCount=0; if (flag==1) loopCount=MAXANTS; else loopCount=MAXANTSFORHEAPS; while (i<MAXITERATIONS) { i++; for(j=0;j<loopCount;j++) { move(&ants[j],flag); if(ants[j].carrying==1) { drop(&ants[j],flag); } else { pick(&ants[j],flag); } } if(debugLevel>0){ for(k=0;k<heapCount;k++) for(j=0;j<heapCount;j++) if((k!=j)&&(heaps[k].x==heaps[j].x)&&(heaps[k].y==heaps[j].y)&&(heaps[k].z==heaps[j].z)) { printf("\nerror two heaps with same co ordinates %d %d",k,j); //getchar(); } k=0; for(j=0;j<heapCount;j++) k+=heaps[j].count; } if(heapCount<minHeapCount) { minHeapCount=heapCount; minPosition=i; } if(debugLevel>0) printf("\n i:%d Heap count %d sum %d",i,heapCount,k); else printf("\n i:%d Heap count %d",i,heapCount); heapSum+=heapCount; } }//end of function /********************************************************************/ /*Function to drop the objects carried by the ants after all the iterations*/ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified:none */ /********************************************************************/ void dropFinal(int flag) { int i=0,j=0,position=-1,loopCount=0; loopCount=currentAntCount; for(i=0;i<loopCount;i++) { if(ants[i].carrying==1) { if(flag==1) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)&&(heaps[j].z==ants[i].z)) { position=i; break; } } if(position!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position,flag); } } else { /*Find if a heap is present */ do { position=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)&&(heaps[j].z==ants[i].z)) { position=i; break; } } if(position!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position,flag); position=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position!=-1); } } } }//end of function /********************************************************************/ /* Function for the implementation of QuickSort */ /* Output: none */ /* Input: data structure, low and high indices */ /* Global structure modified:none */ /********************************************************************/ void quickSort( heap A[], int low,int high ) { int mid=0; if(low<high) { mid=partition(A,low,high); quickSort(A,low,mid); quickSort(A,mid+1,high); } } /********************************************************************/ /* Partition routine used by quick sort */ /* Output: partition location */ /* Input: data , low, high */ /* Global structure modified:none */ /********************************************************************/ int partition(heap a[], int low,int high) { heap temp; int x=0,i=0,j=0; i=low; j=high; x=a[low].count; while (1) { while (a[j].count<x) j=j-1; while (a[i].count>x) i=i+1; if(i<j) { temp=a[i]; a[i]=a[j]; a[j--]=temp; } else return(j); } } /********************************************************************/ /* Function used to write the cluster information */ /* Output: none */ /* Input:iteration flag */ /* Global structure modified:none */ /********************************************************************/ void writeClusterInformation(int flag) { FILE *fp=fopen("center.clt","w"); FILE *fp1; FILE *fp2; FILE *fp3; char temp[80]; int i=0,j=0,k=0; int lastCount=0; int rowCount,colCount,antCount; strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp3=fopen(temp,"a"); if(dataDump) {strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp1=fopen(temp,"a");strcpy(temp,"");sprintf(temp,"%sObj.out",filename);fp2=fopen(temp,"a");if(fp1==NULL||fp2==NULL)printf("\n Error in opening file in the writer Cluster Information routine\n"); } rowCount=currentRow; colCount=currentRow; antCount=currentAntCount; if(fp==NULL) { printf("\n Unable to open cluster center output file \n"); /*return;*/ exit(0); } /*sort the array according to the count value and write the value of the top three elements*/ if((flag==1)&&(!EntireHeaps)) quickSort(heaps,0,heapCount); if(EntireHeaps) {if(flag==1) lastCount=heapCount;else lastCount=newHeapCount; } else lastCount=c; for(i=0;i<lastCount;i++) { if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t ",newHeaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",newHeaps[i].Ocenter[k]); fprintf(fp,"\n"); } else { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); fprintf(fp,"\n"); } } if(dataDump) { fprintf(fp1,"\n Information from the Ant Based Algorithm \n"); fprintf(fp1,"\n Important Parameters : Board %d X %d Ants : %d Iterations %d Total Heaps %d ",rowCount,colCount,antCount,MAXITERATIONS,lastCount); } if(flag==0) { if(dataDump) { fprintf(fp1," TCreateForHeaps %f \n",TcreateForHeaps); fprintf(fp2,"\nTotal Heaps : %d\n",heapCount); fprintf(fp2,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); } fprintf(fp3,"\n@ %d\n",lastCount); } else if(dataDump) fprintf(fp1,"\n");if(dataDump) { fprintf(fp1,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); for(i=0;i<lastCount;i++) if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp2,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); } fprintf(fp1," %5d\n",newHeaps[i].count); fprintf(fp2," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp1," %5d\n",heaps[i].count); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]); fprintf(fp1," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]); fprintf(fp1," %5d\n",heaps[i].count); }} if(flag==0) { if(dataDump) fprintf(fp1,"\n Objects in the heaps \n");for(i=0;i<newHeapCount;i++) { if(dataDump) fprintf(fp1,"\n\n Heap %d\n \n",i+1);fprintf(fp3,"# %d %d\n",i ,newHeaps[i].count); for(j=0;j<newHeaps[i].count;j++){ for(k=0;k<features;k++){ fprintf(fp3,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]); if(dataDump) fprintf(fp1,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]);} if(dataDump) fprintf(fp1,"\n"); fprintf(fp3,"\n"); } } } fclose(fp); fclose(fp3); if(dataDump) { fclose(fp1);fclose(fp2); }}//end of flie /********************************************************************/ /* Function to initialise the new heaps */ /* Output: none */ /* Input:new Heap structure */ /* Global structure modified:none */ /********************************************************************/ void initialiseNewHeaps(heap newHeaps[]) { int i=0,j=0; for(i=0;i<lCount;i++) { newHeaps[i].count=0; for(j=0;j<features;j++) newHeaps[i].Ocenter[j]=V[i][j]; newHeaps[i].x=heaps[i].x; newHeaps[i].y=heaps[i].y; newHeaps[i].z=heaps[i].z; } }//end of function /********************************************************************/ /* Function to initialise the new heaps */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ int findPosition(int i) { int j,position=0; for(j=1;j<lCount;j++) { if(U[j][i]>U[position][i]) position=j; } /*printf("\n Position %d ",position);*/ return(position); }//end of function find position /********************************************************************/ /* Function to add object */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ void addObject(int object,heap * heapPosition) { heapPosition->objects[heapPosition->count]=object; heapPosition->count++; }//end of function add object /********************************************************************/ /* Function to harden the outputs obtained from fcm */ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/ void harden(heap newHeaps[]) { int i,heapPosition=-1,sum=0,j=0; /*initialise the new heaps*/ initialiseNewHeaps(newHeaps); for(i=0;i<count;i++) { heapPosition=findPosition(i); addObject(i,&newHeaps[heapPosition]); } /* To check for degenerate partitions*/ for(i=0;i<lCount;i++) { if(newHeaps[i].count==0){for(j=i;j<lCount-1;j++){newHeaps[j]=newHeaps[j+1];}lCount--;i--;} } newHeapCount=lCount; for(i=0;i<lCount;i++) computeHeapParameters(&newHeaps[i],1); if(debugLevel>0) { for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\n sum = %d",sum);} }//end of function/********************************************************************/ /* Function to initialize the initial parameters for the ant algorithm*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void init() { int i=0,j=0,k; for(k=0;k<zDim;k++)for(i=0;i<MAXROWS;i++) for(j=0;j<MAXCOLS;j++) board[k][i][j]=-9; heapCount=0; newHeapCount=0; heapRow=0; minPosition=-1; minHeapCount=100000; for(i=0;i<MAXANTS;i++) { ants[i].carrying=-1; ants[i].x=-1; ants[i].y=-1; ants[i].z=-1; ants[i].speed=-1; ants[i].dir=-1; } } /********************************************************************/ /* Function to initialize the initial board */ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void initialiseBoard() { int i,j,k;for(k=0;k<zDim;k++) for(i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[k][i][j]=-9; }/********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void harden1() { int i=0,heapPosition=-1; for(i=0;i<heapCount;i++) cenCount[i]=0; for(i=0;i<n;i++) { heapPosition=findPosition1(i); addObject1(i,heapPosition); } writeInfo(); }//end of function/********************************************************************/ /* Function to find to which class the object belongs */ /* Output: Class number */ /* Input: object position */ /* Global Structure Modified: None */ /********************************************************************/int findPosition1(int i) { int j,position=0; for(j=1;j<heapCount;j++) { if(U[j][i]>U[position][i]) position=j; } return(position); }//end of function find position/********************************************************************/ /* Function to Write the output of the hardened objects to file*/ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/void writeInfo(){int i=0,j=0,k=0;FILE *fp=fopen("fcm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}fprintf(fp,"\n@ %d\n",heapCount);for(i=0;i<heapCount;i++){fprintf(fp,"# %d %d\n",i,cenCount[i]);for(j=0;j<cenCount[i];j++){for(k=0;k<s;k++)fprintf(fp,"%8.2f ",clsCen[i][j][k]/scale[k]+minInput[k]);fprintf(fp,"\n");}}fclose(fp);return;}/********************************************************************/ /* Function to add a object to a cluster (2nd stage) */ /* Output: none */ /* Input: object poistion , cluster position */ /* Global Structure Modified: Cluster object and cluster count*/ /*******************************************************************/void addObject1(int object,int heapPosition) { int k=0; for(k=0;k<features;k++) clsCen[heapPosition][cenCount[heapPosition]][k]=X[object][k]; cenCount[heapPosition]++; return; }//end of function add object/********************************************************************/ /* Function to read the original classification of objects */ /* Output: none */ /* Input: none */ /* Global Structure Modified: None */ /*******************************************************************/void readOrg(){int i=0,k=0;char temp[80];FILE *fp;strcpy(temp,"");sprintf(temp,"%sFull.data",filename);fp=fopen(temp,"r");if(fp==NULL){printf("\n Unable to open full data input file ");exit(0);}for(i=0;i<n;i++){fscanf(fp,"%f ",&orgData[i][0]);for(k=1;k<s+1;k++)fscanf(fp,"%f ",&orgData[i][k]);}return;}// end of function/********************************************************************/ /* Function to classify the objects in different clusters */ /* Output: none */ /* Input: the cluster objects, the original classification, */ /* number of objects in the cluster,file to write the data */ /* Global Structure Modified: None *//********************************************************************/int analysisData(float data[][s],float orgData[n][s+1],int count,FILE * fp){int i=0,j=0,k=0,type=1,max=0;heapInfo info;for(i=0;i<c;i++){info.classObj[i]=0;}info.total=0;info.total=count;for(i=0;i<count;i++)for(j=0;j<n;j++){type=1;for(k=0;k<s;k++)if(fabs(data[i][k]-orgData[j][k+1])>errThresh){type=-1;break;}if(type!=-1){info.classObj[(int)orgData[j][0]-1]++;break;}}max=0;for(k=0;k<c;k++){if(info.classObj[k]>info.classObj[max])max=k;fprintf(fp,"\nClass %d Objects % d ",k+1,info.classObj[k]);}j=0;for(k=0;k<c;k++){if(k!=max)j+=info.classObj[k];}fprintf(fp,"\n Total errors : %d \n",j);return(j);}//end of analyse/********************************************************************/ /* Function to read the cluster information */ /* Output: none */ /* Input: input file pointer , output file pointer */ /* Global Structure Modified: None *//********************************************************************/void readFCM(FILE* fp,FILE *fp1){int i=0,j=0,k=0,l=0;int totalRuns=0,heapCount=0,junk=0,sum=0;char ch;FILE *fp2=fopen("errors.out","a");ch = 'c'; while (ch != '$') fscanf (fp, "%c", &ch);fscanf(fp,"%d\n",&totalRuns);printf("\n total runs %d ",totalRuns);for(l=0;l<totalRuns;l++){fscanf(fp,"%c",&ch);fscanf(fp,"%d\n",&heapCount);fprintf(fp1,"\n \nExperiment %d \n ",l+1);sum=0;for(i=0;i<heapCount;i++){fscanf(fp,"%c",&ch);fscanf(fp," %d %d\n",&junk,&clsCenCount[i]);for(j=0;j<clsCenCount[i];j++)for(k=0;k<s;k++)fscanf(fp,"%f",&fcmData[i][j][k]);fscanf(fp,"\n");fprintf(fp1,"\n\n Heap %d Total Objects %d \n",i+1,clsCenCount[i]);sum+=analysisData(fcmData[i],orgData,clsCenCount[i],fp1);}fprintf(fp2," %3d %3d %3d \n",l,sum,heapCount);}fprintf(fp2,"\n --------------------------\n");fclose(fp2);return;}//end of function/********************************************************************/ /* Function to update the board parameters */ /* Output: none */ /* Input: none */ /* Global Structure Modified: board size, ant count *//********************************************************************/void adjustBoard(int flag){int newRow=0,newAnt=0,antDiff=0,antsKilled=0,i=0,position[MAXANTS],position1=-1,j=0,k=0,a,b,z; newRow= (int)ceil(sqrt(4.0*heapSum/updateAfter)); newAnt= (int)ceil((heapSum*1.0/updateAfter)*1.0/3.0); if(newAnt<minAnts)newAnt=minAnts; if(newRow<rowMin)newRow=rowMin;antDiff=currentAntCount-newAnt; if((antDiff>0)&&(newAnt>=minAnts)) {for(i=0;i<currentAntCount;i++){if(ants[i].carrying==-1){position[antsKilled++]=i;}} if(antsKilled>=antDiff){for(i=0;i<antDiff;i++){for(j=position[i];j<currentAntCount-1;j++)ants[j]=ants[j+1];for(k=i+1;k<antsKilled;k++)position[k]--; } currentAntCount=newAnt; }else {for(i=0;i<antsKilled;i++){for(j=position[i];j<currentAntCount-1;j++)ants[j]=ants[j+1]; for(k=i+1;k<antsKilled;k++)position[k]--; }if(flag==1) { for(i=currentAntCount-1,k=0;k<antDiff-antsKilled;k++,i--) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)&&(heaps[j].z==ants[i].z)) { position1=i; break; } } if(position1!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position1,flag); }else { dropObject(&ants[i],flag); } } } else { //flag is zero 2nd stage /*Find if a heap is present */ do { position1=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)&&(heaps[j].z==ants[i].z)) { position1=i; break; } } if(position1!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position1].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position1,flag); position1=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position1!=-1); }//end else currentAntCount=newAnt;}//end elsecurrentAntCount=newAnt;}//end iffor(i=0;i<newAnt;i++){if((ants[i].x>=newRow)||(ants[i].y>=newRow)||ants[i].z>zDim) { a=lrand48()%newRow; b=lrand48()%newRow; z=lrand48()%zDim; ants[i].x=a; ants[i].y=b; ants[i].z=z; }} //Done with ants, now adjust the boardif((currentRow>newRow)&&(newRow>=rowMin)){for(i=0;i<heapCount;i++){if((heaps[i].x>=newRow)||(heaps[i].y>=newRow)||(heaps[i].z>=zDim)) {do { a=lrand48()%newRow; b=lrand48()%newRow; z=lrand48()%zDim; } while(found(z,a,b));//adjust the heap poistionheaps[i].x=a;heaps[i].y=b;heaps[i].z=z;board[z][a][b]=9;}//end if}//end for for(i=0;i<currentAntCount;i++){if((ants[i].carrying==1)&&((ants[i].x>=newRow)||(ants[i].y>=newRow)||(ants[i].z>=zDim))){//adjust the ant locationdo { a=lrand48()%newRow; b=lrand48()%newRow; z=lrand48()%zDim; } while(found(z,a,b));//adjust the heap poistionants[i].x=a;ants[i].y=b;ants[i].z=z;}//end if}//end for currentRow=newRow;}//end if }//end of function void writeIncremental(int iteration,int flag){FILE *fp=fopen("dump.txt","w");int i=0,j=0,k=0;fprintf(fp,"Stage %d\n",flag);fprintf(fp,"Board %d\n",currentRow);fprintf(fp,"Ants %d\n",currentAntCount);fprintf(fp,"Iteration %d\n",iteration);fprintf(fp,"Heaps %d\n",heapCount); if(flag==0)fprintf(fp,"NewHeapCount %d\n",newHeapCount); fprintf(fp,"Ants\n");for(i=0;i<currentAntCount;i++){fprintf(fp,"%d %d %d %d %d ",ants[i].x,ants[i].y,ants[i].carrying,ants[i].speed,ants[i].dir);for(k=0;k<features;k++)fprintf(fp,"%8.4f ",ants[i].data[k]);fprintf(fp,"\n");}fprintf(fp,"Heaps\n");for(i=0;i<heapCount;i++){fprintf(fp,"%d %d %d %d %8.2f %8.2f \n",heaps[i].x,heaps[i].y,heaps[i].count,heaps[i].disPosition,heaps[i].Dmax,heaps[i].Dmean);for(k=0;k<features;k++)fprintf(fp,"%8.4f ",heaps[i].Ocenter[k]);fprintf(fp,"\n");for(k=0;k<features;k++)fprintf(fp,"%8.4f ",heaps[i].Odissim[k]);fprintf(fp,"\n");if(flag==1){for(j=0;j<heaps[i].count;j++)fprintf(fp,"%d ",heaps[i].objects[j]);fprintf(fp,"\n");}}if(flag==0){fprintf(fp,"newHeaps\n");for(i=0;i<newHeapCount;i++){fprintf(fp,"%d\n",newHeaps[i].count);for(j=0;j<newHeaps[i].count;j++)fprintf(fp,"%d ",newHeaps[i].objects[j]);fprintf(fp,"\n");}}fclose(fp);}//end of function/********************************************************************/ /* Function Main */ /********************************************************************/ int main(int argc ,char** argv) { int sum=0,k=0,i=0,j=0; char temp[80]; FILE *fp=fopen("fcm.out","w"); FILE *fp1; FILE *fp2=fopen("antAnalysis.out","a"); FILE *fp3=fopen("fcmAnalysis.out","a"); FILE *fp4=fopen("errors.out","w"); FILE *fp5; FILE *fpJm=fopen("jm.out","w"); fclose(fp4); fclose(fpJm); sprintf(temp,"%sObj.out",filename); fp5=fopen(temp,"w"); fclose(fp5); strcpy(temp,""); sprintf(temp,"%sTot.out",filename); fp5=fopen(temp,"w"); fclose(fp5); strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp1=fopen(temp,"w"); fprintf(fp,"\n$%d",EXPRUN); fprintf(fp1,"\n$%d",EXPRUN); fclose(fp); fclose(fp1); if(argc==2)sscanf(argv[1],"%d",&debugLevel); printf("\n Debug level %d\n",debugLevel); for(k=0;k<zDim;k++) for( i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[k][i][j]=-9; //Read from the file readInput(); //Compute the distance of all the points if(maxDist) { Dmax=computeDistance(); //Compute the maximum distance fp5=fopen("maxData.txt","w");fprintf(fp5,"%f",Dmax);fclose(fp5); } else {/*if((fp5=fopen("maxData.txt","r"))==NULL);{printf("\n Max Distance file not found, exiting . . . \n");exit(-1);}*/fp5=fopen("maxData.txt","r");fscanf(fp5,"%f",&Dmax);fclose(fp5); } printf("\n %f \n",Dmax); //Initialise the board for(k=0;k<EXPRUN;k++) { init(); sum=0; srand48(state1[k]); initialiseBoard(); initialise(1); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(1); printf("\n %d \n",heapCount); //Simulate the working of ants iterate(1); //drop the objects after all the iterations dropFinal(1); //to verify the correctness for(i=0;i<heapCount;i++) sum+=heaps[i].count; //write the cluster centers to the file writeClusterInformation(1); printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); printf("\n\n k %d \n\n",k); Fcm(data,0); //compute new board size. lCount=heapCount; heapRow=(int)ceil(sqrt(heapCount*4.0/3.0)); newHeapCount=lCount; printf("\n LCount %d newHeapCount %d heap Row %d \n",lCount,newHeapCount,heapRow); //harden the objects harden(newHeaps); //iterate with the heaps as the objects. initialiseBoard(); initialise(0); for(i=0;i<lCount;i++) computeHeapParameters(&heaps[i],0); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(0); printf("\n %d \n",heapCount); //getchar(); //Simulate the working of ants iterate(0); dropFinal(0); writeClusterInformation(0); sum=0; for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); // getchar(); fpJm=fopen("jm.out","a"); fprintf(fpJm,"Experiment %2d: ",k); fclose(fpJm); Fcm(data,1); harden1(); } fp=fopen("fcm.out","a"); fp1=fopen(temp,"a"); fprintf(fp,"\n"); fprintf(fp1,"\n"); fclose(fp); fclose(fp1); fp=fopen("fcm.out","r"); fp1=fopen(temp,"r");printf("\n Before read org\n");readOrg();printf("\nafter read org\n");readFCM(fp1,fp2);printf("\n After 1st read fcm\n");readFCM(fp,fp3);printf("\n After 2 fcm read \n");return(1); }//end of main /****************************************************************/ float NORM(feature , feature); void find_covar(); void Fcm (irisData Data[MAXDATA],int stage) { int i,j,l; FILE *ofile; char temp[80]; int flag=0;//added by parag FILE *iniClsCen=fopen("center.clt","a+"),*fpJm; float jmFinal=0; if(iniClsCen==NULL) { printf("\n Unable to open the initial cluster center file\n"); printf("\n choosing random cluster centers....\n"); flag=1; } strcpy(temp,""); sprintf(temp,"%s.data",filename); strcpy (fname,temp); /* Read from this file */ m=2.0; /* The exponent */ /*GET_INPUTS1(fname);*/ /* read in feature vectors */ for (j=0; j<n ; j++) /* Read File in row-major */ for (i=0; i<s ; i++) /* format */ X[j][i]=Data[j][i]; Random_seed=state1[run];/*randseed*/ srand48(Random_seed); for (run=0;run<TOTRUNS;run++) { //flag=1;//for testing random values if (EntireHeaps) lCount=heapCount; else lCount=c; for(i=0;i<lCount;i++) for(j=0;j<s;j++) V[i][j]=0; for(i=0;i<lCount;i++) { for(l=0;l<s;l++) { if(flag==1) { rand_index = lrand48() % n; V[i][l] = X[rand_index][l]; } else { fscanf(iniClsCen,"%f ",&V[i][l]); } }//end for } eps=0.001; stop = FALSE; /* This loop finds the final cluster center and the corresponding U values */ z = 0; while (stop == FALSE) { z++; UMAT(U,V,X); /** Update the U matrix **/ stop = FINISHED(U1,eps); CLUSTER(U,X); /** Get new cluster center **/ printf("Iteration: %d\n\n", z); } /* end while */ printf("Number of iterations: %d\n\n", z);strcpy(temp,""); sprintf(temp,"%sFCM.out",filename);if(stage>0){jmFinal=Compute_Jm(V,X);fpJm=fopen("jm.out","a");fprintf(fpJm," classes %2d Jm: %8.5f\n",lCount,jmFinal);fclose(fpJm);}if(dataDump) Output(temp); /* output the results */ }/*end for*/ if(dataDump) { ofile=fopen(temp,"a"); for (i=0; i <= mins; i++) fprintf(ofile, "Min %d of %10.3f occurred %d times\n", i, mintab[i], mincount[i]); fprintf(ofile, "Average iterations %f\n", (float)aveit/(float)TOTRUNS); fclose(ofile); } }/* end Main */ /* The GET_INPUTS1 procedure opens an input file, reads the number of input vectors, the number of features per vector, the number of clusters, and epsilon, then it reads the input values */ void GET_INPUTS1(char fname[]) //char fname[]; { /* Parameters: fname -- name of file containing input info. */ FILE *infile ;//*fopen(); //char junk[10][80]; char realfname[80]; char ch; char fileName1[80]; //int temp[10], i, j, inputs, outputs, count; int i,j; sprintf (realfname, "%s", fname); /* open input file */ if(normalize==1) strcpy(fileName1,"gtest1.data"); else strcpy(fileName1,fname); if((infile = fopen (fileName1, "r")) == NULL) printf("\n CAN'T OPEN %s\n",realfname); else { ch = 'c'; /* Discard leading comments */ while (ch != '#') fscanf (infile, "%c", &ch); for (j=0; j<n ; j++) /* Read File in row-major */ { /* format */ for (i=0; i<s ; i++) fscanf (infile, "%f", &X[j][i]); /* fscanf (infile, "%d", &temp[1]); */ /* end for */ } /* end for */ fclose(infile); /* close input file */ }/* end else */ } /* end GET_INPUTS1 */ /* The NORM function computes the norm of two vectors, x and v, of dimension s. This gives an indication of the distance between them */ float NORM( feature x, feature v) //feature x, v; { /* Parameters : x -- a local copy of an input vector v -- a local copy of a cluster center */ int i,j; /* loop variable */ double sum; /* the square root of sum is the norm; sum is the sum of the differences squared */ float mat1[s]; float mat2[s]; if (maha) { for (i=0; i<s; i++) mat1[i]=x[i]-v[i]; for(i=0;i<s;i++) { sum =0; for(j=0;j<s;j++) sum += mat1[j] * inv_cov[j][i]; mat2[i] = sum; } sum =0; for(j=0;j<s;j++) sum += mat1[j]*mat2[j]; } else { sum = 0; for(j=0;j<s;j++) sum += (x[j] - v[j])* (x[j] - v[j]); } return (float)(sqrt(sum)); } /* end NORM */ /* The UMAT procedure calculates the updated U matrix according to Bezdek's formula. Its side-effect is the update of the global array U1 */ void UMAT( matrix U,center V, objects X) /*matrix U; center V; objects X;*/ { /* Parameters: U, V, and X: same as in main program */ int i, j, k,l ;/* loop variables *///, class /* class whose cluster center //is closest to pattern k */; float d1,d2,d; float sum; int exact; for (k=0;k<n;k++) { /*printf("\n k %d ",k);*/ exact=false; for (i=0;i<lCount;i++) { sum=0; for (j=0;j<lCount;j++) { d1=NORM(X[k],V[i]); d2=NORM(X[k],V[j]); if(d2!=0) { d= d1/d2; d= d * d;/* pow(d1/d2,2.0/(m-1.0)); */ sum+=d; } else { exact = true; for(l=0;l<lCount;l++) if (l==j) U1[l][k]=1.0; else U1[l][k]=0.0; break; } } if (exact!=true) U1[i][k]=1.0/sum; } } } /* end UMAT */ /* The function FINISHED determines if a termination occurs. If not it updates U with U1, so its side-effect is the potential modification of U */ BOOLEAN FINISHED( matrix U1,float eps) //matrix U1; float eps; { /* Parameters: U1, eps-- same as in main */ float sum; /* sum of squared differences between corresponding elements of U and U1 */ int i,k; /* loop control variabes */ BOOLEAN finished; /* set to true if all corresponding elements are within epsilon */ finished = TRUE; sum = 0; for (i=0; i<lCount; i++) for (k=0; k<n; k++) sum += (U[i][k] - U1[i][k])*(U[i][k]-U1[i][k]); if (sqrt(sum) > eps) { finished = FALSE; for (i=0; i<lCount; i++) for (k=0; k<n; k++) U[i][k] = U1[i][k]; } /* end if */ return finished; } /* end FINISHED */ /* The CLUSTER procedure determines cluster centers in V for the c classes being searched for. The global array V is updated */ void CLUSTER( matrix U, objects X) //matrix U; objects X; { /* Parameters: U, X -- as in main */ int i, j, k; /* loop control variables */ feature num; /* numerator in the cluster center computation formula presented by Bezdek */ float denum; /* denumenator in the obove formula */ for (i=0; i<lCount; i++) { denum = 0; for (j=0; j<s; j++) { num[j] = 0; for (k=0; k<n; k++) num[j] += U[i][k] * U[i][k] * X[k][j]; /* pow(U[i][k],m) * X[k][j]; */ } /* end for */ for (k=0; k<n; k++) denum += U[i][k] * U[i][k]; /* pow(U[i][k],m); */ for (j=0; j<s; j++) V[i][j] = num[j] / denum; } /* end for */ } /* end CLUSTER */ float Compute_Jm(center V, objects X) //center V; //objects X; { int i, k,found; double Jm_value; float u,dik; Jm_value = 0; for (k=0; k<n; k++) { found = FALSE; for (i=0; i<lCount; i++) { dik=NORM(X[k],V[i]); dik=dik*dik; if (dik==0.0) { if (found == FALSE) { u=1.0; found = TRUE; printf("found the case with dik=0\n"); } else { printf("\n cant proceed with this initialization"); exit(1); } } else { if (found != TRUE) u=U1[i][k]; /* get_Uvalue(V,X,dik,k); */ else u=0.0; } Jm_value += u * u * dik;/* pow(u, m) * dik; */ } } return (Jm_value); } /* end Compute_Jm */ /* The Output procedure outputs the U matrix which gives the fuzzy measure of each feature belonging to a cluster center. It also outputs the cluster center for each class */ void Output(char fname[]) //char fname[80]; /* Parameters: U, V -- same as in main */ { int i, j, found; /* loop control variables */ float J2c; if ((fp=fopen(fname,"a"))==NULL) printf("\n CAN'T OPEN %s\n",fname); else { fprintf(fp,"\n The cluster centers are: \n"); fprintf(fp,"\n \n"); for (i=0; i<lCount; i++) { fprintf(fp,"\nCluster center %d: ",i+1); fprintf(fp,"\n"); for (j=0; j<s; j++) fprintf(fp,"%8.7f ",V[i][j]); } /* end for */ fprintf(fp,"\nThe membership grades for the feature vectors are:\n"); for (j=0; j<n; j++) { fprintf(fp,"\n"); for (i=0; i<lCount; i++) fprintf(fp," %8.7f",U[i][j]); } /* end for */ } /* end if */ J2c= Compute_Jm(V,X); found =0; for (i=0;i<= mins; i++){ if ((found == 0) && ((J2c - mintab[i]) < .001) && ((J2c - mintab[i]) > -.001)){ mincount[i]++; found = 1; } } if (found == 0){ mins++; mintab[mins]=J2c; mincount[mins]=1; } fprintf(fp,"\nJ%3.1f = %f", m,J2c); fprintf(fp, "Number of iterations: %d\n\n", z); aveit=aveit + z; fclose(fp); } /***************************************************************/ void find_covar(int m) //int m; { int i,j,k; float mean[s],sum; for (i=0;i<s;i++) mean[i]=0; for (i=0;i<s;i++) for (j=0;j<m;j++) mean[i]+=X[j][i]; for (i=0;i<s;i++) mean[i]/=m; for (i=0;i<s;i++) for (j=0;j<s;j++) { sum=0; for (k=0;k<m;k++) sum+=(X[k][i]-mean[i])*(X[k][j]-mean[j]); Y[i][j]=sum/m; } }

Parag Kanade/fuzzy ants/3D Board/ant_xyz.c

#include "header_xyz.h" /*******************************************************************/ /*** Global Variables ***/ irisData data[MAXDATA]; //To hold the data read from the file int count=0; //Count of the total number of data elements int heapCount=0; // Total number of heaps int heapRow=0; // Rows for the second iteration with heaps int newHeapCount=0; // Count of the heaps for the second iteration heap heaps[MAXDATA]; // Data structure to hold the heap information heap newHeaps[MAXDATA]; // Data Structure to hold the new heap information // For the second iteration int board[zDim][MAXROWS][MAXCOLS]; // Board on which the objects are located ant ants[MAXANTS]; // Data structure to hold the ants float Dmax=0; // Distance of the most dissimilar object pair int minHeapCount=100000; // Counter to hold the minimum heap count, used only for academic purpose // no use in the algorithm int minPosition; // the minimum heap iteration , only for academic purpose // no use in the algorithm float clsCen[MAXDATA][MAXDATA][s];//to hold the objects after hardening int cenCount[MAXDATA];//to hold the number of objects after hardening /********************************************************************/ /*** Varaibles to normalize the features between 0 and 1 */ float minInput[features], maxInput[features],scale[features],minForHeaps[features],maxForHeaps[features],scaleForHeaps[features]; /********************************************************************/ /** Variables for the FCM algorithm */ int temp[200]; /* for square matrix tests */ float eps,m; matrix U, U1; BOOLEAN stop; center V; objects X; char fname[80]; FILE *fp; int run; float inv_cov[s+1][s+1]; /* inverse of covariance matrix Y */ float Y[s][s]; long rand_index, Random_seed; long state1[52] = { 9347, 3835, 5194, 8310, 346, 535, 5297, 6711, 77, 3834, 668, 4175, 6868, 5890, 9304, 8462, 5269, 920, 65392, 41600, 70119, 91032, 76220, 26245, 4746, 73608, 32823, 63264, 75641, 99104, 36534, 24704, 98255, 72266, 75336, 65152, 7269, 63163, 88471, 27271, 43641, 76649, 47773, 23777, 27491, 35926, 16651, 48652, 89766, 90921, 6056 }; int z; int mins=0; int aveit =0; float mintab[200]; int mincount[200]; long start,end,total; int lCount;//added by parag /********************************************************************/ /********************************************************************/ /* Function to read the input data set */ /* The file name is assumed to be itest.data */ /* Status: Tested */ /* Output: Data from the file into the "data" (Global) data structure */ /* Output: Count (Global), the count of the number of objects */ /********************************************************************/ void readInput() { FILE *fp; char fname[80]; int i=0,j=0; char ch; sprintf(fname,"%s.data",filename); /* Check if the input file is opened */ if((fp=fopen(fname,"r"))==NULL) { printf("\n Unable to open input file \n"); exit(0); } /*initialise the minimum maximum and the scale */ for(i=0;i<features;i++) { minInput[i]=1000; maxInput[i]=0; minForHeaps[i]=1000; maxForHeaps[i]=0; scale[i]=0; scaleForHeaps[i]=0; } /* Discard leading comments */ ch = 'c'; while (ch != '#') fscanf (fp, "%c", &ch); i=0; /* Parse the file and populate the data structure */ while(!feof(fp)) { for(j=0;j<features;j++) { fscanf(fp,"%f ",&data[i][j]); } fscanf(fp,"\n"); for(j=0;j<features;j++) { if(data[i][j]<minInput[j]) minInput[j]=data[i][j]; if(data[i][j]>maxInput[j]) maxInput[j]=data[i][j]; } i++; } count=i; /* Normailze the feature values in between 0 and 1 */ for(j=0;j<features;j++) { if(minInput[j]!=maxInput[j]) scale[j]=1.0/(maxInput[j]-minInput[j]); else scale[j]=1.0; } for(i=0;i<count;i++) { for(j=0;j<features;j++) data[i][j]=(data[i][j]-minInput[j])*scale[j]; } fclose(fp); return; }//end of function /********************************************************************/ /*Function to compute the eucldiean distance for all input data set */ /* Output : an array conatining the distance of each object with the other */ /********************************************************************/ float computeDistance() { float distance,dist=0; int i=0,j=0,k=0; distance=0; for(j=0;j<count;j++) for(i=0;i<count;i++) { for(k=0;k<features;k++) dist+=sqrt((data[j][k]-data[i][k])*(data[j][k]-data[i][k])); if(distance<dist)distance=dist; dist=0; } return(distance); } /********************************************************************/ /* Function to compute the distance between two objects */ /* Output : Euclidean d istance between the two input objects */ /********************************************************************/ float computeDistanceBetween2(irisData a,irisData b) { float temp=0; int i=0; for(i=0;i<features;i++) temp+=(a[i]-b[i])*(a[i]-b[i]); temp=sqrt(temp); return(temp); } /********************************************************************/ /* Function to compute the value of an object */ /********************************************************************/ float value(irisData a) { int i=0; float temp=0; for(i=0;i<features;i++) temp+=a[i]*a[i]; return(sqrt(temp)); } /********************************************************************/ /* Function to find the object in the data structure */ /* Output : The position of the object in the data */ /* Input : The object */ /********************************************************************/ int findInData(irisData object,int iFlag) { int i=0,lastCount=0,j,position=-1,flag[features],flg;if(iFlag==1)lastCount=MAXDATA;else{}for(i=0;i<lastCount;i++){ for(j=0;j<features;j++) if(fabs(object[j]-data[i][j])<0.00001) flag[j]=1; else { flag[j]=0; break;} flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /* Function to find whether there is an object on the board at a,b */ /* Output : a flag indicating whether an object is found or not */ /* Input : The row and column indicating the board position */ /********************************************************************/ int found(int z,int a , int b) { if(board[z][a][b]==9) return 1; else return 0; } /********************************************************************/ /* Function to initialize the initial board */ /* Output : None */ /* Global Structures modified : heaps */ /* Input : Flag indicating the iteration 1: First 0: Second */ /********************************************************************/ void initialise(int flag) { int a,b,i=0,k=0,z; int loopCount,rowCount,colCount,zCount; /* Initialize the row, column and number of heaps */ if(flag==1) { loopCount=count; rowCount=MAXROWS; colCount=MAXCOLS; zCount=zDim; } else { loopCount=lCount; rowCount=heapRow; colCount=heapRow; zCount=heapRow; } /* Initialize the heaps */ for(i=0;i<loopCount;i++) { heaps[i].count=0; heaps[i].x=0; heaps[i].y=0; heaps[i].z=0; heaps[i].disPosition=0; heaps[i].Dmax=0; heaps[i].Dmean=0; } for(i=0;i<loopCount;i++) { /*find a board position which is empty*/ do { a=lrand48()%rowCount; b=lrand48()%colCount; z=lrand48()%zCount; } while(found(z,a,b)); /*Allocate the object to that place and assign a heap to it*/ heaps[i].x=a; heaps[i].y=b; heaps[i].z=z; heaps[i].count=1; heaps[i].Dmax=0; heaps[i].Dmean=0; if(flag==1) { heaps[i].objects[0]=i; for(k=0;k<features;k++)heaps[i].Ocenter[k]=data[i][k]; } else { for(k=0;k<features;k++) heaps[i].Ocenter[k]=newHeaps[i].Ocenter[k]; } heaps[i].disPosition=0; board[z][a][b]=9; if((debugLevel>0)&&(flag==1))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],data[heaps[i].objects[0]][0]); if((debugLevel>0)&&(flag==0))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],newHeaps[i].Ocenter[0]); } /*initialise the initial heap count*/ heapCount=loopCount; currentRow=rowCount; } /********************************************************************/ /* Function to find whether an heap is present at the location a b */ /* Output : Flag indicating the presencs of the heap */ /* Input : Roe and column indicating the board position */ /********************************************************************/ int findObject(int z,int a,int b) { int i; int position=-1; for(i=0;i<heapCount;i++) { if((heaps[i].z==z)&&(heaps[i].x==a)&&(heaps[i].y==b)) { position=i; break; } } return(position); } /********************************************************************/ /* Function initialize the ants */ /* Output : None */ /* Input : Flag indicating the iteration */ /* Global Variables Modified : ant, heapCount, heaps, board */ /********************************************************************/ void initialiseAnts(int flag) { int i=0,a=0,b=0,position,j=0,loopCount=0,rowCount,colCount,k=0,z=0,zCount=0; if(flag==1) { loopCount=MAXANTS; rowCount=MAXROWS; colCount=MAXCOLS; zCount=zDim; } else { loopCount=MAXANTSFORHEAPS; rowCount=heapRow; colCount=heapRow; zCount=heapRow; } for(i=0;i<loopCount;i++) { /*generate random positions for the ants*/ a=lrand48()%rowCount; b=lrand48()%colCount; z=lrand48()%zCount; ants[i].x=a; ants[i].y=b; ants[i].z=z; /*Find if an object is present at that position or not*/ if(board[z][a][b]==9) { ants[i].carrying=1; /*find the heap at that location*/ position=findObject(z,a,b); if(position==-1) { printf("\n Error in logic: Ant init \n"); //getchar(); break; } else { /*Assign the heap data to the ant data and decrement the heap count*/ if(flag==1){ for(k=0;k<features;k++)ants[i].data[k]=data[heaps[position].objects[0]][k]; }else{for(k=0;k<features;k++)ants[i].data[k]=heaps[position].Ocenter[k];} for(j=position;j<heapCount-1;j++) heaps[j]=heaps[j+1]; heapCount--; } /*Clear the board position */ board[z][a][b]=-9; } else ants[i].carrying=-1; ants[i].speed=0; ants[i].dir=lrand48()%26; printf("\n Ant %2d X: %2d Y: %2d Z:%2d Carying : %2d ",i,ants[i].x,ants[i].y,ants[i].z,ants[i].carrying); } currentAntCount=loopCount; } /********************************************************************/ /* Function to move the ant */ /* Output: None */ /* Input: Ant to be moved and the flag indicating the iteration */ /********************************************************************/ void moveNext(ant* a,int flag) { int rowCount,colCount,zCount; rowCount=currentRow; colCount=currentRow; if(flag==1)zCount=zDim;else zCount=heapRow; /*Depending upon the direction move the ant*/ switch (a->dir) { case 0: a->y++; if(a->y>=colCount)a->y=0; break; case 1: a->x--; a->y++; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 2: a->x--; if(a->x<0)a->x=rowCount-1; break; case 3: a->x--; a->y--; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 4: a->y--; if(a->y<0)a->y=colCount-1; break; case 5: a->x++; a->y--; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 6: a->x++; if(a->x>=rowCount) a->x=0; break; case 7: a->x++; a->y++; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break; case 8: a->y++; if(a->y>=colCount)a->y=0; a->z--; if(a->z<0)a->z=zCount-1; break; case 9: a->x--; a->y++; a->z--; if(a->z<0)a->z=zCount-1; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 10: a->x--; a->z--; if(a->z<0)a->z=zCount-1; if(a->x<0)a->x=rowCount-1; break; case 11: a->x--; a->y--; a->z--; if(a->z<0)a->z=zCount-1; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 12: a->y--; a->z--; if(a->z<0)a->z=zCount-1; if(a->y<0)a->y=colCount-1; break; case 13: a->x++; a->y--; a->z--; if(a->z<0)a->z=zCount-1; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 14: a->x++; a->z--; if(a->z<0)a->z=zCount-1; if(a->x>=rowCount) a->x=0; break; case 15: a->x++; a->y++; a->z--; if(a->z<0)a->z=zCount-1; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break; case 16: a->z--; if(a->z<0)a->z=zCount-1; break; case 17: a->y++; a->z++; if(a->z>=zCount)a->z=0; if(a->y>=colCount)a->y=0; break; case 18: a->x--; a->y++; a->z++; if(a->z>=zCount)a->z=0; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 19: a->x--; a->z++; if(a->z>=zCount)a->z=0; if(a->x<0)a->x=rowCount-1; break; case 20: a->x--; a->y--; a->z++; if(a->z>=zCount)a->z=0; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 21: a->y--; a->z++; if(a->z>=zCount)a->z=0; if(a->y<0)a->y=colCount-1; break; case 22: a->x++; a->y--; a->z++; if(a->z>=zCount)a->z=0; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 23: a->x++; a->z++; if(a->z>=zCount)a->z=0; if(a->x>=rowCount) a->x=0; break; case 24: a->x++; a->y++; a->z++; if(a->z>=zCount)a->z=0; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break;case 25: a->z++; if(a->z>=zCount)a->z=0; break;default: break; } }//end of function /********************************************************************/ /* Function to compute the parameters of the heap */ /* Output: None */ /* Input: The heap whose parameters are to be computed iteration flag*/ /********************************************************************/ void computeHeapParameters(heap* heapData,int flag) { int i=0,j=0,k=0,position=0; float distance=0; float Dmean=0.0; float *dis; float featureSum[features]; float max=0; float sum=0; /* init*/ for(k=0;k<features;k++) featureSum[k]=0; /*Compute the distance of each object and find the maximum distance*/ max=0; if(flag==1){ for(j=0;j<heapData->count;j++) for(i=0;i<heapData->count;i++) { for(k=0;k<features;k++) sum+= (data[heapData->objects[j]][k]-data[heapData->objects[i]][k])*(data[heapData->objects[j]][k]-data[heapData->objects[i]][k]); distance=sqrt(sum); if(max<distance) max=distance; sum=0; } /* Assign the maximum distance to the heap */ heapData->Dmax=max;/*Compute the centre object*/for(i=0;i<heapData->count;i++)for(k=0;k<features;k++) featureSum[k]+=data[heapData->objects[i]][k];for(k=0;k<features;k++)heapData->Ocenter[k]=featureSum[k]*1.0/heapData->count; /* Find the mean distance */if((dis=(float *) calloc(heapData->count,sizeof(float)))==NULL){printf("\nUnable to allocate memory in the compute heap parameters procedure\n");exit(0);} for(i=0;i<heapData->count;i++) { Dmean+=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); dis[i]=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); } heapData->Dmean=Dmean*1.0/heapData->count; /*Compute the most dissimilar object*/ max=dis[0]; for(i=1;i<heapData->count;i++) if(max<dis[i]) { max=dis[i]; position=i; } /*Assign the most dissimilar object to the heap variable*/ for(k=0;k<features;k++) heapData->Odissim[k]=data[heapData->objects[position]][k]; heapData->disPosition=position;free(dis); } else{/* Heap Parameters for the second stage */for(k=0;k<features;k++){ heapData->Odissim[k]=heapData->Ocenter[k];}heapData->disPosition=0;heapData->Dmean=0;heapData->Dmax=0;} }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a single object*/ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject1(ant *a,int position,int flag) { int i,k=0; /*Clear the board*/ board[heaps[position].z][heaps[position].x][heaps[position].y]=-9; /* Assign the data to the ant*/if(flag==1){ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[0]][k];}else {for(k=0;k<features;k++) a->data[k]=heaps[position].Ocenter[k];} a->carrying=1; /*Copy all the heaps remaining heaps in the structure*/ for(i=position;i<heapCount-1;i++) heaps[i]=heaps[i+1]; /*Decrement the heap count*/ heapCount--; }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a two objects */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ int pickObject2(ant *a , int position,int flag) { float Pdestroy1=-1; int i=0,k=0; /*Compute the probability to destroy the heap*/ Pdestroy1=lrand48()%13*1.0/12.0; /*If the probability is more than Pdestroy then destroy the heap */ if(Pdestroy1<Pdestroy) { /*pick the object randomly*/ i=lrand48()%2; /* Assign the data to the ant */ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[i]][k]; a->carrying=1; /*Correct the heap parameters*/ heaps[position].count=1; heaps[position].objects[0]=heaps[position].objects[1-i]; computeHeapParameters(&heaps[position],flag); return(1); } else return(0); }//end of function /********************************************************************/ /*Function to pick a object from a heap consisting of more than two object */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /********************************************************************/ int pickObjectn(ant *a,int position,int flag) { int i=0,k=0; /*If the criteria is satsfied then pick the object*/ if((computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)*1.0/heaps[position].Dmean)>Tremove) { a->carrying=1; for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[heaps[position].disPosition]][k]; for(i=heaps[position].disPosition;i<heaps[position].count-1;i++) heaps[position].objects[i]=heaps[position].objects[i+1]; heaps[position].count--; computeHeapParameters(&heaps[position],1); return(1);}elsereturn(0); }//end of function /********************************************************************/ /* Function to pick a object */ /********************************************************************/ void pick(ant *a,int flag) { int position=-1,i=0,j=0; float Pload1=-1.0; int hCount=0,hPos[26],picked=0; for(i=0;i<heapCount;i++) { /*Check if a heap is present in the eight neighbouring cells*/ if(abs(heaps[i].z-a->z)<=1&&abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; hPos[hCount++]=i; /*break;*/ } } /*Check if Heap is found*/ /*if(position!=-1)*/ if(hCount>0) { /* heap or object found.*/ /*Depending upon the number present in the heap call the corresponding routine*/ j=0; while(j<hCount && !picked) { switch(heaps[hPos[j]].count) { case 1: Pload1 = lrand48()%151*1.0/150.0; if(Pload1<Pload) { pickObject1(a,hPos[j],flag);picked=1; }else picked=0; break; case 2: picked=pickObject2(a,hPos[j],flag); break; default: picked=pickObjectn(a,hPos[j],flag); break; }j++;} } } /********************************************************************/ /* Function to drop a object to form a new heap */ /* Output: None */ /* Input: Ant, iteration flag */ /* Global Structure modified : heap, heapCount */ /********************************************************************/ void dropObject(ant* a,int flag) { /*Create a new heap*/ int k=0; heaps[heapCount].count=1; heaps[heapCount].x=a->x; heaps[heapCount].y=a->y; heaps[heapCount].z=a->z; heaps[heapCount].Dmax=0; heaps[heapCount].Dmean=0; heaps[heapCount].disPosition=0; for(k=0;k<features;k++) { heaps[heapCount].Odissim[k]=a->data[k]; heaps[heapCount].Ocenter[k]=a->data[k]; } if(flag==1) {heaps[heapCount].objects[0]=findInData(a->data,flag); /*computeHeapParameters(&heaps[heapCount],flag);*/ } computeHeapParameters(&heaps[heapCount],flag); heapCount++; a->carrying=-1; board[a->z][a->x][a->y]=9; }//end of function /********************************************************************/ /* Function to find the heap position during the second iteration */ /* Output: position of the heap */ /* Input: object center of the heap that the ant is carrying */ /* Global Structure modified : none */ /********************************************************************/ int findInNewheaps(irisData object) { int position=-1,i,j=0,flag[features],flg; for(i=0;i<newHeapCount;i++) { for(j=0;j<features;j++) { if(fabs(object[j]-newHeaps[i].Ocenter[j])<0.00001) flag[j]=1; else {flag[j]=0;break;} } flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /*Function to drop a object to a heap consisting of a one or more object */ /* Output: none */ /* Input: Ant, position of the heap , iteration flag */ /* Global Structure modified: heaps,newHeaps */ /********************************************************************/ void dropObject1(ant* a, int position,int flag) { int heapPosition=-1,heapPosition1=-1,i,j,k=0; irisData newData,temp; if(flag==1) { heaps[position].objects[heaps[position].count]=findInData(a->data,flag); heaps[position].count++; computeHeapParameters(&heaps[position],flag); } else { for(k=0;k<features;k++)temp[k]=heaps[position].Ocenter[k]; heapPosition=findInNewheaps(a->data); heapPosition1=findInNewheaps(heaps[position].Ocenter); if(heapPosition==-1||heapPosition1==-1) { printf("\n error in logic:new heaps\n"); //getchar(); return; } for(k=0;k<features;k++) {newData[k]=(newHeaps[heapPosition].Ocenter[k]*newHeaps[heapPosition].count+newHeaps[heapPosition1].Ocenter[k]*newHeaps[heapPosition1].count)*1.0/(newHeaps[heapPosition].count+newHeaps[heapPosition1].count);newHeaps[heapPosition].Ocenter[k]=newData[k];heaps[position].Ocenter[k]=newData[k]; } computeHeapParameters(&heaps[position],flag); for(i=newHeaps[heapPosition].count,j=0;j<newHeaps[heapPosition1].count;i++,j++) { newHeaps[heapPosition].objects[i]=newHeaps[heapPosition1].objects[j]; } newHeaps[heapPosition].count+=newHeaps[heapPosition1].count; for(i=heapPosition1;i<newHeapCount-1;i++) newHeaps[i]=newHeaps[i+1]; newHeapCount--; } a->carrying=-1; }//end of function /********************************************************************/ /* Function to drop a object */ /* Output: None */ /* Input: Ant, iteration flag */ /********************************************************************/ void drop(ant* a,int flag) { float Pdrop1=0.0; int position=-1,i=0,j=0; int hCount=0,hPos[26],dropped=0; for(i=0;i<heapCount;i++) { /*Check if a heap is present in the eight neighbouring cells*/ if(abs(heaps[i].z-a->z)<=1&&abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; hPos[hCount++]=i; /*break;*/ } } /*Check if Heap is found*/ /*if(position!=-1)*/ if(hCount>0) { /*heap or object found.*/ /*Depending on the number of objects present take action*/j=0;while(j<hCount && !dropped){ switch(heaps[hPos[j]].count) { /*If criteria is satisfied then drop the object*/ case 1: if(flag==1) { if((computeDistanceBetween2(a->data,data[heaps[position].objects[0]])*1.0/Dmax)<Tcreate) { dropObject1(a,position,flag);dropped=1; } else dropped =0; } else { if((computeDistanceBetween2(a->data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(a,position,flag); dropped=1; } else dropped=0; } break; default: if(computeDistanceBetween2(a->data,heaps[position].Ocenter)<computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)) { dropObject1(a,position,flag); dropped=1; } else dropped=0; break; }j++;} } else { /*Compute the probability to drop the object*/ Pdrop1 = lrand48()%151*1.0/150; if(Pdrop1<Pdrop) dropObject(a,flag); } }//end of function /********************************************************************/ /* Function to move the ant */ /* Output: none */ /* Input: Ant, iteration flag */ /********************************************************************/ void move(ant* a,int flag) { float pDir; /*Check the speed */ if (a->speed!=0) { /*move in the same direction*/ a->speed--; moveNext(a,flag); } else { /*generate the new direction*/ pDir=lrand48()%17*1.0/16.0; if(pDir<.85) { a->dir=lrand48()%26; } else if(pDir>.95) { a->speed=2; } moveNext(a,flag); } } /********************************************************************/ /* Function to simulate the movement of the ants */ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified: minHeapCount, minHeapPosition */ /********************************************************************/ void iterate(int flag) { int i=0,j=0,k=0,loopCount=0; if (flag==1) loopCount=MAXANTS; else loopCount=MAXANTSFORHEAPS; while (i<MAXITERATIONS) { i++; for(j=0;j<loopCount;j++) { move(&ants[j],flag); if(ants[j].carrying==1) { drop(&ants[j],flag); } else { pick(&ants[j],flag); } } if(debugLevel>0){ for(k=0;k<heapCount;k++) for(j=0;j<heapCount;j++) if((k!=j)&&(heaps[k].x==heaps[j].x)&&(heaps[k].y==heaps[j].y)&&(heaps[k].z==heaps[j].z)) { printf("\nerror two heaps with same co ordinates %d %d",k,j); //getchar(); } k=0; for(j=0;j<heapCount;j++) k+=heaps[j].count; } if(heapCount<minHeapCount) { minHeapCount=heapCount; minPosition=i; } if(debugLevel>0) printf("\n i:%d Heap count %d sum %d",i,heapCount,k); else printf("\n i:%d Heap count %d",i,heapCount); heapSum+=heapCount; } }//end of function /********************************************************************/ /*Function to drop the objects carried by the ants after all the iterations*/ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified:none */ /********************************************************************/ void dropFinal(int flag) { int i=0,j=0,position=-1,loopCount=0; loopCount=currentAntCount; for(i=0;i<loopCount;i++) { if(ants[i].carrying==1) { if(flag==1) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)&&(heaps[j].z==ants[i].z)) { position=i; break; } } if(position!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position,flag); } } else { /*Find if a heap is present */ do { position=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)&&(heaps[j].z==ants[i].z)) { position=i; break; } } if(position!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position,flag); position=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position!=-1); } } } }//end of function /********************************************************************/ /* Function for the implementation of QuickSort */ /* Output: none */ /* Input: data structure, low and high indices */ /* Global structure modified:none */ /********************************************************************/ void quickSort( heap A[], int low,int high ) { int mid=0; if(low<high) { mid=partition(A,low,high); quickSort(A,low,mid); quickSort(A,mid+1,high); } } /********************************************************************/ /* Partition routine used by quick sort */ /* Output: partition location */ /* Input: data , low, high */ /* Global structure modified:none */ /********************************************************************/ int partition(heap a[], int low,int high) { heap temp; int x=0,i=0,j=0; i=low; j=high; x=a[low].count; while (1) { while (a[j].count<x) j=j-1; while (a[i].count>x) i=i+1; if(i<j) { temp=a[i]; a[i]=a[j]; a[j--]=temp; } else return(j); } } /********************************************************************/ /* Function used to write the cluster information */ /* Output: none */ /* Input:iteration flag */ /* Global structure modified:none */ /********************************************************************/ void writeClusterInformation(int flag) { FILE *fp=fopen("center.clt","w"); FILE *fp1; FILE *fp2; FILE *fp3; char temp[80]; int i=0,j=0,k=0; int lastCount=0; int rowCount,colCount,antCount; strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp3=fopen(temp,"a"); if(dataDump) {strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp1=fopen(temp,"a");strcpy(temp,"");sprintf(temp,"%sObj.out",filename);fp2=fopen(temp,"a");if(fp1==NULL||fp2==NULL)printf("\n Error in opening file in the writer Cluster Information routine\n"); } rowCount=currentRow; colCount=currentRow; antCount=currentAntCount; if(fp==NULL) { printf("\n Unable to open cluster center output file \n"); /*return;*/ exit(0); } /*sort the array according to the count value and write the value of the top three elements*/ if((flag==1)&&(!EntireHeaps)) quickSort(heaps,0,heapCount); if(EntireHeaps) {if(flag==1) lastCount=heapCount;else lastCount=newHeapCount; } else lastCount=c; for(i=0;i<lastCount;i++) { if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t ",newHeaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",newHeaps[i].Ocenter[k]); fprintf(fp,"\n"); } else { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); fprintf(fp,"\n"); } } if(dataDump) { fprintf(fp1,"\n Information from the Ant Based Algorithm \n"); fprintf(fp1,"\n Important Parameters : Board %d X %d Ants : %d Iterations %d Total Heaps %d ",rowCount,colCount,antCount,MAXITERATIONS,lastCount); } if(flag==0) { if(dataDump) { fprintf(fp1," TCreateForHeaps %f \n",TcreateForHeaps); fprintf(fp2,"\nTotal Heaps : %d\n",heapCount); fprintf(fp2,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); } fprintf(fp3,"\n@ %d\n",lastCount); } else if(dataDump) fprintf(fp1,"\n");if(dataDump) { fprintf(fp1,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); for(i=0;i<lastCount;i++) if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp2,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); } fprintf(fp1," %5d\n",newHeaps[i].count); fprintf(fp2," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp1," %5d\n",heaps[i].count); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]); fprintf(fp1," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]); fprintf(fp1," %5d\n",heaps[i].count); }} if(flag==0) { if(dataDump) fprintf(fp1,"\n Objects in the heaps \n");for(i=0;i<newHeapCount;i++) { if(dataDump) fprintf(fp1,"\n\n Heap %d\n \n",i+1);fprintf(fp3,"# %d %d\n",i ,newHeaps[i].count); for(j=0;j<newHeaps[i].count;j++){ for(k=0;k<features;k++){ fprintf(fp3,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]); if(dataDump) fprintf(fp1,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]);} if(dataDump) fprintf(fp1,"\n"); fprintf(fp3,"\n"); } } } fclose(fp); fclose(fp3); if(dataDump) { fclose(fp1);fclose(fp2); }}//end of flie /********************************************************************/ /* Function to initialise the new heaps */ /* Output: none */ /* Input:new Heap structure */ /* Global structure modified:none */ /********************************************************************/ void initialiseNewHeaps(heap newHeaps[]) { int i=0,j=0; for(i=0;i<lCount;i++) { newHeaps[i].count=0; for(j=0;j<features;j++) newHeaps[i].Ocenter[j]=V[i][j]; newHeaps[i].x=heaps[i].x; newHeaps[i].y=heaps[i].y; newHeaps[i].z=heaps[i].z; } }//end of function /********************************************************************/ /* Function to initialise the new heaps */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ int findPosition(int i) { int j,position=0; for(j=1;j<lCount;j++) { if(U[j][i]>U[position][i]) position=j; } printf("\n Position %d ",position); return(position); }//end of function find position /********************************************************************/ /* Function to add object */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ void addObject(int object,heap * heapPosition) { heapPosition->objects[heapPosition->count]=object; heapPosition->count++; }//end of function add object /********************************************************************/ /* Function to harden the outputs obtained from fcm */ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/ void harden(heap newHeaps[]) { int i,heapPosition=-1,sum=0,j=0; /*initialise the new heaps*/ initialiseNewHeaps(newHeaps); for(i=0;i<count;i++) { heapPosition=findPosition(i); addObject(i,&newHeaps[heapPosition]); } /* To check for degenerate partitions*/ for(i=0;i<lCount;i++) { if(newHeaps[i].count==0){for(j=i;j<lCount-1;j++){newHeaps[j]=newHeaps[j+1];}lCount--;i--;} } newHeapCount=lCount; for(i=0;i<lCount;i++) computeHeapParameters(&newHeaps[i],1); if(debugLevel>0) { for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\n sum = %d",sum);} }//end of function/********************************************************************/ /* Function to initialize the initial parameters for the ant algorithm*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void init() { int i=0,j=0,k; for(k=0;k<zDim;k++)for(i=0;i<MAXROWS;i++) for(j=0;j<MAXCOLS;j++) board[k][i][j]=-9; heapCount=0; newHeapCount=0; heapRow=0; minPosition=-1; minHeapCount=100000; for(i=0;i<MAXANTS;i++) { ants[i].carrying=-1; ants[i].x=-1; ants[i].y=-1; ants[i].z=-1; ants[i].speed=-1; ants[i].dir=-1; } } /********************************************************************/ /* Function to initialize the initial board */ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void initialiseBoard() { int i,j,k;for(k=0;k<zDim;k++) for(i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[k][i][j]=-9; }/********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void harden1() { int i=0,heapPosition=-1; for(i=0;i<heapCount;i++) cenCount[i]=0; for(i=0;i<n;i++) { heapPosition=findPosition1(i); addObject1(i,heapPosition); } writeInfo(); }//end of function/********************************************************************/ /* Function to find to which class the object belongs */ /* Output: Class number */ /* Input: object position */ /* Global Structure Modified: None */ /********************************************************************/int findPosition1(int i) { int j,position=0; for(j=1;j<heapCount;j++) { if(U[j][i]>U[position][i]) position=j; } return(position); }//end of function find position/********************************************************************/ /* Function to Write the output of the hardened objects to file*/ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/void writeInfo(){int i=0,j=0,k=0;FILE *fp=fopen("fcm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}fprintf(fp,"\n@ %d\n",heapCount);for(i=0;i<heapCount;i++){fprintf(fp,"# %d %d\n",i,cenCount[i]);for(j=0;j<cenCount[i];j++){for(k=0;k<s;k++)fprintf(fp,"%8.2f ",clsCen[i][j][k]/scale[k]+minInput[k]);fprintf(fp,"\n");}}fclose(fp);return;}/********************************************************************/ /* Function to add a object to a cluster (2nd stage) */ /* Output: none */ /* Input: object poistion , cluster position */ /* Global Structure Modified: Cluster object and cluster count*/ /*******************************************************************/void addObject1(int object,int heapPosition) { int k=0; for(k=0;k<features;k++) clsCen[heapPosition][cenCount[heapPosition]][k]=X[object][k]; cenCount[heapPosition]++; return; }//end of function add object/********************************************************************/ /* Function to read the original classification of objects */ /* Output: none */ /* Input: none */ /* Global Structure Modified: None */ /*******************************************************************/void readOrg(){int i=0,k=0;char temp[80];FILE *fp;strcpy(temp,"");sprintf(temp,"%sFull.data",filename);fp=fopen(temp,"r");if(fp==NULL){printf("\n Unable to open full data input file ");exit(0);}for(i=0;i<n;i++){fscanf(fp,"%f ",&orgData[i][0]);for(k=1;k<s+1;k++)fscanf(fp,"%f ",&orgData[i][k]);}return;}// end of function/********************************************************************/ /* Function to classify the objects in different clusters */ /* Output: none */ /* Input: the cluster objects, the original classification, */ /* number of objects in the cluster,file to write the data */ /* Global Structure Modified: None *//********************************************************************/int analysisData(float data[][s],float orgData[n][s+1],int count,FILE * fp){int i=0,j=0,k=0,type=1,max=0;heapInfo info;for(i=0;i<c;i++){info.classObj[i]=0;}info.total=0;info.total=count;for(i=0;i<count;i++)for(j=0;j<n;j++){type=1;for(k=0;k<s;k++)if(fabs(data[i][k]-orgData[j][k+1])>errThresh){type=-1;break;}if(type!=-1){info.classObj[(int)orgData[j][0]-1]++;break;}}max=0;for(k=0;k<c;k++){if(info.classObj[k]>info.classObj[max])max=k;fprintf(fp,"\nClass %d Objects % d ",k+1,info.classObj[k]);}j=0;for(k=0;k<c;k++){if(k!=max)j+=info.classObj[k];}fprintf(fp,"\n Total errors : %d \n",j);return(j);}//end of analyse/********************************************************************/ /* Function to read the cluster information */ /* Output: none */ /* Input: input file pointer , output file pointer */ /* Global Structure Modified: None *//********************************************************************/void readFCM(FILE* fp,FILE *fp1){int i=0,j=0,k=0,l=0;int totalRuns=0,heapCount=0,junk=0,sum=0;char ch;FILE *fp2=fopen("errors.out","a");ch = 'c'; while (ch != '$') fscanf (fp, "%c", &ch);fscanf(fp,"%d\n",&totalRuns);printf("\n total runs %d ",totalRuns);for(l=0;l<totalRuns;l++){fscanf(fp,"%c",&ch);fscanf(fp,"%d\n",&heapCount);fprintf(fp1,"\n \nExperiment %d \n ",l+1);sum=0;for(i=0;i<heapCount;i++){fscanf(fp,"%c",&ch);fscanf(fp," %d %d\n",&junk,&clsCenCount[i]);for(j=0;j<clsCenCount[i];j++)for(k=0;k<s;k++)fscanf(fp,"%f",&fcmData[i][j][k]);fscanf(fp,"\n");fprintf(fp1,"\n\n Heap %d Total Objects %d \n",i+1,clsCenCount[i]);sum+=analysisData(fcmData[i],orgData,clsCenCount[i],fp1);}fprintf(fp2," %3d %3d %3d \n",l,sum,heapCount);}fprintf(fp2,"\n --------------------------\n");fclose(fp2);return;}//end of function/********************************************************************/ /* Function to update the board parameters */ /* Output: none */ /* Input: none */ /* Global Structure Modified: board size, ant count *//********************************************************************/void adjustBoard(int flag){int newRow=0,newAnt=0,antDiff=0,antsKilled=0,i=0,position[MAXANTS],position1=-1,j=0,k=0,a,b,z; newRow= (int)ceil(sqrt(4.0*heapSum/updateAfter)); newAnt= (int)ceil((heapSum*1.0/updateAfter)*1.0/3.0); if(newAnt<minAnts)newAnt=minAnts; if(newRow<rowMin)newRow=rowMin;antDiff=currentAntCount-newAnt; if((antDiff>0)&&(newAnt>=minAnts)) {for(i=0;i<currentAntCount;i++){if(ants[i].carrying==-1){position[antsKilled++]=i;}} if(antsKilled>=antDiff){for(i=0;i<antDiff;i++){for(j=position[i];j<currentAntCount-1;j++)ants[j]=ants[j+1];for(k=i+1;k<antsKilled;k++)position[k]--; } currentAntCount=newAnt; }else {for(i=0;i<antsKilled;i++){for(j=position[i];j<currentAntCount-1;j++)ants[j]=ants[j+1]; for(k=i+1;k<antsKilled;k++)position[k]--; }if(flag==1) { for(i=currentAntCount-1,k=0;k<antDiff-antsKilled;k++,i--) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)&&(heaps[j].z==ants[i].z)) { position1=i; break; } } if(position1!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position1,flag); }else { dropObject(&ants[i],flag); } } } else { //flag is zero 2nd stage /*Find if a heap is present */ do { position1=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)&&(heaps[j].z==ants[i].z)) { position1=i; break; } } if(position1!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position1].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position1,flag); position1=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position1!=-1); }//end else currentAntCount=newAnt;}//end elsecurrentAntCount=newAnt;}//end iffor(i=0;i<newAnt;i++){if((ants[i].x>=newRow)||(ants[i].y>=newRow)||ants[i].z>zDim) { a=lrand48()%newRow; b=lrand48()%newRow; z=lrand48()%zDim; ants[i].x=a; ants[i].y=b; ants[i].z=z; }} //Done with ants, now adjust the boardif((currentRow>newRow)&&(newRow>=rowMin)){for(i=0;i<heapCount;i++){if((heaps[i].x>=newRow)||(heaps[i].y>=newRow)||(heaps[i].z>=zDim)) {do { a=lrand48()%newRow; b=lrand48()%newRow; z=lrand48()%zDim; } while(found(z,a,b));//adjust the heap poistionheaps[i].x=a;heaps[i].y=b;heaps[i].z=z;board[z][a][b]=9;}//end if}//end for for(i=0;i<currentAntCount;i++){if((ants[i].carrying==1)&&((ants[i].x>=newRow)||(ants[i].y>=newRow)||(ants[i].z>=zDim))){//adjust the ant locationdo { a=lrand48()%newRow; b=lrand48()%newRow; z=lrand48()%zDim; } while(found(z,a,b));//adjust the heap poistionants[i].x=a;ants[i].y=b;ants[i].z=z;}//end if}//end for currentRow=newRow;}//end if }//end of function void writeIncremental(int iteration,int flag){FILE *fp=fopen("dump.txt","w");int i=0,j=0,k=0;fprintf(fp,"Stage %d\n",flag);fprintf(fp,"Board %d\n",currentRow);fprintf(fp,"Ants %d\n",currentAntCount);fprintf(fp,"Iteration %d\n",iteration);fprintf(fp,"Heaps %d\n",heapCount); if(flag==0)fprintf(fp,"NewHeapCount %d\n",newHeapCount); fprintf(fp,"Ants\n");for(i=0;i<currentAntCount;i++){fprintf(fp,"%d %d %d %d %d ",ants[i].x,ants[i].y,ants[i].carrying,ants[i].speed,ants[i].dir);for(k=0;k<features;k++)fprintf(fp,"%8.4f ",ants[i].data[k]);fprintf(fp,"\n");}fprintf(fp,"Heaps\n");for(i=0;i<heapCount;i++){fprintf(fp,"%d %d %d %d %8.2f %8.2f \n",heaps[i].x,heaps[i].y,heaps[i].count,heaps[i].disPosition,heaps[i].Dmax,heaps[i].Dmean);for(k=0;k<features;k++)fprintf(fp,"%8.4f ",heaps[i].Ocenter[k]);fprintf(fp,"\n");for(k=0;k<features;k++)fprintf(fp,"%8.4f ",heaps[i].Odissim[k]);fprintf(fp,"\n");if(flag==1){for(j=0;j<heaps[i].count;j++)fprintf(fp,"%d ",heaps[i].objects[j]);fprintf(fp,"\n");}}if(flag==0){fprintf(fp,"newHeaps\n");for(i=0;i<newHeapCount;i++){fprintf(fp,"%d\n",newHeaps[i].count);for(j=0;j<newHeaps[i].count;j++)fprintf(fp,"%d ",newHeaps[i].objects[j]);fprintf(fp,"\n");}}fclose(fp);}//end of function/********************************************************************/ /* Function Main */ /********************************************************************/ int main(int argc ,char** argv) { int sum=0,k=0,i=0,j=0; char temp[80]; FILE *fp=fopen("fcm.out","w"); FILE *fp1; FILE *fp2=fopen("antAnalysis.out","a"); FILE *fp3=fopen("fcmAnalysis.out","a"); FILE *fp4=fopen("errors.out","w"); FILE *fp5; FILE *fpJm=fopen("jm.out","w"); fclose(fp4); fclose(fpJm); sprintf(temp,"%sObj.out",filename); fp5=fopen(temp,"w"); fclose(fp5); strcpy(temp,""); sprintf(temp,"%sTot.out",filename); fp5=fopen(temp,"w"); fclose(fp5); strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp1=fopen(temp,"w"); fprintf(fp,"\n$%d",EXPRUN); fprintf(fp1,"\n$%d",EXPRUN); fclose(fp); fclose(fp1); if(argc==2)sscanf(argv[1],"%d",&debugLevel); printf("\n Debug level %d\n",debugLevel); for(k=0;k<zDim;k++) for( i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[k][i][j]=-9; //Read from the file readInput(); //Compute the distance of all the points if(maxDist) { Dmax=computeDistance(); //Compute the maximum distance fp5=fopen("maxData.txt","w");fprintf(fp5,"%f",Dmax);fclose(fp5); } else {/*if((fp5=fopen("maxData.txt","r"))==NULL);{printf("\n Max Distance file not found, exiting . . . \n");exit(-1);}*/fp5=fopen("maxData.txt","r");fscanf(fp5,"%f",&Dmax);fclose(fp5); } printf("\n %f \n",Dmax); //Initialise the board for(k=0;k<EXPRUN;k++) { init(); sum=0; srand48(state1[k]); initialiseBoard(); initialise(1); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(1); printf("\n %d \n",heapCount); //Simulate the working of ants iterate(1); //drop the objects after all the iterations dropFinal(1); //to verify the correctness for(i=0;i<heapCount;i++) sum+=heaps[i].count; //write the cluster centers to the file writeClusterInformation(1); printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); printf("\n\n k %d \n\n",k); Fcm(data,0); //compute new board size. lCount=heapCount; heapRow=(int)ceil(pow(4*heapCount,1.0/3.0)); newHeapCount=lCount; printf("\n LCount %d newHeapCount %d heap Row %d \n",lCount,newHeapCount,heapRow); //harden the objects harden(newHeaps); //iterate with the heaps as the objects. initialiseBoard(); initialise(0); for(i=0;i<lCount;i++) computeHeapParameters(&heaps[i],0); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(0); printf("\n %d \n",heapCount); //getchar(); //Simulate the working of ants iterate(0); dropFinal(0); writeClusterInformation(0); sum=0; for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); // getchar(); fpJm=fopen("jm.out","a"); fprintf(fpJm,"Experiment %2d: ",k); fclose(fpJm); Fcm(data,1); harden1(); } fp=fopen("fcm.out","a"); fp1=fopen(temp,"a"); fprintf(fp,"\n"); fprintf(fp1,"\n"); fclose(fp); fclose(fp1); fp=fopen("fcm.out","r"); fp1=fopen(temp,"r");printf("\n Before read org\n");readOrg();printf("\nafter read org\n");readFCM(fp1,fp2);printf("\n After 1st read fcm\n");readFCM(fp,fp3);printf("\n After 2 fcm read \n");return(1); }//end of main /****************************************************************/ float NORM(feature , feature); void find_covar(); void Fcm (irisData Data[MAXDATA],int stage) { int i,j,l; FILE *ofile; char temp[80]; int flag=0;//added by parag FILE *iniClsCen=fopen("center.clt","a+"),*fpJm; float jmFinal=0; if(iniClsCen==NULL) { printf("\n Unable to open the initial cluster center file\n"); printf("\n choosing random cluster centers....\n"); flag=1; } strcpy(temp,""); sprintf(temp,"%s.data",filename); strcpy (fname,temp); /* Read from this file */ m=2.0; /* The exponent */ /*GET_INPUTS1(fname);*/ /* read in feature vectors */ for (j=0; j<n ; j++) /* Read File in row-major */ for (i=0; i<s ; i++) /* format */ X[j][i]=Data[j][i]; Random_seed=state1[run];/*randseed*/ srand48(Random_seed); for (run=0;run<TOTRUNS;run++) { //flag=1;//for testing random values if (EntireHeaps) lCount=heapCount; else lCount=c; for(i=0;i<lCount;i++) for(j=0;j<s;j++) V[i][j]=0; for(i=0;i<lCount;i++) { for(l=0;l<s;l++) { if(flag==1) { rand_index = lrand48() % n; V[i][l] = X[rand_index][l]; } else { fscanf(iniClsCen,"%f ",&V[i][l]); } }//end for } eps=0.001; stop = FALSE; /* This loop finds the final cluster center and the corresponding U values */ z = 0; while (stop == FALSE) { z++; UMAT(U,V,X); /** Update the U matrix **/ stop = FINISHED(U1,eps); CLUSTER(U,X); /** Get new cluster center **/ printf("Iteration: %d\n\n", z); } /* end while */ printf("Number of iterations: %d\n\n", z);strcpy(temp,""); sprintf(temp,"%sFCM.out",filename);if(stage>0){jmFinal=Compute_Jm(V,X);fpJm=fopen("jm.out","a");fprintf(fpJm," classes %2d Jm: %8.5f\n",lCount,jmFinal);fclose(fpJm);}if(dataDump) Output(temp); /* output the results */ }/*end for*/ if(dataDump) { ofile=fopen(temp,"a"); for (i=0; i <= mins; i++) fprintf(ofile, "Min %d of %10.3f occurred %d times\n", i, mintab[i], mincount[i]); fprintf(ofile, "Average iterations %f\n", (float)aveit/(float)TOTRUNS); fclose(ofile); } }/* end Main */ /* The GET_INPUTS1 procedure opens an input file, reads the number of input vectors, the number of features per vector, the number of clusters, and epsilon, then it reads the input values */ void GET_INPUTS1(char fname[]) //char fname[]; { /* Parameters: fname -- name of file containing input info. */ FILE *infile ;//*fopen(); //char junk[10][80]; char realfname[80]; char ch; char fileName1[80]; //int temp[10], i, j, inputs, outputs, count; int i,j; sprintf (realfname, "%s", fname); /* open input file */ if(normalize==1) strcpy(fileName1,"gtest1.data"); else strcpy(fileName1,fname); if((infile = fopen (fileName1, "r")) == NULL) printf("\n CAN'T OPEN %s\n",realfname); else { ch = 'c'; /* Discard leading comments */ while (ch != '#') fscanf (infile, "%c", &ch); for (j=0; j<n ; j++) /* Read File in row-major */ { /* format */ for (i=0; i<s ; i++) fscanf (infile, "%f", &X[j][i]); /* fscanf (infile, "%d", &temp[1]); */ /* end for */ } /* end for */ fclose(infile); /* close input file */ }/* end else */ } /* end GET_INPUTS1 */ /* The NORM function computes the norm of two vectors, x and v, of dimension s. This gives an indication of the distance between them */ float NORM( feature x, feature v) //feature x, v; { /* Parameters : x -- a local copy of an input vector v -- a local copy of a cluster center */ int i,j; /* loop variable */ double sum; /* the square root of sum is the norm; sum is the sum of the differences squared */ float mat1[s]; float mat2[s]; if (maha) { for (i=0; i<s; i++) mat1[i]=x[i]-v[i]; for(i=0;i<s;i++) { sum =0; for(j=0;j<s;j++) sum += mat1[j] * inv_cov[j][i]; mat2[i] = sum; } sum =0; for(j=0;j<s;j++) sum += mat1[j]*mat2[j]; } else { sum = 0; for(j=0;j<s;j++) sum += (x[j] - v[j])* (x[j] - v[j]); } return (float)(sqrt(sum)); } /* end NORM */ /* The UMAT procedure calculates the updated U matrix according to Bezdek's formula. Its side-effect is the update of the global array U1 */ void UMAT( matrix U,center V, objects X) /*matrix U; center V; objects X;*/ { /* Parameters: U, V, and X: same as in main program */ int i, j, k,l ;/* loop variables *///, class /* class whose cluster center //is closest to pattern k */; float d1,d2,d; float sum; int exact; for (k=0;k<n;k++) { /*printf("\n k %d ",k);*/ exact=false; for (i=0;i<lCount;i++) { sum=0; for (j=0;j<lCount;j++) { d1=NORM(X[k],V[i]); d2=NORM(X[k],V[j]); if(d2!=0) { d= d1/d2; d= d * d;/* pow(d1/d2,2.0/(m-1.0)); */ sum+=d; } else { exact = true; for(l=0;l<lCount;l++) if (l==j) U1[l][k]=1.0; else U1[l][k]=0.0; break; } } if (exact!=true) U1[i][k]=1.0/sum; } } } /* end UMAT */ /* The function FINISHED determines if a termination occurs. If not it updates U with U1, so its side-effect is the potential modification of U */ BOOLEAN FINISHED( matrix U1,float eps) //matrix U1; float eps; { /* Parameters: U1, eps-- same as in main */ float sum; /* sum of squared differences between corresponding elements of U and U1 */ int i,k; /* loop control variabes */ BOOLEAN finished; /* set to true if all corresponding elements are within epsilon */ finished = TRUE; sum = 0; for (i=0; i<lCount; i++) for (k=0; k<n; k++) sum += (U[i][k] - U1[i][k])*(U[i][k]-U1[i][k]); if (sqrt(sum) > eps) { finished = FALSE; for (i=0; i<lCount; i++) for (k=0; k<n; k++) U[i][k] = U1[i][k]; } /* end if */ return finished; } /* end FINISHED */ /* The CLUSTER procedure determines cluster centers in V for the c classes being searched for. The global array V is updated */ void CLUSTER( matrix U, objects X) //matrix U; objects X; { /* Parameters: U, X -- as in main */ int i, j, k; /* loop control variables */ feature num; /* numerator in the cluster center computation formula presented by Bezdek */ float denum; /* denumenator in the obove formula */ for (i=0; i<lCount; i++) { denum = 0; for (j=0; j<s; j++) { num[j] = 0; for (k=0; k<n; k++) num[j] += U[i][k] * U[i][k] * X[k][j]; /* pow(U[i][k],m) * X[k][j]; */ } /* end for */ for (k=0; k<n; k++) denum += U[i][k] * U[i][k]; /* pow(U[i][k],m); */ for (j=0; j<s; j++) V[i][j] = num[j] / denum; } /* end for */ } /* end CLUSTER */ float Compute_Jm(center V, objects X) //center V; //objects X; { int i, k,found; double Jm_value; float u,dik; Jm_value = 0; for (k=0; k<n; k++) { found = FALSE; for (i=0; i<lCount; i++) { dik=NORM(X[k],V[i]); dik=dik*dik; if (dik==0.0) { if (found == FALSE) { u=1.0; found = TRUE; printf("found the case with dik=0\n"); } else { printf("\n cant proceed with this initialization"); exit(1); } } else { if (found != TRUE) u=U1[i][k]; /* get_Uvalue(V,X,dik,k); */ else u=0.0; } Jm_value += u * u * dik;/* pow(u, m) * dik; */ } } return (Jm_value); } /* end Compute_Jm */ /* The Output procedure outputs the U matrix which gives the fuzzy measure of each feature belonging to a cluster center. It also outputs the cluster center for each class */ void Output(char fname[]) //char fname[80]; /* Parameters: U, V -- same as in main */ { int i, j, found; /* loop control variables */ float J2c; if ((fp=fopen(fname,"a"))==NULL) printf("\n CAN'T OPEN %s\n",fname); else { fprintf(fp,"\n The cluster centers are: \n"); fprintf(fp,"\n \n"); for (i=0; i<lCount; i++) { fprintf(fp,"\nCluster center %d: ",i+1); fprintf(fp,"\n"); for (j=0; j<s; j++) fprintf(fp,"%8.7f ",V[i][j]); } /* end for */ fprintf(fp,"\nThe membership grades for the feature vectors are:\n"); for (j=0; j<n; j++) { fprintf(fp,"\n"); for (i=0; i<lCount; i++) fprintf(fp," %8.7f",U[i][j]); } /* end for */ } /* end if */ J2c= Compute_Jm(V,X); found =0; for (i=0;i<= mins; i++){ if ((found == 0) && ((J2c - mintab[i]) < .001) && ((J2c - mintab[i]) > -.001)){ mincount[i]++; found = 1; } } if (found == 0){ mins++; mintab[mins]=J2c; mincount[mins]=1; } fprintf(fp,"\nJ%3.1f = %f", m,J2c); fprintf(fp, "Number of iterations: %d\n\n", z); aveit=aveit + z; fclose(fp); } /***************************************************************/ void find_covar(int m) //int m; { int i,j,k; float mean[s],sum; for (i=0;i<s;i++) mean[i]=0; for (i=0;i<s;i++) for (j=0;j<m;j++) mean[i]+=X[j][i]; for (i=0;i<s;i++) mean[i]/=m; for (i=0;i<s;i++) for (j=0;j<s;j++) { sum=0; for (k=0;k<m;k++) sum+=(X[k][i]-mean[i])*(X[k][j]-mean[j]); Y[i][j]=sum/m; } }

Parag Kanade/fuzzy ants/3D Board/header_xyz.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include<stdlib.h>#include <ctype.h>#include <time.h>#define MAXANTS 50#define MAXDATA 150#define MAXANTSFORHEAPS 10#define MAXITERATIONS 1000#define MAXROWS 9#define MAXCOLS 9#define zDim 9#define EXPRUN 50#define Tremove 1.50#define Tcreate 0.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.12#define features 4#define filename "iris"#define errThresh 0.1#define dataDump 1#define maxDist 0#define INITIALOBJECTS 75#define OBJINC 10/* some type definitions to make C code a little more readable */#define TRUE 1#define FALSE 0#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define c 3 /* number of clusters */#define TOTRUNS 1 /* number of V's to write out */#define false 0#define true 1#define maha 0#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap {float Dmax;//maximum dissimilar distanceirisData Ocenter;//center objectfloat Dmean;//mean of the distance between the center and all the objectsirisData Odissim;//most dissimilar objectint x;//x position on the boardint y;//y position on the boardint z;//z position on the boardint count;//number of objects in the heapsint disPosition;//most dissimilar objectint objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int z;int carrying;int speed;int dir;irisData data;}ant;float orgData[n][s+1];float fcmData[260][n][s];int clsCenCount[260];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int z,int a , int b);void initialise(int flag);int findObject(int z,int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);int pickObject2(ant *a , int position,int flag);int pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);/********************************************************************//***For FCM algorithm*/void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects);void Output(char[]);void Fcm(irisData data[],int stage);float Compute_Jm(center V, objects X);/********************************************************************/


Gauss data

#

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5 17.3724251 17.838472

5 17.3996649 15.71695

5 17.4002773 14.6913483

5 17.4324959 15.5923088

5 17.4391638 18.273311

5 17.474673 16.1187519

5 17.4783074 16.7469574

5 17.4828174 18.835584

5 17.5082782 17.96041

5 17.5131746 16.1271108

5 17.5226118 20.37399

5 17.5411115 18.970615

5 17.5478529 20.084663

5 17.5593776 18.467168

5 17.5604301 16.6251063

5 17.5621066 15.6151074

5 17.5639823 15.6570201

5 17.5727543 16.2284542

5 17.5999234 19.382793

5 17.6097383 16.6748319

5 17.6335421 17.659876

5 17.6346179 19.120215

5 17.6394592 14.6308142

5 17.6442026 19.402978

5 17.6624146 18.518399

5 17.6824201 19.045062

5 17.6933922 18.000639

5 17.6988315 18.716886

5 17.7180367 18.103054

5 17.7549039 16.4974324

5 17.7672336 18.161612

5 17.7675093 19.396513

5 17.7926923 18.684571

5 17.8061956 18.138528

5 17.8161585 17.744709

5 17.8442842 16.5564664

5 17.8517083 18.048076

5 17.8798602 18.078834

5 17.894805 17.364323

5 17.897217 16.2556095

5 17.9274089 17.295782

5 17.9540314 17.790538

5 17.9833606 17.01958

5 18.010122 16.2967178

5 18.011117 17.84965

5 18.02912 18.488563

5 18.036761 16.4478485

5 18.037256 18.574155

5 18.063049 18.151774

5 18.090068 18.56643

5 18.094963 18.018778

5 18.10096 15.8177448

5 18.122916 17.674656

5 18.129248 18.158393

5 18.13386 14.7555909

5 18.149812 16.2079389

5 18.152682 20.011784

5 18.176491 19.079545

5 18.178535 17.691873

5 18.183269 18.910638

5 18.215687 15.8817595

5 18.235729 15.7945041

5 18.259081 17.087312

5 18.264561 17.029223

5 18.274332 15.8333249

5 18.330272 16.7431158

5 18.368219 16.0200739

5 18.379084 19.194089

5 18.379872 18.124247

5 18.385792 18.179336

5 18.398364 19.606996

5 18.409342 17.693611

5 18.437678 17.926307

5 18.455588 19.845492

5 18.475238 18.791697

5 18.508271 16.4602961

5 18.527687 19.682064

5 18.536628 17.831121

5 18.542094 17.563114

5 18.545504 17.37607

5 18.547878 17.319933

5 18.553336 19.569272

5 18.557667 15.180909

5 18.582784 19.483361

5 18.590279 17.149325

5 18.634664 18.86855

5 18.639971 21.681327

5 18.643215 16.5278489

5 18.650616 18.021068

5 18.659338 19.160969

5 18.664585 17.421214

5 18.67864 13.6651535

5 18.706178 19.788597

5 18.743804 18.332319

5 18.754485 17.600224

5 18.776854 18.299507

5 18.788332 18.461131

5 18.797314 14.9900939

5 18.812277 16.7877808

5 18.818708 17.767132

5 18.825362 17.998126

5 18.827257 17.411798

5 18.855049 20.06313

5 18.895247 15.6882882

5 18.89677 18.241082

5 18.90694 16.3827126

5 18.931778 19.185287

5 18.935962 17.947167

5 18.936954 19.25114

5 18.958115 18.59757

5 19.026628 19.205066

5 19.037261 17.780271

5 19.061187 19.016911

5 19.097614 18.740769

5 19.099027 18.797805

5 19.1145 17.767203

5 19.131498 17.562132

5 19.170495 18.460691

5 19.188678 19.321407

5 19.191673 15.5556509

5 19.201821 18.442701

5 19.237079 15.6778069

5 19.25179 18.030541

5 19.270079 19.044006

5 19.333913 15.3620963

5 19.35918 17.033823

5 19.361986 17.354937

5 19.365055 19.033464

5 19.365386 16.7474302

5 19.367864 18.965754

5 19.432841 19.920374

5 19.455325 17.293297

5 19.457648 18.584263

5 19.482073 16.4698072

5 19.495794 15.2869317

5 19.520868 17.981305

5 19.601644 15.5475595

5 19.626402 17.237819

5 19.639048 17.63884

5 19.664319 18.12114

5 19.678355 18.234852

5 19.706041 19.347441

5 19.946172 18.768173

5 19.954228 17.480191

5 20.016013 20.799353

5 20.025227 18.322595

5 20.065571 19.06146

5 20.080158 17.622466

5 20.260129 17.286994

5 20.385613 19.526589

5 20.591116 20.438186

5 20.774989 19.963467

5 20.973478 18.539765

5 21.657494 15.7015363






1.748873


Gauss Data

#

0.53448803 2.3396181

0.85845981 2.6467355

0.93852626 2.910021

0.99039336 2.6959237

1.13522751 3.3371545

1.15816343 2.74101

1.16902868 2.7145902

1.20321506 2.3230994

1.24094079 2.3069846

1.25245225 3.1839526

1.26993324 3.5483323

1.28680391 2.6052432

1.3192367 3.2276855

1.36114833 3.8009784

1.4244067 2.8634449

1.46037797 2.4532789

1.46098959 3.3555875

1.48936047 2.8126198

1.493417 1.52432961

1.5579586 3.0069093

1.59517099 1.74266104

1.601286 3.447746

1.64249205 1.22137058

1.64603926 3.098612

1.68936857 3.3436606

1.70125847 2.9881431

1.70954162 2.8340884

1.71271143 2.1911658

1.73828501 3.9157625

1.76121259 3.887774

1.76300537 4.36236

1.78715506 2.7998882

1.79603202 2.3837631

1.81552587 1.7659359

1.81671088 3.0235945

1.83646559 2.7292928

1.84905869 1.90959966

1.87280583 2.9185033

1.93911004 3.2377149

1.94658989 2.8099329

1.95071896 4.3593771

1.95477185 1.82422933

2.0475351 4.7358953

2.0483555 3.6222619

2.0663274 3.0772458

2.0719789 3.3051114

2.0841659 3.8982847

2.0917331 2.4965835

2.1268271 2.7541776

2.1308673 3.1353612

2.1428466 3.0972579

2.1493513 2.8227118

2.1819349 2.8738619

2.1900377 2.5835831

2.2101874 2.8796963

2.2181118 4.0111068

2.2277972 3.0808183

2.2335498 3.9573163

2.2448562 2.6017201

2.2555436 2.9472067

2.2597352 1.95874592

2.2601195 1.81798378

2.2715242 1.46185734

2.2749423 4.2592336

2.2888759 3.4162943

2.319464 3.1180977

2.3266557 2.9249356

2.3269461 3.8783508

2.3458998 2.6047048

2.3471548 3.0660325

2.3753515 3.4089999

2.3793406 3.1089793

2.3872928 4.3455047

2.3979966 3.3829163

2.4001717 4.0482038

2.4054732 2.6215221

2.4312392 1.79191521

2.4354818 4.0271297

2.4410139 3.7986203

2.4641202 1.58155858

2.4646153 4.9791516

2.5055329 2.0990125

2.5286615 3.6943944

2.5534019 4.741445

2.5735908 2.5571016

2.5856639 1.99127551

2.6083411 3.3154722

2.6167515 4.4641362

2.6315791 3.9447703

2.6485825 2.9459715

2.6558902 2.1154736

2.6603318 3.4801616

2.6657018 2.9762646

2.6740867 3.9751515

2.7109312 2.5716093

2.7172934 2.9542265

2.7245997 3.387059

2.7280016 4.2168608

2.7407356 2.6770587

2.7446611 4.8440441

2.7479167 2.3913499

2.7507312 3.769599

2.7567404 2.4447784

2.7591262 4.9952283

2.7610234 4.5877581

2.7736913 2.4399875

2.7826518 4.1827802

2.7882331 3.7558682

2.8048077 2.4894358

2.806578 3.2980184

2.8105734 4.0728863

2.8132452 1.11900227

2.8581162 3.3816176

2.8735926 3.1120087

2.8771577 2.9606585

2.8791867 1.81713304

2.8866894 2.5761363

2.8881009 1.12698201

2.8980516 3.4640572

2.911659 1.92031474

2.9163593 3.2659011

2.9217044 2.9429349

2.9240625 2.2683148

2.933498 1.99443935

2.9366692 4.0426166

2.9572064 4.2689621

2.987931 5.1287767

2.9907116 2.6489055

2.9930155 3.6846483

3.0011578 3.3236036

3.0107086 3.1802041

3.0650972 1.62687304

3.0760993 2.8564104

3.076595 4.141211

3.0797088 3.6444816

3.0828643 3.4026682

3.0948837 2.8648059

3.0985622 4.7026465

3.0994979 5.0382142

3.1023642 4.2814187

3.1102441 4.3805769

3.1522589 2.6625872

3.1613626 3.6416719

3.1760954 1.66759943

3.1805424 3.0442384

3.1842685 3.5954692

3.1891405 3.9582391

3.1963915 3.0430046

3.2020264 3.176707

3.204684 1.83693931

3.2052541 2.7499996

3.2066926 3.7208274

3.2109871 1.60017807

3.2146996 2.399128

3.2174554 1.72364346

3.2304159 1.82961734

3.2473506 2.2875368

3.2490397 3.1734422

3.2519844 2.2969987

3.2604619 1.7826934

3.2688021 3.0460717

3.3082186 4.4655713

3.337285 3.3586476

3.3616657 3.9431843

3.372691 2.7305975

3.3783937 2.3535819

3.3816021 2.9007384

3.3967371 2.1569486

3.409683 3.3595594

3.4193469 2.0856312

3.4234583 1.51782552

3.4392568 4.1463499

3.440037 4.2618835

3.4499319 1.4923003

3.4582683 2.3588849

3.4769531 4.5814207

3.4844316 2.9984511

3.4845173 2.8737064

3.4932187 2.66146

3.5161047 3.2298843

3.5216744 3.9607432

3.5229139 2.492989

3.5270772 0.93612531

3.5353904 1.28615059

3.5781225 3.5085023

3.5789043 3.6564247

3.5913078 4.4678316

3.6017795 3.3329228

3.6083691 2.7219107

3.609068 3.4104897

3.6328854 2.4587599

3.6469687 2.7014709

3.6920039 2.295654

3.6979838 3.9314267

3.7126542 3.1619493

3.7176177 3.6184841

3.720342 3.7590754

3.7288016 2.8601606

3.7729759 3.3649024

3.8041777 2.8947211

3.8297566 3.1058471

3.8568165 4.2398304

3.8708006 3.5365312

3.8884565 3.4983996

3.8940135 1.80718637

3.9270099 2.4705192

3.933241 1.61062

3.9722306 2.9105811

3.9772854 1.05260017

3.9773797 3.4382292

3.980513 1.98434561

4.005612 3.8833924

4.0089706 1.48283347

4.0261818 3.8696189

4.0400688 3.2351968

4.0647613 2.3867858

4.0958156 5.7977043

4.0974361 1.61238889

4.123407 4.2756611

4.140026 2.9104996

4.1401996 4.0088845

4.1743328 3.9899578

4.1808794 3.408549

4.1832203 2.0703815

4.2276428 3.180782

4.2372055 2.4413745

4.2952069 3.9016304

4.3387983 4.2365316

4.3562441 4.3595853

4.4416423 5.7357497

4.521928 3.8651324

4.5249241 3.4056552

4.6396121 1.74341452

4.6525806 5.0596418

4.6773415 1.65264229

4.7089812 4.4749576

4.7570425 3.2789297

4.7990812 2.8031089

4.8079212 4.2604948

4.8635523 4.3065351

4.8826769 3.4434805

4.9105801 1.0730681

5.0275824 3.9110729

5.2610977 4.5150765

5.2654032 3.5862794

5.3772226 4.6939538

5.3862929 2.8272327

5.6736332 2.4853571

8.9435583 11.4739571

8.9739184 9.87866

9.187397 11.348852

9.2407109 10.5518535

9.2436521 11.4541242

9.2551838 9.5564442

9.285243 12.2071673

9.3299446 11.0000361

9.3319237 12.0936765

9.373557 11.8826912

9.464094 11.0581829

9.4906742 11.3292611

9.5115338 11.8535287

9.5659971 12.0486414

9.6023175 9.9585361

9.6234813 10.3907157

9.6445082 11.6998504

9.649045 11.8849591

9.6584125 11.0152807

9.6665508 11.4282592

9.6730605 12.5942431

9.763729 10.2835604

9.7755229 12.407463

9.8076254 11.658945

9.8276345 10.4644596

9.8479234 9.756862

9.856574 10.503814

9.8622591 11.9170009

9.9363758 9.2319598

9.943909 10.4875439

9.9889206 12.611449

10.0165059 11.5475354

10.0419705 12.8528666

10.102277 13.0164613

10.1288555 10.7255618

10.1296474 11.9023458

10.1553296 12.365873

10.1730311 10.742746

10.1820635 10.0384904

10.2560427 12.2301575

10.2981906 13.2181469

10.3011218 7.9657148

10.301961 11.6504424

10.3145947 10.2175059

10.3361955 13.0435696

10.3803912 11.9176344

10.4078596 11.5672229

10.40994 11.6915476

10.4850186 10.672043

10.4965347 12.4085449

10.5192183 11.1697117

10.5647357 12.0321895

10.5648727 12.5589759

10.6015189 8.0994906

10.603581 12.3790253

10.6099817 9.1638026

10.6206807 11.6087325

10.6345994 11.1531639

10.6429483 12.3542054

10.6724078 10.4637002

10.7092349 10.7151763

10.714601 9.8089865

10.7182087 9.8359403

10.7245 10.2597278

10.7385045 10.4247141

10.770265 11.7400903

10.8110738 10.4712292

10.8312662 9.5418477

10.8319838 10.9931978

10.8578799 11.0934209

10.8853276 11.0234515

10.913638 11.5415647

10.9267408 10.7133325

11.018074 11.6140025

11.0184744 11.045611

11.0412225 10.2239493

11.0485496 11.0632895

11.0518117 10.0080764

11.0664894 11.1414022

11.0949584 11.125697

11.101395 11.8750952

11.1235131 10.6977035

11.1394266 10.110125

11.1673507 10.731084

11.1958179 11.1986208

11.2270168 12.334217

11.2319251 11.5158861

11.2327407 11.0726365

11.2746275 10.693953

11.2802248 8.3492874

11.307973 10.5744575

11.4089124 10.1652054

11.4206061 9.1979654

11.4332237 11.4580566

11.4413669 10.0914636

11.4696749 10.8581128

11.4830634 13.2508174

11.4864111 9.7290216

11.4928201 12.746028

11.4952661 9.12129

11.4990939 12.0108213

11.5146056 11.6430751

11.5521972 10.328596

11.574167 11.0679398

11.5811656 9.0079888

11.5818258 11.1019273

11.6091367 10.5752111

11.6246849 10.5365028

11.6642143 10.4458003

11.6691018 12.7915455

11.7002922 12.6383879

11.7120615 10.794039

11.7644048 10.8808288

11.7934826 9.4950836

11.9684594 12.0053487

11.9714406 9.2756851

11.9788712 11.9465767

12.0029676 9.9771513

12.024769 8.9830776

12.108955 12.0022074

12.1667502 9.1966235

12.2008513 13.2806987

12.2237865 11.3287031

12.3157701 9.5950945

12.3606768 14.1086731

12.5844491 11.865713

12.6733525 9.6230164

12.682216 11.4233843

12.6856208 11.310157

12.6906946 9.328908

12.7297457 12.1547281

15.382204 11.6953397

6.46197526 29.8670258

6.49022974 29.5352805

6.56415542 30.831295

6.95117941 28.9503718

7.05835481 30.557332

7.10288324 29.5935174

7.17447523 28.0540215

7.19810974 29.12201

7.21934452 29.0784394

7.23439904 29.844791

7.26788561 30.209558

7.47250581 28.6123102

7.5142698 28.4614009

7.54996643 29.2768358

7.55418227 28.884939

7.60602417 30.936468

7.63375978 30.206995

7.63673117 28.7844843

7.7274187 29.0976652

7.78672127 28.6171725

7.84069897 28.7650607

7.885701 30.137431

7.91613686 30.233336

7.92942006 28.5713626

7.95596944 30.535309

7.96854857 29.8038184

8.0145628 28.2879164

8.0177927 30.84433

8.0215634 30.765331

8.0319656 29.6683734

8.0682963 29.1762061

8.0797411 30.497518

8.0890363 29.7297415

8.1020139 29.1104753

8.1111965 30.974068

8.1118762 30.730051

8.1190241 30.626021

8.1290053 30.708113

8.1567038 28.9859388

8.1642717 30.86776

8.1877287 29.0277225

8.2417761 29.9239422

8.2697233 30.58267

8.2805359 30.839587

8.315033 27.800678

8.3374848 29.8847305

8.3436787 31.160946

8.3601605 29.9220547

8.3804341 29.5464206

8.419576 28.5611838

8.44766 29.7976419

8.4497856 30.992422

8.4515913 29.246366

8.462917 29.0683491

8.469379 31.277371

8.476635 29.7049207

8.4789768 28.7587035

8.4864838 27.8367824

8.494347 28.4188009

8.4977578 30.235316

8.5150408 31.469438

8.5465376 30.102797

8.5481756 30.500124

8.5623819 31.11233

8.5657523 29.8653065

8.5680518 29.7992902

8.5787906 29.4795982

8.580711 30.634115

8.5882776 31.154661

8.6154553 29.5561227

8.6240277 28.1513374

8.6271007 29.9594745

8.6423654 29.5207591

8.6532214 28.9213972

8.6708971 29.0647823

8.6816109 30.307332

8.6831495 30.753881

8.6909234 28.3056743

8.694486 28.4031309

8.7029568 28.7852089

8.7209245 31.160009

8.7554475 29.5635625

8.7561105 29.1486694

8.7591644 31.053899

8.7816041 31.754704

8.7876631 30.692215

8.8234422 29.7212479

8.8262062 30.727142

8.8288627 30.673864

8.8297793 30.458916

8.832603 31.074711

8.8353628 29.0803144

8.8556235 29.6185811

8.8563045 30.950317

8.8567533 30.230949

8.8755811 29.7954

8.8808773 31.261579

8.8832988 28.9781784

8.8843075 29.8027541

8.8905587 32.469766

8.8975282 30.28635

8.9109833 30.301225

8.9144448 28.974059

8.9165083 31.676352

8.9209176 29.7131755

8.9781659 31.664901

8.9788063 27.9605311

8.9795792 30.604285

8.9988977 29.3873

9.0085747 30.703644

9.0196867 32.969208

9.025619 30.081437

9.0534619 30.705229

9.0547586 30.305228

9.0547586 30.816871

9.0755392 28.3552001

9.075569 29.5328423

9.1066715 28.9310227

9.1219421 28.0786952

9.1325375 30.278667

9.1346469 29.7929784

9.1377228 29.3141014

9.1424518 29.0621782

9.163728 28.0126414

9.1789909 31.545179

9.1910496 32.74184

9.192648 31.567828

9.2120042 29.5149355

9.2236802 29.7026045

9.235359 31.291386

9.2504888 30.255275

9.2849944 29.6670767

9.2936236 30.04094

9.2943962 28.6098342

9.2972952 31.044405

9.3159775 28.8025205

9.3183388 29.7124052

9.3312717 29.1945021

9.3332788 29.858556

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5 17.2126233 32.627478

5 17.27771 33.301138

5 17.339495 37.147247

5 17.3421613 33.489292

5 17.3577314 32.460453

5 17.3724251 32.838472

5 17.3996649 30.71695

5 17.4002773 29.6913483

5 17.4324959 30.5923088

5 17.4391638 33.273311

5 17.474673 31.1187519

5 17.4783074 31.7469574

5 17.4828174 33.835584

5 17.5082782 32.96041

5 17.5131746 31.1271108

5 17.5226118 35.37399

5 17.5411115 33.970615

5 17.5478529 35.084663

5 17.5593776 33.467168

5 17.5604301 31.6251063

5 17.5621066 30.6151074

5 17.5639823 30.6570201

5 17.5727543 31.2284542

5 17.5999234 34.382793

5 17.6097383 31.6748319

5 17.6335421 32.659876

5 17.6346179 34.120215

5 17.6394592 29.6308142

5 17.6442026 34.402978

5 17.6624146 33.518399

5 17.6824201 34.045062

5 17.6933922 33.000639

5 17.6988315 33.716886

5 17.7180367 33.103054

5 17.7549039 31.4974324

5 17.7672336 33.161612

5 17.7675093 34.396513

5 17.7926923 33.684571

5 17.8061956 33.138528

5 17.8161585 32.744709

5 17.8442842 31.5564664

5 17.8517083 33.048076

5 17.8798602 33.078834

5 17.894805 32.364323

5 17.897217 31.2556095

5 17.9274089 32.295782

5 17.9540314 32.790538

5 17.9833606 32.01958

5 18.010122 31.2967178

5 18.011117 32.84965

5 18.02912 33.488563

5 18.036761 31.4478485

5 18.037256 33.574155

5 18.063049 33.151774

5 18.090068 33.56643

5 18.094963 33.018778

5 18.10096 30.8177448

5 18.122916 32.674656

5 18.129248 33.158393

5 18.13386 29.7555909

5 18.149812 31.2079389

5 18.152682 35.011784

5 18.176491 34.079545

5 18.178535 32.691873

5 18.183269 33.910638

5 18.215687 30.8817595

5 18.235729 30.7945041

5 18.259081 32.087312

5 18.264561 32.029223

5 18.274332 30.8333249

5 18.330272 31.7431158

5 18.368219 31.0200739

5 18.379084 34.194089

5 18.379872 33.124247

5 18.385792 33.179336

5 18.398364 34.606996

5 18.409342 32.693611

5 18.437678 32.926307

5 18.455588 34.845492

5 18.475238 33.791697

5 18.508271 31.4602961

5 18.527687 34.682064

5 18.536628 32.831121

5 18.542094 32.563114

5 18.545504 32.37607

5 18.547878 32.319933

5 18.553336 34.569272

5 18.557667 30.180909

5 18.582784 34.483361

5 18.590279 32.149325

5 18.634664 33.86855

5 18.639971 36.681327

5 18.643215 31.5278489

5 18.650616 33.021068

5 18.659338 34.160969

5 18.664585 32.421214

5 18.67864 28.6651535

5 18.706178 34.788597

5 18.743804 33.332319

5 18.754485 32.600224

5 18.776854 33.299507

5 18.788332 33.461131

5 18.797314 29.9900939

5 18.812277 31.7877808

5 18.818708 32.767132

5 18.825362 32.998126

5 18.827257 32.411798

5 18.855049 35.06313

5 18.895247 30.6882882

5 18.89677 33.241082

5 18.90694 31.3827126

5 18.931778 34.185287

5 18.935962 32.947167

5 18.936954 34.25114

5 18.958115 33.59757

5 19.026628 34.205066

5 19.037261 32.780271

5 19.061187 34.016911

5 19.097614 33.740769

5 19.099027 33.797805

5 19.1145 32.767203

5 19.131498 32.562132

5 19.170495 33.460691

5 19.188678 34.321407

5 19.191673 30.5556509

5 19.201821 33.442701

5 19.237079 30.6778069

5 19.25179 33.030541

5 19.270079 34.044006

5 19.333913 30.3620963

5 19.35918 32.033823

5 19.361986 32.354937

5 19.365055 34.033464

5 19.365386 31.7474302

5 19.367864 33.965754

5 19.432841 34.920374

5 19.455325 32.293297

5 19.457648 33.584263

5 19.482073 31.4698072

5 19.495794 30.2869317

5 19.520868 32.981305

5 19.601644 30.5475595

5 19.626402 32.237819

5 19.639048 32.63884

5 19.664319 33.12114

5 19.678355 33.234852

5 19.706041 34.347441

5 19.946172 33.768173

5 19.954228 32.480191

5 20.016013 35.799353

5 20.025227 33.322595

5 20.065571 34.06146

5 20.080158 32.622466

5 20.260129 32.286994

5 20.385613 34.526589

5 20.591116 35.438186

5 20.774989 34.963467

5 20.973478 33.539765

5 21.657494 30.7015363






1.602038


Gauss data

#

-0.46551197 1.33961810

-0.14154019 1.64673550

-0.06147374 1.91002100

-0.00960664 1.69592370

0.13522751 2.33715450

0.15816343 1.74101000

0.16902868 1.71459020

0.20321506 1.32309940

0.24094079 1.30698460

0.25245225 2.18395260

0.26993324 2.54833230

0.28680391 1.60524320

0.31923670 2.22768550

0.36114833 2.80097840

0.42440670 1.86344490

0.46037797 1.45327890

0.46098959 2.35558750

0.48936047 1.81261980

0.49341700 0.52432961

0.55795860 2.00690930

0.59517099 0.74266104

0.60128600 2.44774600

0.64249205 0.22137058

0.64603926 2.09861200

0.68936857 2.34366060

0.70125847 1.98814310

0.70954162 1.83408840

0.71271143 1.19116580

0.73828501 2.91576250

0.76121259 2.88777400

0.76300537 3.36236000

0.78715506 1.79988820

0.79603202 1.38376310

0.81552587 0.76593590

0.81671088 2.02359450

0.83646559 1.72929280

0.84905869 0.90959966

0.87280583 1.91850330

0.93911004 2.23771490

0.94658989 1.80993290

0.95071896 3.35937710

0.95477185 0.82422933

1.04753510 3.73589530

1.04835550 2.62226190

1.06632740 2.07724580

1.07197890 2.30511140

1.08416590 2.89828470

1.09173310 1.49658350

1.12682710 1.75417760

1.13086730 2.13536120

1.14284660 2.09725790

1.14935130 1.82271180

1.18193490 1.87386190

1.19003770 1.58358310

1.21018740 1.87969630

1.21811180 3.01110680

1.22779720 2.08081830

1.23354980 2.95731630

1.24485620 1.60172010

1.25554360 1.94720670

1.25973520 0.95874592

1.26011950 0.81798378

1.27152420 0.46185734

1.27494230 3.25923360

1.28887590 2.41629430

1.31946400 2.11809770

1.32665570 1.92493560

1.32694610 2.87835080

1.34589980 1.60470480

1.34715480 2.06603250

1.37535150 2.40899990

1.37934060 2.10897930

1.38729280 3.34550470

1.39799660 2.38291630

1.40017170 3.04820380

1.40547320 1.62152210

1.43123920 0.79191521

1.43548180 3.02712970

1.44101390 2.79862030

1.46412020 0.58155858

1.46461530 3.97915160

1.50553290 1.09901250

1.52866150 2.69439440

1.55340190 3.74144500

1.57359080 1.55710160

1.58566390 0.99127551

1.60834110 2.31547220

1.61675150 3.46413620

1.63157910 2.94477030

1.64858250 1.94597150

1.65589020 1.11547360

1.66033180 2.48016160

1.66570180 1.97626460

1.67408670 2.97515150

1.71093120 1.57160930

1.71729340 1.95422650

1.72459970 2.38705900

1.72800160 3.21686080

1.74073560 1.67705870

1.74466110 3.84404410

1.74791670 1.39134990

1.75073120 2.76959900

1.75674040 1.44477840

1.75912620 3.99522830

1.76102340 3.58775810

1.77369130 1.43998750

1.78265180 3.18278020

1.78823310 2.75586820

1.80480770 1.48943580

1.80657800 2.29801840

1.81057340 3.07288630

1.81324520 0.11900227

1.85811620 2.38161760

1.87359260 2.11200870

1.87715770 1.96065850

1.87918670 0.81713304

1.88668940 1.57613630

1.88810090 0.12698201

1.89805160 2.46405720

1.91165900 0.92031474

1.91635930 2.26590110

1.92170440 1.94293490

1.92406250 1.26831480

1.93349800 0.99443935

1.93666920 3.04261660

1.95720640 3.26896210

1.98793100 4.12877670

1.99071160 1.64890550

1.99301550 2.68464830

2.00115780 2.32360360

2.01070860 2.18020410

2.06509720 0.62687304

2.07609930 1.85641040

2.07659500 3.14121100

2.07970880 2.64448160

2.08286430 2.40266820

2.09488370 1.86480590

2.09856220 3.70264650

2.09949790 4.03821420

2.10236420 3.28141870

2.11024410 3.38057690

2.15225890 1.66258720

2.16136260 2.64167190

2.17609540 0.66759943

2.18054240 2.04423840

2.18426850 2.59546920

2.18914050 2.95823910

2.19639150 2.04300460

2.20202640 2.17670700

2.20468400 0.83693931

2.20525410 1.74999960

2.20669260 2.72082740

2.21098710 0.60017807

2.21469960 1.39912800

2.21745540 0.72364346

2.23041590 0.82961734

2.24735060 1.28753680

2.24903970 2.17344220

2.25198440 1.29699870

2.26046190 0.78269340

2.26880210 2.04607170

2.30821860 3.46557130

2.33728500 2.35864760

2.36166570 2.94318430

2.37269100 1.73059750

2.37839370 1.35358190

2.38160210 1.90073840

2.39673710 1.15694860

2.40968300 2.35955940

2.41934690 1.08563120

2.42345830 0.51782552

2.43925680 3.14634990

2.44003700 3.26188350

2.44993190 0.49230030

2.45826830 1.35888490

2.47695310 3.58142070

2.48443160 1.99845110

2.48451730 1.87370640

2.49321870 1.66146000

2.51610470 2.22988430

2.52167440 2.96074320

2.52291390 1.49298900

2.52707720 -0.06387469

2.53539040 0.28615059

2.57812250 2.50850230

2.57890430 2.65642470

2.59130780 3.46783160

2.60177950 2.33292280

2.60836910 1.72191070

2.60906800 2.41048970

2.63288540 1.45875990

2.64696870 1.70147090

2.69200390 1.29565400

2.69798380 2.93142670

2.71265420 2.16194930

2.71761770 2.61848410

2.72034200 2.75907540

2.72880160 1.86016060

2.77297590 2.36490240

2.80417770 1.89472110

2.82975660 2.10584710

2.85681650 3.23983040

2.87080060 2.53653120

2.88845650 2.49839960

2.89401350 0.80718637

2.92700990 1.47051920

2.93324100 0.61062000

2.97223060 1.91058110

2.97728540 0.05260017

2.97737970 2.43822920

2.98051300 0.98434561

3.00561200 2.88339240

3.00897060 0.48283347

3.02618180 2.86961890

3.04006880 2.23519680

3.06476130 1.38678580

3.09581560 4.79770430

3.09743610 0.61238889

3.12340700 3.27566110

3.14002600 1.91049960

3.14019960 3.00888450

3.17433280 2.98995780

3.18087940 2.40854900

3.18322030 1.07038150

3.22764280 2.18078200

3.23720550 1.44137450

3.29520690 2.90163040

3.33879830 3.23653160

3.35624410 3.35958530

3.44164230 4.73574970

3.52192800 2.86513240

3.52492410 2.40565520

3.63961210 0.74341452

3.65258060 4.05964180

3.67734150 0.65264229

3.70898120 3.47495760

3.75704250 2.27892970

3.79908120 1.80310890

3.80792120 3.26049480

3.86355230 3.30653510

3.88267690 2.44348050

3.91058010 0.07306810

4.02758240 2.91107290

4.26109770 3.51507650

4.26540320 2.58627940

4.37722260 3.69395380

4.38629290 1.82723270

4.67363320 1.48535710

3.94355830 6.47395710

3.97391840 4.87866000

4.18739700 6.34885200

4.24071090 5.55185350

4.24365210 6.45412420

4.25518380 4.55644420

4.28524300 7.20716730

4.32994460 6.00003610

4.33192370 7.09367650

4.37355700 6.88269120

4.46409400 6.05818290

4.49067420 6.32926110

4.51153380 6.85352870

4.56599710 7.04864140

4.60231750 4.95853610

4.62348130 5.39071570

4.64450820 6.69985040

4.64904500 6.88495910

4.65841250 6.01528070

4.66655080 6.42825920

4.67306050 7.59424310

4.76372900 5.28356040

4.77552290 7.40746300

4.80762540 6.65894500

4.82763450 5.46445960

4.84792340 4.75686200

4.85657400 5.50381400

4.86225910 6.91700090

4.93637580 4.23195980

4.94390900 5.48754390

4.98892060 7.61144900

5.01650590 6.54753540

5.04197050 7.85286660

5.10227700 8.01646130

5.12885550 5.72556180

5.12964740 6.90234580

5.15532960 7.36587300

5.17303110 5.74274600

5.18206350 5.03849040

5.25604270 7.23015750

5.29819060 8.21814690

5.30112180 2.96571480

5.30196100 6.65044240

5.31459470 5.21750590

5.33619550 8.04356960

5.38039120 6.91763440

5.40785960 6.56722290

5.40994000 6.69154760

5.48501860 5.67204300

5.49653470 7.40854490

5.51921830 6.16971170

5.56473570 7.03218950

5.56487270 7.55897590

5.60151890 3.09949060

5.60358100 7.37902530

5.60998170 4.16380260

5.62068070 6.60873250

5.63459940 6.15316390

5.64294830 7.35420540

5.67240780 5.46370020

5.70923490 5.71517630

5.71460100 4.80898650

5.71820870 4.83594030

5.72450000 5.25972780

5.73850450 5.42471410

5.77026500 6.74009030

5.81107380 5.47122920

5.83126620 4.54184770

5.83198380 5.99319780

5.85787990 6.09342090

5.88532760 6.02345150

5.91363800 6.54156470

5.92674080 5.71333250

6.01807400 6.61400250

6.01847440 6.04561100

6.04122250 5.22394930

6.04854960 6.06328950

6.05181170 5.00807640

6.06648940 6.14140220

6.09495840 6.12569700

6.10139500 6.87509520

6.12351310 5.69770350

6.13942660 5.11012500

6.16735070 5.73108400

6.19581790 6.19862080

6.22701680 7.33421700

6.23192510 6.51588610

6.23274070 6.07263650

6.27462750 5.69395300

6.28022480 3.34928740

6.30797300 5.57445750

6.40891240 5.16520540

6.42060610 4.19796540

6.43322370 6.45805660

6.44136690 5.09146360

6.46967490 5.85811280

6.48306340 8.25081740

6.48641110 4.72902160

6.49282010 7.74602800

6.49526610 4.12129000

6.49909390 7.01082130

6.51460560 6.64307510

6.55219720 5.32859600

6.57416700 6.06793980

6.58116560 4.00798880

6.58182580 6.10192730

6.60913670 5.57521110

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6.66910180 7.79154550

6.70029220 7.63838790

6.71206150 5.79403900

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6.79348260 4.49508360

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6.97144060 4.27568510

6.97887120 6.94657670

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7.10895500 7.00220740

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7.22378650 6.32870310

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7.36067680 9.10867310

7.58444910 6.86571300

7.67335250 4.62301640

7.68221600 6.42338430

7.68562080 6.31015700

7.69069460 4.32890800

7.72974570 7.15472810

10.38220400 6.69533970

-0.53802474 9.86702580

-0.50977026 9.53528050

-0.43584458 10.83129500

-0.04882059 8.95037180

0.05835481 10.55733200

0.10288324 9.59351740

0.17447523 8.05402150

0.19810974 9.12201000

0.21934452 9.07843940

0.23439904 9.84479100

0.26788561 10.20955800

0.47250581 8.61231020

0.51426980 8.46140090

0.54996643 9.27683580

0.55418227 8.88493900

0.60602417 10.93646800

0.63375978 10.20699500

0.63673117 8.78448430

0.72741870 9.09766520

0.78672127 8.61717250

0.84069897 8.76506070

0.88570100 10.13743100

0.91613686 10.23333600

0.92942006 8.57136260

0.95596944 10.53530900

0.96854857 9.80381840

1.01456280 8.28791640

1.01779270 10.84433000

1.02156340 10.76533100

1.03196560 9.66837340

1.06829630 9.17620610

1.07974110 10.49751800

1.08903630 9.72974150

1.10201390 9.11047530

1.11119650 10.97406800

1.11187620 10.73005100

1.11902410 10.62602100

1.12900530 10.70811300

1.15670380 8.98593880

1.16427170 10.86776000

1.18772870 9.02772250

1.24177610 9.92394220

1.26972330 10.58267000

1.28053590 10.83958700

1.31503300 7.80067800

1.33748480 9.88473050

1.34367870 11.16094600

1.36016050 9.92205470

1.38043410 9.54642060

1.41957600 8.56118380

1.44766000 9.79764190

1.44978560 10.99242200

1.45159130 9.24636600

1.46291700 9.06834910

1.46937900 11.27737100

1.47663500 9.70492070

1.47897680 8.75870350

1.48648380 7.83678240

1.49434700 8.41880090

1.49775780 10.23531600

1.51504080 11.46943800

1.54653760 10.10279700

1.54817560 10.50012400

1.56238190 11.11233000

1.56575230 9.86530650

1.56805180 9.79929020

1.57879060 9.47959820

1.58071100 10.63411500

1.58827760 11.15466100

1.61545530 9.55612270

1.62402770 8.15133740

1.62710070 9.95947450

1.64236540 9.52075910

1.65322140 8.92139720

1.67089710 9.06478230

1.68161090 10.30733200

1.68314950 10.75388100

1.69092340 8.30567430

1.69448600 8.40313090

1.70295680 8.78520890

1.72092450 11.16000900

1.75544750 9.56356250

1.75611050 9.14866940

1.75916440 11.05389900

1.78160410 11.75470400

1.78766310 10.69221500

1.82344220 9.72124790

1.82620620 10.72714200

1.82886270 10.67386400

1.82977930 10.45891600

1.83260300 11.07471100

1.83536280 9.08031440

1.85562350 9.61858110

1.85630450 10.95031700

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4 10.87257000 0.23464118

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4 10.92863400 2.63593660

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4 11.03666100 1.70051580

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4 11.06851100 3.95114850

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4 11.13785300 2.00208790

4 11.17317400 3.14803610

4 11.18638600 6.22516560

4 11.20099100 1.19015140

4 11.20883900 3.42794410

4 11.21894800 2.78643090

4 11.22180700 2.07195140

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4 11.26246100 1.95785590

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4 11.29046500 2.28748380

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4 12.41839700 2.60166070

4 12.43804500 4.29621460

4 12.50262800 1.91294910

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4 12.53177400 2.51077500

4 12.53548400 5.34961560

4 12.55590600 1.88128220

4 12.57487900 3.14838260

4 12.57704700 5.57486740

4 12.69811900 4.56844720

4 12.71266600 1.86983480

4 12.75471600 1.88424410

4 12.84054600 4.17589510

4 12.87563700 4.58237630

4 12.91383900 3.72360130

4 12.92143500 3.99211200

4 12.95051000 4.50726300

4 12.96883700 2.81026580

4 13.37212300 4.10562440

4 13.57001500 6.35204170

4 13.87186200 1.28395610

5 6.77625400 8.93326230

5 6.82853500 10.75476800

5 6.99850990 12.00625500

5 7.03320130 12.17173900

5 7.34958640 11.34462900

5 7.36433010 10.30722100

5 7.63086440 11.72930800

5 7.67429150 11.42227600

5 7.67659120 11.95716300

5 7.72421250 11.60357600

5 7.73093580 11.28785800

5 7.82011350 10.31780300

5 8.04405000 11.32677600

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5 8.41343420 12.81313400

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5 8.67928500 11.29584400

5 8.69809170 9.83322040

5 8.72482460 10.81320900

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5 8.74470180 13.61214400

5 8.75924500 13.42558100

5 8.81743790 10.38709600

5 8.83182740 13.13231400

5 8.84119810 11.77572400

5 8.86576390 8.18619000

5 8.90450350 9.62928520

5 8.90605490 10.43295500

5 8.92366520 9.50858180

5 8.92461850 11.50141400

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5 8.95438400 9.62107450

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5 9.04954750 11.48330000

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5 9.12191410 10.02935100

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5 9.14859280 11.23678600

5 9.15599420 9.92740050

5 9.17842650 9.63470770

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5 9.20678430 11.87802700

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5 9.27771000 11.30113800

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5 9.34216130 11.48929200

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5 9.37242510 10.83847200

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5 9.40027730 7.69134830

5 9.43249590 8.59230880

5 9.43916380 11.27331100

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5 9.48281740 11.83558400

5 9.50827820 10.96041000

5 9.51317460 9.12711080

5 9.52261180 13.37399000

5 9.54111150 11.97061500

5 9.54785290 13.08466300

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5 9.56043010 9.62510630

5 9.56210660 8.61510740

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5 9.57275430 9.22845420

5 9.59992340 12.38279300

5 9.60973830 9.67483190

5 9.63354210 10.65987600

5 9.63461790 12.12021500

5 9.63945920 7.63081420

5 9.64420260 12.40297800

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5 9.69339220 11.00063900

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5 10.65933800 12.16096900

5 10.66458500 10.42121400

5 10.67864000 6.66515350

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5 10.85504900 13.06313000

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5 11.09902700 11.79780500

5 11.11450000 10.76720300

5 11.13149800 10.56213200

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5 11.20182100 11.44270100

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5 11.36786400 11.96575400

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5 12.06557100 12.06146000

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5 12.38561300 12.52658900

5 12.59111600 13.43818600

5 12.77498900 12.96346700

5 12.97347800 11.53976500

5 13.65749400 8.70153630




1.688495



Parag Kanade/fuzzy ants/Gauss500-1/gauss500_01.data

gauss Data

#

1.20790910 3.72105890

1.22318330 4.62444200

1.27242740 3.72119890

1.39058610 2.85282570

1.52632060 3.19027090

1.54465150 2.90923660

2.08176410 2.29323270

2.13295880 2.79096560

2.20619080 2.55876980

2.22945410 3.70946740

2.23116050 2.18429310

2.37294590 2.61465910

2.45178300 4.09422380

2.46312140 1.97760290

2.46411810 3.99186090

2.50921260 3.20285370

2.52496350 1.88579450

2.54084840 3.09384190

2.55385850 4.13531910

2.58099800 3.17036370

2.62216610 4.05325020

2.66759260 3.50787480

2.66931780 3.02588030

2.67570580 1.53863590

2.68751670 4.39556030

2.81365690 4.25504410

2.83307330 4.75222820

2.83880630 4.80213920

2.83944630 4.19682650

2.85643290 1.77432030

2.86941540 2.24156170

2.88384730 3.88449670

2.91513020 2.51188730

2.91979180 4.40812900

2.95465960 4.20401090

2.97074170 2.43121040

3.01351070 3.94685690

3.04563820 3.69689970

3.06625130 3.90264770

3.07317570 5.06072370

3.09401840 5.38469030

3.09980030 3.01968540

3.10625880 3.86655360

3.11135740 2.74664260

3.14015020 2.32282300

3.16450400 2.37724910

3.16772100 4.49169200

3.17923370 3.19213120

3.18903400 3.92090930

3.19233550 5.02698590

3.24202770 2.68666690

3.25633940 2.63186020

3.26466530 3.36216560

3.27783900 3.59039120

3.28408270 2.39489040

3.31341280 3.74656750

3.34329660 2.30249760

3.35886880 2.63951690

3.37731540 3.50969100

3.39236820 3.77047540

3.39283960 3.93073540

3.40973750 3.02436640

3.42509050 4.29578070

3.42564670 5.19090880

3.42985900 4.63892740

3.43833830 4.59459460

3.44998160 3.34065440

3.46489990 3.76339640

3.51018710 3.09220030

3.58142780 4.47539960

3.58831510 2.20499400

3.60654970 3.73048950

3.61092180 4.41089130

3.68002160 3.65956820

3.68657310 2.87362660

3.73646180 3.11096490

3.78348040 4.44785110

3.80693700 2.21548770

3.85050150 2.74956110

3.88946560 3.58457720

3.89807100 4.44213340

3.91916380 4.13792570

3.92325950 2.36911640

3.92630710 2.34994830

3.93368750 3.54306570

3.95610750 2.01434500

3.95678700 2.29137210

3.95973750 3.92336320

3.96472580 3.55419380

4.00146640 1.59593080

4.09676130 3.34609180

4.11136190 4.95612900

4.13931330 3.12903690

4.24274510 4.32936230

4.24473240 2.40552780

4.28089580 4.18497110

4.29582480 4.52221000

4.33338810 4.89267550

4.37706670 4.14826300

4.38728110 3.08349770

4.39438060 1.20470410

4.42311330 5.14285450

4.42424630 4.74370290

4.47371880 5.19608090

4.54348320 2.95401650

4.55423010 4.65144570

4.55661300 3.67922140

4.55693430 4.35570380

4.56818780 2.73349690

4.61544290 4.99191540

4.65732870 3.63231810

4.75235850 4.67648360

4.75303290 1.69703220

4.88558470 3.10924050

4.92029430 3.68786210

5.06929180 4.46140430

5.09425100 4.99866570

5.22136030 2.88596540

5.29025000 4.12315510

5.30474090 4.05297750

5.38075760 2.88971840

5.40225010 4.21574110

5.52586830 4.60779350

5.56029020 4.02022760

5.59284930 5.94734880

5.63379210 2.04773990

4.56496330 8.81990280

4.94924760 9.24178310

4.96398430 8.13018410

5.22679600 7.08964560

5.51604770 8.33961800

5.54949660 9.31870340

5.73213920 7.30918760

5.78872000 7.96719220

5.80742230 8.16041300

5.80772180 9.28296680

5.84010640 8.43407780

5.88878260 9.18261600

5.93854940 7.08269680

6.01732790 10.01593400

6.13331750 9.06412020

6.14938460 5.58131310

6.32476160 8.50081580

6.35965520 8.43000210

6.44987570 8.46609640

6.50398860 9.12919810

6.55778340 6.13529350

6.62439690 7.18625970

6.66533610 7.89935120

6.67529210 7.25224350

6.69788670 7.91823120

6.70500830 10.97463400

6.74736090 7.24850780

6.74938060 7.51299370

6.78877990 7.85880770

6.83635100 7.21746830

6.87264510 8.08347420

6.89035080 6.72500990

6.89074810 8.72978990

6.89556320 7.21410970

6.91331220 10.33794100

6.98172740 7.63455400

6.99855530 6.02362240

7.09174110 7.55206390

7.11107720 8.55369310

7.13839200 7.24159980

7.16242050 7.74177850

7.16753040 8.34244030

7.19596080 7.67605230

7.19600340 8.48065380

7.20138350 9.69803770

7.21410180 8.68375570

7.23488380 6.43974970

7.24224830 7.34937430

7.24308230 7.76448210

7.26136760 8.86718760

7.29467750 7.15935710

7.33502880 8.46004700

7.33583100 9.45610450

7.34010160 9.13983420

7.37391630 7.92165490

7.43575000 7.36740770

7.47351160 7.36219690

7.50943180 8.74251930

7.51927650 7.09198750

7.52303360 7.31811160

7.53891530 8.37066250

7.56902300 9.69571210

7.68146310 7.04343990

7.71449540 5.19183500

7.71988930 9.08293360

7.78037680 7.88613490

7.93262710 8.18044940

7.97323520 7.99187600

7.98495340 7.88536950

8.11242610 7.37063310

8.11544250 8.90069810

8.12542490 6.09283890

8.27950630 8.55703860

8.29758040 7.17683620

8.38008940 7.23224310

9.14977290 8.41239770

0.14794639 9.97961100

0.87296376 12.25967700

0.95057589 10.25391200

1.17749740 10.07844500

1.22125760 11.18073600

1.33193920 12.25034600

1.34172490 10.92280400

1.36668230 11.05772600

1.43430450 10.84407300

1.46370020 11.50454500

1.57309490 12.12008300

1.69603260 10.71967200

1.71769480 10.68105200

1.73597100 12.28138600

1.79071270 10.97072200

1.89341280 12.57715300

1.90672830 11.33818500

1.96209270 12.09142100

1.99673980 12.70860600

2.00474310 11.37161400

2.04421500 10.68913300

2.08722420 12.30532500

2.08879850 11.69753500

2.20018500 12.66379400

2.20085780 10.88587900

2.25572220 10.88979800

2.28927610 9.60072350

2.30283070 10.63739200

2.34192850 10.22409200

2.34228770 10.20432700

2.35837570 12.04876100

2.38938590 11.91840600

2.41693470 12.29231400

2.45767500 11.09708100

2.45786590 11.97433900

2.47888920 12.15292500

2.50464770 12.38179100

2.50590350 12.73737100

2.56210210 11.85764700

2.67207820 9.41642710

2.69590010 12.99519600

2.69928480 11.47312700

2.70662150 10.62388600

2.72956480 11.27896300

2.74608850 11.57901500

2.86995370 13.05209000

2.87165180 13.41363400

2.88735720 12.96273800

2.90458980 12.71584900

2.94569430 11.24886600

2.97652460 12.88339300

2.98191090 13.26836000

2.99856800 12.24334200

3.04330960 13.34705800

3.05960440 13.05621500

3.07944220 11.75840700

3.11295220 11.22429300

3.14139420 11.92967100

3.16274410 13.15897700

3.17671860 11.18468300

3.17819640 12.23026600

3.24316480 12.96259800

3.26183990 11.53375400

3.38296950 11.58900800

3.39006150 12.29883300

3.40722920 11.45213100

3.46309720 13.51363000

3.48651150 11.16625900

3.48980800 11.30236500

3.49857810 11.17645400

3.49956030 12.33953700

3.50158880 12.54662300

3.51269070 13.18131800

3.51868070 11.24392300

3.52004940 12.60635900

3.55460590 12.52347500

3.55935130 13.20819000

3.62011300 13.04117400

3.75619870 12.16167200

3.77773560 11.77477900

3.79934290 12.86226800

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5 9.9977637 10.264383

5 10.120108 12.298028

5 10.161642 10.235561

5 10.259619 12.538579

5 10.278865 11.907848

5 10.284851 12.78127

5 10.302126 11.551077

5 10.419082 11.560862

5 10.451229 12.87717

5 10.481022 12.631295

5 10.487289 11.299683

5 10.570844 11.073695

5 10.587373 12.506647

5 10.625944 11.499386

5 10.641761 11.157186

5 10.64511 10.942852

5 10.667898 13.982038

5 10.670485 11.955641

5 10.706356 11.361715

5 10.711061 12.236744

5 10.771428 10.766095

5 10.775607 14.052587

5 10.886805 11.023554

5 10.928196 9.2239141

5 10.950179 12.187592

5 10.966609 11.020396

5 10.984097 11.00589

5 10.997997 12.669185

5 11.089569 12.398868

5 11.108494 11.910334

5 11.147798 11.724342

5 11.155115 14.121416

5 11.158758 12.718643

5 11.213179 10.731326

5 11.222689 11.894866

5 11.237439 12.728269

5 11.247742 13.206662

5 11.256842 12.442514

5 11.269178 11.628728

5 11.350498 11.563816

5 11.469883 12.188571

5 11.4942 12.38967

5 11.52686 10.779428

5 11.553505 12.539572

5 11.559418 13.570712

5 11.567707 12.901948

5 11.634692 10.074948

5 11.634837 12.236684

5 11.641807 12.726728

5 11.677089 12.332538

5 11.767215 13.092209

5 11.781016 10.595779

5 11.878886 12.329098

5 11.891137 11.18777

5 11.895193 9.6599829

5 11.945273 11.156521

5 11.949261 10.944328

5 11.998138 11.186815

5 12.01272 12.270148

5 12.021108 13.495508

5 12.026602 11.861609

5 12.056164 11.728905

5 12.065794 11.845586

5 12.087697 13.523848

5 12.105136 11.638893

5 12.197604 12.022645

5 12.296025 13.509115

5 12.349088 12.002902

5 12.370353 14.051215

5 12.425818 10.55447

5 12.465642 10.745134

5 12.470883 11.280208

5 12.473897 11.425769

5 12.516394 11.222901

5 12.646671 11.889919

5 12.651572 13.064428

5 12.888393 10.709495

5 13.075641 12.576588

5 14.009856 12.548147

Parag Kanade/fuzzy ants/Gauss500-1/gauss.c


Parag Kanade/fuzzy ants/Gauss500-1/headerStatic.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include<stdlib.h>#include <ctype.h>#include <time.h>#define MAXANTS 167#define MAXDATA 500#define MAXANTSFORHEAPS 45#define MAXITERATIONS 1000#define MAXROWS 45#define MAXCOLS 45#define EXPRUN 10#define Tremove 1.50#define Tcreate 0.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.19#define features 2#define filename "gauss500_01"#define errThresh 0.1#define dataDump 1#define maxDist 1#define INITIALOBJECTS 75#define OBJINC 10/* some type definitions to make C code a little more readable */#define TRUE 1#define FALSE 0#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define c 5 /* number of clusters */#define TOTRUNS 1 /* number of V's to write out */#define false 0#define true 1#define maha 0#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap {float Dmax;//maximum dissimilar distanceirisData Ocenter;//center objectfloat Dmean;//mean of the distance between the center and all the objectsirisData Odissim;//most dissimilar objectint x;//x position on the boardint y;//y position on the boardint count;//number of objects in the heapsint disPosition;//most dissimilar objectint objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int carrying;int speed;int dir;irisData data;}ant;float orgData[n][s+1];float fcmData[MAXDATA][n][s];int clsCenCount[MAXDATA];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int a , int b);void initialise(int flag);int findObject(int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);void pickObject2(ant *a , int position,int flag);void pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);/********************************************************************//***For FCM algorithm*/void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects);void Output(char[]);void Fcm();/********************************************************************/

Parag Kanade/fuzzy ants/Gauss500-1/maxData.txt

1.641473


gauss Data

#

1.2079091 3.7210589

1.2231833 4.624442

1.2724274 3.7211989

1.3905861 2.8528257

1.5263206 3.1902709

1.5446515 2.9092366

2.0817641 2.2932327

2.1329588 2.7909656

2.2061908 2.5587698

2.2294541 3.7094674

2.2311605 2.1842931

2.3729459 2.6146591

2.451783 4.0942238

2.4631214 1.9776029

2.4641181 3.9918609

2.5092126 3.2028537

2.5249635 1.8857945

2.5408484 3.0938419

2.5538585 4.1353191

2.580998 3.1703637

2.6221661 4.0532502

2.6675926 3.5078748

2.6693178 3.0258803

2.6757058 1.5386359

2.6875167 4.3955603

2.8136569 4.2550441

2.8330733 4.7522282

2.8388063 4.8021392

2.8394463 4.1968265

2.8564329 1.7743203

2.8694154 2.2415617

2.8838473 3.8844967

2.9151302 2.5118873

2.9197918 4.408129

2.9546596 4.2040109

2.9707417 2.4312104

3.0135107 3.9468569

3.0456382 3.6968997

3.0662513 3.9026477

3.0731757 5.0607237

3.0940184 5.3846903

3.0998003 3.0196854

3.1062588 3.8665536

3.1113574 2.7466426

3.1401502 2.322823

3.164504 2.3772491

3.167721 4.491692

3.1792337 3.1921312

3.189034 3.9209093

3.1923355 5.0269859

3.2420277 2.6866669

3.2563394 2.6318602

3.2646653 3.3621656

3.277839 3.5903912

3.2840827 2.3948904

3.3134128 3.7465675

3.3432966 2.3024976

3.3588688 2.6395169

3.3773154 3.509691

3.3923682 3.7704754

3.3928396 3.9307354

3.4097375 3.0243664

3.4250905 4.2957807

3.4256467 5.1909088

3.429859 4.6389274

3.4383383 4.5945946

3.4499816 3.3406544

3.4648999 3.7633964

3.5101871 3.0922003

3.5814278 4.4753996

3.5883151 2.204994

3.6065497 3.7304895

3.6109218 4.4108913

3.6800216 3.6595682

3.6865731 2.8736266

3.7364618 3.1109649

3.7834804 4.4478511

3.806937 2.2154877

3.8505015 2.7495611

3.8894656 3.5845772

3.898071 4.4421334

3.9191638 4.1379257

3.9232595 2.3691164

3.9263071 2.3499483

3.9336875 3.5430657

3.9561075 2.014345

3.956787 2.2913721

3.9597375 3.9233632

3.9647258 3.5541938

4.0014664 1.5959308

4.0967613 3.3460918

4.1113619 4.956129

4.1393133 3.1290369

4.2427451 4.3293623

4.2447324 2.4055278

4.2808958 4.1849711

4.2958248 4.52221

4.3333881 4.8926755

4.3770667 4.148263

4.3872811 3.0834977

4.3943806 1.2047041

4.4231133 5.1428545

4.4242463 4.7437029

4.4737188 5.1960809

4.5434832 2.9540165

4.5542301 4.6514457

4.556613 3.6792214

4.5569343 4.3557038

4.5681878 2.7334969

4.6154429 4.9919154

4.6573287 3.6323181

4.7523585 4.6764836

4.7530329 1.6970322

4.8855847 3.1092405

4.9202943 3.6878621

5.0692918 4.4614043

5.094251 4.9986657

5.2213603 2.8859654

5.29025 4.1231551

5.3047409 4.0529775

5.3807576 2.8897184

5.4022501 4.2157411

5.5258683 4.6077935

5.5602902 4.0202276

5.5928493 5.9473488

5.6337921 2.0477399

4.5649633 10.8199028

4.9492476 11.2417831

4.9639843 10.1301841

5.226796 9.0896456

5.5160477 10.339618

5.5494966 11.3187034

5.7321392 9.3091876

5.78872 9.9671922

5.8074223 10.160413

5.8077218 11.2829668

5.8401064 10.4340778

5.8887826 11.182616

5.9385494 9.0826968

6.0173279 12.015934

6.1333175 11.0641202

6.1493846 7.5813131

6.3247616 10.5008158

6.3596552 10.4300021

6.4498757 10.4660964

6.5039886 11.1291981

6.5577834 8.1352935

6.6243969 9.1862597

6.6653361 9.8993512

6.6752921 9.2522435

6.6978867 9.9182312

6.7050083 12.974634

6.7473609 9.2485078

6.7493806 9.5129937

6.7887799 9.8588077

6.836351 9.2174683

6.8726451 10.0834742

6.8903508 8.7250099

6.8907481 10.7297899

6.8955632 9.2141097

6.9133122 12.337941

6.9817274 9.634554

6.9985553 8.0236224

7.0917411 9.5520639

7.1110772 10.5536931

7.138392 9.2415998

7.1624205 9.7417785

7.1675304 10.3424403

7.1959608 9.6760523

7.1960034 10.4806538

7.2013835 11.6980377

7.2141018 10.6837557

7.2348838 8.4397497

7.2422483 9.3493743

7.2430823 9.7644821

7.2613676 10.8671876

7.2946775 9.1593571

7.3350288 10.460047

7.335831 11.4561045

7.3401016 11.1398342

7.3739163 9.9216549

7.43575 9.3674077

7.4735116 9.3621969

7.5094318 10.7425193

7.5192765 9.0919875

7.5230336 9.3181116

7.5389153 10.3706625

7.569023 11.6957121

7.6814631 9.0434399

7.7144954 7.191835

7.7198893 11.0829336

7.7803768 9.8861349

7.9326271 10.1804494

7.9732352 9.991876

7.9849534 9.8853695

8.1124261 9.3706331

8.1154425 10.9006981

8.1254249 8.0928389

8.2795063 10.5570386

8.2975804 9.1768362

8.3800894 9.2322431

9.1497729 10.4123977

1.14794639 13.979611

1.87296376 16.259677

1.95057589 14.253912

2.1774974 14.078445

2.2212576 15.180736

2.3319392 16.250346

2.3417249 14.922804

2.3666823 15.057726

2.4343045 14.844073

2.4637002 15.504545

2.5730949 16.120083

2.6960326 14.719672

2.7176948 14.681052

2.735971 16.281386

2.7907127 14.970722

2.8934128 16.577153

2.9067283 15.338185

2.9620927 16.091421

2.9967398 16.708606

3.0047431 15.371614

3.044215 14.689133

3.0872242 16.305325

3.0887985 15.697535

3.200185 16.663794

3.2008578 14.885879

3.2557222 14.889798

3.2892761 13.6007235

3.3028307 14.637392

3.3419285 14.224092

3.3422877 14.204327

3.3583757 16.048761

3.3893859 15.918406

3.4169347 16.292314

3.457675 15.097081

3.4578659 15.974339

3.4788892 16.152925

3.5046477 16.381791

3.5059035 16.737371

3.5621021 15.857647

3.6720782 13.4164271

3.6959001 16.995196

3.6992848 15.473127

3.7066215 14.623886

3.7295648 15.278963

3.7460885 15.579015

3.8699537 17.05209

3.8716518 17.413634

3.8873572 16.962738

3.9045898 16.715849

3.9456943 15.248866

3.9765246 16.883393

3.9819109 17.26836

3.998568 16.243342

4.0433096 17.347058

4.0596044 17.056215

4.0794422 15.758407

4.1129522 15.224293

4.1413942 15.929671

4.1627441 17.158977

4.1767186 15.184683

4.1781964 16.230266

4.2431648 16.962598

4.2618399 15.533754

4.3829695 15.589008

4.3900615 16.298833

4.4072292 15.452131

4.4630972 17.51363

4.4865115 15.166259

4.489808 15.302365

4.4985781 15.176454

4.4995603 16.339537

4.5015888 16.546623

4.5126907 17.181318

4.5186807 15.243923

4.5200494 16.606359

4.5546059 16.523475

4.5593513 17.20819

4.620113 17.041174

4.7561987 16.161672

4.7777356 15.774779

4.7993429 16.862268

4.8108036 17.172305

4.8138488 18.482412

4.9717907 16.813861

4.9946929 17.564215

5.0059337 16.629871

5.0114164 16.947345

5.0376315 15.086702

5.1360659 15.978871

5.1976377 17.45016

5.3760322 15.3188

5.4290531 16.540536

5.4497178 16.767889

5.4527803 16.620024

5.6388008 16.745066

5.6473698 16.450529

5.7566377 17.143698

5.7900453 16.382074

5.8758893 16.90501

5.9023884 16.818273

5.9788072 15.959914

6.0324193 16.252833

6.3479027 15.875618

10.4652107 2.6831135

10.5244255 1.1185327

10.676615 2.1365133

10.7060357 4.8800944

11.0507822 4.9306996

11.0926175 5.5469616

11.1620361 3.4362472

11.528659 4.9653684

11.68202 4.9323634

11.8113105 5.0447482

11.8395293 3.2846371

11.8904436 3.9756688

11.901902 4.0596121

12.00957 3.8387848

12.041757 3.2972722

12.070124 3.5975018

12.125698 5.2145027

12.13737 5.069625

12.179778 2.7118074

12.300714 2.811699

12.302589 4.6895294

12.310044 3.3904743

12.360948 4.048405

12.387776 2.650697

12.404948 2.3650841

12.415097 4.0142043

12.417047 3.4687826

12.442745 5.030264

12.443721 4.1409706

12.447941 5.8988869

12.476582 3.743859

12.515158 5.106076

12.595752 3.8626734

12.615602 3.2022261

12.61961 5.7629745

12.632347 3.3748335

12.657503 3.2747553

12.709758 5.0287697

12.747944 5.0152176

12.752371 4.7189407

12.818769 3.7802197

12.820888 3.1272452

12.830515 5.601528

12.863656 2.6533946

12.88482 5.2657701

12.904148 4.8965637

12.939187 3.1585675

12.952628 2.2741358

12.973613 4.8611944

12.97606 2.9509126

13.000251 4.8797171

13.004435 2.8729802

13.004874 4.9961698

13.008949 4.4582345

13.025455 4.3870708

13.045022 4.9916418

13.096295 2.973044

13.195278 2.9627547

13.21192 4.3851352

13.215603 4.153893

13.362235 2.9408317

13.487338 3.4554507

13.507061 4.9059559

13.544954 3.0748351

13.561125 3.3112401

13.575478 3.2823488

13.61449 4.9872933

13.618037 3.916222

13.63065 4.3280429

13.666668 4.0476106

13.693189 4.5426592

13.71092 2.5744855

13.717479 3.5385358

13.723603 4.8269313

13.740956 4.5362417

13.810342 3.3345241

13.884852 5.2966064

13.88918 2.4088233

13.908124 4.4692953

13.920669 5.1046996

14.066882 4.2443514

14.119596 3.8195427

14.134638 4.0501819

14.15121 5.0939166

14.174712 1.7723559

14.23597 5.8236027

14.270719 3.9357992

14.418402 5.0104769

14.423091 4.655516

14.58717 5.4807475

14.608216 5.5958854

14.621365 5.1009621

14.639837 5.7854218

14.675322 3.0883694

14.885806 4.5369381

15.123712 3.1371023

15.314308 4.2649289

10.1271918 15.296397

11.0410633 15.246086

11.0765704 14.281669

11.1579441 14.821112

11.2049293 14.132894

11.2509463 13.3686

11.2995974 15.315821

11.4488022 13.797879

11.4718758 15.400537

11.6037499 15.167981

11.6299854 12.916744

11.6385609 15.134394

11.722283 12.348765

11.8768788 12.795341

11.8805154 13.427278

11.9245665 14.171992

11.9419753 12.11674

11.9458388 13.572502

11.9963091 13.910162

11.9977637 12.264383

12.120108 14.298028

12.161642 12.235561

12.259619 14.538579

12.278865 13.907848

12.284851 14.78127

12.302126 13.551077

12.419082 13.560862

12.451229 14.87717

12.481022 14.631295

12.487289 13.299683

12.570844 13.073695

12.587373 14.506647

12.625944 13.499386

12.641761 13.157186

12.64511 12.942852

12.667898 15.982038

12.670485 13.955641

12.706356 13.361715

12.711061 14.236744

12.771428 12.766095

12.775607 16.052587

12.886805 13.023554

12.928196 11.2239141

12.950179 14.187592

12.966609 13.020396

12.984097 13.00589

12.997997 14.669185

13.089569 14.398868

13.108494 13.910334

13.147798 13.724342

13.155115 16.121416

13.158758 14.718643

13.213179 12.731326

13.222689 13.894866

13.237439 14.728269

13.247742 15.206662

13.256842 14.442514

13.269178 13.628728

13.350498 13.563816

13.469883 14.188571

13.4942 14.38967

13.52686 12.779428

13.553505 14.539572

13.559418 15.570712

13.567707 14.901948

13.634692 12.074948

13.634837 14.236684

13.641807 14.726728

13.677089 14.332538

13.767215 15.092209

13.781016 12.595779

13.878886 14.329098

13.891137 13.18777

13.895193 11.6599829

13.945273 13.156521

13.949261 12.944328

13.998138 13.186815

14.01272 14.270148

14.021108 15.495508

14.026602 13.861609

14.056164 13.728905

14.065794 13.845586

14.087697 15.523848

14.105136 13.638893

14.197604 14.022645

14.296025 15.509115

14.349088 14.002902

14.370353 16.051215

14.425818 12.55447

14.465642 12.745134

14.470883 13.280208

14.473897 13.425769

14.516394 13.222901

14.646671 13.889919

14.651572 15.064428

14.888393 12.709495

15.075641 14.576588

16.009856 14.548147


1 1.2079091 3.7210589

1 1.2231833 4.624442

1 1.2724274 3.7211989

1 1.3905861 2.8528257

1 1.5263206 3.1902709

1 1.5446515 2.9092366

1 2.0817641 2.2932327

1 2.1329588 2.7909656

1 2.2061908 2.5587698

1 2.2294541 3.7094674

1 2.2311605 2.1842931

1 2.3729459 2.6146591

1 2.451783 4.0942238

1 2.4631214 1.9776029

1 2.4641181 3.9918609

1 2.5092126 3.2028537

1 2.5249635 1.8857945

1 2.5408484 3.0938419

1 2.5538585 4.1353191

1 2.580998 3.1703637

1 2.6221661 4.0532502

1 2.6675926 3.5078748

1 2.6693178 3.0258803

1 2.6757058 1.5386359

1 2.6875167 4.3955603

1 2.8136569 4.2550441

1 2.8330733 4.7522282

1 2.8388063 4.8021392

1 2.8394463 4.1968265

1 2.8564329 1.7743203

1 2.8694154 2.2415617

1 2.8838473 3.8844967

1 2.9151302 2.5118873

1 2.9197918 4.408129

1 2.9546596 4.2040109

1 2.9707417 2.4312104

1 3.0135107 3.9468569

1 3.0456382 3.6968997

1 3.0662513 3.9026477

1 3.0731757 5.0607237

1 3.0940184 5.3846903

1 3.0998003 3.0196854

1 3.1062588 3.8665536

1 3.1113574 2.7466426

1 3.1401502 2.322823

1 3.164504 2.3772491

1 3.167721 4.491692

1 3.1792337 3.1921312

1 3.189034 3.9209093

1 3.1923355 5.0269859

1 3.2420277 2.6866669

1 3.2563394 2.6318602

1 3.2646653 3.3621656

1 3.277839 3.5903912

1 3.2840827 2.3948904

1 3.3134128 3.7465675

1 3.3432966 2.3024976

1 3.3588688 2.6395169

1 3.3773154 3.509691

1 3.3923682 3.7704754

1 3.3928396 3.9307354

1 3.4097375 3.0243664

1 3.4250905 4.2957807

1 3.4256467 5.1909088

1 3.429859 4.6389274

1 3.4383383 4.5945946

1 3.4499816 3.3406544

1 3.4648999 3.7633964

1 3.5101871 3.0922003

1 3.5814278 4.4753996

1 3.5883151 2.204994

1 3.6065497 3.7304895

1 3.6109218 4.4108913

1 3.6800216 3.6595682

1 3.6865731 2.8736266

1 3.7364618 3.1109649

1 3.7834804 4.4478511

1 3.806937 2.2154877

1 3.8505015 2.7495611

1 3.8894656 3.5845772

1 3.898071 4.4421334

1 3.9191638 4.1379257

1 3.9232595 2.3691164

1 3.9263071 2.3499483

1 3.9336875 3.5430657

1 3.9561075 2.014345

1 3.956787 2.2913721

1 3.9597375 3.9233632

1 3.9647258 3.5541938

1 4.0014664 1.5959308

1 4.0967613 3.3460918

1 4.1113619 4.956129

1 4.1393133 3.1290369

1 4.2427451 4.3293623

1 4.2447324 2.4055278

1 4.2808958 4.1849711

1 4.2958248 4.52221

1 4.3333881 4.8926755

1 4.3770667 4.148263

1 4.3872811 3.0834977

1 4.3943806 1.2047041

1 4.4231133 5.1428545

1 4.4242463 4.7437029

1 4.4737188 5.1960809

1 4.5434832 2.9540165

1 4.5542301 4.6514457

1 4.556613 3.6792214

1 4.5569343 4.3557038

1 4.5681878 2.7334969

1 4.6154429 4.9919154

1 4.6573287 3.6323181

1 4.7523585 4.6764836

1 4.7530329 1.6970322

1 4.8855847 3.1092405

1 4.9202943 3.6878621

1 5.0692918 4.4614043

1 5.094251 4.9986657

1 5.2213603 2.8859654

1 5.29025 4.1231551

1 5.3047409 4.0529775

1 5.3807576 2.8897184

1 5.4022501 4.2157411

1 5.5258683 4.6077935

1 5.5602902 4.0202276

1 5.5928493 5.9473488

1 5.6337921 2.0477399

2 4.5649633 10.8199028

2 4.9492476 11.2417831

2 4.9639843 10.1301841

2 5.226796 9.0896456

2 5.5160477 10.339618

2 5.5494966 11.3187034

2 5.7321392 9.3091876

2 5.78872 9.9671922

2 5.8074223 10.160413

2 5.8077218 11.2829668

2 5.8401064 10.4340778

2 5.8887826 11.182616

2 5.9385494 9.0826968

2 6.0173279 12.015934

2 6.1333175 11.0641202

2 6.1493846 7.5813131

2 6.3247616 10.5008158

2 6.3596552 10.4300021

2 6.4498757 10.4660964

2 6.5039886 11.1291981

2 6.5577834 8.1352935

2 6.6243969 9.1862597

2 6.6653361 9.8993512

2 6.6752921 9.2522435

2 6.6978867 9.9182312

2 6.7050083 12.974634

2 6.7473609 9.2485078

2 6.7493806 9.5129937

2 6.7887799 9.8588077

2 6.836351 9.2174683

2 6.8726451 10.0834742

2 6.8903508 8.7250099

2 6.8907481 10.7297899

2 6.8955632 9.2141097

2 6.9133122 12.337941

2 6.9817274 9.634554

2 6.9985553 8.0236224

2 7.0917411 9.5520639

2 7.1110772 10.5536931

2 7.138392 9.2415998

2 7.1624205 9.7417785

2 7.1675304 10.3424403

2 7.1959608 9.6760523

2 7.1960034 10.4806538

2 7.2013835 11.6980377

2 7.2141018 10.6837557

2 7.2348838 8.4397497

2 7.2422483 9.3493743

2 7.2430823 9.7644821

2 7.2613676 10.8671876

2 7.2946775 9.1593571

2 7.3350288 10.460047

2 7.335831 11.4561045

2 7.3401016 11.1398342

2 7.3739163 9.9216549

2 7.43575 9.3674077

2 7.4735116 9.3621969

2 7.5094318 10.7425193

2 7.5192765 9.0919875

2 7.5230336 9.3181116

2 7.5389153 10.3706625

2 7.569023 11.6957121

2 7.6814631 9.0434399

2 7.7144954 7.191835

2 7.7198893 11.0829336

2 7.7803768 9.8861349

2 7.9326271 10.1804494

2 7.9732352 9.991876

2 7.9849534 9.8853695

2 8.1124261 9.3706331

2 8.1154425 10.9006981

2 8.1254249 8.0928389

2 8.2795063 10.5570386

2 8.2975804 9.1768362

2 8.3800894 9.2322431

2 9.1497729 10.4123977

3 1.14794639 13.979611

3 1.87296376 16.259677

3 1.95057589 14.253912

3 2.1774974 14.078445

3 2.2212576 15.180736

3 2.3319392 16.250346

3 2.3417249 14.922804

3 2.3666823 15.057726

3 2.4343045 14.844073

3 2.4637002 15.504545

3 2.5730949 16.120083

3 2.6960326 14.719672

3 2.7176948 14.681052

3 2.735971 16.281386

3 2.7907127 14.970722

3 2.8934128 16.577153

3 2.9067283 15.338185

3 2.9620927 16.091421

3 2.9967398 16.708606

3 3.0047431 15.371614

3 3.044215 14.689133

3 3.0872242 16.305325

3 3.0887985 15.697535

3 3.200185 16.663794

3 3.2008578 14.885879

3 3.2557222 14.889798

3 3.2892761 13.6007235

3 3.3028307 14.637392

3 3.3419285 14.224092

3 3.3422877 14.204327

3 3.3583757 16.048761

3 3.3893859 15.918406

3 3.4169347 16.292314

3 3.457675 15.097081

3 3.4578659 15.974339

3 3.4788892 16.152925

3 3.5046477 16.381791

3 3.5059035 16.737371

3 3.5621021 15.857647

3 3.6720782 13.4164271

3 3.6959001 16.995196

3 3.6992848 15.473127

3 3.7066215 14.623886

3 3.7295648 15.278963

3 3.7460885 15.579015

3 3.8699537 17.05209

3 3.8716518 17.413634

3 3.8873572 16.962738

3 3.9045898 16.715849

3 3.9456943 15.248866

3 3.9765246 16.883393

3 3.9819109 17.26836

3 3.998568 16.243342

3 4.0433096 17.347058

3 4.0596044 17.056215

3 4.0794422 15.758407

3 4.1129522 15.224293

3 4.1413942 15.929671

3 4.1627441 17.158977

3 4.1767186 15.184683

3 4.1781964 16.230266

3 4.2431648 16.962598

3 4.2618399 15.533754

3 4.3829695 15.589008

3 4.3900615 16.298833

3 4.4072292 15.452131

3 4.4630972 17.51363

3 4.4865115 15.166259

3 4.489808 15.302365

3 4.4985781 15.176454

3 4.4995603 16.339537

3 4.5015888 16.546623

3 4.5126907 17.181318

3 4.5186807 15.243923

3 4.5200494 16.606359

3 4.5546059 16.523475

3 4.5593513 17.20819

3 4.620113 17.041174

3 4.7561987 16.161672

3 4.7777356 15.774779

3 4.7993429 16.862268

3 4.8108036 17.172305

3 4.8138488 18.482412

3 4.9717907 16.813861

3 4.9946929 17.564215

3 5.0059337 16.629871

3 5.0114164 16.947345

3 5.0376315 15.086702

3 5.1360659 15.978871

3 5.1976377 17.45016

3 5.3760322 15.3188

3 5.4290531 16.540536

3 5.4497178 16.767889

3 5.4527803 16.620024

3 5.6388008 16.745066

3 5.6473698 16.450529

3 5.7566377 17.143698

3 5.7900453 16.382074

3 5.8758893 16.90501

3 5.9023884 16.818273

3 5.9788072 15.959914

3 6.0324193 16.252833

3 6.3479027 15.875618

4 10.4652107 2.6831135

4 10.5244255 1.1185327

4 10.676615 2.1365133

4 10.7060357 4.8800944

4 11.0507822 4.9306996

4 11.0926175 5.5469616

4 11.1620361 3.4362472

4 11.528659 4.9653684

4 11.68202 4.9323634

4 11.8113105 5.0447482

4 11.8395293 3.2846371

4 11.8904436 3.9756688

4 11.901902 4.0596121

4 12.00957 3.8387848

4 12.041757 3.2972722

4 12.070124 3.5975018

4 12.125698 5.2145027

4 12.13737 5.069625

4 12.179778 2.7118074

4 12.300714 2.811699

4 12.302589 4.6895294

4 12.310044 3.3904743

4 12.360948 4.048405

4 12.387776 2.650697

4 12.404948 2.3650841

4 12.415097 4.0142043

4 12.417047 3.4687826

4 12.442745 5.030264

4 12.443721 4.1409706

4 12.447941 5.8988869

4 12.476582 3.743859

4 12.515158 5.106076

4 12.595752 3.8626734

4 12.615602 3.2022261

4 12.61961 5.7629745

4 12.632347 3.3748335

4 12.657503 3.2747553

4 12.709758 5.0287697

4 12.747944 5.0152176

4 12.752371 4.7189407

4 12.818769 3.7802197

4 12.820888 3.1272452

4 12.830515 5.601528

4 12.863656 2.6533946

4 12.88482 5.2657701

4 12.904148 4.8965637

4 12.939187 3.1585675

4 12.952628 2.2741358

4 12.973613 4.8611944

4 12.97606 2.9509126

4 13.000251 4.8797171

4 13.004435 2.8729802

4 13.004874 4.9961698

4 13.008949 4.4582345

4 13.025455 4.3870708

4 13.045022 4.9916418

4 13.096295 2.973044

4 13.195278 2.9627547

4 13.21192 4.3851352

4 13.215603 4.153893

4 13.362235 2.9408317

4 13.487338 3.4554507

4 13.507061 4.9059559

4 13.544954 3.0748351

4 13.561125 3.3112401

4 13.575478 3.2823488

4 13.61449 4.9872933

4 13.618037 3.916222

4 13.63065 4.3280429

4 13.666668 4.0476106

4 13.693189 4.5426592

4 13.71092 2.5744855

4 13.717479 3.5385358

4 13.723603 4.8269313

4 13.740956 4.5362417

4 13.810342 3.3345241

4 13.884852 5.2966064

4 13.88918 2.4088233

4 13.908124 4.4692953

4 13.920669 5.1046996

4 14.066882 4.2443514

4 14.119596 3.8195427

4 14.134638 4.0501819

4 14.15121 5.0939166

4 14.174712 1.7723559

4 14.23597 5.8236027

4 14.270719 3.9357992

4 14.418402 5.0104769

4 14.423091 4.655516

4 14.58717 5.4807475

4 14.608216 5.5958854

4 14.621365 5.1009621

4 14.639837 5.7854218

4 14.675322 3.0883694

4 14.885806 4.5369381

4 15.123712 3.1371023

4 15.314308 4.2649289

5 10.1271918 15.296397

5 11.0410633 15.246086

5 11.0765704 14.281669

5 11.1579441 14.821112

5 11.2049293 14.132894

5 11.2509463 13.3686

5 11.2995974 15.315821

5 11.4488022 13.797879

5 11.4718758 15.400537

5 11.6037499 15.167981

5 11.6299854 12.916744

5 11.6385609 15.134394

5 11.722283 12.348765

5 11.8768788 12.795341

5 11.8805154 13.427278

5 11.9245665 14.171992

5 11.9419753 12.11674

5 11.9458388 13.572502

5 11.9963091 13.910162

5 11.9977637 12.264383

5 12.120108 14.298028

5 12.161642 12.235561

5 12.259619 14.538579

5 12.278865 13.907848

5 12.284851 14.78127

5 12.302126 13.551077

5 12.419082 13.560862

5 12.451229 14.87717

5 12.481022 14.631295

5 12.487289 13.299683

5 12.570844 13.073695

5 12.587373 14.506647

5 12.625944 13.499386

5 12.641761 13.157186

5 12.64511 12.942852

5 12.667898 15.982038

5 12.670485 13.955641

5 12.706356 13.361715

5 12.711061 14.236744

5 12.771428 12.766095

5 12.775607 16.052587

5 12.886805 13.023554

5 12.928196 11.2239141

5 12.950179 14.187592

5 12.966609 13.020396

5 12.984097 13.00589

5 12.997997 14.669185

5 13.089569 14.398868

5 13.108494 13.910334

5 13.147798 13.724342

5 13.155115 16.121416

5 13.158758 14.718643

5 13.213179 12.731326

5 13.222689 13.894866

5 13.237439 14.728269

5 13.247742 15.206662

5 13.256842 14.442514

5 13.269178 13.628728

5 13.350498 13.563816

5 13.469883 14.188571

5 13.4942 14.38967

5 13.52686 12.779428

5 13.553505 14.539572

5 13.559418 15.570712

5 13.567707 14.901948

5 13.634692 12.074948

5 13.634837 14.236684

5 13.641807 14.726728

5 13.677089 14.332538

5 13.767215 15.092209

5 13.781016 12.595779

5 13.878886 14.329098

5 13.891137 13.18777

5 13.895193 11.6599829

5 13.945273 13.156521

5 13.949261 12.944328

5 13.998138 13.186815

5 14.01272 14.270148

5 14.021108 15.495508

5 14.026602 13.861609

5 14.056164 13.728905

5 14.065794 13.845586

5 14.087697 15.523848

5 14.105136 13.638893

5 14.197604 14.022645

5 14.296025 15.509115

5 14.349088 14.002902

5 14.370353 16.051215

5 14.425818 12.55447

5 14.465642 12.745134

5 14.470883 13.280208

5 14.473897 13.425769

5 14.516394 13.222901

5 14.646671 13.889919

5 14.651572 15.064428

5 14.888393 12.709495

5 15.075641 14.576588

5 16.009856 14.548147






1.662073


gauss Data

#

1.2079091 5.7210589

1.2231833 6.624442

1.2724274 5.7211989

1.3905861 4.8528257

1.5263206 5.1902709

1.5446515 4.9092366

2.0817641 4.2932327

2.1329588 4.7909656

2.2061908 4.5587698

2.2294541 5.7094674

2.2311605 4.1842931

2.3729459 4.6146591

2.451783 6.0942238

2.4631214 3.9776029

2.4641181 5.9918609

2.5092126 5.2028537

2.5249635 3.8857945

2.5408484 5.0938419

2.5538585 6.1353191

2.580998 5.1703637

2.6221661 6.0532502

2.6675926 5.5078748

2.6693178 5.0258803

2.6757058 3.5386359

2.6875167 6.3955603

2.8136569 6.2550441

2.8330733 6.7522282

2.8388063 6.8021392

2.8394463 6.1968265

2.8564329 3.7743203

2.8694154 4.2415617

2.8838473 5.8844967

2.9151302 4.5118873

2.9197918 6.408129

2.9546596 6.2040109

2.9707417 4.4312104

3.0135107 5.9468569

3.0456382 5.6968997

3.0662513 5.9026477

3.0731757 7.0607237

3.0940184 7.3846903

3.0998003 5.0196854

3.1062588 5.8665536

3.1113574 4.7466426

3.1401502 4.322823

3.164504 4.3772491

3.167721 6.491692

3.1792337 5.1921312

3.189034 5.9209093

3.1923355 7.0269859

3.2420277 4.6866669

3.2563394 4.6318602

3.2646653 5.3621656

3.277839 5.5903912

3.2840827 4.3948904

3.3134128 5.7465675

3.3432966 4.3024976

3.3588688 4.6395169

3.3773154 5.509691

3.3923682 5.7704754

3.3928396 5.9307354

3.4097375 5.0243664

3.4250905 6.2957807

3.4256467 7.1909088

3.429859 6.6389274

3.4383383 6.5945946

3.4499816 5.3406544

3.4648999 5.7633964

3.5101871 5.0922003

3.5814278 6.4753996

3.5883151 4.204994

3.6065497 5.7304895

3.6109218 6.4108913

3.6800216 5.6595682

3.6865731 4.8736266

3.7364618 5.1109649

3.7834804 6.4478511

3.806937 4.2154877

3.8505015 4.7495611

3.8894656 5.5845772

3.898071 6.4421334

3.9191638 6.1379257

3.9232595 4.3691164

3.9263071 4.3499483

3.9336875 5.5430657

3.9561075 4.014345

3.956787 4.2913721

3.9597375 5.9233632

3.9647258 5.5541938

4.0014664 3.5959308

4.0967613 5.3460918

4.1113619 6.956129

4.1393133 5.1290369

4.2427451 6.3293623

4.2447324 4.4055278

4.2808958 6.1849711

4.2958248 6.52221

4.3333881 6.8926755

4.3770667 6.148263

4.3872811 5.0834977

4.3943806 3.2047041

4.4231133 7.1428545

4.4242463 6.7437029

4.4737188 7.1960809

4.5434832 4.9540165

4.5542301 6.6514457

4.556613 5.6792214

4.5569343 6.3557038

4.5681878 4.7334969

4.6154429 6.9919154

4.6573287 5.6323181

4.7523585 6.6764836

4.7530329 3.6970322

4.8855847 5.1092405

4.9202943 5.6878621

5.0692918 6.4614043

5.094251 6.9986657

5.2213603 4.8859654

5.29025 6.1231551

5.3047409 6.0529775

5.3807576 4.8897184

5.4022501 6.2157411

5.5258683 6.6077935

5.5602902 6.0202276

5.5928493 7.9473488

5.6337921 4.0477399

9.5649633 15.8199028

9.9492476 16.2417831

9.9639843 15.1301841

10.226796 14.0896456

10.5160477 15.339618

10.5494966 16.3187034

10.7321392 14.3091876

10.78872 14.9671922

10.8074223 15.160413

10.8077218 16.2829668

10.8401064 15.4340778

10.8887826 16.182616

10.9385494 14.0826968

11.0173279 17.015934

11.1333175 16.0641202

11.1493846 12.5813131

11.3247616 15.5008158

11.3596552 15.4300021

11.4498757 15.4660964

11.5039886 16.1291981

11.5577834 13.1352935

11.6243969 14.1862597

11.6653361 14.8993512

11.6752921 14.2522435

11.6978867 14.9182312

11.7050083 17.974634

11.7473609 14.2485078

11.7493806 14.5129937

11.7887799 14.8588077

11.836351 14.2174683

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4 12.310044 3.3904743

4 12.360948 4.048405

4 12.387776 2.650697

4 12.404948 2.3650841

4 12.415097 4.0142043

4 12.417047 3.4687826

4 12.442745 5.030264

4 12.443721 4.1409706

4 12.447941 5.8988869

4 12.476582 3.743859

4 12.515158 5.106076

4 12.595752 3.8626734

4 12.615602 3.2022261

4 12.61961 5.7629745

4 12.632347 3.3748335

4 12.657503 3.2747553

4 12.709758 5.0287697

4 12.747944 5.0152176

4 12.752371 4.7189407

4 12.818769 3.7802197

4 12.820888 3.1272452

4 12.830515 5.601528

4 12.863656 2.6533946

4 12.88482 5.2657701

4 12.904148 4.8965637

4 12.939187 3.1585675

4 12.952628 2.2741358

4 12.973613 4.8611944

4 12.97606 2.9509126

4 13.000251 4.8797171

4 13.004435 2.8729802

4 13.004874 4.9961698

4 13.008949 4.4582345

4 13.025455 4.3870708

4 13.045022 4.9916418

4 13.096295 2.973044

4 13.195278 2.9627547

4 13.21192 4.3851352

4 13.215603 4.153893

4 13.362235 2.9408317

4 13.487338 3.4554507

4 13.507061 4.9059559

4 13.544954 3.0748351

4 13.561125 3.3112401

4 13.575478 3.2823488

4 13.61449 4.9872933

4 13.618037 3.916222

4 13.63065 4.3280429

4 13.666668 4.0476106

4 13.693189 4.5426592

4 13.71092 2.5744855

4 13.717479 3.5385358

4 13.723603 4.8269313

4 13.740956 4.5362417

4 13.810342 3.3345241

4 13.884852 5.2966064

4 13.88918 2.4088233

4 13.908124 4.4692953

4 13.920669 5.1046996

4 14.066882 4.2443514

4 14.119596 3.8195427

4 14.134638 4.0501819

4 14.15121 5.0939166

4 14.174712 1.7723559

4 14.23597 5.8236027

4 14.270719 3.9357992

4 14.418402 5.0104769

4 14.423091 4.655516

4 14.58717 5.4807475

4 14.608216 5.5958854

4 14.621365 5.1009621

4 14.639837 5.7854218

4 14.675322 3.0883694

4 14.885806 4.5369381

4 15.123712 3.1371023

4 15.314308 4.2649289

5 15.1271918 15.296397

5 16.0410633 15.246086

5 16.0765704 14.281669

5 16.1579441 14.821112

5 16.2049293 14.132894

5 16.2509463 13.3686

5 16.2995974 15.315821

5 16.4488022 13.797879

5 16.4718758 15.400537

5 16.6037499 15.167981

5 16.6299854 12.916744

5 16.6385609 15.134394

5 16.722283 12.348765

5 16.8768788 12.795341

5 16.8805154 13.427278

5 16.9245665 14.171992

5 16.9419753 12.11674

5 16.9458388 13.572502

5 16.9963091 13.910162

5 16.9977637 12.264383

5 17.120108 14.298028

5 17.161642 12.235561

5 17.259619 14.538579

5 17.278865 13.907848

5 17.284851 14.78127

5 17.302126 13.551077

5 17.419082 13.560862

5 17.451229 14.87717

5 17.481022 14.631295

5 17.487289 13.299683

5 17.570844 13.073695

5 17.587373 14.506647

5 17.625944 13.499386

5 17.641761 13.157186

5 17.64511 12.942852

5 17.667898 15.982038

5 17.670485 13.955641

5 17.706356 13.361715

5 17.711061 14.236744

5 17.771428 12.766095

5 17.775607 16.052587

5 17.886805 13.023554

5 17.928196 11.2239141

5 17.950179 14.187592

5 17.966609 13.020396

5 17.984097 13.00589

5 17.997997 14.669185

5 18.089569 14.398868

5 18.108494 13.910334

5 18.147798 13.724342

5 18.155115 16.121416

5 18.158758 14.718643

5 18.213179 12.731326

5 18.222689 13.894866

5 18.237439 14.728269

5 18.247742 15.206662

5 18.256842 14.442514

5 18.269178 13.628728

5 18.350498 13.563816

5 18.469883 14.188571

5 18.4942 14.38967

5 18.52686 12.779428

5 18.553505 14.539572

5 18.559418 15.570712

5 18.567707 14.901948

5 18.634692 12.074948

5 18.634837 14.236684

5 18.641807 14.726728

5 18.677089 14.332538

5 18.767215 15.092209

5 18.781016 12.595779

5 18.878886 14.329098

5 18.891137 13.18777

5 18.895193 11.6599829

5 18.945273 13.156521

5 18.949261 12.944328

5 18.998138 13.186815

5 19.01272 14.270148

5 19.021108 15.495508

5 19.026602 13.861609

5 19.056164 13.728905

5 19.065794 13.845586

5 19.087697 15.523848

5 19.105136 13.638893

5 19.197604 14.022645

5 19.296025 15.509115

5 19.349088 14.002902

5 19.370353 16.051215

5 19.425818 12.55447

5 19.465642 12.745134

5 19.470883 13.280208

5 19.473897 13.425769

5 19.516394 13.222901

5 19.646671 13.889919

5 19.651572 15.064428

5 19.888393 12.709495

5 20.075641 14.576588

5 21.009856 14.548147






1.490739


gauss Data

#

1.2079091 5.7210589

1.2231833 6.624442

1.2724274 5.7211989

1.3905861 4.8528257

1.5263206 5.1902709

1.5446515 4.9092366

2.0817641 4.2932327

2.1329588 4.7909656

2.2061908 4.5587698

2.2294541 5.7094674

2.2311605 4.1842931

2.3729459 4.6146591

2.451783 6.0942238

2.4631214 3.9776029

2.4641181 5.9918609

2.5092126 5.2028537

2.5249635 3.8857945

2.5408484 5.0938419

2.5538585 6.1353191

2.580998 5.1703637

2.6221661 6.0532502

2.6675926 5.5078748

2.6693178 5.0258803

2.6757058 3.5386359

2.6875167 6.3955603

2.8136569 6.2550441

2.8330733 6.7522282

2.8388063 6.8021392

2.8394463 6.1968265

2.8564329 3.7743203

2.8694154 4.2415617

2.8838473 5.8844967

2.9151302 4.5118873

2.9197918 6.408129

2.9546596 6.2040109

2.9707417 4.4312104

3.0135107 5.9468569

3.0456382 5.6968997

3.0662513 5.9026477

3.0731757 7.0607237

3.0940184 7.3846903

3.0998003 5.0196854

3.1062588 5.8665536

3.1113574 4.7466426

3.1401502 4.322823

3.164504 4.3772491

3.167721 6.491692

3.1792337 5.1921312

3.189034 5.9209093

3.1923355 7.0269859

3.2420277 4.6866669

3.2563394 4.6318602

3.2646653 5.3621656

3.277839 5.5903912

3.2840827 4.3948904

3.3134128 5.7465675

3.3432966 4.3024976

3.3588688 4.6395169

3.3773154 5.509691

3.3923682 5.7704754

3.3928396 5.9307354

3.4097375 5.0243664

3.4250905 6.2957807

3.4256467 7.1909088

3.429859 6.6389274

3.4383383 6.5945946

3.4499816 5.3406544

3.4648999 5.7633964

3.5101871 5.0922003

3.5814278 6.4753996

3.5883151 4.204994

3.6065497 5.7304895

3.6109218 6.4108913

3.6800216 5.6595682

3.6865731 4.8736266

3.7364618 5.1109649

3.7834804 6.4478511

3.806937 4.2154877

3.8505015 4.7495611

3.8894656 5.5845772

3.898071 6.4421334

3.9191638 6.1379257

3.9232595 4.3691164

3.9263071 4.3499483

3.9336875 5.5430657

3.9561075 4.014345

3.956787 4.2913721

3.9597375 5.9233632

3.9647258 5.5541938

4.0014664 3.5959308

4.0967613 5.3460918

4.1113619 6.956129

4.1393133 5.1290369

4.2427451 6.3293623

4.2447324 4.4055278

4.2808958 6.1849711

4.2958248 6.52221

4.3333881 6.8926755

4.3770667 6.148263

4.3872811 5.0834977

4.3943806 3.2047041

4.4231133 7.1428545

4.4242463 6.7437029

4.4737188 7.1960809

4.5434832 4.9540165

4.5542301 6.6514457

4.556613 5.6792214

4.5569343 6.3557038

4.5681878 4.7334969

4.6154429 6.9919154

4.6573287 5.6323181

4.7523585 6.6764836

4.7530329 3.6970322

4.8855847 5.1092405

4.9202943 5.6878621

5.0692918 6.4614043

5.094251 6.9986657

5.2213603 4.8859654

5.29025 6.1231551

5.3047409 6.0529775

5.3807576 4.8897184

5.4022501 6.2157411

5.5258683 6.6077935

5.5602902 6.0202276

5.5928493 7.9473488

5.6337921 4.0477399

9.5649633 15.8199028

9.9492476 16.2417831

9.9639843 15.1301841

10.226796 14.0896456

10.5160477 15.339618

10.5494966 16.3187034

10.7321392 14.3091876

10.78872 14.9671922

10.8074223 15.160413

10.8077218 16.2829668

10.8401064 15.4340778

10.8887826 16.182616

10.9385494 14.0826968

11.0173279 17.015934

11.1333175 16.0641202

11.1493846 12.5813131

11.3247616 15.5008158

11.3596552 15.4300021

11.4498757 15.4660964

11.5039886 16.1291981

11.5577834 13.1352935

11.6243969 14.1862597

11.6653361 14.8993512

11.6752921 14.2522435

11.6978867 14.9182312

11.7050083 17.974634

11.7473609 14.2485078

11.7493806 14.5129937

11.7887799 14.8588077

11.836351 14.2174683

11.8726451 15.0834742

11.8903508 13.7250099

11.8907481 15.7297899

11.8955632 14.2141097

11.9133122 17.337941

11.9817274 14.634554

11.9985553 13.0236224

12.0917411 14.5520639

12.1110772 15.5536931

12.138392 14.2415998

12.1624205 14.7417785

12.1675304 15.3424403

12.1959608 14.6760523

12.1960034 15.4806538

12.2013835 16.6980377

12.2141018 15.6837557

12.2348838 13.4397497

12.2422483 14.3493743

12.2430823 14.7644821

12.2613676 15.8671876

12.2946775 14.1593571

12.3350288 15.460047

12.335831 16.4561045

12.3401016 16.1398342

12.3739163 14.9216549

12.43575 14.3674077

12.4735116 14.3621969

12.5094318 15.7425193

12.5192765 14.0919875

12.5230336 14.3181116

12.5389153 15.3706625

12.569023 16.6957121

12.6814631 14.0434399

12.7144954 12.191835

12.7198893 16.0829336

12.7803768 14.8861349

12.9326271 15.1804494

12.9732352 14.991876

12.9849534 14.8853695

13.1124261 14.3706331

13.1154425 15.9006981

13.1254249 13.0928389

13.2795063 15.5570386

13.2975804 14.1768362

13.3800894 14.2322431

14.1497729 15.4123977

1.14794639 18.979611

1.87296376 21.259677

1.95057589 19.253912

2.1774974 19.078445

2.2212576 20.180736

2.3319392 21.250346

2.3417249 19.922804

2.3666823 20.057726

2.4343045 19.844073

2.4637002 20.504545

2.5730949 21.120083

2.6960326 19.719672

2.7176948 19.681052

2.735971 21.281386

2.7907127 19.970722

2.8934128 21.577153

2.9067283 20.338185

2.9620927 21.091421

2.9967398 21.708606

3.0047431 20.371614

3.044215 19.689133

3.0872242 21.305325

3.0887985 20.697535

3.200185 21.663794

3.2008578 19.885879

3.2557222 19.889798

3.2892761 18.6007235

3.3028307 19.637392

3.3419285 19.224092

3.3422877 19.204327

3.3583757 21.048761

3.3893859 20.918406

3.4169347 21.292314

3.457675 20.097081

3.4578659 20.974339

3.4788892 21.152925

3.5046477 21.381791

3.5059035 21.737371

3.5621021 20.857647

3.6720782 18.4164271

3.6959001 21.995196

3.6992848 20.473127

3.7066215 19.623886

3.7295648 20.278963

3.7460885 20.579015

3.8699537 22.05209

3.8716518 22.413634

3.8873572 21.962738

3.9045898 21.715849

3.9456943 20.248866

3.9765246 21.883393

3.9819109 22.26836

3.998568 21.243342

4.0433096 22.347058

4.0596044 22.056215

4.0794422 20.758407

4.1129522 20.224293

4.1413942 20.929671

4.1627441 22.158977

4.1767186 20.184683

4.1781964 21.230266

4.2431648 21.962598

4.2618399 20.533754

4.3829695 20.589008

4.3900615 21.298833

4.4072292 20.452131

4.4630972 22.51363

4.4865115 20.166259

4.489808 20.302365

4.4985781 20.176454

4.4995603 21.339537

4.5015888 21.546623

4.5126907 22.181318

4.5186807 20.243923

4.5200494 21.606359

4.5546059 21.523475

4.5593513 22.20819

4.620113 22.041174

4.7561987 21.161672

4.7777356 20.774779

4.7993429 21.862268

4.8108036 22.172305

4.8138488 23.482412

4.9717907 21.813861

4.9946929 22.564215

5.0059337 21.629871

5.0114164 21.947345

5.0376315 20.086702

5.1360659 20.978871

5.1976377 22.45016

5.3760322 20.3188

5.4290531 21.540536

5.4497178 21.767889

5.4527803 21.620024

5.6388008 21.745066

5.6473698 21.450529

5.7566377 22.143698

5.7900453 21.382074

5.8758893 21.90501

5.9023884 21.818273

5.9788072 20.959914

6.0324193 21.252833

6.3479027 20.875618

10.4652107 2.6831135

10.5244255 1.1185327

10.676615 2.1365133

10.7060357 4.8800944

11.0507822 4.9306996

11.0926175 5.5469616

11.1620361 3.4362472

11.528659 4.9653684

11.68202 4.9323634

11.8113105 5.0447482

11.8395293 3.2846371

11.8904436 3.9756688

11.901902 4.0596121

12.00957 3.8387848

12.041757 3.2972722

12.070124 3.5975018

12.125698 5.2145027

12.13737 5.069625

12.179778 2.7118074

12.300714 2.811699

12.302589 4.6895294

12.310044 3.3904743

12.360948 4.048405

12.387776 2.650697

12.404948 2.3650841

12.415097 4.0142043

12.417047 3.4687826

12.442745 5.030264

12.443721 4.1409706

12.447941 5.8988869

12.476582 3.743859

12.515158 5.106076

12.595752 3.8626734

12.615602 3.2022261

12.61961 5.7629745

12.632347 3.3748335

12.657503 3.2747553

12.709758 5.0287697

12.747944 5.0152176

12.752371 4.7189407

12.818769 3.7802197

12.820888 3.1272452

12.830515 5.601528

12.863656 2.6533946

12.88482 5.2657701

12.904148 4.8965637

12.939187 3.1585675

12.952628 2.2741358

12.973613 4.8611944

12.97606 2.9509126

13.000251 4.8797171

13.004435 2.8729802

13.004874 4.9961698

13.008949 4.4582345

13.025455 4.3870708

13.045022 4.9916418

13.096295 2.973044

13.195278 2.9627547

13.21192 4.3851352

13.215603 4.153893

13.362235 2.9408317

13.487338 3.4554507

13.507061 4.9059559

13.544954 3.0748351

13.561125 3.3112401

13.575478 3.2823488

13.61449 4.9872933

13.618037 3.916222

13.63065 4.3280429

13.666668 4.0476106

13.693189 4.5426592

13.71092 2.5744855

13.717479 3.5385358

13.723603 4.8269313

13.740956 4.5362417

13.810342 3.3345241

13.884852 5.2966064

13.88918 2.4088233

13.908124 4.4692953

13.920669 5.1046996

14.066882 4.2443514

14.119596 3.8195427

14.134638 4.0501819

14.15121 5.0939166

14.174712 1.7723559

14.23597 5.8236027

14.270719 3.9357992

14.418402 5.0104769

14.423091 4.655516

14.58717 5.4807475

14.608216 5.5958854

14.621365 5.1009621

14.639837 5.7854218

14.675322 3.0883694

14.885806 4.5369381

15.123712 3.1371023

15.314308 4.2649289

20.1271918 15.296397

21.0410633 15.246086

21.0765704 14.281669

21.1579441 14.821112

21.2049293 14.132894

21.2509463 13.3686

21.2995974 15.315821

21.4488022 13.797879

21.4718758 15.400537

21.6037499 15.167981

21.6299854 12.916744

21.6385609 15.134394

21.722283 12.348765

21.8768788 12.795341

21.8805154 13.427278

21.9245665 14.171992

21.9419753 12.11674

21.9458388 13.572502

21.9963091 13.910162

21.9977637 12.264383

22.120108 14.298028

22.161642 12.235561

22.259619 14.538579

22.278865 13.907848

22.284851 14.78127

22.302126 13.551077

22.419082 13.560862

22.451229 14.87717

22.481022 14.631295

22.487289 13.299683

22.570844 13.073695

22.587373 14.506647

22.625944 13.499386

22.641761 13.157186

22.64511 12.942852

22.667898 15.982038

22.670485 13.955641

22.706356 13.361715

22.711061 14.236744

22.771428 12.766095

22.775607 16.052587

22.886805 13.023554

22.928196 11.2239141

22.950179 14.187592

22.966609 13.020396

22.984097 13.00589

22.997997 14.669185

23.089569 14.398868

23.108494 13.910334

23.147798 13.724342

23.155115 16.121416

23.158758 14.718643

23.213179 12.731326

23.222689 13.894866

23.237439 14.728269

23.247742 15.206662

23.256842 14.442514

23.269178 13.628728

23.350498 13.563816

23.469883 14.188571

23.4942 14.38967

23.52686 12.779428

23.553505 14.539572

23.559418 15.570712

23.567707 14.901948

23.634692 12.074948

23.634837 14.236684

23.641807 14.726728

23.677089 14.332538

23.767215 15.092209

23.781016 12.595779

23.878886 14.329098

23.891137 13.18777

23.895193 11.6599829

23.945273 13.156521

23.949261 12.944328

23.998138 13.186815

24.01272 14.270148

24.021108 15.495508

24.026602 13.861609

24.056164 13.728905

24.065794 13.845586

24.087697 15.523848

24.105136 13.638893

24.197604 14.022645

24.296025 15.509115

24.349088 14.002902

24.370353 16.051215

24.425818 12.55447

24.465642 12.745134

24.470883 13.280208

24.473897 13.425769

24.516394 13.222901

24.646671 13.889919

24.651572 15.064428

24.888393 12.709495

25.075641 14.576588

26.009856 14.548147


1 1.2079091 5.7210589

1 1.2231833 6.624442

1 1.2724274 5.7211989

1 1.3905861 4.8528257

1 1.5263206 5.1902709

1 1.5446515 4.9092366

1 2.0817641 4.2932327

1 2.1329588 4.7909656

1 2.2061908 4.5587698

1 2.2294541 5.7094674

1 2.2311605 4.1842931

1 2.3729459 4.6146591

1 2.451783 6.0942238

1 2.4631214 3.9776029

1 2.4641181 5.9918609

1 2.5092126 5.2028537

1 2.5249635 3.8857945

1 2.5408484 5.0938419

1 2.5538585 6.1353191

1 2.580998 5.1703637

1 2.6221661 6.0532502

1 2.6675926 5.5078748

1 2.6693178 5.0258803

1 2.6757058 3.5386359

1 2.6875167 6.3955603

1 2.8136569 6.2550441

1 2.8330733 6.7522282

1 2.8388063 6.8021392

1 2.8394463 6.1968265

1 2.8564329 3.7743203

1 2.8694154 4.2415617

1 2.8838473 5.8844967

1 2.9151302 4.5118873

1 2.9197918 6.408129

1 2.9546596 6.2040109

1 2.9707417 4.4312104

1 3.0135107 5.9468569

1 3.0456382 5.6968997

1 3.0662513 5.9026477

1 3.0731757 7.0607237

1 3.0940184 7.3846903

1 3.0998003 5.0196854

1 3.1062588 5.8665536

1 3.1113574 4.7466426

1 3.1401502 4.322823

1 3.164504 4.3772491

1 3.167721 6.491692

1 3.1792337 5.1921312

1 3.189034 5.9209093

1 3.1923355 7.0269859

1 3.2420277 4.6866669

1 3.2563394 4.6318602

1 3.2646653 5.3621656

1 3.277839 5.5903912

1 3.2840827 4.3948904

1 3.3134128 5.7465675

1 3.3432966 4.3024976

1 3.3588688 4.6395169

1 3.3773154 5.509691

1 3.3923682 5.7704754

1 3.3928396 5.9307354

1 3.4097375 5.0243664

1 3.4250905 6.2957807

1 3.4256467 7.1909088

1 3.429859 6.6389274

1 3.4383383 6.5945946

1 3.4499816 5.3406544

1 3.4648999 5.7633964

1 3.5101871 5.0922003

1 3.5814278 6.4753996

1 3.5883151 4.204994

1 3.6065497 5.7304895

1 3.6109218 6.4108913

1 3.6800216 5.6595682

1 3.6865731 4.8736266

1 3.7364618 5.1109649

1 3.7834804 6.4478511

1 3.806937 4.2154877

1 3.8505015 4.7495611

1 3.8894656 5.5845772

1 3.898071 6.4421334

1 3.9191638 6.1379257

1 3.9232595 4.3691164

1 3.9263071 4.3499483

1 3.9336875 5.5430657

1 3.9561075 4.014345

1 3.956787 4.2913721

1 3.9597375 5.9233632

1 3.9647258 5.5541938

1 4.0014664 3.5959308

1 4.0967613 5.3460918

1 4.1113619 6.956129

1 4.1393133 5.1290369

1 4.2427451 6.3293623

1 4.2447324 4.4055278

1 4.2808958 6.1849711

1 4.2958248 6.52221

1 4.3333881 6.8926755

1 4.3770667 6.148263

1 4.3872811 5.0834977

1 4.3943806 3.2047041

1 4.4231133 7.1428545

1 4.4242463 6.7437029

1 4.4737188 7.1960809

1 4.5434832 4.9540165

1 4.5542301 6.6514457

1 4.556613 5.6792214

1 4.5569343 6.3557038

1 4.5681878 4.7334969

1 4.6154429 6.9919154

1 4.6573287 5.6323181

1 4.7523585 6.6764836

1 4.7530329 3.6970322

1 4.8855847 5.1092405

1 4.9202943 5.6878621

1 5.0692918 6.4614043

1 5.094251 6.9986657

1 5.2213603 4.8859654

1 5.29025 6.1231551

1 5.3047409 6.0529775

1 5.3807576 4.8897184

1 5.4022501 6.2157411

1 5.5258683 6.6077935

1 5.5602902 6.0202276

1 5.5928493 7.9473488

1 5.6337921 4.0477399

2 9.5649633 15.8199028

2 9.9492476 16.2417831

2 9.9639843 15.1301841

2 10.226796 14.0896456

2 10.5160477 15.339618

2 10.5494966 16.3187034

2 10.7321392 14.3091876

2 10.78872 14.9671922

2 10.8074223 15.160413

2 10.8077218 16.2829668

2 10.8401064 15.4340778

2 10.8887826 16.182616

2 10.9385494 14.0826968

2 11.0173279 17.015934

2 11.1333175 16.0641202

2 11.1493846 12.5813131

2 11.3247616 15.5008158

2 11.3596552 15.4300021

2 11.4498757 15.4660964

2 11.5039886 16.1291981

2 11.5577834 13.1352935

2 11.6243969 14.1862597

2 11.6653361 14.8993512

2 11.6752921 14.2522435

2 11.6978867 14.9182312

2 11.7050083 17.974634

2 11.7473609 14.2485078

2 11.7493806 14.5129937

2 11.7887799 14.8588077

2 11.836351 14.2174683

2 11.8726451 15.0834742

2 11.8903508 13.7250099

2 11.8907481 15.7297899

2 11.8955632 14.2141097

2 11.9133122 17.337941

2 11.9817274 14.634554

2 11.9985553 13.0236224

2 12.0917411 14.5520639

2 12.1110772 15.5536931

2 12.138392 14.2415998

2 12.1624205 14.7417785

2 12.1675304 15.3424403

2 12.1959608 14.6760523

2 12.1960034 15.4806538

2 12.2013835 16.6980377

2 12.2141018 15.6837557

2 12.2348838 13.4397497

2 12.2422483 14.3493743

2 12.2430823 14.7644821

2 12.2613676 15.8671876

2 12.2946775 14.1593571

2 12.3350288 15.460047

2 12.335831 16.4561045

2 12.3401016 16.1398342

2 12.3739163 14.9216549

2 12.43575 14.3674077

2 12.4735116 14.3621969

2 12.5094318 15.7425193

2 12.5192765 14.0919875

2 12.5230336 14.3181116

2 12.5389153 15.3706625

2 12.569023 16.6957121

2 12.6814631 14.0434399

2 12.7144954 12.191835

2 12.7198893 16.0829336

2 12.7803768 14.8861349

2 12.9326271 15.1804494

2 12.9732352 14.991876

2 12.9849534 14.8853695

2 13.1124261 14.3706331

2 13.1154425 15.9006981

2 13.1254249 13.0928389

2 13.2795063 15.5570386

2 13.2975804 14.1768362

2 13.3800894 14.2322431

2 14.1497729 15.4123977

3 1.14794639 18.979611

3 1.87296376 21.259677

3 1.95057589 19.253912

3 2.1774974 19.078445

3 2.2212576 20.180736

3 2.3319392 21.250346

3 2.3417249 19.922804

3 2.3666823 20.057726

3 2.4343045 19.844073

3 2.4637002 20.504545

3 2.5730949 21.120083

3 2.6960326 19.719672

3 2.7176948 19.681052

3 2.735971 21.281386

3 2.7907127 19.970722

3 2.8934128 21.577153

3 2.9067283 20.338185

3 2.9620927 21.091421

3 2.9967398 21.708606

3 3.0047431 20.371614

3 3.044215 19.689133

3 3.0872242 21.305325

3 3.0887985 20.697535

3 3.200185 21.663794

3 3.2008578 19.885879

3 3.2557222 19.889798

3 3.2892761 18.6007235

3 3.3028307 19.637392

3 3.3419285 19.224092

3 3.3422877 19.204327

3 3.3583757 21.048761

3 3.3893859 20.918406

3 3.4169347 21.292314

3 3.457675 20.097081

3 3.4578659 20.974339

3 3.4788892 21.152925

3 3.5046477 21.381791

3 3.5059035 21.737371

3 3.5621021 20.857647

3 3.6720782 18.4164271

3 3.6959001 21.995196

3 3.6992848 20.473127

3 3.7066215 19.623886

3 3.7295648 20.278963

3 3.7460885 20.579015

3 3.8699537 22.05209

3 3.8716518 22.413634

3 3.8873572 21.962738

3 3.9045898 21.715849

3 3.9456943 20.248866

3 3.9765246 21.883393

3 3.9819109 22.26836

3 3.998568 21.243342

3 4.0433096 22.347058

3 4.0596044 22.056215

3 4.0794422 20.758407

3 4.1129522 20.224293

3 4.1413942 20.929671

3 4.1627441 22.158977

3 4.1767186 20.184683

3 4.1781964 21.230266

3 4.2431648 21.962598

3 4.2618399 20.533754

3 4.3829695 20.589008

3 4.3900615 21.298833

3 4.4072292 20.452131

3 4.4630972 22.51363

3 4.4865115 20.166259

3 4.489808 20.302365

3 4.4985781 20.176454

3 4.4995603 21.339537

3 4.5015888 21.546623

3 4.5126907 22.181318

3 4.5186807 20.243923

3 4.5200494 21.606359

3 4.5546059 21.523475

3 4.5593513 22.20819

3 4.620113 22.041174

3 4.7561987 21.161672

3 4.7777356 20.774779

3 4.7993429 21.862268

3 4.8108036 22.172305

3 4.8138488 23.482412

3 4.9717907 21.813861

3 4.9946929 22.564215

3 5.0059337 21.629871

3 5.0114164 21.947345

3 5.0376315 20.086702

3 5.1360659 20.978871

3 5.1976377 22.45016

3 5.3760322 20.3188

3 5.4290531 21.540536

3 5.4497178 21.767889

3 5.4527803 21.620024

3 5.6388008 21.745066

3 5.6473698 21.450529

3 5.7566377 22.143698

3 5.7900453 21.382074

3 5.8758893 21.90501

3 5.9023884 21.818273

3 5.9788072 20.959914

3 6.0324193 21.252833

3 6.3479027 20.875618

4 10.4652107 2.6831135

4 10.5244255 1.1185327

4 10.676615 2.1365133

4 10.7060357 4.8800944

4 11.0507822 4.9306996

4 11.0926175 5.5469616

4 11.1620361 3.4362472

4 11.528659 4.9653684

4 11.68202 4.9323634

4 11.8113105 5.0447482

4 11.8395293 3.2846371

4 11.8904436 3.9756688

4 11.901902 4.0596121

4 12.00957 3.8387848

4 12.041757 3.2972722

4 12.070124 3.5975018

4 12.125698 5.2145027

4 12.13737 5.069625

4 12.179778 2.7118074

4 12.300714 2.811699

4 12.302589 4.6895294

4 12.310044 3.3904743

4 12.360948 4.048405

4 12.387776 2.650697

4 12.404948 2.3650841

4 12.415097 4.0142043

4 12.417047 3.4687826

4 12.442745 5.030264

4 12.443721 4.1409706

4 12.447941 5.8988869

4 12.476582 3.743859

4 12.515158 5.106076

4 12.595752 3.8626734

4 12.615602 3.2022261

4 12.61961 5.7629745

4 12.632347 3.3748335

4 12.657503 3.2747553

4 12.709758 5.0287697

4 12.747944 5.0152176

4 12.752371 4.7189407

4 12.818769 3.7802197

4 12.820888 3.1272452

4 12.830515 5.601528

4 12.863656 2.6533946

4 12.88482 5.2657701

4 12.904148 4.8965637

4 12.939187 3.1585675

4 12.952628 2.2741358

4 12.973613 4.8611944

4 12.97606 2.9509126

4 13.000251 4.8797171

4 13.004435 2.8729802

4 13.004874 4.9961698

4 13.008949 4.4582345

4 13.025455 4.3870708

4 13.045022 4.9916418

4 13.096295 2.973044

4 13.195278 2.9627547

4 13.21192 4.3851352

4 13.215603 4.153893

4 13.362235 2.9408317

4 13.487338 3.4554507

4 13.507061 4.9059559

4 13.544954 3.0748351

4 13.561125 3.3112401

4 13.575478 3.2823488

4 13.61449 4.9872933

4 13.618037 3.916222

4 13.63065 4.3280429

4 13.666668 4.0476106

4 13.693189 4.5426592

4 13.71092 2.5744855

4 13.717479 3.5385358

4 13.723603 4.8269313

4 13.740956 4.5362417

4 13.810342 3.3345241

4 13.884852 5.2966064

4 13.88918 2.4088233

4 13.908124 4.4692953

4 13.920669 5.1046996

4 14.066882 4.2443514

4 14.119596 3.8195427

4 14.134638 4.0501819

4 14.15121 5.0939166

4 14.174712 1.7723559

4 14.23597 5.8236027

4 14.270719 3.9357992

4 14.418402 5.0104769

4 14.423091 4.655516

4 14.58717 5.4807475

4 14.608216 5.5958854

4 14.621365 5.1009621

4 14.639837 5.7854218

4 14.675322 3.0883694

4 14.885806 4.5369381

4 15.123712 3.1371023

4 15.314308 4.2649289

5 20.1271918 15.296397

5 21.0410633 15.246086

5 21.0765704 14.281669

5 21.1579441 14.821112

5 21.2049293 14.132894

5 21.2509463 13.3686

5 21.2995974 15.315821

5 21.4488022 13.797879

5 21.4718758 15.400537

5 21.6037499 15.167981

5 21.6299854 12.916744

5 21.6385609 15.134394

5 21.722283 12.348765

5 21.8768788 12.795341

5 21.8805154 13.427278

5 21.9245665 14.171992

5 21.9419753 12.11674

5 21.9458388 13.572502

5 21.9963091 13.910162

5 21.9977637 12.264383

5 22.120108 14.298028

5 22.161642 12.235561

5 22.259619 14.538579

5 22.278865 13.907848

5 22.284851 14.78127

5 22.302126 13.551077

5 22.419082 13.560862

5 22.451229 14.87717

5 22.481022 14.631295

5 22.487289 13.299683

5 22.570844 13.073695

5 22.587373 14.506647

5 22.625944 13.499386

5 22.641761 13.157186

5 22.64511 12.942852

5 22.667898 15.982038

5 22.670485 13.955641

5 22.706356 13.361715

5 22.711061 14.236744

5 22.771428 12.766095

5 22.775607 16.052587

5 22.886805 13.023554

5 22.928196 11.2239141

5 22.950179 14.187592

5 22.966609 13.020396

5 22.984097 13.00589

5 22.997997 14.669185

5 23.089569 14.398868

5 23.108494 13.910334

5 23.147798 13.724342

5 23.155115 16.121416

5 23.158758 14.718643

5 23.213179 12.731326

5 23.222689 13.894866

5 23.237439 14.728269

5 23.247742 15.206662

5 23.256842 14.442514

5 23.269178 13.628728

5 23.350498 13.563816

5 23.469883 14.188571

5 23.4942 14.38967

5 23.52686 12.779428

5 23.553505 14.539572

5 23.559418 15.570712

5 23.567707 14.901948

5 23.634692 12.074948

5 23.634837 14.236684

5 23.641807 14.726728

5 23.677089 14.332538

5 23.767215 15.092209

5 23.781016 12.595779

5 23.878886 14.329098

5 23.891137 13.18777

5 23.895193 11.6599829

5 23.945273 13.156521

5 23.949261 12.944328

5 23.998138 13.186815

5 24.01272 14.270148

5 24.021108 15.495508

5 24.026602 13.861609

5 24.056164 13.728905

5 24.065794 13.845586

5 24.087697 15.523848

5 24.105136 13.638893

5 24.197604 14.022645

5 24.296025 15.509115

5 24.349088 14.002902

5 24.370353 16.051215

5 24.425818 12.55447

5 24.465642 12.745134

5 24.470883 13.280208

5 24.473897 13.425769

5 24.516394 13.222901

5 24.646671 13.889919

5 24.651572 15.064428

5 24.888393 12.709495

5 25.075641 14.576588

5 26.009856 14.548147






1.432105


gauss Data

#

3.2079091 7.7210589

3.2231833 8.624442

3.2724274 7.7211989

3.3905861 6.8528257

3.5263206 7.1902709

3.5446515 6.9092366

4.0817641 6.2932327

4.1329588 6.7909656

4.2061908 6.5587698

4.2294541 7.7094674

4.2311605 6.1842931

4.3729459 6.6146591

4.451783 8.0942238

4.4631214 5.9776029

4.4641181 7.9918609

4.5092126 7.2028537

4.5249635 5.8857945

4.5408484 7.0938419

4.5538585 8.1353191

4.580998 7.1703637

4.6221661 8.0532502

4.6675926 7.5078748

4.6693178 7.0258803

4.6757058 5.5386359

4.6875167 8.3955603

4.8136569 8.2550441

4.8330733 8.7522282

4.8388063 8.8021392

4.8394463 8.1968265

4.8564329 5.7743203

4.8694154 6.2415617

4.8838473 7.8844967

4.9151302 6.5118873

4.9197918 8.408129

4.9546596 8.2040109

4.9707417 6.4312104

5.0135107 7.9468569

5.0456382 7.6968997

5.0662513 7.9026477

5.0731757 9.0607237

5.0940184 9.3846903

5.0998003 7.0196854

5.1062588 7.8665536

5.1113574 6.7466426

5.1401502 6.322823

5.164504 6.3772491

5.167721 8.491692

5.1792337 7.1921312

5.189034 7.9209093

5.1923355 9.0269859

5.2420277 6.6866669

5.2563394 6.6318602

5.2646653 7.3621656

5.277839 7.5903912

5.2840827 6.3948904

5.3134128 7.7465675

5.3432966 6.3024976

5.3588688 6.6395169

5.3773154 7.509691

5.3923682 7.7704754

5.3928396 7.9307354

5.4097375 7.0243664

5.4250905 8.2957807

5.4256467 9.1909088

5.429859 8.6389274

5.4383383 8.5945946

5.4499816 7.3406544

5.4648999 7.7633964

5.5101871 7.0922003

5.5814278 8.4753996

5.5883151 6.204994

5.6065497 7.7304895

5.6109218 8.4108913

5.6800216 7.6595682

5.6865731 6.8736266

5.7364618 7.1109649

5.7834804 8.4478511

5.806937 6.2154877

5.8505015 6.7495611

5.8894656 7.5845772

5.898071 8.4421334

5.9191638 8.1379257

5.9232595 6.3691164

5.9263071 6.3499483

5.9336875 7.5430657

5.9561075 6.014345

5.956787 6.2913721

5.9597375 7.9233632

5.9647258 7.5541938

6.0014664 5.5959308

6.0967613 7.3460918

6.1113619 8.956129

6.1393133 7.1290369

6.2427451 8.3293623

6.2447324 6.4055278

6.2808958 8.1849711

6.2958248 8.52221

6.3333881 8.8926755

6.3770667 8.148263

6.3872811 7.0834977

6.3943806 5.2047041

6.4231133 9.1428545

6.4242463 8.7437029

6.4737188 9.1960809

6.5434832 6.9540165

6.5542301 8.6514457

6.556613 7.6792214

6.5569343 8.3557038

6.5681878 6.7334969

6.6154429 8.9919154

6.6573287 7.6323181

6.7523585 8.6764836

6.7530329 5.6970322

6.8855847 7.1092405

6.9202943 7.6878621

7.0692918 8.4614043

7.094251 8.9986657

7.2213603 6.8859654

7.29025 8.1231551

7.3047409 8.0529775

7.3807576 6.8897184

7.4022501 8.2157411

7.5258683 8.6077935

7.5602902 8.0202276

7.5928493 9.9473488

7.6337921 6.0477399

12.5649633 14.8199028

12.9492476 15.2417831

12.9639843 14.1301841

13.226796 13.0896456

13.5160477 14.339618

13.5494966 15.3187034

13.7321392 13.3091876

13.78872 13.9671922

13.8074223 14.160413

13.8077218 15.2829668

13.8401064 14.4340778

13.8887826 15.182616

13.9385494 13.0826968

14.0173279 16.015934

14.1333175 15.0641202

14.1493846 11.5813131

14.3247616 14.5008158

14.3596552 14.4300021

14.4498757 14.4660964

14.5039886 15.1291981

14.5577834 12.1352935

14.6243969 13.1862597

14.6653361 13.8993512

14.6752921 13.2522435

14.6978867 13.9182312

14.7050083 16.974634

14.7473609 13.2485078

14.7493806 13.5129937

14.7887799 13.8588077

14.836351 13.2174683

14.8726451 14.0834742

14.8903508 12.7250099

14.8907481 14.7297899

14.8955632 13.2141097

14.9133122 16.337941

14.9817274 13.634554

14.9985553 12.0236224

15.0917411 13.5520639

15.1110772 14.5536931

15.138392 13.2415998

15.1624205 13.7417785

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1.532994

Parag Kanade/fuzzy ants/Multiple Sclerosis/headerStatic.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include<stdlib.h>#include <ctype.h>#include <time.h>#define MAXANTS 31#define MAXDATA 98#define MAXANTSFORHEAPS 10#define MAXITERATIONS 1000#define MAXROWS 20#define MAXCOLS 20#define EXPRUN 50#define Tremove 2.50#define Tcreate 0.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.22#define features 5#define filename "ms"#define errThresh 0.1#define dataDump 1#define maxDist 0#define INITIALOBJECTS 75#define OBJINC 10/* some type definitions to make C code a little more readable */#define TRUE 1#define FALSE 0#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define c 2 /* number of clusters */#define TOTRUNS 1 /* number of V's to write out */#define false 0#define true 1#define maha 0#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap {float Dmax;//maximum dissimilar distanceirisData Ocenter;//center objectfloat Dmean;//mean of the distance between the center and all the objectsirisData Odissim;//most dissimilar objectint x;//x position on the boardint y;//y position on the boardint count;//number of objects in the heapsint disPosition;//most dissimilar objectint objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int carrying;int speed;int dir;irisData data;}ant;float orgData[n][s+1];float fcmData[260][n][s];int clsCenCount[260];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int a , int b);void initialise(int flag);int findObject(int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);void pickObject2(ant *a , int position,int flag);void pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);/********************************************************************//***For FCM algorithm*/void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects);void Output(char[]);void Fcm();/********************************************************************/

Parag Kanade/fuzzy ants/Multiple Sclerosis/maxData.txt

3.829970

Parag Kanade/fuzzy ants/Multiple Sclerosis/ms.c


Parag Kanade/fuzzy ants/Multiple Sclerosis/ms.data

Multiple Sclerosis Group Data#18.0 152.0 1.6 198.4 0.019.0 138.0 0.4 180.8 1.620.0 144.0 0.0 186.4 0.820.0 143.6 3.2 194.8 0.020.0 148.8 0.0 217.6 0.021.0 141.6 0.8 181.6 0.821.0 136.0 1.6 180.0 0.821.0 137.6 1.6 185.6 3.222.0 140.4 3.2 182.0 3.222.0 137.2 0.0 181.8 0.222.0 125.4 1.0 169.2 0.022.0 142.4 4.8 185.6 0.022.0 150.4 0.0 214.4 3.222.0 145.6 1.6 203.6 5.223.0 147.2 3.2 196.8 1.623.0 139.2 1.6 179.2 0.024.0 169.6 0.0 204.8 0.024.0 139.2 1.6 176.0 3.224.0 153.6 0.0 212.0 0.825.0 146.8 0.0 194.8 3.225.0 139.2 1.6 198.4 3.225.0 136.0 1.6 181.6 2.426.0 138.8 1.6 191.6 0.026.0 150.4 0.0 205.2 0.426.0 139.0 1.4 178.6 0.227.0 133.8 0.2 180.8 0.027.0 139.0 1.8 190.4 1.628.0 136.0 1.6 193.2 3.628.0 146.4 0.8 195.6 2.829.0 145.2 4.8 194.2 3.829.0 146.4 0.8 208.2 0.229.0 138.0 2.8 181.2 0.430.0 148.8 1.6 196.4 1.631.0 137.2 0.0 184.0 0.031.0 147.2 0.0 197.6 0.832.0 144.0 0.0 185.8 0.232.0 156.0 0.0 192.8 2.434.0 137.0 0.2 182.4 0.035.0 143.2 2.4 184.0 1.636.0 141.6 0.8 187.2 1.637.0 152.0 1.6 189.2 2.839.0 157.4 3.4 227.0 2.640.0 141.4 0.6 209.2 1.642.0 156.0 2.4 195.2 3.243.0 150.4 1.6 180.0 0.843.0 142.4 1.6 188.8 0.046.0 158.0 2.0 192.0 3.248.0 130.0 3.6 190.0 0.449.0 152.2 1.4 200.0 4.849.0 150.0 3.2 206.6 2.250.0 146.4 2.4 191.6 2.854.0 146.0 1.2 203.2 1.655.0 140.8 0.0 184.0 1.656.0 140.0 0.4 203.2 1.656.0 155.8 3.0 187.8 2.656.0 141.6 0.8 196.8 1.657.0 144.8 0.8 188.0 0.857.0 146.8 3.2 191.6 0.059.0 176.8 2.4 232.8 0.860.0 171.0 1.8 202.0 3.660.0 163.2 0.0 224.0 0.060.0 171.6 1.2 213.8 3.460.0 146.4 4.0 203.2 4.862.0 146.8 3.6 201.6 3.267.0 154.4 2.4 205.2 6.069.0 171.2 1.6 210.4 0.873.0 157.2 0.4 204.8 0.074.0 176.2 5.6 236.6 0.479.0 155.0 1.4 204.4 0.023.0 148.0 0.8 205.4 0.625.0 195.2 3.2 262.8 0.425.0 158.0 8.0 209.8 12.228.0 134.4 0.0 198.4 3.229.0 190.2 14.2 243.8 10.629.0 160.4 18.4 222.8 31.231.0 227.8 90.2 270.2 83.034.0 211.0 3.0 250.8 5.235.0 204.8 12.8 254.4 11.236.0 141.2 6.8 194.4 21.639.0 157.4 3.4 227.0 2.642.0 166.4 0.0 226.0 0.043.0 191.8 35.4 243.6 40.844.0 156.8 0.0 203.2 0.044.0 202.8 29.2 246.4 24.844.0 165.2 18.4 254.0 46.445.0 162.0 5.6 224.4 8.845.0 138.4 0.8 176.8 4.045.0 158.4 1.6 214.4 0.046.0 155.4 1.8 201.2 6.046.0 214.8 9.2 290.6 0.647.0 185.0 19.0 274.4 7.648.0 236.0 20.0 328.0 0.057.0 170.8 24.0 228.4 33.657.0 165.6 16.8 229.2 15.658.0 238.4 8.0 304.4 6.058.0 164.0 0.8 216.8 0.858.0 169.8 0.0 219.2 1.659.0 199.8 4.6 250.2 1.0

Parag Kanade/fuzzy ants/Multiple Sclerosis/msFull.data

1 18 152 1.6 198.4 01 19 138 0.4 180.8 1.61 20 144 0 186.4 0.81 20 143.6 3.2 194.8 01 20 148.8 0 217.6 01 21 141.6 0.8 181.6 0.81 21 136 1.6 180 0.81 21 137.6 1.6 185.6 3.21 22 140.4 3.2 182 3.21 22 137.2 0 181.8 0.21 22 125.4 1 169.2 01 22 142.4 4.8 185.6 01 22 150.4 0 214.4 3.21 22 145.6 1.6 203.6 5.21 23 147.2 3.2 196.8 1.61 23 139.2 1.6 179.2 01 24 169.6 0 204.8 01 24 139.2 1.6 176 3.21 24 153.6 0 212 0.81 25 146.8 0 194.8 3.21 25 139.2 1.6 198.4 3.21 25 136 1.6 181.6 2.41 26 138.8 1.6 191.6 01 26 150.4 0 205.2 0.41 26 139 1.4 178.6 0.21 27 133.8 0.2 180.8 01 27 139 1.8 190.4 1.61 28 136 1.6 193.2 3.61 28 146.4 0.8 195.6 2.81 29 145.2 4.8 194.2 3.81 29 146.4 0.8 208.2 0.21 29 138 2.8 181.2 0.41 30 148.8 1.6 196.4 1.61 31 137.2 0 184 01 31 147.2 0 197.6 0.81 32 144 0 185.8 0.21 32 156 0 192.8 2.41 34 137 0.2 182.4 01 35 143.2 2.4 184 1.61 36 141.6 0.8 187.2 1.61 37 152 1.6 189.2 2.81 39 157.4 3.4 227 2.61 40 141.4 0.6 209.2 1.61 42 156 2.4 195.2 3.21 43 150.4 1.6 180 0.81 43 142.4 1.6 188.8 01 46 158 2 192 3.21 48 130 3.6 190 0.41 49 152.2 1.4 200 4.81 49 150 3.2 206.6 2.21 50 146.4 2.4 191.6 2.81 54 146 1.2 203.2 1.61 55 140.8 0 184 1.61 56 140 0.4 203.2 1.61 56 155.8 3 187.8 2.61 56 141.6 0.8 196.8 1.61 57 144.8 0.8 188 0.81 57 146.8 3.2 191.6 01 59 176.8 2.4 232.8 0.81 60 171 1.8 202 3.61 60 163.2 0 224 01 60 171.6 1.2 213.8 3.41 60 146.4 4 203.2 4.81 62 146.8 3.6 201.6 3.21 67 154.4 2.4 205.2 61 69 171.2 1.6 210.4 0.81 73 157.2 0.4 204.8 01 74 176.2 5.6 236.6 0.41 79 155 1.4 204.4 02 23 148 0.8 205.4 0.62 25 195.2 3.2 262.8 0.42 25 158 8 209.8 12.22 28 134.4 0 198.4 3.22 29 190.2 14.2 243.8 10.62 29 160.4 18.4 222.8 31.22 31 227.8 90.2 270.2 832 34 211 3 250.8 5.22 35 204.8 12.8 254.4 11.22 36 141.2 6.8 194.4 21.62 39 157.4 3.4 227 2.62 42 166.4 0 226 02 43 191.8 35.4 243.6 40.82 44 156.8 0 203.2 02 44 202.8 29.2 246.4 24.82 44 165.2 18.4 254 46.42 45 162 5.6 224.4 8.82 45 138.4 0.8 176.8 42 45 158.4 1.6 214.4 0 2 46 155.4 1.8 201.2 62 46 214.8 9.2 290.6 0.62 47 185 19 274.4 7.62 48 236 20 328 02 57 170.8 24 228.4 33.62 57 165.6 16.8 229.2 15.62 58 238.4 8 304.4 62 58 164 0.8 216.8 0.82 58 169.8 0 219.2 1.62 59 199.8 4.6 250.2 1

Parag Kanade/fuzzy ants/Multiple Sclerosis/ignoring age/headerStatic.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include<stdlib.h>#include <ctype.h>#include <time.h>#define MAXANTS 31#define MAXDATA 98#define MAXANTSFORHEAPS 10#define MAXITERATIONS 1000#define MAXROWS 20#define MAXCOLS 20#define EXPRUN 50#define Tremove 2.50#define Tcreate 0.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.24#define features 4#define filename "ms"#define errThresh 0.1#define dataDump 1#define maxDist 0#define INITIALOBJECTS 75#define OBJINC 10/* some type definitions to make C code a little more readable */#define TRUE 1#define FALSE 0#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define c 2 /* number of clusters */#define TOTRUNS 1 /* number of V's to write out */#define false 0#define true 1#define maha 0#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap {float Dmax;//maximum dissimilar distanceirisData Ocenter;//center objectfloat Dmean;//mean of the distance between the center and all the objectsirisData Odissim;//most dissimilar objectint x;//x position on the boardint y;//y position on the boardint count;//number of objects in the heapsint disPosition;//most dissimilar objectint objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int carrying;int speed;int dir;irisData data;}ant;float orgData[n][s+1];float fcmData[260][n][s];int clsCenCount[260];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int a , int b);void initialise(int flag);int findObject(int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);void pickObject2(ant *a , int position,int flag);void pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);/********************************************************************//***For FCM algorithm*/void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects);void Output(char[]);void Fcm();/********************************************************************/

Parag Kanade/fuzzy ants/Multiple Sclerosis/ignoring age/ms.data

Multiple Sclerosis Group Data#152.0 1.6 198.4 0.0138.0 0.4 180.8 1.6144.0 0.0 186.4 0.8143.6 3.2 194.8 0.0148.8 0.0 217.6 0.0141.6 0.8 181.6 0.8136.0 1.6 180.0 0.8137.6 1.6 185.6 3.2140.4 3.2 182.0 3.2137.2 0.0 181.8 0.2125.4 1.0 169.2 0.0142.4 4.8 185.6 0.0150.4 0.0 214.4 3.2145.6 1.6 203.6 5.2147.2 3.2 196.8 1.6139.2 1.6 179.2 0.0169.6 0.0 204.8 0.0139.2 1.6 176.0 3.2153.6 0.0 212.0 0.8146.8 0.0 194.8 3.2139.2 1.6 198.4 3.2136.0 1.6 181.6 2.4138.8 1.6 191.6 0.0150.4 0.0 205.2 0.4139.0 1.4 178.6 0.2133.8 0.2 180.8 0.0139.0 1.8 190.4 1.6136.0 1.6 193.2 3.6146.4 0.8 195.6 2.8145.2 4.8 194.2 3.8146.4 0.8 208.2 0.2138.0 2.8 181.2 0.4148.8 1.6 196.4 1.6137.2 0.0 184.0 0.0147.2 0.0 197.6 0.8144.0 0.0 185.8 0.2156.0 0.0 192.8 2.4137.0 0.2 182.4 0.0143.2 2.4 184.0 1.6141.6 0.8 187.2 1.6152.0 1.6 189.2 2.8157.4 3.4 227.0 2.6141.4 0.6 209.2 1.6156.0 2.4 195.2 3.2150.4 1.6 180.0 0.8142.4 1.6 188.8 0.0158.0 2.0 192.0 3.2130.0 3.6 190.0 0.4152.2 1.4 200.0 4.8150.0 3.2 206.6 2.2146.4 2.4 191.6 2.8146.0 1.2 203.2 1.6140.8 0.0 184.0 1.6140.0 0.4 203.2 1.6155.8 3.0 187.8 2.6141.6 0.8 196.8 1.6144.8 0.8 188.0 0.8146.8 3.2 191.6 0.0176.8 2.4 232.8 0.8171.0 1.8 202.0 3.6163.2 0.0 224.0 0.0171.6 1.2 213.8 3.4146.4 4.0 203.2 4.8146.8 3.6 201.6 3.2154.4 2.4 205.2 6.0171.2 1.6 210.4 0.8157.2 0.4 204.8 0.0176.2 5.6 236.6 0.4155.0 1.4 204.4 0.0148.0 0.8 205.4 0.6195.2 3.2 262.8 0.4158.0 8.0 209.8 12.2134.4 0.0 198.4 3.2190.2 14.2 243.8 10.6160.4 18.4 222.8 31.2227.8 90.2 270.2 83.0211.0 3.0 250.8 5.2204.8 12.8 254.4 11.2141.2 6.8 194.4 21.6157.4 3.4 227.0 2.6166.4 0.0 226.0 0.0191.8 35.4 243.6 40.8156.8 0.0 203.2 0.0202.8 29.2 246.4 24.8165.2 18.4 254.0 46.4162.0 5.6 224.4 8.8138.4 0.8 176.8 4.0158.4 1.6 214.4 0.0155.4 1.8 201.2 6.0214.8 9.2 290.6 0.6185.0 19.0 274.4 7.6236.0 20.0 328.0 0.0170.8 24.0 228.4 33.6165.6 16.8 229.2 15.6238.4 8.0 304.4 6.0164.0 0.8 216.8 0.8169.8 0.0 219.2 1.6199.8 4.6 250.2 1.0

Parag Kanade/fuzzy ants/Multiple Sclerosis/ignoring age/msFull.data

1 152 1.6 198.4 01 138 0.4 180.8 1.61 144 0 186.4 0.81 143.6 3.2 194.8 01 148.8 0 217.6 01 141.6 0.8 181.6 0.81 136 1.6 180 0.81 137.6 1.6 185.6 3.21 140.4 3.2 182 3.21 137.2 0 181.8 0.21 125.4 1 169.2 01 142.4 4.8 185.6 01 150.4 0 214.4 3.21 145.6 1.6 203.6 5.21 147.2 3.2 196.8 1.61 139.2 1.6 179.2 01 169.6 0 204.8 01 139.2 1.6 176 3.21 153.6 0 212 0.81 146.8 0 194.8 3.21 139.2 1.6 198.4 3.21 136 1.6 181.6 2.41 138.8 1.6 191.6 01 150.4 0 205.2 0.41 139 1.4 178.6 0.21 133.8 0.2 180.8 01 139 1.8 190.4 1.61 136 1.6 193.2 3.61 146.4 0.8 195.6 2.81 145.2 4.8 194.2 3.81 146.4 0.8 208.2 0.21 138 2.8 181.2 0.41 148.8 1.6 196.4 1.61 137.2 0 184 01 147.2 0 197.6 0.81 144 0 185.8 0.21 156 0 192.8 2.41 137 0.2 182.4 01 143.2 2.4 184 1.61 141.6 0.8 187.2 1.61 152 1.6 189.2 2.81 157.4 3.4 227 2.61 141.4 0.6 209.2 1.61 156 2.4 195.2 3.21 150.4 1.6 180 0.81 142.4 1.6 188.8 01 158 2 192 3.21 130 3.6 190 0.41 152.2 1.4 200 4.81 150 3.2 206.6 2.21 146.4 2.4 191.6 2.81 146 1.2 203.2 1.61 140.8 0 184 1.61 140 0.4 203.2 1.61 155.8 3 187.8 2.61 141.6 0.8 196.8 1.61 144.8 0.8 188 0.81 146.8 3.2 191.6 01 176.8 2.4 232.8 0.81 171 1.8 202 3.61 163.2 0 224 01 171.6 1.2 213.8 3.41 146.4 4 203.2 4.81 146.8 3.6 201.6 3.21 154.4 2.4 205.2 61 171.2 1.6 210.4 0.81 157.2 0.4 204.8 01 176.2 5.6 236.6 0.41 155 1.4 204.4 02 148 0.8 205.4 0.62 195.2 3.2 262.8 0.42 158 8 209.8 12.22 134.4 0 198.4 3.22 190.2 14.2 243.8 10.62 160.4 18.4 222.8 31.22 227.8 90.2 270.2 832 211 3 250.8 5.22 204.8 12.8 254.4 11.22 141.2 6.8 194.4 21.62 157.4 3.4 227 2.62 166.4 0 226 02 191.8 35.4 243.6 40.82 156.8 0 203.2 02 202.8 29.2 246.4 24.82 165.2 18.4 254 46.42 162 5.6 224.4 8.82 138.4 0.8 176.8 42 158.4 1.6 214.4 0 2 155.4 1.8 201.2 62 214.8 9.2 290.6 0.62 185 19 274.4 7.62 236 20 328 02 170.8 24 228.4 33.62 165.6 16.8 229.2 15.62 238.4 8 304.4 62 164 0.8 216.8 0.82 169.8 0 219.2 1.62 199.8 4.6 250.2 1

Parag Kanade/fuzzy ants/Multiple Sclerosis/ignoring age/ms.c


Parag Kanade/Partition with centroids/FCM/british.c

#include "header.h"/***************************************************************************//* Function to read the input data set *//* The input data is in filename.data *//* Status: under developement *//* Output: Data from the file into the "data" (Global) data structure *//* Output: Count (Global), the count of the number of objects *//***************************************************************************/void readInput() { FILE *fp; char fname[80]; int i=0,j=0; char ch; sprintf(fname,"%s.data",filename); /* Check if the input file is opened */ if((fp=fopen(fname,"r"))==NULL) { printf("\n Unable to open input file %s \n",fname); exit(0); } /*initialise the minimum maximum and the scale */ for(i=0;i<features;i++) { minInput[i]=1000.0; maxInput[i]=0.0; scale[i]=0.0; } /* Discard leading comments */ ch = 'c'; while (ch != '#') fscanf (fp, "%c", &ch); i=0; /* Parse the file and populate the data structure */ while(!feof(fp)) { for(j=0;j<features;j++) { fscanf(fp,"%f ",&data[i][j]); } fscanf(fp,"\n"); for(j=0;j<features;j++) { if(data[i][j]<minInput[j]) minInput[j]=data[i][j]; if(data[i][j]>maxInput[j]) maxInput[j]=data[i][j]; } i++; } count=i; /* Normailze the feature values in between 0 and 1 */ for(j=0;j<features;j++) { if(minInput[j]!=maxInput[j]) scale[j]=1.0/(maxInput[j]-minInput[j]); else scale[j]=1.0; } for(i=0;i<count;i++) { for(j=0;j<features;j++) data[i][j]=(data[i][j]-minInput[j])*scale[j]; } fclose(fp); return; }/*end of function*//***************************************************************************//* Function to initialise the ants *//* Status: under developement *//* Output: the ant structure is modified with initial random values *//* Input : Ant structure (this is modified) *//***************************************************************************/void initializeAnts(antdata ants[antCount]){int i,j,k;/* Initialize all the ants with initial values and the rest and direction*/for(i=0;i<antCount;i++){ants[i].currPos=drand48();for(j=0;j<memory;j++){ants[i].Rm[j]=inf;for(k=0;k<antsPerFeature;k++)Rm[k][j]=inf;}ants[i].rest=1;ants[i].direction=drand48()<0.5?1:-1;}}/*end of function*//***************************************************************************//* Function that runs one epoch *//* Status: under developement *//* Output: the ant structure is modified with according to the epoch *//* Input : Ant structure (this is modified) *//***************************************************************************/void epoch(antdata ants[antCount]){int i,j;float Trest=0,Tcontinue=0,Tinc=0;float dDiff=0.0;float temp=0;dDiff=Dmax-Dmin;for(j=0;j<iterations;j++){for(i=0;i<antCount;i++){if(ants[i].rest!=0){Trest=drand48();if(Trest<Prest){ants[i].rest=0;continue;}Tcontinue=drand48();if(Tcontinue>Pcontinue)ants[i].direction=-1*ants[i].direction;Tinc=drand48();temp=ants[i].currPos+ants[i].direction*(Dmin+Tinc*dDiff);if(temp>1||temp<0)ants[i].direction=-1*ants[i].direction;ants[i].currPos+=ants[i].direction*(Dmin+Tinc*dDiff);}}}for(i=0;i<antCount;i++)ants[i].rest=1;}/*end of function*//***************************************************************************//* Function that computes Rm *//* Status: under developement *//* Output: the ant structure is modified, the worst Rm is removed *//* Input : Ant structure (this is modified) *//***************************************************************************/void computeRm(antdata ants[antCount],float data[MAXDATA][features],float newRm[antsPerFeature]){int i,j,k=0,l;float clsCenters[antsPerFeature][classes][features];float Rm=0.0,temp=0.0,Dik=0;for(k=0;k<antsPerFeature;k++)for(j=0;j<features;j++)for(i=0;i<classes;i++){clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;}for(k=0;k<antsPerFeature;k++){Rm=0;for(i=0;i<count;i++){Dik=0;for(j=0;j<classes;j++){temp=0;for(l=0;l<features;l++)temp+=(data[i][l]-clsCenters[k][j][l])*(data[i][l]-clsCenters[k][j][l]);/*temp=sqrt(temp);*/Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}newRm[k]=Rm;}return;}/*end of function*//********************************************************************//* Function to read the original classification of objects *//* Output: none *//* Input: none *//* Global Structure Modified: OrgData *//********************************************************************/void readOrg(){int i=0,k=0;char temp[80];FILE *fp;strcpy(temp,"");sprintf(temp,"%sFull.data",filename);fp=fopen(temp,"r");if(fp==NULL){printf("\n Unable to open %s file\n",temp);exit(-1);}for(i=0;i<count;i++){fscanf(fp,"%f ",&orgData[i][0]);for(k=1;k<features+1;k++)fscanf(fp,"%f ",&orgData[i][k]);}return;}/*end of function*//********************************************************************//* Function to update the memory of the ants *//* Output: none *//* Input: ant structure and the new Rm, old memory *//* Global Structure Modified: none *//********************************************************************/void updateMemory(antdata ants[antCount],float newRm[antsPerFeature],float Rm[antsPerFeature][memory]){int i,minPosition[antsPerFeature],maxPosition[antsPerFeature],flag[antsPerFeature],k,j,b[memory],infCount=0;float temp=0,tempRm[memory],tChoose=0;for(i=0;i<antsPerFeature;i++){minPosition[i]=0;maxPosition[i]=0;flag[i]=1;}for(k=0;k<antsPerFeature;k++){b[0]=0;for(i=1;i<memory;i++){if(Rm[k][i]<Rm[k][minPosition[k]])minPosition[k]=i;if(Rm[k][i]>Rm[k][maxPosition[k]])maxPosition[k]=i;if(Rm[k][i]==newRm[k]){flag[k]=0;break;}temp=(newRm[k]-Rm[k][i])*1.0/Rm[k][i];if(temp<0) temp=-1*temp;if(temp<minChange){flag[k]=0;break;}b[i]=i;}if(flag[k]==1){if(newRm[k]<Rm[k][maxPosition[k]]){for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];}else{/* find min Rm and restore its position*/infCount=0;for(i=0;i<memory;i++){tempRm[i]=Rm[k][i];if(Rm[k][i]==inf) infCount++;}for(i=0;i<memory;i++)for(j=0;j<memory-1-i;j++){if(tempRm[j]>tempRm[j+1]){temp=tempRm[j];tempRm[j]=tempRm[j+1];tempRm[j+1]=temp;temp=b[j];b[j]=b[j+1];b[j+1]=temp;}}tChoose=drand48();if(tChoose<0.6){minPosition[k]=b[0];}else if (tChoose<0.8){minPosition[k]=b[1];}else if(tChoose<0.9){minPosition[k]=b[2];}else if(tChoose<0.975){minPosition[k]=b[3];}else {minPosition[k]=b[4];}if(Rm[k][minPosition[k]]!=inf)for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].currPos=ants[k*classes*features+j*classes+i].Rm[minPosition[k]];}}}}}/* end of function *//********************************************************************//* Function to cluster according to the cluster centroids *//* Output: none *//* Input: ant structure and the data points *//* Global Structure Modified: none *//********************************************************************/void finalCluster(antdata ants[antCount],float data[MAXDATA][features],float Rm[antsPerFeature][memory]){int i,j,position[MAXDATA],minPosition=0,k=0,l=0,minRm=0;float clsCenters[antsPerFeature][classes][features];float distance[classes];int cenCount[classes],classCount[classes][classes];float temp=0;int clsObjects[classes][MAXDATA],label[classes];int minPosition1[antsPerFeature],maxPosition1[antsPerFeature];FILE *fp,*fpOut,*fpCls,*fpMinRm,*fpOrgClass,*fpRm;fp=fopen("fcm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpMinRm=fopen("minRm.out","a");fpOrgClass=fopen("orgClass.out","a");fpRm=fopen("Rm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}for(k=0;k<antsPerFeature;k++){for(j=0;j<features;j++)for(i=0;i<classes;i++)clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;minPosition1[k]=0;maxPosition1[k]=0;}for(k=0;k<antsPerFeature;k++){for(i=1;i<memory;i++){if(Rm[l][i]<Rm[l][minPosition1[l]])minPosition1[l]=i;if(Rm[l][i]>Rm[l][maxPosition1[l]])maxPosition1[l]=i;}}minRm=0;for(k=1;k<antsPerFeature;k++){if(Rm[k][minPosition1[k]]<Rm[minRm][minPosition1[minRm]])minRm=k;}fprintf(fpRm,"Rm %8.4f\n",Rm[minRm][minPosition1[minRm]]);fclose(fpRm);Fcm(clsCenters[minRm],data);for(l=0;l<antsPerFeature;l++){fprintf(fpCls,"Cluster number %2d\n",l);for(j=0;j<classes;j++){distance[j]=inf;cenCount[j]=0;for(k=0;k<classes;k++)classCount[j][k]=0;}fprintf(fp,"* %d\n",l);if(l==minRm)fprintf(fpMinRm,"* %d\n",l);for(i=0;i<count;i++){minPosition=0;for(k=0;k<classes;k++){temp=0.0;for(j=0;j<features;j++)temp+= (clsCenters[l][k][j]-data[i][j])*(clsCenters[l][k][j]-data[i][j]);temp=sqrt(temp);distance[k]=temp;if(distance[k]<distance[minPosition])minPosition=k;}position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++;}for(j=0;j<classes;j++){for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++;}/* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/fprintf(fpOrgClass,"\nCluster number: %2d \n",l);for(j=0;j<classes;j++){minPosition=0;fprintf(fpOrgClass,"\n Cluster %2d \n",j+1);fprintf(fpOrgClass,"%2d %2d\n",1,classCount[j][0]);for(i=1;i<classes;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;fprintf(fpOrgClass,"%2d %2d\n",i+1,classCount[j][i]);}}for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");fprintf(fpOut,"\nCluster number: %2d \n",l);for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);for(j=0;j<features;j++)fprintf(fpCls,"%8.7f ",clsCenters[l][i][j]/scale[j]+minInput[j]);fprintf(fpCls,"\n");}}/* Write the data to the file */fclose(fp);fclose(fpOut);fclose(fpCls);fclose(fpMinRm);fclose(fpOrgClass);}/* end of function *//********************************************************************//* Function to amnalyze the results *//* Output: none *//* Input: *//* Global Structure Modified: none *//********************************************************************/void analysis(){int i,j,k,temp;char junk;int totalRuns=0,errors[antsPerFeature],epoch=0,position=0,antsperfeature=0,clsNumber=0,min=0,currMinRm;FILE *fp,*fpError,*fpMin,*fpMinRm,*fpMinIp;fp=fopen("fcm.out","r");fpMinIp=fopen("minRm.out","r");fpMinRm=fopen("minRmError.out","w");fpError=fopen("error.out","w");fpMin=fopen("minError.out","w");if(fp==NULL||fpError==NULL){printf("\n unable to open files in the analysis module \n");return;}fscanf(fp,"%c %d\n",&junk,&totalRuns);fscanf(fp,"%c %d\n",&junk,&antsperfeature);fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&temp);for(i=0;i<totalRuns;i++){for(j=0;j<antsPerFeature;j++){errors[j]=0;}fscanf(fp,"%c %d\n",&junk,&epoch);fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&currMinRm);fprintf(fpError,"Experiment %3d \n",i);for(k=0;k<antsperfeature;k++){fscanf(fp,"%c %d\n",&junk,&clsNumber);for(j=0;j<count;j++){fscanf(fp,"%d ",&position);if(orgData[j][0]!=position)errors[k]++;}fscanf(fp,"\n");fprintf(fpError,"Cluster %3d Errors %5d \n",k,errors[k]);if(k==currMinRm){fprintf(fpMinRm,"Cluster %2d Errors %5d\n",k,errors[k]);}}min=0;for(j=1;j<antsPerFeature;j++){if(errors[j]<errors[min])min=j;}fprintf(fpMin,"Exp %2d Min Error %5d\n",i,errors[min]);}fclose(fp);fclose(fpError);fclose(fpMin);fclose(fpMinRm);fclose(fpMinIp);}/* end of function */int main(){int k=0,i=0,flag[antsPerFeature],j=0,flg;float newRm[antsPerFeature],oldRm[antsPerFeature],temp;FILE *fp=fopen("fcm.out","w");FILE *fpOut=fopen("ClassData.out","w");FILE *fpCls=fopen("clsCenters.out","w");FILE *fpMinRm=fopen("minRm.out","w");FILE *fpOrgClass=fopen("orgClass.out","w");FILE *fpRm=fopen("Rm.out","w");FILE *fpJm=fopen("Jm.out","w");fclose(fpJm);fprintf(fp,"# %d\n",EXPRUN);fprintf(fp,"@ %d\n",antsPerFeature);fclose(fp);fprintf(fpMinRm,"# %d\n",EXPRUN);fprintf(fpMinRm,"@ %d\n",antsPerFeature);fclose(fpMinRm);fclose(fpOut);fclose(fpCls);fclose(fpOrgClass);fclose(fpRm);readInput();/*readOrg();*/for(k=0;k<EXPRUN;k++){srand48(state1[k]);fp=fopen("fcm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpOrgClass=fopen("orgClass.out","a");fpMinRm=fopen("minRm.out","a");fpRm=fopen("Rm.out","a");fprintf(fpRm,"Exp: %2d ",k);fclose(fpRm);fprintf(fpCls,"EXP No %2d \n",k);fclose(fpCls);fprintf(fp,"$ %d\n",k);fprintf(fpMinRm,"$ %d\n",k);fprintf(fpOut,"Experiment %d\n",k);fprintf(fpOrgClass,"Experiment %d\n",k);fclose(fp);fclose(fpOut);fclose(fpMinRm);fclose(fpOrgClass);initializeAnts(ants);for(i=0;i<antsPerFeature;i++){oldRm[i]=inf;newRm[i]=inf;}flg=1;i=0;while(i<maxEpocs){epoch(ants);for(j=0;j<antsPerFeature;j++){oldRm[j]=newRm[j];flag[j]=1;}computeRm(ants,data,newRm);updateMemory(ants,newRm,Rm);flg=0;for(j=0;j<antsPerFeature;j++){if(newRm[j]!=inf){if(oldRm[j]!=inf){temp=0;temp=newRm[j]-oldRm[j]<0? (oldRm[j]-newRm[j]):(newRm[j]-oldRm[j]);/*printf("\n new %f old %f temp %f ",newRm[j],oldRm[j],temp);*/if(temp/oldRm[j]>minChange){flg=1;break;}}else flg=1;}}i++;printf("\nExp %d Epoch %d ",k,i);}/* Do the final clustering*/finalCluster(ants,data,Rm);/* Compute the error */}/* end for*//*analysis();*/printf("\n");return 0;}/* End of main */ void Fcm (center V,objects X) { int i; FILE *ofile; char temp[80]; Random_seed=state1[run];/*randseed*/ srand48(Random_seed); for (run=0;run<TOTRUNS;run++) { eps=0.001; stop = FALSE; /* This loop finds the final cluster center and the corresponding U values */ z = 0; while (stop == FALSE) { z++; UMAT(U,V,X); /** Update the U matrix **/stop = FINISHED(U1,eps); CLUSTER(U,X,V); /** Get new cluster center **/printf("\nIteration: %d \n",z); } /* end while */ printf("Number of iterations: %d\n\n", z);strcpy(temp,""); sprintf(temp,"%sFCM.out",filename); Output(temp,V,X); /* output the results */ }/*end for*/ ofile=fopen(temp,"a"); for (i=0; i <= mins; i++) fprintf(ofile, "Min %d of %10.3f occurred %d times\n", i, mintab[i], mincount[i]); fprintf(ofile, "Average iterations %f\n", (float)aveit/(float)TOTRUNS); fclose(ofile); }/* end Main */ /* The NORM function computes the norm of two vectors, x and v, of dimension s. This gives an indication of the distance between them */ float NORM( feature x, feature v) //feature x, v; { /* Parameters : x -- a local copy of an input vector v -- a local copy of a cluster center */ int i,j; /* loop variable */ double sum; /* the square root of sum is the norm; sum is the sum of the differences squared */ float mat1[features]; float mat2[features]; if (maha) { for (i=0; i<features; i++) mat1[i]=x[i]-v[i]; for(i=0;i<features;i++) { sum =0; for(j=0;j<features;j++) sum += mat1[j] * inv_cov[j][i]; mat2[i] = sum; } sum =0; for(j=0;j<features;j++) sum += mat1[j]*mat2[j]; } else { sum = 0; for(j=0;j<features;j++) sum += (x[j] - v[j])* (x[j] - v[j]); } return (float)(sqrt(sum)); } /* end NORM */ /* The UMAT procedure calculates the updated U matrix according to Bezdek's formula. Its side-effect is the update of the global array U1 */ void UMAT( matrix U,center V, objects X) /*matrix U; center V; objects X;*/ { /* Parameters: U, V, and X: same as in main program */ int i, j, k,l ;/* loop variables *///, class /* class whose cluster center //is closest to pattern k */; float d1,d2,d; float sum; int exact; for (k=0;k<MAXDATA;k++) { /*printf("\n k %d ",k);*/ exact=false; for (i=0;i<classes;i++) { sum=0; for (j=0;j<classes;j++) { d1=NORM(X[k],V[i]); d2=NORM(X[k],V[j]); if(d2!=0) { d= d1/d2; d= d * d;/* pow(d1/d2,2.0/(m-1.0)); */ sum+=d; } else { exact = true; for(l=0;l<classes;l++) if (l==j) U1[l][k]=1.0; else U1[l][k]=0.0; break; } } if (exact!=true) U1[i][k]=1.0/sum; } } } /* end UMAT */ /* The function FINISHED determines if a termination occurs. If not it updates U with U1, so its side-effect is the potential modification of U */ BOOLEAN FINISHED( matrix U1,float eps) //matrix U1; float eps; { /* Parameters: U1, eps-- same as in main */ float sum; /* sum of squared differences between corresponding elements of U and U1 */ int i,k; /* loop control variabes */ BOOLEAN finished; /* set to true if all corresponding elements are within epsilon */ finished = TRUE; sum = 0; for (i=0; i<classes; i++) for (k=0; k<MAXDATA; k++) sum += (U[i][k] - U1[i][k])*(U[i][k]-U1[i][k]); if (sqrt(sum) > eps) { finished = FALSE; for (i=0; i<classes; i++) for (k=0; k<MAXDATA; k++) U[i][k] = U1[i][k]; } /* end if */ return finished; } /* end FINISHED */ /* The CLUSTER procedure determines cluster centers in V for the c classes being searched for. The global array V is updated */ void CLUSTER( matrix U, objects X,center V) //matrix U; objects X; { /* Parameters: U, X -- as in main */ int i, j, k; /* loop control variables */ feature num; /* numerator in the cluster center computation formula presented by Bezdek */ float denum; /* denumenator in the obove formula */ for (i=0; i<classes; i++) { denum = 0; for (j=0; j<features; j++) { num[j] = 0; for (k=0; k<MAXDATA; k++) num[j] += U[i][k] * U[i][k] * X[k][j]; /* pow(U[i][k],m) * X[k][j]; */ } /* end for */ for (k=0; k<MAXDATA; k++) denum += U[i][k] * U[i][k]; /* pow(U[i][k],m); */ for (j=0; j<features; j++) V[i][j] = num[j] / denum; } /* end for */ } /* end CLUSTER */ float Compute_Jm(center V, objects X) //center V; //objects X; { int i, k,found; double Jm_value; float u,dik; Jm_value = 0; for (k=0; k<MAXDATA; k++) { found = FALSE; for (i=0; i<classes; i++) { dik=NORM(X[k],V[i]); dik=dik*dik; if (dik==0.0) { if (found == FALSE) { u=1.0; found = TRUE; printf("found the case with dik=0\n"); } else { printf("\n cant proceed with this initialization"); exit(1); } } else { if (found != TRUE) u=U1[i][k]; /* get_Uvalue(V,X,dik,k); */ else u=0.0; } Jm_value += u * u * dik;/* pow(u, m) * dik; */ } } return (Jm_value); } /* end Compute_Jm */ /* The Output procedure outputs the U matrix which gives the fuzzy measure of each feature belonging to a cluster center. It also outputs the cluster center for each class */ void Output(char fname[],center V,objects X) //char fname[80]; /* Parameters: U, V -- same as in main */ { int i, j, found; /* loop control variables */ float J2c,R2c; FILE *fpJm=fopen("Jm.out","a"); if ((fp=fopen(fname,"a"))==NULL) printf("\n CAN'T OPEN %s\n",fname); else { fprintf(fp,"\n \n"); for (i=0; i<classes; i++) { fprintf(fp,"\n"); for (j=0; j<features; j++) fprintf(fp,"%4.3f ",V[i][j]); } /* end for */ } /* end if */ J2c= Compute_Jm(V,X); R2c=Compute_Rm(V,X); fprintf(fpJm,"Jm: %10.6f Rm: %10.6f \n",J2c,R2c); found =0; for (i=0;i<= mins; i++) { if ((found == 0) && ((J2c - mintab[i]) < .001) && ((J2c - mintab[i]) > -.001)){ mincount[i]++; found = 1; } } if (found == 0) { mins++; mintab[mins]=J2c; mincount[mins]=1; } fprintf(fp,"\nJ%2d = %f", m,J2c); fprintf(fp, "Number of iterations: %d\n\n", z); aveit=aveit + z; fclose(fp); fclose(fpJm); } /***************************************************************/ void find_covar( ) { int i,j,k; float mean[features],sum; for (i=0;i<features;i++) mean[i]=0; for (i=0;i<features;i++) for (j=0;j<m;j++) mean[i]+=X[j][i]; for (i=0;i<features;i++) mean[i]/=m; for (i=0;i<features;i++) for (j=0;j<features;j++) { sum=0; for (k=0;k<m;k++) sum+=(X[k][i]-mean[i])*(X[k][j]-mean[j]); Y[i][j]=sum/m; } }float Compute_Rm(center V, objects X){int i=0,j=0,l;float Dik=0,temp=0,Rm=0;Rm=0;for(i=0;i<MAXDATA;i++){Dik=0;for(j=0;j<classes;j++){temp=0;for(l=0;l<features;l++)temp+=(X[i][l]-V[j][l])*(X[i][l]-V[j][l]);/*temp=sqrt(temp);*/Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}return(Rm);}/* End of function */

Parag Kanade/Partition with centroids/FCM/header.h

/********************************************************************//* Ant Class parameters *//* Prest: Probability that the ant rests for this epoch *//* Pcontinue:Probability that the ant continues in the current *//* direction *//* *//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include <stdlib.h>#include <ctype.h>#include <time.h>#define Prest 0.01#define Pcontinue 0.75#define Dmin 0.00001#define Dmax 0.0001#define MAXDATA 50#define classes 5#define features 4#define filename "british"#define iterations 50#define memory 5#define antsPerFeature 30#define antCount antsPerFeature*features*classes#define inf 10e100#define m 2#define TRUE 1#define maxEpocs 1000#define EXPRUN 50#define minChange 0.00075#define maha 0#define FALSE 0#define true 1#define false 0#define TOTRUNS 1 /* number of V's to write out */typedef struct a{float Rm[memory];int rest;int direction;float currPos;}antdata;typedef struct HeapInfo{int total;int classObj[MAXDATA];} heapInfo;typedef int BOOLEAN;typedef float feature[features]; /* Type of each input; made up of input features */typedef float irisData[features];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */antdata ants[antCount]; /* Ants */float data[MAXDATA][features]; /* Normailzed input data points */ float minInput[features]; /* Minimum value for all features */float maxInput[features]; /* Maximum value for all features */float scale[features]; /* Scale value for all features */int count; /* Number of data points */float orgData[MAXDATA][features+1];/* Original Data */float Rm[antsPerFeature][memory];long state1[52] = { 9347, 3835, 5194, 8310, 346, 535, 5297, 6711, 77, 3834, 668, 4175, 6868, 5890, 9304, 8462, 5269, 920, 65392, 41600, 70119, 91032, 76220, 26245, 4746, 73608, 32823, 63264, 75641, 99104, 36534, 24704, 98255, 72266, 75336, 65152, 7269, 63163, 88471, 27271, 43641, 76649, 47773, 23777, 27491, 35926, 16651, 48652, 89766, 90921, 6056 }; matrix U, U1; float inv_cov[features+1][features+1]; int temp[200]; /* for square matrix tests */ float eps; BOOLEAN stop; center V; objects X; char fname[80]; FILE *fp; int run; float Y[features][features]; long rand_index, Random_seed; int z; int mins=0; int aveit =0; float mintab[200]; int mincount[200]; long start,end,total; void readInput();void initializeAnts(antdata ants[antCount]);void epoch(antdata ants[antCount]);void computeRm(antdata ants[antCount],float data[MAXDATA][features],float newRm[antsPerFeature]);void updateMemory(antdata ants[antCount],float newRm[antsPerFeature], float Rm[antsPerFeature][memory]);void analysis();void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects,center);void Output(char[],center V,objects X);void Fcm(center V,objects X);float Compute_Jm(center V, objects X);float Compute_Rm(center V, objects X);

Parag Kanade/Partition with centroids/HCM/header.h

/********************************************************************//* Ant Class parameters *//* Prest: Probability that the ant rests for this epoch *//* Pcontinue:Probability that the ant continues in the current *//* direction *//* *//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include <stdlib.h>#include <ctype.h>#include <time.h>#define Prest 0.01#define Pcontinue 0.75#define tContinueCurr 0.2#define Dmin 0.00001#define Dmax 0.0001#define MAXDATA 50#define classes 5#define features 4#define filename "british"#define iterations 50#define memory 5#define antsPerFeature 75#define antCount antsPerFeature*features*classes#define inf 10e100#define m 2#define TRUE 1#define maxEpocs 2000#define EXPRUN 50#define minChange 0.00075#define maha 0#define FALSE 0#define true 1#define false 0#define TOTRUNS 1 /* number of V's to write out */typedef struct a{float Rm[memory];int rest;int direction;float currPos;}antdata;typedef struct HeapInfo{int total;int classObj[MAXDATA];} heapInfo;typedef int BOOLEAN;typedef float feature[features]; /* Type of each input; made up of input features */typedef float irisData[features];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */antdata ants[antCount]; /* Ants */float data[MAXDATA][features]; /* Normailzed input data points */ float minInput[features]; /* Minimum value for all features */float maxInput[features]; /* Maximum value for all features */float scale[features]; /* Scale value for all features */int count; /* Number of data points */float orgData[MAXDATA][features+1];/* Original Data */float Rm[antsPerFeature][memory];long state1[52] = { 9347, 3835, 5194, 8310, 346, 535, 5297, 6711, 77, 3834, 668, 4175, 6868, 5890, 9304, 8462, 5269, 920, 65392, 41600, 70119, 91032, 76220, 26245, 4746, 73608, 32823, 63264, 75641, 99104, 36534, 24704, 98255, 72266, 75336, 65152, 7269, 63163, 88471, 27271, 43641, 76649, 47773, 23777, 27491, 35926, 16651, 48652, 89766, 90921, 6056 }; matrix U, U1; float inv_cov[features+1][features+1]; int degenerate[classes]; int temp[200]; /* for square matrix tests */ float eps; BOOLEAN stop; center V; objects X; char fname[80]; FILE *fp; int run; float Y[features][features]; long rand_index, Random_seed; int z; int mins=0; int aveit =0; float mintab[200]; int mincount[200]; long start,end,total; void readInput();void initializeAnts(antdata ants[antCount]);void epoch(antdata ants[antCount]);void computeRm(antdata ants[antCount],float data[MAXDATA][features],float newRm[antsPerFeature]);void updateMemory(antdata ants[antCount],float newRm[antsPerFeature], float Rm[antsPerFeature][memory]);void analysis();void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects,center);void Output(char[],center V,objects X);void Fcm(center V,objects X);float Compute_Jm(center V, objects X);float Compute_Rm(center V, objects X);void harden();int findPosition(int i);

Parag Kanade/Partition with centroids/HCM/british.c

#include "header.h"/***************************************************************************//* Function to read the input data set *//* The input data is in filename.data *//* Status: under developement *//* Output: Data from the file into the "data" (Global) data structure *//* Output: Count (Global), the count of the number of objects *//***************************************************************************/void readInput() { FILE *fp; char fname[80]; int i=0,j=0; char ch; sprintf(fname,"%s.data",filename); /* Check if the input file is opened */ if((fp=fopen(fname,"r"))==NULL) { printf("\n Unable to open input file %s \n",fname); exit(0); } /*initialise the minimum maximum and the scale */ for(i=0;i<features;i++) { minInput[i]=1000.0; maxInput[i]=0.0; scale[i]=0.0; } /* Discard leading comments */ ch = 'c'; while (ch != '#') fscanf (fp, "%c", &ch); i=0; /* Parse the file and populate the data structure */ while(!feof(fp)) { for(j=0;j<features;j++) { fscanf(fp,"%f ",&data[i][j]); } fscanf(fp,"\n"); for(j=0;j<features;j++) { if(data[i][j]<minInput[j]) minInput[j]=data[i][j]; if(data[i][j]>maxInput[j]) maxInput[j]=data[i][j]; } i++; } count=i; /* Normailze the feature values in between 0 and 1 */ for(j=0;j<features;j++) { if(minInput[j]!=maxInput[j]) scale[j]=1.0/(maxInput[j]-minInput[j]); else scale[j]=1.0; } for(i=0;i<count;i++) { for(j=0;j<features;j++) data[i][j]=(data[i][j]-minInput[j])*scale[j]; } fclose(fp); return; }/*end of function*//***************************************************************************//* Function to initialise the ants *//* Status: under developement *//* Output: the ant structure is modified with initial random values *//* Input : Ant structure (this is modified) *//***************************************************************************/void initializeAnts(antdata ants[antCount]){int i,j,k;/* Initialize all the ants with initial values and the rest and direction*/for(i=0;i<antCount;i++){ants[i].currPos=drand48();for(j=0;j<memory;j++){ants[i].Rm[j]=inf;for(k=0;k<antsPerFeature;k++)Rm[k][j]=inf;}ants[i].rest=1;ants[i].direction=drand48()<0.5?1:-1;}}/*end of function*//***************************************************************************//* Function that runs one epoch *//* Status: under developement *//* Output: the ant structure is modified with according to the epoch *//* Input : Ant structure (this is modified) *//***************************************************************************/void epoch(antdata ants[antCount]){int i,j;float Trest=0,Tcontinue=0,Tinc=0;float dDiff=0.0;float temp=0;dDiff=Dmax-Dmin;for(j=0;j<iterations;j++){for(i=0;i<antCount;i++){if(ants[i].rest!=0){Trest=drand48();if(Trest<Prest){ants[i].rest=0;continue;}Tcontinue=drand48();if(Tcontinue>Pcontinue)ants[i].direction=-1*ants[i].direction;Tinc=drand48();temp=ants[i].currPos+ants[i].direction*(Dmin+Tinc*dDiff);if(temp>1||temp<0)ants[i].direction=-1*ants[i].direction;ants[i].currPos+=ants[i].direction*(Dmin+Tinc*dDiff);}}}for(i=0;i<antCount;i++)ants[i].rest=1;}/*end of function*//***************************************************************************//* Function that computes Rm *//* Status: under developement *//* Output: the ant structure is modified, the worst Rm is removed *//* Input : Ant structure (this is modified) *//***************************************************************************/void computeRm(antdata ants[antCount],float data[MAXDATA][features],float newRm[antsPerFeature]){int i,j,k=0,l=0,minPos=0;float clsCenters[antsPerFeature][classes][features];double Rm=0.0,temp=0.0,Dik=0,min=0;for(k=0;k<antsPerFeature;k++)for(j=0;j<features;j++)for(i=0;i<classes;i++){clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;}for(k=0;k<antsPerFeature;k++){Rm=0;for(i=0;i<count;i++){Dik=0;min=10e10;minPos=0;for(j=0;j<classes;j++){temp=0;for(l=0;l<features;l++)temp+=(data[i][l]-clsCenters[k][j][l])*(data[i][l]-clsCenters[k][j][l]);if(temp<min){min=temp;minPos=j;}}Rm+=min;}newRm[k]=Rm;}return;}/*end of function*//********************************************************************//* Function to read the original classification of objects *//* Output: none *//* Input: none *//* Global Structure Modified: OrgData *//********************************************************************/void readOrg(){int i=0,k=0;char temp[80];FILE *fp;strcpy(temp,"");sprintf(temp,"%sFull.data",filename);fp=fopen(temp,"r");if(fp==NULL){printf("\n Unable to open %s file\n",temp);exit(-1);}for(i=0;i<count;i++){fscanf(fp,"%f ",&orgData[i][0]);for(k=1;k<features+1;k++)fscanf(fp,"%f ",&orgData[i][k]);}return;}/*end of function*//********************************************************************//* Function to update the memory of the ants *//* Output: none *//* Input: ant structure and the new Rm, old memory *//* Global Structure Modified: none *//********************************************************************/void updateMemory(antdata ants[antCount],float newRm[antsPerFeature],float Rm[antsPerFeature][memory]){int i,minPosition[antsPerFeature],maxPosition[antsPerFeature],flag[antsPerFeature],k,j,b[memory],infCount=0;float temp=0,tempRm[memory],tChoose=0,pContinueCurr=0;for(i=0;i<antsPerFeature;i++){minPosition[i]=0;maxPosition[i]=0;flag[i]=1;}for(k=0;k<antsPerFeature;k++){b[0]=0;for(i=1;i<memory;i++){if(Rm[k][i]<Rm[k][minPosition[k]])minPosition[k]=i;if(Rm[k][i]>Rm[k][maxPosition[k]])maxPosition[k]=i;if(Rm[k][i]==newRm[k]){flag[k]=0;break;}temp=(newRm[k]-Rm[k][i])*1.0/Rm[k][i];if(temp<0) temp=-1*temp;if(temp<minChange){flag[k]=0;break;}b[i]=i;}if(flag[k]==1){if(newRm[k]<Rm[k][maxPosition[k]]){for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];}else{/* find min Rm and restore its position*/infCount=0;for(i=0;i<memory;i++){tempRm[i]=Rm[k][i];if(Rm[k][i]==inf) infCount++;}for(i=0;i<memory;i++)for(j=0;j<memory-1-i;j++){if(tempRm[j]>tempRm[j+1]){temp=tempRm[j];tempRm[j]=tempRm[j+1];tempRm[j+1]=temp;temp=b[j];b[j]=b[j+1];b[j+1]=temp;}}tChoose=drand48();if(tChoose<0.6){minPosition[k]=b[0];}else if (tChoose<0.8){minPosition[k]=b[1];}else if(tChoose<0.9){minPosition[k]=b[2];}else if(tChoose<0.975){minPosition[k]=b[3];}else {minPosition[k]=b[4];}pContinueCurr=drand48();if(pContinueCurr<tContinueCurr){/*for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];*/}else if(Rm[k][minPosition[k]]!=inf)for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].currPos=ants[k*classes*features+j*classes+i].Rm[minPosition[k]];}}}}}/* end of function *//********************************************************************//* Function to cluster according to the cluster centroids *//* Output: none *//* Input: ant structure and the data points *//* Global Structure Modified: none *//********************************************************************/void finalCluster(antdata ants[antCount],float data[MAXDATA][features],float Rm[antsPerFeature][memory]){int i,j,position[MAXDATA],minPosition=0,k=0,l=0,minRm=0;float clsCenters[antsPerFeature][classes][features];float distance[classes];int cenCount[classes],classCount[classes][classes];float temp=0;int clsObjects[classes][MAXDATA],label[classes];int minPosition1[antsPerFeature],maxPosition1[antsPerFeature];FILE *fp,*fpOut,*fpCls,*fpMinRm,*fpOrgClass,*fpRm;fp=fopen("fcm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpMinRm=fopen("minRm.out","a");fpOrgClass=fopen("orgClass.out","a");fpRm=fopen("Rm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}for(k=0;k<antsPerFeature;k++){for(j=0;j<features;j++)for(i=0;i<classes;i++)clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;minPosition1[k]=0;maxPosition1[k]=0;}for(k=0;k<antsPerFeature;k++){for(i=1;i<memory;i++){if(Rm[l][i]<Rm[l][minPosition1[l]])minPosition1[l]=i;if(Rm[l][i]>Rm[l][maxPosition1[l]])maxPosition1[l]=i;}}minRm=0;for(k=1;k<antsPerFeature;k++){if(Rm[k][minPosition1[k]]<Rm[minRm][minPosition1[minRm]])minRm=k;}fprintf(fpRm,"Rm %8.4f\n",Rm[minRm][minPosition1[minRm]]);fclose(fpRm);Fcm(clsCenters[minRm],data);harden();for(l=0;l<antsPerFeature;l++){fprintf(fpCls,"Cluster number %2d\n",l);for(j=0;j<classes;j++){distance[j]=inf;cenCount[j]=0;for(k=0;k<classes;k++)classCount[j][k]=0;}fprintf(fp,"* %d\n",l);if(l==minRm)fprintf(fpMinRm,"* %d\n",l);for(i=0;i<count;i++){minPosition=0;for(k=0;k<classes;k++){temp=0.0;for(j=0;j<features;j++)temp+= (clsCenters[l][k][j]-data[i][j])*(clsCenters[l][k][j]-data[i][j]);temp=sqrt(temp);distance[k]=temp;if(distance[k]<distance[minPosition])minPosition=k;}position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++;}for(j=0;j<classes;j++){for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++;}/* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/fprintf(fpOrgClass,"\nCluster number: %2d \n",l);for(j=0;j<classes;j++){minPosition=0;fprintf(fpOrgClass,"\n Cluster %2d \n",j+1);fprintf(fpOrgClass,"%2d %2d\n",1,classCount[j][0]);for(i=1;i<classes;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;fprintf(fpOrgClass,"%2d %2d\n",i+1,classCount[j][i]);}}for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");fprintf(fpOut,"\nCluster number: %2d \n",l);for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);for(j=0;j<features;j++)fprintf(fpCls,"%8.7f ",clsCenters[l][i][j]/scale[j]+minInput[j]);fprintf(fpCls,"\n");}}/* Write the data to the file */fclose(fp);fclose(fpOut);fclose(fpCls);fclose(fpMinRm);fclose(fpOrgClass);}/* end of function *//********************************************************************//* Function to amnalyze the results *//* Output: none *//* Input: *//* Global Structure Modified: none *//********************************************************************/void analysis(int flag){int i,j,k,temp;char junk;int totalRuns=0,errors[antsPerFeature],epoch=0,position=0,antsperfeature=0,clsNumber=0,min=0,currMinRm;FILE *fp,*fpError,*fpMin,*fpMinRm,*fpMinIp;if(flag==1){fp=fopen("fcm.out","r");fpMinIp=fopen("minRm.out","r");fpMinRm=fopen("minRmError.out","w");fpError=fopen("error.out","w");fpMin=fopen("minError.out","w");}else{fp=fopen("fcmFCM.out","r");fpError=fopen("errorFCM.out","w");}if(fp==NULL||fpError==NULL){printf("\n unable to open files in the analysis module \n");return;}fscanf(fp,"%c %d\n",&junk,&totalRuns);fscanf(fp,"%c %d\n",&junk,&antsperfeature);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&temp);}for(i=0;i<totalRuns;i++){for(j=0;j<antsperfeature;j++){errors[j]=0;}fscanf(fp,"%c %d\n",&junk,&epoch);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&currMinRm);fprintf(fpError,"\n");}fprintf(fpError,"Experiment %3d ",i);for(k=0;k<antsperfeature;k++){fscanf(fp,"%c %d\n",&junk,&clsNumber);for(j=0;j<count;j++){fscanf(fp,"%d ",&position);if(orgData[j][0]!=position)errors[k]++;}fscanf(fp,"\n");if(flag==1){fprintf(fpError,"\nCluster %3d Errors %5d",k,errors[k]);if(k==currMinRm){fprintf(fpMinRm,"Cluster %2d Errors %5d\n",k,errors[k]);}}else fprintf(fpError," Errors %5d\n",errors[k]);}if(flag==1){min=0;for(j=1;j<antsPerFeature;j++){if(errors[j]<errors[min])min=j;}fprintf(fpMin,"Exp %2d Min Error %5d\n",i,errors[min]);}}fclose(fp);fclose(fpError);if(flag==1){fclose(fpMin);fclose(fpMinRm);fclose(fpMinIp);}}/* end of function */int main(){int k=0,i=0,flag[antsPerFeature],j=0,flg;float newRm[antsPerFeature],oldRm[antsPerFeature],temp;FILE *fp=fopen("fcm.out","w");FILE *fpFCM=fopen("fcmFCM.out","w");FILE *fpOut=fopen("ClassData.out","w");FILE *fpCls=fopen("clsCenters.out","w");FILE *fpMinRm=fopen("minRm.out","w");FILE *fpOrgClass=fopen("orgClass.out","w");FILE *fpRm=fopen("Rm.out","w");FILE *fpJm=fopen("Jm.out","w");FILE *fpTime=fopen("time.out","w");struct rusage start_usage, end_usage,epoch_start_usage, epoch_end_usage;double *perf_times;fprintf(fpTime,"Experiments %d\n",EXPRUN);fclose(fpJm);fprintf(fp,"# %d\n",EXPRUN);fprintf(fp,"@ %d\n",antsPerFeature);fclose(fp);fprintf(fpFCM,"# %d\n",EXPRUN);fprintf(fpFCM,"@ %d\n",1);fclose(fpFCM);fprintf(fpMinRm,"# %d\n",EXPRUN);fprintf(fpMinRm,"@ %d\n",antsPerFeature);fclose(fpMinRm);fclose(fpOut);fclose(fpCls);fclose(fpOrgClass);fclose(fpRm);readInput();/*readOrg();*/for(k=0;k<EXPRUN;k++){srand48(state1[k]);fp=fopen("fcm.out","a");fpFCM=fopen("fcmFCM.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpOrgClass=fopen("orgClass.out","a");fpMinRm=fopen("minRm.out","a");fpRm=fopen("Rm.out","a");fprintf(fpRm,"Exp: %2d ",k);fclose(fpRm);fprintf(fpCls,"EXP No %2d \n",k);fclose(fpCls);fprintf(fp,"$ %d\n",k);fprintf(fpFCM,"$ %d\n",k);fprintf(fpMinRm,"$ %d\n",k);fprintf(fpOut,"Experiment %d\n",k);fprintf(fpOrgClass,"Experiment %d\n",k);fclose(fp);fclose(fpFCM);fclose(fpOut);fclose(fpMinRm);fclose(fpOrgClass);initializeAnts(ants);for(i=0;i<antsPerFeature;i++){oldRm[i]=inf;newRm[i]=inf;}flg=1;i=0;getrusage(RUSAGE_SELF, &start_usage);while(i<maxEpocs){getrusage(RUSAGE_SELF, &epoch_start_usage);epoch(ants);getrusage(RUSAGE_SELF, &epoch_end_usage);perf_times=timing_of(epoch_start_usage,epoch_end_usage); fprintf(fpTime,"Epoch %3d Timing: %f user, %f system, %f total.\n",i, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);for(j=0;j<antsPerFeature;j++){oldRm[j]=newRm[j];flag[j]=1;}computeRm(ants,data,newRm);updateMemory(ants,newRm,Rm);flg=0;for(j=0;j<antsPerFeature;j++){if(newRm[j]!=inf){if(oldRm[j]!=inf){temp=0;temp=newRm[j]-oldRm[j]<0? (oldRm[j]-newRm[j]):(newRm[j]-oldRm[j]);/*printf("\n new %f old %f temp %f ",newRm[j],oldRm[j],temp);*/if(temp/oldRm[j]>minChange){flg=1;break;}}else flg=1;}}i++;printf("\nExp %d Epoch %d ",k,i);}/* Do the final clustering*/finalCluster(ants,data,Rm);getrusage(RUSAGE_SELF, &end_usage);perf_times=timing_of(start_usage,end_usage); fprintf(fpTime,"Exp %3d Timing: %f user, %f system, %f total.\n",k, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);/* Compute the error */}/* end for*//*analysis(1);analysis(0);*/printf("\n");fclose(fpTime);return 0;}/* End of main */ void Fcm (center V,objects X) { int i,k; FILE *ofile; char temp[80]; Random_seed=state1[run];/*randseed*/ srand48(Random_seed); for(k=0;k<MAXDATA;k++) for(i=0;i<classes;i++){U[i][k]=0;U1[i][k]=0;} for(i=0;i<classes;i++) degenerate[i]=0; for (run=0;run<TOTRUNS;run++) { eps=0.00001; stop = FALSE; /* This loop finds the final cluster center and the corresponding U values */ z = 0; while (stop == FALSE) { z++; UMAT(U,V,X); /** Update the U matrix **/stop = FINISHED(U1,eps); CLUSTER(U,X,V); /** Get new cluster center **/printf("\nIteration: %d \n",z); } /* end while */ printf("Number of iterations: %d\n\n", z);strcpy(temp,""); sprintf(temp,"%sFCM.out",filename); Output(temp,V,X); /* output the results */ }/*end for*/ ofile=fopen(temp,"a"); for (i=0; i <= mins; i++) fprintf(ofile, "Min %d of %10.3f occurred %d times\n", i, mintab[i], mincount[i]); fprintf(ofile, "Average iterations %f\n", (float)aveit/(float)TOTRUNS); fclose(ofile); }/* end Main */ /* The NORM function computes the norm of two vectors, x and v, of dimension s. This gives an indication of the distance between them */ float NORM( feature x, feature v) //feature x, v; { /* Parameters : x -- a local copy of an input vector v -- a local copy of a cluster center */ int i,j; /* loop variable */ double sum; /* the square root of sum is the norm; sum is the sum of the differences squared */ float mat1[features]; float mat2[features]; if (maha) { for (i=0; i<features; i++) mat1[i]=x[i]-v[i]; for(i=0;i<features;i++) { sum =0; for(j=0;j<features;j++) sum += mat1[j] * inv_cov[j][i]; mat2[i] = sum; } sum =0; for(j=0;j<features;j++) sum += mat1[j]*mat2[j]; } else { sum = 0; for(j=0;j<features;j++) sum += (x[j] - v[j])* (x[j] - v[j]); } return (float)(sqrt(sum)); } /* end NORM */ /* The UMAT procedure calculates the updated U matrix according to Bezdek's formula. Its side-effect is the update of the global array U1 */ void UMAT( matrix U,center V, objects X) /*matrix U; center V; objects X;*/ { /* Parameters: U, V, and X: same as in main program */ int i, k ;/* loop variables *///, class /* class whose cluster center //is closest to pattern k */; int minPos; float distance[classes],min; for (i=0;i<classes;i++) distance[i]=0; for (k=0;k<MAXDATA;k++) { min=10e10;minPos=0;for (i=0;i<classes;i++) { distance[i]=NORM(X[k],V[i]);if(distance[i]==0)printf("\n Found a case with Dik =0\n");if(distance[i]<min){min=distance[i];minPos=i;}U1[i][k]=0; } U1[minPos][k]=1.0; } } /* end UMAT */ /* The function FINISHED determines if a termination occurs. If not it updates U with U1, so its side-effect is the potential modification of U */ BOOLEAN FINISHED( matrix U1,float eps) //matrix U1; float eps; { /* Parameters: U1, eps-- same as in main */ float sum; /* sum of squared differences between corresponding elements of U and U1 */ int i,k; /* loop control variabes */ BOOLEAN finished; /* set to true if all corresponding elements are within epsilon */ finished = TRUE; sum = 0; for (i=0; i<classes; i++) for (k=0; k<MAXDATA; k++) sum += (U[i][k] - U1[i][k])*(U[i][k]-U1[i][k]); if (sqrt(sum) > eps) { finished = FALSE; for (i=0; i<classes; i++) for (k=0; k<MAXDATA; k++) U[i][k] = U1[i][k]; } /* end if */ return finished; } /* end FINISHED */ /* The CLUSTER procedure determines cluster centers in V for the c classes being searched for. The global array V is updated */ void CLUSTER( matrix U, objects X,center V) //matrix U; objects X; { /* Parameters: U, X -- as in main */ int i, j, k; /* loop control variables */ feature num; /* numerator in the cluster center computation formula presented by Bezdek */ float denum; /* denumenator in the obove formula */ for (i=0; i<classes; i++) { denum = 0; for (j=0; j<features; j++) { num[j] = 0; for (k=0; k<MAXDATA; k++) num[j] += U[i][k] * X[k][j]; /* pow(U[i][k],m) * X[k][j]; */ } /* end for */ for (k=0; k<MAXDATA; k++) denum += U[i][k]; /* pow(U[i][k],m); */ if(denum==0)degenerate[i]=1; for (j=0; j<features; j++) { if(denum!=0) V[i][j] = num[j] / denum;else V[i][j] = inf; } } /* end for */ } /* end CLUSTER */ /* The Output procedure outputs the U matrix which gives the fuzzy measure of each feature belonging to a cluster center. It also outputs the cluster center for each class */ void Output(char fname[],center V,objects X) //char fname[80]; /* Parameters: U, V -- same as in main */ { int i, j, found; /* loop control variables */ float J2c,R2c; FILE *fpJm=fopen("Jm.out","a"); if ((fp=fopen(fname,"a"))==NULL) printf("\n CAN'T OPEN %s\n",fname); else { fprintf(fp,"\n \n"); for (i=0; i<classes; i++) { fprintf(fp,"\n"); for (j=0; j<features; j++) fprintf(fp,"%4.3f ",V[i][j]); } /* end for */ } /* end if */ J2c= Compute_Jm(V,X); R2c=Compute_Rm(V,X); fprintf(fpJm,"Jm: %10.6f Rm: %10.6f \n",J2c,R2c); found =0; for (i=0;i<= mins; i++) { if ((found == 0) && ((J2c - mintab[i]) < .001) && ((J2c - mintab[i]) > -.001)){ mincount[i]++; found = 1; } } if (found == 0) { mins++; mintab[mins]=J2c; mincount[mins]=1; } fprintf(fp,"\nJ%2d = %f", m,J2c); fprintf(fp, "Number of iterations: %d\n\n", z); aveit=aveit + z; fclose(fp); fclose(fpJm); } /***************************************************************/ void find_covar( ) { int i,j,k; float mean[features],sum; for (i=0;i<features;i++) mean[i]=0; for (i=0;i<features;i++) for (j=0;j<m;j++) mean[i]+=X[j][i]; for (i=0;i<features;i++) mean[i]/=m; for (i=0;i<features;i++) for (j=0;j<features;j++) { sum=0; for (k=0;k<m;k++) sum+=(X[k][i]-mean[i])*(X[k][j]-mean[j]); Y[i][j]=sum/m; } } float Compute_Jm(center V, objects X) //center V; //objects X; { int i, k,found; double Jm_value; float u,dik; Jm_value = 0; for (k=0; k<MAXDATA; k++) { found = FALSE; for (i=0; i<classes; i++) { if(degenerate[i]==1)continue; dik=NORM(X[k],V[i]); dik=dik*dik; u=0; if (dik==0.0) { if (found == FALSE) { u=1.0; found = TRUE; printf("found the case with dik=0\n"); } else { printf("\n cant proceed with this initialization"); exit(1); } } else { if (found != TRUE) u=U1[i][k]; /* get_Uvalue(V,X,dik,k); */ else u=0.0; } Jm_value += u * dik;/* pow(u, m) * dik; */ } } return (Jm_value); } /* end Compute_Jm */float Compute_Rm(center V, objects X){int i=0,j=0,l=0;double dik=0,temp=0,Rm=0,min=0;Rm=0;for(i=0;i<MAXDATA;i++){dik=0;min=10e10;for(j=0;j<classes;j++){temp=0;for(l=0;l<features;l++)temp+=(X[i][l]-V[j][l])*(X[i][l]-V[j][l]); /*dik=NORM(X[i],V[j]); dik=dik*dik;*/if(temp<min)min=temp;}Rm+=min;}return(Rm);}/* End of function *//********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void harden() { FILE *fp=fopen("fcmFCM.out","a"); FILE *fpOut=fopen("ClassDataFCM.out","a"); int i=0,minPosition=-1,position[MAXDATA],j=0,k=0; int cenCount[classes],classCount[classes][classes],label[classes], clsObjects[classes][MAXDATA]; for(j=0;j<classes;j++) {cenCount[j]=0;for(k=0;k<classes;k++)classCount[j][k]=0; } for(i=0;i<MAXDATA;i++) { minPosition=findPosition(i); position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++; } for(j=0;j<classes;j++) {for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++; } /* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/for(j=0;j<classes;j++){minPosition=0;for(i=1;i<classes;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;}}fprintf(fp,"* %d\n",0);for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);}fclose(fpOut);fclose(fp);return; }/*end of function*/ /********************************************************************/ /* Function to find to which class the object belongs */ /* Output: Class number */ /* Input: object position */ /* Global Structure Modified: None */ /********************************************************************/int findPosition(int i) { int j,position=0; for(j=1;j<classes;j++) { if(U[j][i]>U[position][i]) position=j; } return(position); }/*end of function find position*/

Parag Kanade/Partition with centroids/HCM/british.data

Normalized British towns data. Each feature is normalized to start at 0.# 12.5900 1.4400 4.4700 4.7100 11.7600 0.1100 0.5800 6.4200 8.9400 0.4000 3.1900 4.8700 10.6200 1.5000 2.7200 2.9200 10.2800 0 2.6800 6.0400 9.5500 0.9700 2.6200 5.1700 9.7800 1.9600 0.5400 4.2500 10.0900 1.3200 2.7300 4.5900 6.5300 0.9200 2.5100 5.4500 8.3000 2.3300 1.2800 3.9300 6.8000 3.6800 2.0900 2.8700 5.0900 2.0700 4.8200 4.0000 5.7200 5.6600 4.0800 2.3900 5.2300 3.9400 1.7800 2.3000 6.7100 3.8600 3.3600 2.1000 5.4800 3.6800 3.5500 2.5000 5.1100 4.3700 3.7300 2.2300 4.9700 4.6500 3.6900 1.4600 4.9700 4.4700 5.1500 0.6500 7.8300 4.4700 2.6000 4.2100 2.1800 5.1200 4.5500 3.2400 3.3200 4.2300 3.6000 2.2100 2.2400 4.5200 3.8200 2.9100 3.4400 5.1100 1.8000 2.6600 2.6800 4.8200 3.6500 2.0200 3.2400 3.1900 4.1800 0.7400 3.5300 3.2400 3.0700 1.1400 3.2800 5.0300 4.2700 0.6700 2.3100 4.0000 3.9900 0.7600 3.0100 4.1600 4.0500 1.0900 3.4600 4.2600 2.8000 2.5300 3.6800 4.5900 1.0800 0.4300 2.5200 3.7800 2.3700 2.5700 2.9500 4.1200 2.7400 2.5300 3.4800 5.0900 2.2700 0.8500 2.0200 5.1000 2.6400 4.4400 0.4300 6.2800 1.6600 0 1.5600 5.3400 2.0400 2.4900 1.3600 5.3800 4.7400 0.9800 1.4100 5.7500 3.3600 0.8500 0.2500 4.4700 1.3400 0.4500 1.1300 4.7400 3.1100 3.1200 0.9000 6.2300 2.7800 1.6300 0.6000 6.2600 2.3200 1.7200 0.6100 5.4900 3.1800 1.0300 0 6.9500 2.8200 0.9900 0.5000 5.6600 0 2.4200 0.7700 4.5000 2.5400 2.1700 2.0100 5.0200 0.3500 1.7600 0.4600 7.6900 2.9800 1.6700

Parag Kanade/Combined Algorithm/combErr.c

#include "headerStatic.h" /********************************************************************/ /********************************************************************/ /* Function to read the input data set */ /* The file name is assumed to be itest.data */ /* Status: Tested */ /* Output: Data from the file into the "data" (Global) data structure */ /* Output: Count (Global), the count of the number of objects */ /********************************************************************/ void readInput() { FILE *fp; char fname[80]; int i=0,j=0; char ch; sprintf(fname,"%s.data",filename); /* Check if the input file is opened */ if((fp=fopen(fname,"r"))==NULL) { printf("\n Unable to open input file \n"); exit(0); } /*initialise the minimum maximum and the scale */ for(i=0;i<features;i++) { minInput[i]=1000; maxInput[i]=0; minForHeaps[i]=1000; maxForHeaps[i]=0; scale[i]=0; scaleForHeaps[i]=0; } /* Discard leading comments */ ch = 'c'; while (ch != '#') fscanf (fp, "%c", &ch); i=0; /* Parse the file and populate the data structure */ while(!feof(fp)) { for(j=0;j<features;j++) { fscanf(fp,"%f ",&data[i][j]); } fscanf(fp,"\n"); for(j=0;j<features;j++) { if(data[i][j]<minInput[j]) minInput[j]=data[i][j]; if(data[i][j]>maxInput[j]) maxInput[j]=data[i][j]; } i++; } count=i; /* Normailze the feature values in between 0 and 1 */ for(j=0;j<features;j++) { if(minInput[j]!=maxInput[j]) scale[j]=1.0/(maxInput[j]-minInput[j]); else scale[j]=1.0; } for(i=0;i<count;i++) { for(j=0;j<features;j++) data[i][j]=(data[i][j]-minInput[j])*scale[j]; } fclose(fp); return; }//end of function /********************************************************************/ /*Function to compute the eucldiean distance for all input data set */ /* Output : an array conatining the distance of each object with the other */ /********************************************************************/ float computeDistance() { float distance,dist=0; int i=0,j=0,k=0; distance=0; for(j=0;j<count;j++) for(i=0;i<count;i++) { for(k=0;k<features;k++) dist+=sqrt((data[j][k]-data[i][k])*(data[j][k]-data[i][k])); if(distance<dist)distance=dist; dist=0; } return(distance); } /********************************************************************/ /* Function to compute the distance between two objects */ /* Output : Euclidean d istance between the two input objects */ /********************************************************************/ float computeDistanceBetween2(irisData a,irisData b) { float temp=0; int i=0; for(i=0;i<features;i++) temp+=(a[i]-b[i])*(a[i]-b[i]); temp=sqrt(temp); return(temp); } /********************************************************************/ /* Function to compute the value of an object */ /********************************************************************/ float value(irisData a) { int i=0; float temp=0; for(i=0;i<features;i++) temp+=a[i]*a[i]; return(sqrt(temp)); } /********************************************************************/ /* Function to find the object in the data structure */ /* Output : The position of the object in the data */ /* Input : The object */ /********************************************************************/ int findInData(irisData object,int iFlag) { int i=0,lastCount=0,j,position=-1,flag[features],flg;if(iFlag==1)lastCount=MAXDATA;else{}for(i=0;i<lastCount;i++){ for(j=0;j<features;j++) if(fabs(object[j]-data[i][j])<0.00001) flag[j]=1; else { flag[j]=0; break;} flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /* Function to find whether there is an object on the board at a,b */ /* Output : a flag indicating whether an object is found or not */ /* Input : The row and column indicating the board position */ /********************************************************************/ int found(int a , int b) { if(board[a][b]==9) return 1; else return 0; } /********************************************************************/ /* Function to initialize the initial board */ /* Output : None */ /* Global Structures modified : heaps */ /* Input : Flag indicating the iteration 1: First 0: Second */ /********************************************************************/ void initialise(int flag) { int a,b,i=0,k=0; int loopCount,rowCount,colCount; /* Initialize the row, column and number of heaps */ if(flag==1) { loopCount=count; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=lCount; rowCount=heapRow; colCount=heapRow; } /* Initialize the heaps */ for(i=0;i<loopCount;i++) { heaps[i].count=0; heaps[i].x=0; heaps[i].y=0; heaps[i].disPosition=0; heaps[i].Dmax=0; heaps[i].Dmean=0; } for(i=0;i<loopCount;i++) { /*find a board position which is empty*/ do { a=lrand48()%rowCount; b=lrand48()%colCount; } while(found(a,b)); /*Allocate the object to that place and assign a heap to it*/ heaps[i].x=a; heaps[i].y=b; heaps[i].count=1; heaps[i].Dmax=0; heaps[i].Dmean=0; if(flag==1) { heaps[i].objects[0]=i; for(k=0;k<features;k++)heaps[i].Ocenter[k]=data[i][k]; } else { for(k=0;k<features;k++) heaps[i].Ocenter[k]=newHeaps[i].Ocenter[k]; } heaps[i].disPosition=0; board[a][b]=9; if((debugLevel>0)&&(flag==1))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],data[heaps[i].objects[0]][0]); if((debugLevel>0)&&(flag==0))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],newHeaps[i].Ocenter[0]); } /*initialise the initial heap count*/ heapCount=loopCount; currentRow=rowCount; } /********************************************************************/ /* Function to find whether an heap is present at the location a b */ /* Output : Flag indicating the presencs of the heap */ /* Input : Roe and column indicating the board position */ /********************************************************************/ int findObject(int a,int b) { int i; int position=-1; for(i=0;i<heapCount;i++) { if((heaps[i].x==a)&&(heaps[i].y==b)) { position=i; break; } } return(position); } /********************************************************************/ /* Function initialize the ants */ /* Output : None */ /* Input : Flag indicating the iteration */ /* Global Variables Modified : ant, heapCount, heaps, board */ /********************************************************************/ void initialiseAnts(int flag) { int i=0,a=0,b=0,position,j=0,loopCount=0,rowCount,colCount,k=0; if(flag==1) { loopCount=MAXANTS; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=MAXANTSFORHEAPS; rowCount=heapRow; colCount=heapRow; } for(i=0;i<loopCount;i++) { /*generate random positions for the ants*/ a=lrand48()%rowCount; b=lrand48()%colCount; ants[i].x=a; ants[i].y=b; /*Find if an object is present at that position or not*/ if(board[a][b]==9) { ants[i].carrying=1; /*find the heap at that location*/ position=findObject(a,b); if(position==-1) { printf("\n Error in logic: Ant init \n"); //getchar(); break; } else { /*Assign the heap data to the ant data and decrement the heap count*/ if(flag==1){ for(k=0;k<features;k++)ants[i].data[k]=data[heaps[position].objects[0]][k]; }else{for(k=0;k<features;k++)ants[i].data[k]=heaps[position].Ocenter[k];} for(j=position;j<heapCount-1;j++) heaps[j]=heaps[j+1]; heapCount--; } /*Clear the board position */ board[a][b]=-9; } else ants[i].carrying=-1; ants[i].speed=0; ants[i].dir=lrand48()%8; printf("\n Ant %2d X: %2d Y: %2d Carying : %2d ",i,ants[i].x,ants[i].y,ants[i].carrying); } currentAntCount=loopCount; } /********************************************************************/ /* Function to move the ant */ /* Output: None */ /* Input: Ant to be moved and the flag indicating the iteration */ /********************************************************************/ void moveNext(ant* a,int flag) { int rowCount,colCount; rowCount=currentRow;colCount=currentRow; /*Depending upon the direction move the ant*/ switch (a->dir) { case 0: a->y++; if(a->y>=colCount)a->y=0; break; case 1: a->x--; a->y++;; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 2: a->x--; if(a->x<0)a->x=rowCount-1; break; case 3: a->x--; a->y--; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 4: a->y--; if(a->y<0)a->y=colCount-1; break; case 5: a->x++; a->y--; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 6: a->x++; if(a->x>=rowCount) a->x=0; break; case 7: a->x++; a->y++; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break; } }//end of function /********************************************************************/ /* Function to compute the parameters of the heap */ /* Output: None */ /* Input: The heap whose parameters are to be computed iteration flag*/ /********************************************************************/ void computeHeapParameters(heap* heapData,int flag) { int i=0,j=0,k=0,position=0; float distance=0; float Dmean=0.0; float *dis; float featureSum[features]; float max=0; float sum=0; /* init*/ for(k=0;k<features;k++) featureSum[k]=0; /*Compute the distance of each object and find the maximum distance*/ max=0; if(flag==1){ for(j=0;j<heapData->count;j++) for(i=0;i<heapData->count;i++) { for(k=0;k<features;k++) sum+= (data[heapData->objects[j]][k]-data[heapData->objects[i]][k])*(data[heapData->objects[j]][k]-data[heapData->objects[i]][k]); distance=sqrt(sum); if(max<distance) max=distance; sum=0; } /* Assign the maximum distance to the heap */ heapData->Dmax=max;/*Compute the centre object*/for(i=0;i<heapData->count;i++)for(k=0;k<features;k++) featureSum[k]+=data[heapData->objects[i]][k];for(k=0;k<features;k++)heapData->Ocenter[k]=featureSum[k]*1.0/heapData->count; /* Find the mean distance */if((dis=(float *) calloc(heapData->count,sizeof(float)))==NULL){printf("\nUnable to allocate memory in the compute heap parameters procedure\n");exit(0);} for(i=0;i<heapData->count;i++) { Dmean+=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); dis[i]=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); } heapData->Dmean=Dmean*1.0/heapData->count; /*Compute the most dissimilar object*/ max=dis[0]; for(i=1;i<heapData->count;i++) if(max<dis[i]) { max=dis[i]; position=i; } /*Assign the most dissimilar object to the heap variable*/ for(k=0;k<features;k++) heapData->Odissim[k]=data[heapData->objects[position]][k]; heapData->disPosition=position;free(dis); } else{/* Heap Parameters for the second stage */for(k=0;k<features;k++){ heapData->Odissim[k]=heapData->Ocenter[k];}heapData->disPosition=0;heapData->Dmean=0;heapData->Dmax=0;} }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a single object*/ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject1(ant *a,int position,int flag) { int i,k=0; /*Clear the board*/ board[heaps[position].x][heaps[position].y]=-9; /* Assign the data to the ant*/if(flag==1){ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[0]][k];}else {for(k=0;k<features;k++) a->data[k]=heaps[position].Ocenter[k];} a->carrying=1; /*Copy all the heaps remaining heaps in the structure*/ for(i=position;i<heapCount-1;i++) heaps[i]=heaps[i+1]; /*Decrement the heap count*/ heapCount--; }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a two objects */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject2(ant *a , int position,int flag) { float Pdestroy1=-1; int i=0,k=0; /*Compute the probability to destroy the heap*/ Pdestroy1=lrand48()%13*1.0/12.0; /*If the probability is more than Pdestroy then destroy the heap */ if(Pdestroy1<Pdestroy) { /*pick the object randomly*/ i=lrand48()%2; /* Assign the data to the ant */ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[i]][k]; a->carrying=1; /*Correct the heap parameters*/ heaps[position].count=1; heaps[position].objects[0]=heaps[position].objects[1-i]; computeHeapParameters(&heaps[position],flag); } }//end of function /********************************************************************/ /*Function to pick a object from a heap consisting of more than two object */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /********************************************************************/ void pickObjectn(ant *a,int position,int flag) { int i=0,k=0; /*If the criteria is satsfied then pick the object*/ if((computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)*1.0/heaps[position].Dmean)>Tremove) { a->carrying=1; for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[heaps[position].disPosition]][k]; for(i=heaps[position].disPosition;i<heaps[position].count-1;i++) heaps[position].objects[i]=heaps[position].objects[i+1]; heaps[position].count--; computeHeapParameters(&heaps[position],1); } }//end of function /********************************************************************/ /* Function to pick a object */ /********************************************************************/ void pick(ant *a,int flag) { int position=-1,i=0; float Pload1=-1.0; for(i=0;i<heapCount;i++) { /*Check if a heap is present in the eight neighbouring cells*/ if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if Heap is found*/ if(position!=-1) { /* heap or object found.*/ /*Depending upon the number present in the heap call the corresponding routine*/ switch(heaps[i].count) { case 1: Pload1 = lrand48()%151*1.0/150.0; if(Pload1<Pload) { pickObject1(a,position,flag); } break; case 2: pickObject2(a,position,flag); break; default: pickObjectn(a,position,flag); break; } } } /********************************************************************/ /* Function to drop a object to form a new heap */ /* Output: None */ /* Input: Ant, iteration flag */ /* Global Structure modified : heap, heapCount */ /********************************************************************/ void dropObject(ant* a,int flag) { /*Create a new heap*/ int k=0; heaps[heapCount].count=1; heaps[heapCount].x=a->x; heaps[heapCount].y=a->y; heaps[heapCount].Dmax=0; heaps[heapCount].Dmean=0; heaps[heapCount].disPosition=0; for(k=0;k<features;k++) { heaps[heapCount].Odissim[k]=a->data[k]; heaps[heapCount].Ocenter[k]=a->data[k]; } if(flag==1) {heaps[heapCount].objects[0]=findInData(a->data,flag); /*computeHeapParameters(&heaps[heapCount],flag);*/ } computeHeapParameters(&heaps[heapCount],flag); heapCount++; a->carrying=-1; board[a->x][a->y]=9; }//end of function /********************************************************************/ /* Function to find the heap position during the second iteration */ /* Output: position of the heap */ /* Input: object center of the heap that the ant is carrying */ /* Global Structure modified : none */ /********************************************************************/ int findInNewheaps(irisData object) { int position=-1,i,j=0,flag[features],flg; for(i=0;i<newHeapCount;i++) { for(j=0;j<features;j++) { if(fabs(object[j]-newHeaps[i].Ocenter[j])<0.00001) flag[j]=1; else {flag[j]=0;break;} } flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /*Function to drop a object to a heap consisting of a one or more object */ /* Output: none */ /* Input: Ant, position of the heap , iteration flag */ /* Global Structure modified: heaps,newHeaps */ /********************************************************************/ void dropObject1(ant* a, int position,int flag) { int heapPosition=-1,heapPosition1=-1,i,j,k=0; irisData newData,temp; if(flag==1) { heaps[position].objects[heaps[position].count]=findInData(a->data,flag); heaps[position].count++; computeHeapParameters(&heaps[position],flag); } else { for(k=0;k<features;k++)temp[k]=heaps[position].Ocenter[k]; heapPosition=findInNewheaps(a->data); heapPosition1=findInNewheaps(heaps[position].Ocenter); if(heapPosition==-1||heapPosition1==-1) { printf("\n error in logic:new heaps\n"); //getchar(); return; } for(k=0;k<features;k++) {newData[k]=(newHeaps[heapPosition].Ocenter[k]*newHeaps[heapPosition].count+newHeaps[heapPosition1].Ocenter[k]*newHeaps[heapPosition1].count)*1.0/(newHeaps[heapPosition].count+newHeaps[heapPosition1].count);newHeaps[heapPosition].Ocenter[k]=newData[k];heaps[position].Ocenter[k]=newData[k]; } computeHeapParameters(&heaps[position],flag); for(i=newHeaps[heapPosition].count,j=0;j<newHeaps[heapPosition1].count;i++,j++) { newHeaps[heapPosition].objects[i]=newHeaps[heapPosition1].objects[j]; } newHeaps[heapPosition].count+=newHeaps[heapPosition1].count; for(i=heapPosition1;i<newHeapCount-1;i++) newHeaps[i]=newHeaps[i+1]; newHeapCount--; } a->carrying=-1; }//end of function /********************************************************************/ /* Function to drop a object */ /* Output: None */ /* Input: Ant, iteration flag */ /********************************************************************/ void drop(ant* a,int flag) { int i, position=-1; float Pdrop1=0.0; /*Find whether we have a heap in the neighbourhood of the cell*/ for(i=0;i<heapCount;i++) { if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if a heap is found*/ if(position!=-1) { /*heap or object found.*/ /*Depending on the number of objects present take action*/ switch(heaps[position].count) { /*If criteria is satisfied then drop the object*/ case 1: if(flag==1) { if((computeDistanceBetween2(a->data,data[heaps[position].objects[0]])*1.0/Dmax)<Tcreate) dropObject1(a,position,flag); } else { if((computeDistanceBetween2(a->data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) dropObject1(a,position,flag); } break; default: if(computeDistanceBetween2(a->data,heaps[position].Ocenter)<computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)) dropObject1(a,position,flag); break; } } else { /*Compute the probability to drop the object*/ Pdrop1 = lrand48()%151*1.0/150; if(Pdrop1<Pdrop) dropObject(a,flag); } }//end of function /********************************************************************/ /* Function to move the ant */ /* Output: none */ /* Input: Ant, iteration flag */ /********************************************************************/ void move(ant* a,int flag) { float pDir; /*Check the speed */ if (a->speed!=0) { /*move in the same direction*/ a->speed--; moveNext(a,flag); } else { /*generate the new direction*/ pDir=lrand48()%17*1.0/16.0; if(pDir<.85) { a->dir=lrand48()%8; } else if(pDir>.95) { a->speed=2; } moveNext(a,flag); } } /********************************************************************/ /* Function to simulate the movement of the ants */ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified: minHeapCount, minHeapPosition */ /********************************************************************/ void iterate(int flag) { int i=0,j=0,k=0,loopCount=0; if (flag==1) loopCount=MAXANTS; else loopCount=MAXANTSFORHEAPS; while (i<MAXITERATIONS) { i++; for(j=0;j<loopCount;j++) { move(&ants[j],flag); if(ants[j].carrying==1) { drop(&ants[j],flag); } else { pick(&ants[j],flag); } } if(debugLevel>0){ for(k=0;k<heapCount;k++) for(j=0;j<heapCount;j++) if((k!=j)&&(heaps[k].x==heaps[j].x)&&(heaps[k].y==heaps[j].y)) { printf("\nerror two heaps with same co ordinates %d %d ",k,j); //getchar(); } k=0; for(j=0;j<heapCount;j++) k+=heaps[j].count; } if(heapCount<minHeapCount) { minHeapCount=heapCount; minPosition=i; } if(debugLevel>0) printf("\n i:%d Heap count %d sum %d",i,heapCount,k); else printf("\n i:%d Heap count %d",i,heapCount); heapSum+=heapCount; } }//end of function /********************************************************************/ /*Function to drop the objects carried by the ants after all the iterations*/ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified:none */ /********************************************************************/ void dropFinal(int flag) { int i=0,j=0,position=-1,loopCount=0; loopCount=currentAntCount; for(i=0;i<loopCount;i++) { if(ants[i].carrying==1) { if(flag==1) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position,flag); } } else { /*Find if a heap is present */ do { position=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position,flag); position=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position!=-1); } } } }//end of function /********************************************************************/ /* Function for the implementation of QuickSort */ /* Output: none */ /* Input: data structure, low and high indices */ /* Global structure modified:none */ /********************************************************************/ void quickSort( heap A[], int low,int high ) { int mid=0; if(low<high) { mid=partition(A,low,high); quickSort(A,low,mid); quickSort(A,mid+1,high); } } /********************************************************************/ /* Partition routine used by quick sort */ /* Output: partition location */ /* Input: data , low, high */ /* Global structure modified:none */ /********************************************************************/ int partition(heap a[], int low,int high) { heap temp; int x=0,i=0,j=0; i=low; j=high; x=a[low].count; while (1) { while (a[j].count<x) j=j-1; while (a[i].count>x) i=i+1; if(i<j) { temp=a[i]; a[i]=a[j]; a[j--]=temp; } else return(j); } } /********************************************************************/ /* Function used to write the cluster information */ /* Output: none */ /* Input:iteration flag */ /* Global structure modified:none */ /********************************************************************/ void writeClusterInformation(int flag) { FILE *fp=fopen("center.clt","w"); FILE *fp1; FILE *fp2; FILE *fp3; char temp[80]; int i=0,j=0,k=0; int lastCount=0; int rowCount,colCount,antCount; strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp3=fopen(temp,"a"); if(dataDump) {strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp1=fopen(temp,"a");strcpy(temp,"");sprintf(temp,"%sObj.out",filename);fp2=fopen(temp,"a");if(fp1==NULL||fp2==NULL)printf("\n Error in opening file in the writer Cluster Information routine\n"); } rowCount=currentRow; colCount=currentRow; antCount=currentAntCount; if(fp==NULL) { printf("\n Unable to open cluster center output file \n"); /*return;*/ exit(0); } /*sort the array according to the count value and write the value of the top three elements*/ if((flag==1)&&(!EntireHeaps)) quickSort(heaps,0,heapCount); if(EntireHeaps) {if(flag==1) lastCount=heapCount;else lastCount=newHeapCount; } else lastCount=c; for(i=0;i<lastCount;i++) { if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t ",newHeaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",newHeaps[i].Ocenter[k]); fprintf(fp,"\n"); } else { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); fprintf(fp,"\n"); } } if(dataDump) { fprintf(fp1,"\n Information from the Ant Based Algorithm \n"); fprintf(fp1,"\n Important Parameters : Board %d X %d Ants : %d Iterations %d Total Heaps %d ",rowCount,colCount,antCount,MAXITERATIONS,lastCount); } if(flag==0) { if(dataDump) { fprintf(fp1," TCreateForHeaps %f \n",TcreateForHeaps); fprintf(fp2,"\nTotal Heaps : %d\n",heapCount); fprintf(fp2,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); } fprintf(fp3,"\n@ %d\n",lastCount); } else if(dataDump) fprintf(fp1,"\n");if(dataDump) { fprintf(fp1,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); for(i=0;i<lastCount;i++) if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp2,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); } fprintf(fp1," %5d\n",newHeaps[i].count); fprintf(fp2," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp1," %5d\n",heaps[i].count); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]); fprintf(fp1," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]); fprintf(fp1," %5d\n",heaps[i].count); }} if(flag==0) { if(dataDump) fprintf(fp1,"\n Objects in the heaps \n");for(i=0;i<newHeapCount;i++) { if(dataDump) fprintf(fp1,"\n\n Heap %d\n \n",i+1);fprintf(fp3,"# %d %d\n",i ,newHeaps[i].count); for(j=0;j<newHeaps[i].count;j++){ for(k=0;k<features;k++){ fprintf(fp3,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]); if(dataDump) fprintf(fp1,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]);} if(dataDump) fprintf(fp1,"\n"); fprintf(fp3,"\n"); } } } fclose(fp); fclose(fp3); if(dataDump) { fclose(fp1);fclose(fp2); }}//end of flie /********************************************************************/ /* Function to initialise the new heaps */ /* Output: none */ /* Input:new Heap structure */ /* Global structure modified:none */ /********************************************************************/ void initialiseNewHeaps(heap newHeaps[]) { int i=0,j=0; for(i=0;i<lCount;i++) { newHeaps[i].count=0; for(j=0;j<features;j++) newHeaps[i].Ocenter[j]=V[i][j]; newHeaps[i].x=heaps[i].x; newHeaps[i].y=heaps[i].y; } }//end of function /********************************************************************/ /* Function to initialise the new heaps */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ int findPosition(int i) { int j,position=0; for(j=1;j<lCount;j++) { if(U[j][i]>U[position][i]) position=j; } /*printf("\n Position %d ",position);*/ return(position); }//end of function find position /********************************************************************/ /* Function to add object */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ void addObject(int object,heap * heapPosition) { heapPosition->objects[heapPosition->count]=object; heapPosition->count++; }//end of function add object /********************************************************************/ /* Function to harden the outputs obtained from fcm */ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/ void harden(heap newHeaps[]) { int i,heapPosition=-1,sum=0,j=0; /*initialise the new heaps*/ initialiseNewHeaps(newHeaps); for(i=0;i<count;i++) { heapPosition=findPosition(i); addObject(i,&newHeaps[heapPosition]); } /* To check for degenerate partitions*/ for(i=0;i<lCount;i++) { if(newHeaps[i].count==0){for(j=i;j<lCount-1;j++){newHeaps[j]=newHeaps[j+1];}lCount--;i--;} } newHeapCount=lCount; for(i=0;i<lCount;i++) computeHeapParameters(&newHeaps[i],1); if(debugLevel>0) { for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\n sum = %d",sum);} }//end of function/********************************************************************/ /* Function to initialize the initial parameters for the ant algorithm*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void init() { int i=0,j=0; for(i=0;i<MAXROWS;i++) for(j=0;j<MAXCOLS;j++) board[i][j]=-9; heapCount=0; newHeapCount=0; heapRow=0; minPosition=-1; minHeapCount=100000; for(i=0;i<MAXANTS;i++) { ants[i].carrying=-1; ants[i].x=-1; ants[i].y=-1; ants[i].speed=-1; ants[i].dir=-1; } } /********************************************************************/ /* Function to initialize the initial board */ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void initialiseBoard() { int i,j; for(i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; }/********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void harden1() { int i=0,heapPosition=-1; for(i=0;i<heapCount;i++) cenCount[i]=0; for(i=0;i<n;i++) { heapPosition=findPosition1(i); addObject1(i,heapPosition); } writeInfo(); }//end of function/********************************************************************/ /* Function to find to which class the object belongs */ /* Output: Class number */ /* Input: object position */ /* Global Structure Modified: None */ /********************************************************************/int findPosition1(int i) { int j,position=0; for(j=1;j<heapCount;j++) { if(U[j][i]>U[position][i]) position=j; } return(position); }//end of function find position/********************************************************************/ /* Function to Write the output of the hardened objects to file*/ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/void writeInfo(){int i=0,j=0,k=0;FILE *fp=fopen("fcm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}fprintf(fp,"\n@ %d\n",heapCount);for(i=0;i<heapCount;i++){fprintf(fp,"# %d %d\n",i,cenCount[i]);for(j=0;j<cenCount[i];j++){for(k=0;k<s;k++)fprintf(fp,"%8.2f ",clsCen[i][j][k]/scale[k]+minInput[k]);fprintf(fp,"\n");}}fclose(fp);return;}/********************************************************************/ /* Function to add a object to a cluster (2nd stage) */ /* Output: none */ /* Input: object poistion , cluster position */ /* Global Structure Modified: Cluster object and cluster count*/ /*******************************************************************/void addObject1(int object,int heapPosition) { int k=0; for(k=0;k<features;k++) clsCen[heapPosition][cenCount[heapPosition]][k]=X[object][k]; cenCount[heapPosition]++; return; }//end of function add object/********************************************************************/ /* Function to read the original classification of objects */ /* Output: none */ /* Input: none */ /* Global Structure Modified: None */ /*******************************************************************/void readOrg(){int i=0,k=0;char temp[80];FILE *fp;strcpy(temp,"");sprintf(temp,"%sFull.data",filename);fp=fopen(temp,"r");if(fp==NULL){printf("\n Unable to open full data input file ");exit(0);}for(i=0;i<n;i++){fscanf(fp,"%f ",&orgData[i][0]);for(k=1;k<s+1;k++)fscanf(fp,"%f ",&orgData[i][k]);}return;}// end of function/********************************************************************/ /* Function to classify the objects in different clusters */ /* Output: none */ /* Input: the cluster objects, the original classification, */ /* number of objects in the cluster,file to write the data */ /* Global Structure Modified: None *//********************************************************************/int analysisData(float data[][s],float orgData[n][s+1],int count,FILE * fp){int i=0,j=0,k=0,type=1,max=0;heapInfo info;for(i=0;i<c;i++){info.classObj[i]=0;}info.total=0;info.total=count;for(i=0;i<count;i++)for(j=0;j<n;j++){type=1;for(k=0;k<s;k++)if(fabs(data[i][k]-orgData[j][k+1])>errThresh){type=-1;break;}if(type!=-1){info.classObj[(int)orgData[j][0]-1]++;break;}}max=0;for(k=0;k<c;k++){if(info.classObj[k]>info.classObj[max])max=k;fprintf(fp,"\nClass %d Objects % d ",k+1,info.classObj[k]);}j=0;for(k=0;k<c;k++){if(k!=max)j+=info.classObj[k];}fprintf(fp,"\n Total errors : %d \n",j);return(j);}//end of analyse/********************************************************************/ /* Function to read the cluster information */ /* Output: none */ /* Input: input file pointer , output file pointer */ /* Global Structure Modified: None *//********************************************************************/void readFCM(FILE* fp,FILE *fp1){int i=0,j=0,k=0,l=0;int totalRuns=0,heapCount=0,junk=0,sum=0;char ch;FILE *fp2=fopen("errors.out","a");ch = 'c'; while (ch != '$') fscanf (fp, "%c", &ch);fscanf(fp,"%d\n",&totalRuns);printf("\n total runs %d ",totalRuns);for(l=0;l<totalRuns;l++){fscanf(fp,"%c",&ch);fscanf(fp,"%d\n",&heapCount);fprintf(fp1,"\n \nExperiment %d \n ",l+1);sum=0;for(i=0;i<heapCount;i++){fscanf(fp,"%c",&ch);fscanf(fp," %d %d\n",&junk,&clsCenCount[i]);for(j=0;j<clsCenCount[i];j++)for(k=0;k<s;k++)fscanf(fp,"%f",&fcmData[i][j][k]);fscanf(fp,"\n");fprintf(fp1,"\n\n Heap %d Total Objects %d \n",i+1,clsCenCount[i]);sum+=analysisData(fcmData[i],orgData,clsCenCount[i],fp1);}fprintf(fp2," %3d %3d %3d \n",l,sum,heapCount);}fprintf(fp2,"\n --------------------------\n");fclose(fp2);return;}//end of functionvoid initRm(int heapCount) { antCount=heapCount*antsPerFeature*features;classes=heapCount;newAnts=(antdata*) calloc(antCount,sizeof(antdata));if(ants==NULL){printf("\n Cannot allocate memory to ants in Rm stage");printf("\n Exiting ... ");exit(-1);}} /***************************************************************************//* Function to initialise the ants *//* Status: under developement *//* Output: the ant structure is modified with initial random values *//* Input : Ant structure (this is modified) *//***************************************************************************/void initializeAntsRm(antdata* ants,center clsCen){int i,j,k;/* Initialize all the ants with initial values and the rest and direction*/for(i=0;i<antCount;i++){ants[i].currPos=drand48();for(j=0;j<memory;j++){ants[i].Rm[j]=inf;for(k=0;k<antsPerFeature;k++)Rm[k][j]=inf;}ants[i].rest=1;ants[i].direction=drand48()<0.5?1:-1;}for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[j*classes+i].currPos=clsCen[i][j];}}/*end of function*//***************************************************************************//* Function that runs one epoch *//* Status: under developement *//* Output: the ant structure is modified with according to the epoch *//* Input : Ant structure (this is modified) *//***************************************************************************/void epoch(antdata* ants){int i,j;float Trest=0,Tcontinue=0,Tinc=0;float dDiff=0.0;float temp=0;dDiff=DmaxRm-Dmin;for(j=0;j<iterations;j++){for(i=0;i<antCount;i++){if(ants[i].rest!=0){Trest=drand48();if(Trest<Prest){ants[i].rest=0;continue;}Tcontinue=drand48();if(Tcontinue>Pcontinue)ants[i].direction=-1*ants[i].direction;Tinc=drand48();temp=ants[i].currPos+ants[i].direction*(Dmin+Tinc*dDiff);if(temp>1||temp<0)ants[i].direction=-1*ants[i].direction;ants[i].currPos+=ants[i].direction*(Dmin+Tinc*dDiff);}}}for(i=0;i<antCount;i++)ants[i].rest=1;}/*end of function*//***************************************************************************//* Function that computes Rm *//* Status: under developement *//* Output: the ant structure is modified, the worst Rm is removed *//* Input : Ant structure (this is modified) *//***************************************************************************/void computeRm(antdata* ants,float data[MAXDATA][features],float newRm[antsPerFeature]){int i,j,k=0,l=0;float clsCenters[antsPerFeature][MAXDATA][features];double Rm=0.0,temp=0.0,Dik=0;for(k=0;k<antsPerFeature;k++)for(j=0;j<features;j++)for(i=0;i<classes;i++){clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;}for(k=0;k<antsPerFeature;k++){Rm=0;for(i=0;i<count;i++){Dik=0;for(j=0;j<classes;j++){temp=0;for(l=0;l<features;l++)temp+=(data[i][l]-clsCenters[k][j][l])*(data[i][l]-clsCenters[k][j][l]);/*temp=sqrt(temp);*//*temp=NORM(data[i],clsCenters[k][j]);temp=temp*temp;*/Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}newRm[k]=Rm;}return;}/*end of function*//********************************************************************//* Function to update the memory of the ants *//* Output: none *//* Input: ant structure and the new Rm, old memory *//* Global Structure Modified: none *//********************************************************************/void updateMemory(antdata* ants,float newRm[antsPerFeature],float Rm[antsPerFeature][memory]){int i,minPosition[antsPerFeature],maxPosition[antsPerFeature],flag[antsPerFeature],k,j,b[memory],infCount=0;float temp=0,tempRm[memory],tChoose=0,pContinueCurr=0;for(i=0;i<antsPerFeature;i++){minPosition[i]=0;maxPosition[i]=0;flag[i]=1;}for(k=0;k<antsPerFeature;k++){b[0]=0;for(i=1;i<memory;i++){if(Rm[k][i]<Rm[k][minPosition[k]])minPosition[k]=i;if(Rm[k][i]>Rm[k][maxPosition[k]])maxPosition[k]=i;if(Rm[k][i]==newRm[k]){flag[k]=0;break;}temp=(newRm[k]-Rm[k][i])*1.0/Rm[k][i];if(temp<0) temp=-1*temp;if(temp<minChange){flag[k]=0;break;}b[i]=i;}if(flag[k]==1){if(newRm[k]<Rm[k][maxPosition[k]]){for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];}else{/* find min Rm and restore its position*/infCount=0;for(i=0;i<memory;i++){tempRm[i]=Rm[k][i];if(Rm[k][i]==inf) infCount++;}for(i=0;i<memory;i++)for(j=0;j<memory-1-i;j++){if(tempRm[j]>tempRm[j+1]){temp=tempRm[j];tempRm[j]=tempRm[j+1];tempRm[j+1]=temp;temp=b[j];b[j]=b[j+1];b[j+1]=temp;}}tChoose=drand48();if(tChoose<0.6){minPosition[k]=b[0];}else if (tChoose<0.8){minPosition[k]=b[1];}else if(tChoose<0.9){minPosition[k]=b[2];}else if(tChoose<0.975){minPosition[k]=b[3];}else {minPosition[k]=b[4];}pContinueCurr=drand48();if(pContinueCurr<tContinueCurr){/*for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];*/}else if(Rm[k][minPosition[k]]!=inf)for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].currPos=ants[k*classes*features+j*classes+i].Rm[minPosition[k]];}}}}}/* end of function *//********************************************************************//* Function to cluster according to the cluster centroids *//* Output: none *//* Input: ant structure and the data points *//* Global Structure Modified: none *//********************************************************************/void finalCluster(antdata* ants,float data[MAXDATA][features],float Rm[antsPerFeature][memory]){int i,j,position[MAXDATA],minPosition=0,k=0,l=0,minRm=0;float clsCenters[antsPerFeature][MAXDATA][features];float distance[MAXDATA];int cenCount[MAXDATA],classCount[MAXDATA][c];float temp=0;int clsObjects[MAXDATA][MAXDATA],label[MAXDATA];int minPosition1[antsPerFeature],maxPosition1[antsPerFeature];FILE *fp,*fpOut,*fpCls,*fpMinRm,*fpOrgClass,*fpRm;fp=fopen("fcmRm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpMinRm=fopen("minRm.out","a");fpOrgClass=fopen("orgClass.out","a");fpRm=fopen("Rm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}for(k=0;k<antsPerFeature;k++){for(j=0;j<features;j++)for(i=0;i<classes;i++)clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;minPosition1[k]=0;maxPosition1[k]=0;}for(k=0;k<antsPerFeature;k++){for(i=1;i<memory;i++){if(Rm[l][i]<Rm[l][minPosition1[l]])minPosition1[l]=i;if(Rm[l][i]>Rm[l][maxPosition1[l]])maxPosition1[l]=i;}}minRm=0;for(k=1;k<antsPerFeature;k++){if(Rm[k][minPosition1[k]]<Rm[minRm][minPosition1[minRm]])minRm=k;}fprintf(fpRm,"Rm %8.4f\n",Rm[minRm][minPosition1[minRm]]);fclose(fpRm);Fcm(data,clsCenters[minRm],2);hardenRm();for(l=0;l<antsPerFeature;l++){fprintf(fpCls,"Cluster number %2d\n",l);for(j=0;j<classes;j++){distance[j]=inf;cenCount[j]=0;for(k=0;k<c;k++)classCount[j][k]=0;}fprintf(fp,"* %d\n",l);if(l==minRm)fprintf(fpMinRm,"* %d\n",l);for(i=0;i<count;i++){minPosition=0;for(k=0;k<classes;k++){temp=0.0;for(j=0;j<features;j++)temp+= (clsCenters[l][k][j]-data[i][j])*(clsCenters[l][k][j]-data[i][j]);temp=sqrt(temp);distance[k]=temp;if(distance[k]<distance[minPosition])minPosition=k;}position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++;}for(j=0;j<classes;j++){for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++;}/* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/fprintf(fpOrgClass,"\nCluster number: %2d \n",l);for(j=0;j<classes;j++){minPosition=0;fprintf(fpOrgClass,"\n Cluster %2d \n",j+1);fprintf(fpOrgClass,"%2d %2d\n",1,classCount[j][0]);for(i=1;i<c;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;fprintf(fpOrgClass,"%2d %2d\n",i+1,classCount[j][i]);}}for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");fprintf(fpOut,"\nCluster number: %2d \n",l);for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);for(j=0;j<features;j++)fprintf(fpCls,"%8.7f ",clsCenters[l][i][j]/scale[j]+minInput[j]);fprintf(fpCls,"\n");}}/* Write the data to the file */fclose(fp);fclose(fpOut);fclose(fpCls);fclose(fpMinRm);fclose(fpOrgClass);}/* end of function *//********************************************************************//* Function to amnalyze the results *//* Output: none *//* Input: *//* Global Structure Modified: none *//********************************************************************/void analysis(int flag){int i,j,k,temp;char junk;int totalRuns=0,errors[antsPerFeature],epoch=0,position=0,antsperfeature=0,clsNumber=0,min=0,currMinRm;FILE *fp,*fpError,*fpMin,*fpMinRm,*fpMinIp;if(flag==1){fp=fopen("fcmRm.out","r");fpMinIp=fopen("minRm.out","r");fpMinRm=fopen("minRmError.out","w");fpError=fopen("error.out","w");fpMin=fopen("minError.out","w");}else{fp=fopen("fcmFCM.out","r");fpError=fopen("errorFCM.out","w");}if(fp==NULL||fpError==NULL){printf("\n unable to open files in the analysis module \n");return;}fscanf(fp,"%c %d\n",&junk,&totalRuns);fscanf(fp,"%c %d\n",&junk,&antsperfeature);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&temp);}for(i=0;i<totalRuns;i++){for(j=0;j<antsperfeature;j++){errors[j]=0;}fscanf(fp,"%c %d\n",&junk,&epoch);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&currMinRm);}fprintf(fpError,"Experiment %3d ",i);for(k=0;k<antsperfeature;k++){fscanf(fp,"%c %d\n",&junk,&clsNumber);for(j=0;j<count;j++){fscanf(fp,"%d ",&position);if(orgData[j][0]!=position)errors[k]++;}fscanf(fp,"\n");if(flag==1){fprintf(fpError,"\nCluster %3d Errors %5d",k,errors[k]);if(k==currMinRm){fprintf(fpMinRm,"Cluster %2d Errors %5d\n",k,errors[k]);}}else fprintf(fpError," Errors %5d\n",errors[k]);}if(flag==1){min=0;for(j=1;j<antsPerFeature;j++){if(errors[j]<errors[min])min=j;}fprintf(fpMin,"Exp %2d Min Error %5d\n",i,errors[min]);}}fclose(fp);fclose(fpError);if(flag==1){fclose(fpMin);fclose(fpMinRm);fclose(fpMinIp);}}/* end of function */ void RmStage(int heapCount,center clsCen) { FILE *fpTime=fopen("time.out","a");int flag[antsPerFeature],flg;struct rusage start_usage, end_usage,epoch_start_usage, epoch_end_usage;double *perf_times;float newRm[antsPerFeature],oldRm[antsPerFeature],temp;int i=0,j=0,k=0;initRm(heapCount);initializeAntsRm(newAnts,clsCen);for(i=0;i<antsPerFeature;i++){oldRm[i]=inf;newRm[i]=inf;}flg=1;i=0;getrusage(RUSAGE_SELF, &start_usage);while(i<maxEpocs){getrusage(RUSAGE_SELF, &epoch_start_usage);epoch(newAnts);getrusage(RUSAGE_SELF, &epoch_end_usage);perf_times=timing_of(epoch_start_usage,epoch_end_usage); fprintf(fpTime,"Epoch %3d Timing: %f user, %f system, %f total.\n",i, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);for(j=0;j<antsPerFeature;j++){oldRm[j]=newRm[j];flag[j]=1;}computeRm(newAnts,data,newRm);updateMemory(newAnts,newRm,Rm);flg=0;for(j=0;j<antsPerFeature;j++){if(newRm[j]!=inf){if(oldRm[j]!=inf){temp=0;temp=newRm[j]-oldRm[j]<0? (oldRm[j]-newRm[j]):(newRm[j]-oldRm[j]);/*printf("\n new %f old %f temp %f ",newRm[j],oldRm[j],temp);*/if(temp/oldRm[j]>minChange){flg=1;break;}}else flg=1;}}i++;printf("\n Epoch %d ",i);}/* Do the final clustering*/finalCluster(newAnts,data,Rm);getrusage(RUSAGE_SELF, &end_usage);perf_times=timing_of(start_usage,end_usage); fprintf(fpTime,"Exp %3d Timing: %f user, %f system, %f total.\n",k, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);/* Compute the error */} /****************************************************************/ void Fcm (objects X1,center V,int flag) { int i,j,l; FILE *ofile,*fpJm; FILE *iniClsCen=fopen("center.clt","a+"); char temp[80]; float jmFinal=0,rmFinal=0; Random_seed=state1[run];/*randseed*/ srand48(Random_seed); for(i=0;i<MAXDATA;i++) for(j=0;j<features;j++)X[i][j]=X1[i][j]; if(iniClsCen==NULL) { printf("\n Unable to open the initial cluster center file\n"); printf("\n choosing random cluster centers....\n"); flag=1; } for (run=0;run<TOTRUNS;run++) { if(flag!=2) { if (EntireHeaps) lCount=heapCount; else lCount=c; for(i=0;i<lCount;i++) for(j=0;j<s;j++) V[i][j]=0; for(i=0;i<lCount;i++) { for(l=0;l<s;l++) { if(flag==1) { rand_index = lrand48() % n; V[i][l] = X[rand_index][l]; } else { fscanf(iniClsCen,"%f ",&V[i][l]); } }//end for }}else lCount=heapCount;eps=0.001; stop = FALSE; /* This loop finds the final cluster center and the corresponding U values */ z = 0; while (stop == FALSE) { z++; UMAT(U,V,X); /** Update the U matrix **/stop = FINISHED(U1,eps); CLUSTER(U,X,V); /** Get new cluster center **/printf("\nIteration: %d \n",z); } /* end while */ printf("Number of iterations: %d\n\n", z);strcpy(temp,""); sprintf(temp,"%sFCM.out",filename);if(flag==1){jmFinal=Compute_Jm(V,X);rmFinal=Compute_Rm(V,X);fpJm=fopen("jm.out","a");fprintf(fpJm," classes %2d Jm: %10.6f Rm: %10.6f\n",lCount,jmFinal,rmFinal);fclose(fpJm);} if(flag==2) Output(temp,V,X); /* output the results */ }/*end for*/ ofile=fopen(temp,"a"); for (i=0; i <= mins; i++) fprintf(ofile, "Min %d of %10.3f occurred %d times\n", i, mintab[i], mincount[i]); fprintf(ofile, "Average iterations %f\n", (float)aveit/(float)TOTRUNS); fclose(ofile); }/* end Main */ /* The NORM function computes the norm of two vectors, x and v, of dimension s. This gives an indication of the distance between them */ float NORM( feature x, feature v) //feature x, v; { /* Parameters : x -- a local copy of an input vector v -- a local copy of a cluster center */ int i,j; /* loop variable */ double sum; /* the square root of sum is the norm; sum is the sum of the differences squared */ float mat1[features]; float mat2[features]; if (maha) { for (i=0; i<features; i++) mat1[i]=x[i]-v[i]; for(i=0;i<features;i++) { sum =0; for(j=0;j<features;j++) sum += mat1[j] * inv_cov[j][i]; mat2[i] = sum; } sum =0; for(j=0;j<features;j++) sum += mat1[j]*mat2[j]; } else { sum = 0; for(j=0;j<features;j++) sum += (x[j] - v[j])* (x[j] - v[j]); } return (float)(sqrt(sum)); } /* end NORM */ /* The UMAT procedure calculates the updated U matrix according to Bezdek's formula. Its side-effect is the update of the global array U1 */ void UMAT( matrix U,center V, objects X) /*matrix U; center V; objects X;*/ { /* Parameters: U, V, and X: same as in main program */ int i, j, k,l ;/* loop variables *///, class /* class whose cluster center //is closest to pattern k */; float d1,d2,d; float sum; int exact; for (k=0;k<MAXDATA;k++) { /*printf("\n k %d ",k);*/ exact=false; for (i=0;i<lCount;i++) { sum=0; for (j=0;j<lCount;j++) { d1=NORM(X[k],V[i]); d2=NORM(X[k],V[j]); if(d2!=0) { d= d1/d2; d= d * d;/* pow(d1/d2,2.0/(m-1.0)); */ sum+=d; } else { exact = true; for(l=0;l<lCount;l++) if (l==j) U1[l][k]=1.0; else U1[l][k]=0.0; break; } } if (exact!=true) U1[i][k]=1.0/sum; } } } /* end UMAT */ /* The function FINISHED determines if a termination occurs. If not it updates U with U1, so its side-effect is the potential modification of U */ BOOLEAN FINISHED( matrix U1,float eps) //matrix U1; float eps; { /* Parameters: U1, eps-- same as in main */ float sum; /* sum of squared differences between corresponding elements of U and U1 */ int i,k; /* loop control variabes */ BOOLEAN finished; /* set to true if all corresponding elements are within epsilon */ finished = TRUE; sum = 0; for (i=0; i<lCount; i++) for (k=0; k<MAXDATA; k++) sum += (U[i][k] - U1[i][k])*(U[i][k]-U1[i][k]); if (sqrt(sum) > eps) { finished = FALSE; for (i=0; i<lCount; i++) for (k=0; k<MAXDATA; k++) U[i][k] = U1[i][k]; } /* end if */ return finished; } /* end FINISHED */ /* The CLUSTER procedure determines cluster centers in V for the c classes being searched for. The global array V is updated */ void CLUSTER( matrix U, objects X,center V) //matrix U; objects X; { /* Parameters: U, X -- as in main */ int i, j, k; /* loop control variables */ feature num; /* numerator in the cluster center computation formula presented by Bezdek */ float denum; /* denumenator in the obove formula */ for (i=0; i<lCount; i++) { denum = 0; for (j=0; j<features; j++) { num[j] = 0; for (k=0; k<MAXDATA; k++) num[j] += U[i][k] * U[i][k] * X[k][j]; /* pow(U[i][k],m) * X[k][j]; */ } /* end for */ for (k=0; k<MAXDATA; k++) denum += U[i][k] * U[i][k]; /* pow(U[i][k],m); */ for (j=0; j<features; j++) V[i][j] = num[j] / denum; } /* end for */ } /* end CLUSTER */ float Compute_Jm(center V, objects X) //center V; //objects X; { int i, k,found; double Jm_value; float u,dik; Jm_value = 0; for (k=0; k<MAXDATA; k++) { found = FALSE; for (i=0; i<lCount; i++) { dik=NORM(X[k],V[i]); dik=dik*dik; if (dik==0.0) { if (found == FALSE) { u=1.0; found = TRUE; printf("found the case with dik=0\n"); } else { printf("\n cant proceed with this initialization"); exit(1); } } else { if (found != TRUE) u=U1[i][k]; /* get_Uvalue(V,X,dik,k); */ else u=0.0; } Jm_value += u * u * dik;/* pow(u, m) * dik; */ } } return (Jm_value); } /* end Compute_Jm */ /* The Output procedure outputs the U matrix which gives the fuzzy measure of each feature belonging to a cluster center. It also outputs the cluster center for each class */ void Output(char fname[],center V,objects X) //char fname[80]; /* Parameters: U, V -- same as in main */ { int i, j, found; /* loop control variables */ float J2c,R2c; FILE *fpJm=fopen("JmRm.out","a"),*fp; if ((fp=fopen(fname,"a"))==NULL) printf("\n CAN'T OPEN %s\n",fname); else { fprintf(fp,"\n \n"); for (i=0; i<lCount; i++) { fprintf(fp,"\n"); for (j=0; j<features; j++) fprintf(fp,"%4.3f ",V[i][j]); } /* end for */ } /* end if */ J2c= Compute_Jm(V,X); R2c=Compute_Rm(V,X); fprintf(fpJm,"Classes: %3d Jm: %10.6f Rm: %10.6f \n",lCount,J2c,R2c); found =0; for (i=0;i<= mins; i++) { if ((found == 0) && ((J2c - mintab[i]) < .001) && ((J2c - mintab[i]) > -.001)){ mincount[i]++; found = 1; } } if (found == 0) { mins++; mintab[mins]=J2c; mincount[mins]=1; } fprintf(fp,"\nJ%2d = %f", m,J2c); fprintf(fp, "Number of iterations: %d\n\n", z); aveit=aveit + z; fclose(fp); fclose(fpJm); } /***************************************************************/ void find_covar( ) { int i,j,k; float mean[features],sum; for (i=0;i<features;i++) mean[i]=0; for (i=0;i<features;i++) for (j=0;j<m;j++) mean[i]+=X[j][i]; for (i=0;i<features;i++) mean[i]/=m; for (i=0;i<features;i++) for (j=0;j<features;j++) { sum=0; for (k=0;k<m;k++) sum+=(X[k][i]-mean[i])*(X[k][j]-mean[j]); Y[i][j]=sum/m; } }float Compute_Rm(center V, objects X){int i=0,j=0,l=0;double Dik=0,temp=0,Rm=0;Rm=0;for(i=0;i<MAXDATA;i++){Dik=0;for(j=0;j<lCount;j++){temp=0;for(l=0;l<features;l++)temp+=(X[i][l]-V[j][l])*(X[i][l]-V[j][l]);/*temp=sqrt(temp);*//*temp=NORM(X[i],V[j]);temp=temp*temp;*/Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}return(Rm);}/* End of function *//********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void hardenRm() { FILE *fp=fopen("fcmFCM.out","a"); FILE *fpOut=fopen("ClassDataFCM.out","a"); int i=0,minPosition=-1,position[MAXDATA],j=0,k=0; int cenCount[MAXDATA],classCount[MAXDATA][c],label[c], clsObjects[MAXDATA][MAXDATA]; for(j=0;j<classes;j++) {cenCount[j]=0;for(k=0;k<classes;k++)classCount[j][k]=0; } for(i=0;i<MAXDATA;i++) { minPosition=findPosition(i); position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++; } for(j=0;j<classes;j++) {for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++; } /* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/for(j=0;j<classes;j++){minPosition=0;for(i=1;i<c;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;}}fprintf(fp,"* %d\n",0);for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);}fclose(fpOut);fclose(fp);return; }/*end of function*/ /********************************************************************//* Function Main *//********************************************************************/ int main(int argc, char** argv){int k=0,i=0,j=0;int sum=0;char temp[80];FILE *fpRmOut=fopen("fcmRm.out","w");FILE *fpOut=fopen("ClassData.out","w");FILE *fpCls=fopen("clsCenters.out","w");FILE *fpMinRm=fopen("minRm.out","w");FILE *fpOrgClass=fopen("orgClass.out","w");FILE *fpRm=fopen("Rm.out","w");FILE *fpJmRm=fopen("JmRm.out","w");FILE *fp=fopen("fcm.out","w");FILE *fp1; FILE *fp2=fopen("antAnalysis.out","a");FILE *fp3=fopen("fcmAnalysis.out","a"); FILE *fp4=fopen("errors.out","w");FILE *fp5;FILE *fpJm=fopen("jm.out","w");FILE *fpTime=fopen("time.out","w");FILE *fpFCM=fopen("fcmFCM.out","w");FILE *fpOutFCM=fopen("ClassDataFCM.out","w");fprintf(fpFCM,"# %d\n",EXPRUN);fprintf(fpFCM,"@ %d\n",1);fclose(fpFCM);fclose(fpOutFCM);fclose(fp4);fclose(fpJm);sprintf(temp,"%sObj.out",filename);fp5=fopen(temp,"w");fclose(fp5);strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp5=fopen(temp,"w");fclose(fp5);strcpy(temp,"");sprintf(temp,"%sOp.out",filename);fp1=fopen(temp,"w");fprintf(fp,"\n$%d",EXPRUN);fprintf(fp1,"\n$%d",EXPRUN);fclose(fp);fclose(fp1);fclose(fpJmRm);fprintf(fpRmOut,"# %d\n",EXPRUN);fprintf(fpRmOut,"@ %d\n",antsPerFeature);fclose(fpRmOut);fprintf(fpTime,"Experiments %d\n",EXPRUN);fprintf(fpMinRm,"# %d\n",EXPRUN);fprintf(fpMinRm,"@ %d\n",antsPerFeature);fclose(fpMinRm);fclose(fpOut);fclose(fpCls);fclose(fpOrgClass);fclose(fpRm);fclose(fpTime);/*readInput();*/ if(argc==2)sscanf(argv[1],"%d",&debugLevel); printf("\n Debug level %d\n",debugLevel); for( i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; readInput(); readOrg(); //Compute the distance of all the points if(maxDist) { Dmax=computeDistance(); //Compute the maximum distance fp5=fopen("maxData.txt","w");fprintf(fp5,"%f",Dmax);fclose(fp5); } else {fp5=fopen("maxData.txt","r");fscanf(fp5,"%f",&Dmax);fclose(fp5); } printf("\n %f \n",Dmax); //Initialise the board for(k=0;k<EXPRUN;k++) { fpRmOut=fopen("fcmRm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpOrgClass=fopen("orgClass.out","a");fpMinRm=fopen("minRm.out","a");fpRm=fopen("Rm.out","a");fpTime=fopen("time.out","a");fpFCM=fopen("fcmFCM.out","a");fpOutFCM=fopen("ClassDataFCM.out","a");fprintf(fpFCM,"$ %d\n",k);fprintf(fpOutFCM,"Experiment %d\n",k);fprintf(fpRm,"Exp: %2d ",k);fclose(fpRm);fprintf(fpCls,"EXP No %2d \n",k);fclose(fpCls);fprintf(fpRmOut,"$ %d\n",k);fprintf(fpMinRm,"$ %d\n",k);fprintf(fpOut,"Experiment %d\n",k);fprintf(fpOrgClass,"Experiment %d\n",k);fprintf(fpTime,"Experiment %d\n",k);fclose(fpRmOut);fclose(fpOut);fclose(fpMinRm);fclose(fpOrgClass);fclose(fpTime); fclose(fpFCM);fclose(fpOutFCM); init(); sum=0; srand48(state1[k]); initialiseBoard(); initialise(1); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(1); printf("\n %d \n",heapCount); //Simulate the working of ants iterate(1); //drop the objects after all the iterations dropFinal(1); //to verify the correctness for(i=0;i<heapCount;i++) sum+=heaps[i].count; //write the cluster centers to the file writeClusterInformation(1); printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); //getchar(); //Run FCM // heapCount=2973;/* To be commented */ printf("\n\n k %d \n\n",k); Fcm(data,V,0); //compute new board size. lCount=heapCount; heapRow=(int)ceil(sqrt(4*heapCount)); newHeapCount=lCount; printf("\n LCount %d newHeapCount %d heap Row %d \n",lCount,newHeapCount,heapRow); //harden the objects harden(newHeaps); //iterate with the heaps as the objects. initialiseBoard(); initialise(0); for(i=0;i<lCount;i++) computeHeapParameters(&heaps[i],0); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(0); printf("\n %d \n",heapCount); //getchar(); //Simulate the working of ants iterate(0); dropFinal(0); writeClusterInformation(0); sum=0; for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); // getchar(); fpJm=fopen("jm.out","a"); fprintf(fpJm,"Experiment %2d: ",k); fclose(fpJm); Fcm(data,V,1); harden1(); RmStage(newHeapCount,V); } fp=fopen("fcm.out","a"); fp1=fopen(temp,"a"); fprintf(fp,"\n"); fprintf(fp1,"\n"); fclose(fp); fclose(fp1); fp=fopen("fcm.out","r"); fp1=fopen(temp,"r");printf("\n Before read org\n");printf("\nafter read org\n");readFCM(fp1,fp2);printf("\n After 1st read fcm\n");readFCM(fp,fp3);printf("\n After 2 fcm read \n");analysis(1);analysis(0);return(1); }//end of main

Parag Kanade/Combined Algorithm/combWoErr.c

#include "headerStatic.h" /********************************************************************/ /********************************************************************/ /* Function to read the input data set */ /* The file name is assumed to be itest.data */ /* Status: Tested */ /* Output: Data from the file into the "data" (Global) data structure */ /* Output: Count (Global), the count of the number of objects */ /********************************************************************/ void readInput() { FILE *fp; char fname[80]; int i=0,j=0; char ch; sprintf(fname,"%s.data",filename); /* Check if the input file is opened */ if((fp=fopen(fname,"r"))==NULL) { printf("\n Unable to open input file \n"); exit(0); } /*initialise the minimum maximum and the scale */ for(i=0;i<features;i++) { minInput[i]=1000; maxInput[i]=0; minForHeaps[i]=1000; maxForHeaps[i]=0; scale[i]=0; scaleForHeaps[i]=0; } /* Discard leading comments */ ch = 'c'; while (ch != '#') fscanf (fp, "%c", &ch); i=0; /* Parse the file and populate the data structure */ while(!feof(fp)) { for(j=0;j<features;j++) { fscanf(fp,"%f ",&data[i][j]); } fscanf(fp,"\n"); for(j=0;j<features;j++) { if(data[i][j]<minInput[j]) minInput[j]=data[i][j]; if(data[i][j]>maxInput[j]) maxInput[j]=data[i][j]; } i++; } count=i; /* Normailze the feature values in between 0 and 1 */ for(j=0;j<features;j++) { if(minInput[j]!=maxInput[j]) scale[j]=1.0/(maxInput[j]-minInput[j]); else scale[j]=1.0; } for(i=0;i<count;i++) { for(j=0;j<features;j++) data[i][j]=(data[i][j]-minInput[j])*scale[j]; } fclose(fp); return; }//end of function /********************************************************************/ /*Function to compute the eucldiean distance for all input data set */ /* Output : an array conatining the distance of each object with the other */ /********************************************************************/ float computeDistance() { float distance,dist=0; int i=0,j=0,k=0; distance=0; for(j=0;j<count;j++) for(i=0;i<count;i++) { for(k=0;k<features;k++) dist+=sqrt((data[j][k]-data[i][k])*(data[j][k]-data[i][k])); if(distance<dist)distance=dist; dist=0; } return(distance); } /********************************************************************/ /* Function to compute the distance between two objects */ /* Output : Euclidean d istance between the two input objects */ /********************************************************************/ float computeDistanceBetween2(irisData a,irisData b) { float temp=0; int i=0; for(i=0;i<features;i++) temp+=(a[i]-b[i])*(a[i]-b[i]); temp=sqrt(temp); return(temp); } /********************************************************************/ /* Function to compute the value of an object */ /********************************************************************/ float value(irisData a) { int i=0; float temp=0; for(i=0;i<features;i++) temp+=a[i]*a[i]; return(sqrt(temp)); } /********************************************************************/ /* Function to find the object in the data structure */ /* Output : The position of the object in the data */ /* Input : The object */ /********************************************************************/ int findInData(irisData object,int iFlag) { int i=0,lastCount=0,j,position=-1,flag[features],flg;if(iFlag==1)lastCount=MAXDATA;else{}for(i=0;i<lastCount;i++){ for(j=0;j<features;j++) if(fabs(object[j]-data[i][j])<0.00001) flag[j]=1; else { flag[j]=0; break;} flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /* Function to find whether there is an object on the board at a,b */ /* Output : a flag indicating whether an object is found or not */ /* Input : The row and column indicating the board position */ /********************************************************************/ int found(int a , int b) { if(board[a][b]==9) return 1; else return 0; } /********************************************************************/ /* Function to initialize the initial board */ /* Output : None */ /* Global Structures modified : heaps */ /* Input : Flag indicating the iteration 1: First 0: Second */ /********************************************************************/ void initialise(int flag) { int a,b,i=0,k=0; int loopCount,rowCount,colCount; /* Initialize the row, column and number of heaps */ if(flag==1) { loopCount=count; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=lCount; rowCount=heapRow; colCount=heapRow; } /* Initialize the heaps */ for(i=0;i<loopCount;i++) { heaps[i].count=0; heaps[i].x=0; heaps[i].y=0; heaps[i].disPosition=0; heaps[i].Dmax=0; heaps[i].Dmean=0; } for(i=0;i<loopCount;i++) { /*find a board position which is empty*/ do { a=lrand48()%rowCount; b=lrand48()%colCount; } while(found(a,b)); /*Allocate the object to that place and assign a heap to it*/ heaps[i].x=a; heaps[i].y=b; heaps[i].count=1; heaps[i].Dmax=0; heaps[i].Dmean=0; if(flag==1) { heaps[i].objects[0]=i; for(k=0;k<features;k++)heaps[i].Ocenter[k]=data[i][k]; } else { for(k=0;k<features;k++) heaps[i].Ocenter[k]=newHeaps[i].Ocenter[k]; } heaps[i].disPosition=0; board[a][b]=9; if((debugLevel>0)&&(flag==1))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],data[heaps[i].objects[0]][0]); if((debugLevel>0)&&(flag==0))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],newHeaps[i].Ocenter[0]); } /*initialise the initial heap count*/ heapCount=loopCount; currentRow=rowCount; } /********************************************************************/ /* Function to find whether an heap is present at the location a b */ /* Output : Flag indicating the presencs of the heap */ /* Input : Roe and column indicating the board position */ /********************************************************************/ int findObject(int a,int b) { int i; int position=-1; for(i=0;i<heapCount;i++) { if((heaps[i].x==a)&&(heaps[i].y==b)) { position=i; break; } } return(position); } /********************************************************************/ /* Function initialize the ants */ /* Output : None */ /* Input : Flag indicating the iteration */ /* Global Variables Modified : ant, heapCount, heaps, board */ /********************************************************************/ void initialiseAnts(int flag) { int i=0,a=0,b=0,position,j=0,loopCount=0,rowCount,colCount,k=0; if(flag==1) { loopCount=MAXANTS; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=MAXANTSFORHEAPS; rowCount=heapRow; colCount=heapRow; } for(i=0;i<loopCount;i++) { /*generate random positions for the ants*/ a=lrand48()%rowCount; b=lrand48()%colCount; ants[i].x=a; ants[i].y=b; /*Find if an object is present at that position or not*/ if(board[a][b]==9) { ants[i].carrying=1; /*find the heap at that location*/ position=findObject(a,b); if(position==-1) { printf("\n Error in logic: Ant init \n"); //getchar(); break; } else { /*Assign the heap data to the ant data and decrement the heap count*/ if(flag==1){ for(k=0;k<features;k++)ants[i].data[k]=data[heaps[position].objects[0]][k]; }else{for(k=0;k<features;k++)ants[i].data[k]=heaps[position].Ocenter[k];} for(j=position;j<heapCount-1;j++) heaps[j]=heaps[j+1]; heapCount--; } /*Clear the board position */ board[a][b]=-9; } else ants[i].carrying=-1; ants[i].speed=0; ants[i].dir=lrand48()%8; printf("\n Ant %2d X: %2d Y: %2d Carying : %2d ",i,ants[i].x,ants[i].y,ants[i].carrying); } currentAntCount=loopCount; } /********************************************************************/ /* Function to move the ant */ /* Output: None */ /* Input: Ant to be moved and the flag indicating the iteration */ /********************************************************************/ void moveNext(ant* a,int flag) { int rowCount,colCount; rowCount=currentRow;colCount=currentRow; /*Depending upon the direction move the ant*/ switch (a->dir) { case 0: a->y++; if(a->y>=colCount)a->y=0; break; case 1: a->x--; a->y++;; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 2: a->x--; if(a->x<0)a->x=rowCount-1; break; case 3: a->x--; a->y--; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 4: a->y--; if(a->y<0)a->y=colCount-1; break; case 5: a->x++; a->y--; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 6: a->x++; if(a->x>=rowCount) a->x=0; break; case 7: a->x++; a->y++; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break; } }//end of function /********************************************************************/ /* Function to compute the parameters of the heap */ /* Output: None */ /* Input: The heap whose parameters are to be computed iteration flag*/ /********************************************************************/ void computeHeapParameters(heap* heapData,int flag) { int i=0,j=0,k=0,position=0; float distance=0; float Dmean=0.0; float *dis; float featureSum[features]; float max=0; float sum=0; /* init*/ for(k=0;k<features;k++) featureSum[k]=0; /*Compute the distance of each object and find the maximum distance*/ max=0; if(flag==1){ for(j=0;j<heapData->count;j++) for(i=0;i<heapData->count;i++) { for(k=0;k<features;k++) sum+= (data[heapData->objects[j]][k]-data[heapData->objects[i]][k])*(data[heapData->objects[j]][k]-data[heapData->objects[i]][k]); distance=sqrt(sum); if(max<distance) max=distance; sum=0; } /* Assign the maximum distance to the heap */ heapData->Dmax=max;/*Compute the centre object*/for(i=0;i<heapData->count;i++)for(k=0;k<features;k++) featureSum[k]+=data[heapData->objects[i]][k];for(k=0;k<features;k++)heapData->Ocenter[k]=featureSum[k]*1.0/heapData->count; /* Find the mean distance */if((dis=(float *) calloc(heapData->count,sizeof(float)))==NULL){printf("\nUnable to allocate memory in the compute heap parameters procedure\n");exit(0);} for(i=0;i<heapData->count;i++) { Dmean+=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); dis[i]=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); } heapData->Dmean=Dmean*1.0/heapData->count; /*Compute the most dissimilar object*/ max=dis[0]; for(i=1;i<heapData->count;i++) if(max<dis[i]) { max=dis[i]; position=i; } /*Assign the most dissimilar object to the heap variable*/ for(k=0;k<features;k++) heapData->Odissim[k]=data[heapData->objects[position]][k]; heapData->disPosition=position;free(dis); } else{/* Heap Parameters for the second stage */for(k=0;k<features;k++){ heapData->Odissim[k]=heapData->Ocenter[k];}heapData->disPosition=0;heapData->Dmean=0;heapData->Dmax=0;} }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a single object*/ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject1(ant *a,int position,int flag) { int i,k=0; /*Clear the board*/ board[heaps[position].x][heaps[position].y]=-9; /* Assign the data to the ant*/if(flag==1){ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[0]][k];}else {for(k=0;k<features;k++) a->data[k]=heaps[position].Ocenter[k];} a->carrying=1; /*Copy all the heaps remaining heaps in the structure*/ for(i=position;i<heapCount-1;i++) heaps[i]=heaps[i+1]; /*Decrement the heap count*/ heapCount--; }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a two objects */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject2(ant *a , int position,int flag) { float Pdestroy1=-1; int i=0,k=0; /*Compute the probability to destroy the heap*/ Pdestroy1=lrand48()%13*1.0/12.0; /*If the probability is more than Pdestroy then destroy the heap */ if(Pdestroy1<Pdestroy) { /*pick the object randomly*/ i=lrand48()%2; /* Assign the data to the ant */ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[i]][k]; a->carrying=1; /*Correct the heap parameters*/ heaps[position].count=1; heaps[position].objects[0]=heaps[position].objects[1-i]; computeHeapParameters(&heaps[position],flag); } }//end of function /********************************************************************/ /*Function to pick a object from a heap consisting of more than two object */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /********************************************************************/ void pickObjectn(ant *a,int position,int flag) { int i=0,k=0; /*If the criteria is satsfied then pick the object*/ if((computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)*1.0/heaps[position].Dmean)>Tremove) { a->carrying=1; for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[heaps[position].disPosition]][k]; for(i=heaps[position].disPosition;i<heaps[position].count-1;i++) heaps[position].objects[i]=heaps[position].objects[i+1]; heaps[position].count--; computeHeapParameters(&heaps[position],1); } }//end of function /********************************************************************/ /* Function to pick a object */ /********************************************************************/ void pick(ant *a,int flag) { int position=-1,i=0; float Pload1=-1.0; for(i=0;i<heapCount;i++) { /*Check if a heap is present in the eight neighbouring cells*/ if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if Heap is found*/ if(position!=-1) { /* heap or object found.*/ /*Depending upon the number present in the heap call the corresponding routine*/ switch(heaps[i].count) { case 1: Pload1 = lrand48()%151*1.0/150.0; if(Pload1<Pload) { pickObject1(a,position,flag); } break; case 2: pickObject2(a,position,flag); break; default: pickObjectn(a,position,flag); break; } } } /********************************************************************/ /* Function to drop a object to form a new heap */ /* Output: None */ /* Input: Ant, iteration flag */ /* Global Structure modified : heap, heapCount */ /********************************************************************/ void dropObject(ant* a,int flag) { /*Create a new heap*/ int k=0; heaps[heapCount].count=1; heaps[heapCount].x=a->x; heaps[heapCount].y=a->y; heaps[heapCount].Dmax=0; heaps[heapCount].Dmean=0; heaps[heapCount].disPosition=0; for(k=0;k<features;k++) { heaps[heapCount].Odissim[k]=a->data[k]; heaps[heapCount].Ocenter[k]=a->data[k]; } if(flag==1) {heaps[heapCount].objects[0]=findInData(a->data,flag); /*computeHeapParameters(&heaps[heapCount],flag);*/ } computeHeapParameters(&heaps[heapCount],flag); heapCount++; a->carrying=-1; board[a->x][a->y]=9; }//end of function /********************************************************************/ /* Function to find the heap position during the second iteration */ /* Output: position of the heap */ /* Input: object center of the heap that the ant is carrying */ /* Global Structure modified : none */ /********************************************************************/ int findInNewheaps(irisData object) { int position=-1,i,j=0,flag[features],flg; for(i=0;i<newHeapCount;i++) { for(j=0;j<features;j++) { if(fabs(object[j]-newHeaps[i].Ocenter[j])<0.00001) flag[j]=1; else {flag[j]=0;break;} } flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /*Function to drop a object to a heap consisting of a one or more object */ /* Output: none */ /* Input: Ant, position of the heap , iteration flag */ /* Global Structure modified: heaps,newHeaps */ /********************************************************************/ void dropObject1(ant* a, int position,int flag) { int heapPosition=-1,heapPosition1=-1,i,j,k=0; irisData newData,temp; if(flag==1) { heaps[position].objects[heaps[position].count]=findInData(a->data,flag); heaps[position].count++; computeHeapParameters(&heaps[position],flag); } else { for(k=0;k<features;k++)temp[k]=heaps[position].Ocenter[k]; heapPosition=findInNewheaps(a->data); heapPosition1=findInNewheaps(heaps[position].Ocenter); if(heapPosition==-1||heapPosition1==-1) { printf("\n error in logic:new heaps\n"); //getchar(); return; } for(k=0;k<features;k++) {newData[k]=(newHeaps[heapPosition].Ocenter[k]*newHeaps[heapPosition].count+newHeaps[heapPosition1].Ocenter[k]*newHeaps[heapPosition1].count)*1.0/(newHeaps[heapPosition].count+newHeaps[heapPosition1].count);newHeaps[heapPosition].Ocenter[k]=newData[k];heaps[position].Ocenter[k]=newData[k]; } computeHeapParameters(&heaps[position],flag); for(i=newHeaps[heapPosition].count,j=0;j<newHeaps[heapPosition1].count;i++,j++) { newHeaps[heapPosition].objects[i]=newHeaps[heapPosition1].objects[j]; } newHeaps[heapPosition].count+=newHeaps[heapPosition1].count; for(i=heapPosition1;i<newHeapCount-1;i++) newHeaps[i]=newHeaps[i+1]; newHeapCount--; } a->carrying=-1; }//end of function /********************************************************************/ /* Function to drop a object */ /* Output: None */ /* Input: Ant, iteration flag */ /********************************************************************/ void drop(ant* a,int flag) { int i, position=-1; float Pdrop1=0.0; /*Find whether we have a heap in the neighbourhood of the cell*/ for(i=0;i<heapCount;i++) { if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if a heap is found*/ if(position!=-1) { /*heap or object found.*/ /*Depending on the number of objects present take action*/ switch(heaps[position].count) { /*If criteria is satisfied then drop the object*/ case 1: if(flag==1) { if((computeDistanceBetween2(a->data,data[heaps[position].objects[0]])*1.0/Dmax)<Tcreate) dropObject1(a,position,flag); } else { if((computeDistanceBetween2(a->data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) dropObject1(a,position,flag); } break; default: if(computeDistanceBetween2(a->data,heaps[position].Ocenter)<computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)) dropObject1(a,position,flag); break; } } else { /*Compute the probability to drop the object*/ Pdrop1 = lrand48()%151*1.0/150; if(Pdrop1<Pdrop) dropObject(a,flag); } }//end of function /********************************************************************/ /* Function to move the ant */ /* Output: none */ /* Input: Ant, iteration flag */ /********************************************************************/ void move(ant* a,int flag) { float pDir; /*Check the speed */ if (a->speed!=0) { /*move in the same direction*/ a->speed--; moveNext(a,flag); } else { /*generate the new direction*/ pDir=lrand48()%17*1.0/16.0; if(pDir<.85) { a->dir=lrand48()%8; } else if(pDir>.95) { a->speed=2; } moveNext(a,flag); } } /********************************************************************/ /* Function to simulate the movement of the ants */ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified: minHeapCount, minHeapPosition */ /********************************************************************/ void iterate(int flag) { int i=0,j=0,k=0,loopCount=0; if (flag==1) loopCount=MAXANTS; else loopCount=MAXANTSFORHEAPS; while (i<MAXITERATIONS) { i++; for(j=0;j<loopCount;j++) { move(&ants[j],flag); if(ants[j].carrying==1) { drop(&ants[j],flag); } else { pick(&ants[j],flag); } } if(debugLevel>0){ for(k=0;k<heapCount;k++) for(j=0;j<heapCount;j++) if((k!=j)&&(heaps[k].x==heaps[j].x)&&(heaps[k].y==heaps[j].y)) { printf("\nerror two heaps with same co ordinates %d %d ",k,j); //getchar(); } k=0; for(j=0;j<heapCount;j++) k+=heaps[j].count; } if(heapCount<minHeapCount) { minHeapCount=heapCount; minPosition=i; } if(debugLevel>0) printf("\n i:%d Heap count %d sum %d",i,heapCount,k); else printf("\n i:%d Heap count %d",i,heapCount); heapSum+=heapCount; } }//end of function /********************************************************************/ /*Function to drop the objects carried by the ants after all the iterations*/ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified:none */ /********************************************************************/ void dropFinal(int flag) { int i=0,j=0,position=-1,loopCount=0; loopCount=currentAntCount; for(i=0;i<loopCount;i++) { if(ants[i].carrying==1) { if(flag==1) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position,flag); } } else { /*Find if a heap is present */ do { position=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position,flag); position=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position!=-1); } } } }//end of function /********************************************************************/ /* Function for the implementation of QuickSort */ /* Output: none */ /* Input: data structure, low and high indices */ /* Global structure modified:none */ /********************************************************************/ void quickSort( heap A[], int low,int high ) { int mid=0; if(low<high) { mid=partition(A,low,high); quickSort(A,low,mid); quickSort(A,mid+1,high); } } /********************************************************************/ /* Partition routine used by quick sort */ /* Output: partition location */ /* Input: data , low, high */ /* Global structure modified:none */ /********************************************************************/ int partition(heap a[], int low,int high) { heap temp; int x=0,i=0,j=0; i=low; j=high; x=a[low].count; while (1) { while (a[j].count<x) j=j-1; while (a[i].count>x) i=i+1; if(i<j) { temp=a[i]; a[i]=a[j]; a[j--]=temp; } else return(j); } } /********************************************************************/ /* Function used to write the cluster information */ /* Output: none */ /* Input:iteration flag */ /* Global structure modified:none */ /********************************************************************/ void writeClusterInformation(int flag) { FILE *fp=fopen("center.clt","w"); FILE *fp1; FILE *fp2; FILE *fp3; char temp[80]; int i=0,j=0,k=0; int lastCount=0; int rowCount,colCount,antCount; strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp3=fopen(temp,"a"); if(dataDump) {strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp1=fopen(temp,"a");strcpy(temp,"");sprintf(temp,"%sObj.out",filename);fp2=fopen(temp,"a");if(fp1==NULL||fp2==NULL)printf("\n Error in opening file in the writer Cluster Information routine\n"); } rowCount=currentRow; colCount=currentRow; antCount=currentAntCount; if(fp==NULL) { printf("\n Unable to open cluster center output file \n"); /*return;*/ exit(0); } /*sort the array according to the count value and write the value of the top three elements*/ if((flag==1)&&(!EntireHeaps)) quickSort(heaps,0,heapCount); if(EntireHeaps) {if(flag==1) lastCount=heapCount;else lastCount=newHeapCount; } else lastCount=c; for(i=0;i<lastCount;i++) { if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t ",newHeaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",newHeaps[i].Ocenter[k]); fprintf(fp,"\n"); } else { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); fprintf(fp,"\n"); } } if(dataDump) { fprintf(fp1,"\n Information from the Ant Based Algorithm \n"); fprintf(fp1,"\n Important Parameters : Board %d X %d Ants : %d Iterations %d Total Heaps %d ",rowCount,colCount,antCount,MAXITERATIONS,lastCount); } if(flag==0) { if(dataDump) { fprintf(fp1," TCreateForHeaps %f \n",TcreateForHeaps); fprintf(fp2,"\nTotal Heaps : %d\n",heapCount); fprintf(fp2,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); } fprintf(fp3,"\n@ %d\n",lastCount); } else if(dataDump) fprintf(fp1,"\n");if(dataDump) { fprintf(fp1,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); for(i=0;i<lastCount;i++) if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp2,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); } fprintf(fp1," %5d\n",newHeaps[i].count); fprintf(fp2," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp1," %5d\n",heaps[i].count); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]); fprintf(fp1," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]); fprintf(fp1," %5d\n",heaps[i].count); }} if(flag==0) { if(dataDump) fprintf(fp1,"\n Objects in the heaps \n");for(i=0;i<newHeapCount;i++) { if(dataDump) fprintf(fp1,"\n\n Heap %d\n \n",i+1);fprintf(fp3,"# %d %d\n",i ,newHeaps[i].count); for(j=0;j<newHeaps[i].count;j++){ for(k=0;k<features;k++){ fprintf(fp3,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]); if(dataDump) fprintf(fp1,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]);} if(dataDump) fprintf(fp1,"\n"); fprintf(fp3,"\n"); } } } fclose(fp); fclose(fp3); if(dataDump) { fclose(fp1);fclose(fp2); }}//end of flie /********************************************************************/ /* Function to initialise the new heaps */ /* Output: none */ /* Input:new Heap structure */ /* Global structure modified:none */ /********************************************************************/ void initialiseNewHeaps(heap newHeaps[]) { int i=0,j=0; for(i=0;i<lCount;i++) { newHeaps[i].count=0; for(j=0;j<features;j++) newHeaps[i].Ocenter[j]=V[i][j]; newHeaps[i].x=heaps[i].x; newHeaps[i].y=heaps[i].y; } }//end of function /********************************************************************/ /* Function to initialise the new heaps */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ int findPosition(int i) { int j,position=0; for(j=1;j<lCount;j++) { if(U[j][i]>U[position][i]) position=j; } /*printf("\n Position %d ",position);*/ return(position); }//end of function find position /********************************************************************/ /* Function to add object */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ void addObject(int object,heap * heapPosition) { heapPosition->objects[heapPosition->count]=object; heapPosition->count++; }//end of function add object /********************************************************************/ /* Function to harden the outputs obtained from fcm */ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/ void harden(heap newHeaps[]) { int i,heapPosition=-1,sum=0,j=0; /*initialise the new heaps*/ initialiseNewHeaps(newHeaps); for(i=0;i<count;i++) { heapPosition=findPosition(i); addObject(i,&newHeaps[heapPosition]); } /* To check for degenerate partitions*/ for(i=0;i<lCount;i++) { if(newHeaps[i].count==0){for(j=i;j<lCount-1;j++){newHeaps[j]=newHeaps[j+1];}lCount--;i--;} } newHeapCount=lCount; for(i=0;i<lCount;i++) computeHeapParameters(&newHeaps[i],1); if(debugLevel>0) { for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\n sum = %d",sum);} }//end of function/********************************************************************/ /* Function to initialize the initial parameters for the ant algorithm*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void init() { int i=0,j=0; for(i=0;i<MAXROWS;i++) for(j=0;j<MAXCOLS;j++) board[i][j]=-9; heapCount=0; newHeapCount=0; heapRow=0; minPosition=-1; minHeapCount=100000; for(i=0;i<MAXANTS;i++) { ants[i].carrying=-1; ants[i].x=-1; ants[i].y=-1; ants[i].speed=-1; ants[i].dir=-1; } } /********************************************************************/ /* Function to initialize the initial board */ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void initialiseBoard() { int i,j; for(i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; }/********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void harden1() { int i=0,heapPosition=-1; for(i=0;i<heapCount;i++) cenCount[i]=0; for(i=0;i<n;i++) { heapPosition=findPosition1(i); addObject1(i,heapPosition); } writeInfo(); }//end of function/********************************************************************/ /* Function to find to which class the object belongs */ /* Output: Class number */ /* Input: object position */ /* Global Structure Modified: None */ /********************************************************************/int findPosition1(int i) { int j,position=0; for(j=1;j<heapCount;j++) { if(U[j][i]>U[position][i]) position=j; } return(position); }//end of function find position/********************************************************************/ /* Function to Write the output of the hardened objects to file*/ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/void writeInfo(){int i=0,j=0,k=0;FILE *fp=fopen("fcm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}fprintf(fp,"\n@ %d\n",heapCount);for(i=0;i<heapCount;i++){fprintf(fp,"# %d %d\n",i,cenCount[i]);for(j=0;j<cenCount[i];j++){for(k=0;k<s;k++)fprintf(fp,"%8.2f ",clsCen[i][j][k]/scale[k]+minInput[k]);fprintf(fp,"\n");}}fclose(fp);return;}/********************************************************************/ /* Function to add a object to a cluster (2nd stage) */ /* Output: none */ /* Input: object poistion , cluster position */ /* Global Structure Modified: Cluster object and cluster count*/ /*******************************************************************/void addObject1(int object,int heapPosition) { int k=0; for(k=0;k<features;k++) clsCen[heapPosition][cenCount[heapPosition]][k]=X[object][k]; cenCount[heapPosition]++; return; }//end of function add object/********************************************************************/ /* Function to read the original classification of objects */ /* Output: none */ /* Input: none */ /* Global Structure Modified: None */ /*******************************************************************/void readOrg(){int i=0,k=0;char temp[80];FILE *fp;strcpy(temp,"");sprintf(temp,"%sFull.data",filename);fp=fopen(temp,"r");if(fp==NULL){printf("\n Unable to open full data input file ");exit(0);}for(i=0;i<n;i++){fscanf(fp,"%f ",&orgData[i][0]);for(k=1;k<s+1;k++)fscanf(fp,"%f ",&orgData[i][k]);}return;}// end of function/********************************************************************/ /* Function to classify the objects in different clusters */ /* Output: none */ /* Input: the cluster objects, the original classification, */ /* number of objects in the cluster,file to write the data */ /* Global Structure Modified: None *//********************************************************************/int analysisData(float data[][s],float orgData[n][s+1],int count,FILE * fp){int i=0,j=0,k=0,type=1,max=0;heapInfo info;for(i=0;i<c;i++){info.classObj[i]=0;}info.total=0;info.total=count;for(i=0;i<count;i++)for(j=0;j<n;j++){type=1;for(k=0;k<s;k++)if(fabs(data[i][k]-orgData[j][k+1])>errThresh){type=-1;break;}if(type!=-1){info.classObj[(int)orgData[j][0]-1]++;break;}}max=0;for(k=0;k<c;k++){if(info.classObj[k]>info.classObj[max])max=k;fprintf(fp,"\nClass %d Objects % d ",k+1,info.classObj[k]);}j=0;for(k=0;k<c;k++){if(k!=max)j+=info.classObj[k];}fprintf(fp,"\n Total errors : %d \n",j);return(j);}//end of analyse/********************************************************************/ /* Function to read the cluster information */ /* Output: none */ /* Input: input file pointer , output file pointer */ /* Global Structure Modified: None *//********************************************************************/void readFCM(FILE* fp,FILE *fp1){int i=0,j=0,k=0,l=0;int totalRuns=0,heapCount=0,junk=0,sum=0;char ch;FILE *fp2=fopen("errors.out","a");ch = 'c'; while (ch != '$') fscanf (fp, "%c", &ch);fscanf(fp,"%d\n",&totalRuns);printf("\n total runs %d ",totalRuns);for(l=0;l<totalRuns;l++){fscanf(fp,"%c",&ch);fscanf(fp,"%d\n",&heapCount);fprintf(fp1,"\n \nExperiment %d \n ",l+1);sum=0;for(i=0;i<heapCount;i++){fscanf(fp,"%c",&ch);fscanf(fp," %d %d\n",&junk,&clsCenCount[i]);for(j=0;j<clsCenCount[i];j++)for(k=0;k<s;k++)fscanf(fp,"%f",&fcmData[i][j][k]);fscanf(fp,"\n");fprintf(fp1,"\n\n Heap %d Total Objects %d \n",i+1,clsCenCount[i]);sum+=analysisData(fcmData[i],orgData,clsCenCount[i],fp1);}fprintf(fp2," %3d %3d %3d \n",l,sum,heapCount);}fprintf(fp2,"\n --------------------------\n");fclose(fp2);return;}//end of function void initRm(int heapCount) { antCount=heapCount*antsPerFeature*features;classes=heapCount;newAnts=(antdata*) calloc(antCount,sizeof(antdata));if(ants==NULL){printf("\n Cannot allocate memory to ants in Rm stage");printf("\n Exiting ... ");exit(-1);}} /***************************************************************************//* Function to initialise the ants *//* Status: under developement *//* Output: the ant structure is modified with initial random values *//* Input : Ant structure (this is modified) *//***************************************************************************/void initializeAntsRm(antdata* ants,center clsCen){int i,j,k;/* Initialize all the ants with initial values and the rest and direction*/for(i=0;i<antCount;i++){ants[i].currPos=drand48();for(j=0;j<memory;j++){ants[i].Rm[j]=inf;for(k=0;k<antsPerFeature;k++)Rm[k][j]=inf;}ants[i].rest=1;ants[i].direction=drand48()<0.5?1:-1;}for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[j*classes+i].currPos=clsCen[i][j];}}/*end of function*//***************************************************************************//* Function that runs one epoch *//* Status: under developement *//* Output: the ant structure is modified with according to the epoch *//* Input : Ant structure (this is modified) *//***************************************************************************/void epoch(antdata* ants){int i,j;float Trest=0,Tcontinue=0,Tinc=0;float dDiff=0.0;float temp=0;dDiff=DmaxRm-Dmin;for(j=0;j<iterations;j++){for(i=0;i<antCount;i++){if(ants[i].rest!=0){Trest=drand48();if(Trest<Prest){ants[i].rest=0;continue;}Tcontinue=drand48();if(Tcontinue>Pcontinue)ants[i].direction=-1*ants[i].direction;Tinc=drand48();temp=ants[i].currPos+ants[i].direction*(Dmin+Tinc*dDiff);if(temp>1||temp<0)ants[i].direction=-1*ants[i].direction;ants[i].currPos+=ants[i].direction*(Dmin+Tinc*dDiff);}}}for(i=0;i<antCount;i++)ants[i].rest=1;}/*end of function*//***************************************************************************//* Function that computes Rm *//* Status: under developement *//* Output: the ant structure is modified, the worst Rm is removed *//* Input : Ant structure (this is modified) *//***************************************************************************/void computeRm(antdata* ants,float data[MAXDATA][features],float newRm[antsPerFeature]){int i,j,k=0,l=0;float clsCenters[antsPerFeature][MAXDATA][features];double Rm=0.0,temp=0.0,Dik=0;for(k=0;k<antsPerFeature;k++)for(j=0;j<features;j++)for(i=0;i<classes;i++){clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;}for(k=0;k<antsPerFeature;k++){Rm=0;for(i=0;i<count;i++){Dik=0;for(j=0;j<classes;j++){temp=0;for(l=0;l<features;l++)temp+=(data[i][l]-clsCenters[k][j][l])*(data[i][l]-clsCenters[k][j][l]);/*temp=sqrt(temp);*//*temp=NORM(data[i],clsCenters[k][j]);temp=temp*temp;*/Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}newRm[k]=Rm;}return;}/*end of function*//********************************************************************//* Function to update the memory of the ants *//* Output: none *//* Input: ant structure and the new Rm, old memory *//* Global Structure Modified: none *//********************************************************************/void updateMemory(antdata* ants,float newRm[antsPerFeature],float Rm[antsPerFeature][memory]){int i,minPosition[antsPerFeature],maxPosition[antsPerFeature],flag[antsPerFeature],k,j,b[memory],infCount=0;float temp=0,tempRm[memory],tChoose=0,pContinueCurr=0;for(i=0;i<antsPerFeature;i++){minPosition[i]=0;maxPosition[i]=0;flag[i]=1;}for(k=0;k<antsPerFeature;k++){b[0]=0;for(i=1;i<memory;i++){if(Rm[k][i]<Rm[k][minPosition[k]])minPosition[k]=i;if(Rm[k][i]>Rm[k][maxPosition[k]])maxPosition[k]=i;if(Rm[k][i]==newRm[k]){flag[k]=0;break;}temp=(newRm[k]-Rm[k][i])*1.0/Rm[k][i];if(temp<0) temp=-1*temp;if(temp<minChange){flag[k]=0;break;}b[i]=i;}if(flag[k]==1){if(newRm[k]<Rm[k][maxPosition[k]]){for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];}else{/* find min Rm and restore its position*/infCount=0;for(i=0;i<memory;i++){tempRm[i]=Rm[k][i];if(Rm[k][i]==inf) infCount++;}for(i=0;i<memory;i++)for(j=0;j<memory-1-i;j++){if(tempRm[j]>tempRm[j+1]){temp=tempRm[j];tempRm[j]=tempRm[j+1];tempRm[j+1]=temp;temp=b[j];b[j]=b[j+1];b[j+1]=temp;}}tChoose=drand48();if(tChoose<0.6){minPosition[k]=b[0];}else if (tChoose<0.8){minPosition[k]=b[1];}else if(tChoose<0.9){minPosition[k]=b[2];}else if(tChoose<0.975){minPosition[k]=b[3];}else {minPosition[k]=b[4];}pContinueCurr=drand48();if(pContinueCurr<tContinueCurr){/*for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];*/}else if(Rm[k][minPosition[k]]!=inf)for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].currPos=ants[k*classes*features+j*classes+i].Rm[minPosition[k]];}}}}}/* end of function *//********************************************************************//* Function to cluster according to the cluster centroids *//* Output: none *//* Input: ant structure and the data points *//* Global Structure Modified: none *//********************************************************************/void finalCluster(antdata* ants,float data[MAXDATA][features],float Rm[antsPerFeature][memory]){int i,j,position[MAXDATA],minPosition=0,k=0,l=0,minRm=0;float clsCenters[antsPerFeature][MAXDATA][features];float distance[MAXDATA];int cenCount[MAXDATA],classCount[MAXDATA][c];float temp=0;int clsObjects[MAXDATA][MAXDATA],label[MAXDATA];int minPosition1[antsPerFeature],maxPosition1[antsPerFeature];FILE *fp,*fpOut,*fpCls,*fpMinRm,*fpOrgClass,*fpRm;fp=fopen("fcmRm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpMinRm=fopen("minRm.out","a");fpOrgClass=fopen("orgClass.out","a");fpRm=fopen("Rm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}for(k=0;k<antsPerFeature;k++){for(j=0;j<features;j++)for(i=0;i<classes;i++)clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;minPosition1[k]=0;maxPosition1[k]=0;}for(k=0;k<antsPerFeature;k++){for(i=1;i<memory;i++){if(Rm[l][i]<Rm[l][minPosition1[l]])minPosition1[l]=i;if(Rm[l][i]>Rm[l][maxPosition1[l]])maxPosition1[l]=i;}}minRm=0;for(k=1;k<antsPerFeature;k++){if(Rm[k][minPosition1[k]]<Rm[minRm][minPosition1[minRm]])minRm=k;}fprintf(fpRm,"Rm %8.4f\n",Rm[minRm][minPosition1[minRm]]);fclose(fpRm);Fcm(data,clsCenters[minRm],2);hardenRm();for(l=0;l<antsPerFeature;l++){fprintf(fpCls,"Cluster number %2d\n",l);for(j=0;j<classes;j++){distance[j]=inf;cenCount[j]=0;for(k=0;k<c;k++)classCount[j][k]=0;}fprintf(fp,"* %d\n",l);if(l==minRm)fprintf(fpMinRm,"* %d\n",l);for(i=0;i<count;i++){minPosition=0;for(k=0;k<classes;k++){temp=0.0;for(j=0;j<features;j++)temp+= (clsCenters[l][k][j]-data[i][j])*(clsCenters[l][k][j]-data[i][j]);temp=sqrt(temp);distance[k]=temp;if(distance[k]<distance[minPosition])minPosition=k;}position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++;}for(j=0;j<classes;j++){for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++;}/* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/fprintf(fpOrgClass,"\nCluster number: %2d \n",l);for(j=0;j<classes;j++){minPosition=0;fprintf(fpOrgClass,"\n Cluster %2d \n",j+1);fprintf(fpOrgClass,"%2d %2d\n",1,classCount[j][0]);for(i=1;i<c;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;fprintf(fpOrgClass,"%2d %2d\n",i+1,classCount[j][i]);}}for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");fprintf(fpOut,"\nCluster number: %2d \n",l);for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);for(j=0;j<features;j++)fprintf(fpCls,"%8.7f ",clsCenters[l][i][j]/scale[j]+minInput[j]);fprintf(fpCls,"\n");}}/* Write the data to the file */fclose(fp);fclose(fpOut);fclose(fpCls);fclose(fpMinRm);fclose(fpOrgClass);}/* end of function *//********************************************************************//* Function to amnalyze the results *//* Output: none *//* Input: *//* Global Structure Modified: none *//********************************************************************/void analysis(int flag){int i,j,k,temp;char junk;int totalRuns=0,errors[antsPerFeature],epoch=0,position=0,antsperfeature=0,clsNumber=0,min=0,currMinRm;FILE *fp,*fpError,*fpMin,*fpMinRm,*fpMinIp;if(flag==1){fp=fopen("fcmRm.out","r");fpMinIp=fopen("minRm.out","r");fpMinRm=fopen("minRmError.out","w");fpError=fopen("error.out","w");fpMin=fopen("minError.out","w");}else{fp=fopen("fcmFCM.out","r");fpError=fopen("errorFCM.out","w");}if(fp==NULL||fpError==NULL){printf("\n unable to open files in the analysis module \n");return;}fscanf(fp,"%c %d\n",&junk,&totalRuns);fscanf(fp,"%c %d\n",&junk,&antsperfeature);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&temp);}for(i=0;i<totalRuns;i++){for(j=0;j<antsperfeature;j++){errors[j]=0;}fscanf(fp,"%c %d\n",&junk,&epoch);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&currMinRm);}fprintf(fpError,"Experiment %3d ",i);for(k=0;k<antsperfeature;k++){fscanf(fp,"%c %d\n",&junk,&clsNumber);for(j=0;j<count;j++){fscanf(fp,"%d ",&position);if(orgData[j][0]!=position)errors[k]++;}fscanf(fp,"\n");if(flag==1){fprintf(fpError,"\nCluster %3d Errors %5d",k,errors[k]);if(k==currMinRm){fprintf(fpMinRm,"Cluster %2d Errors %5d\n",k,errors[k]);}}else fprintf(fpError," Errors %5d\n",errors[k]);}if(flag==1){min=0;for(j=1;j<antsPerFeature;j++){if(errors[j]<errors[min])min=j;}fprintf(fpMin,"Exp %2d Min Error %5d\n",i,errors[min]);}}fclose(fp);fclose(fpError);if(flag==1){fclose(fpMin);fclose(fpMinRm);fclose(fpMinIp);}}/* end of function */ void RmStage(int heapCount,center clsCen) { FILE *fpTime=fopen("time.out","a");int flag[antsPerFeature],flg;struct rusage start_usage, end_usage,epoch_start_usage, epoch_end_usage;double *perf_times;float newRm[antsPerFeature],oldRm[antsPerFeature],temp;int i=0,j=0,k=0;initRm(heapCount);initializeAntsRm(newAnts,clsCen);for(i=0;i<antsPerFeature;i++){oldRm[i]=inf;newRm[i]=inf;}flg=1;i=0;getrusage(RUSAGE_SELF, &start_usage);while(i<maxEpocs){getrusage(RUSAGE_SELF, &epoch_start_usage);epoch(newAnts);getrusage(RUSAGE_SELF, &epoch_end_usage);perf_times=timing_of(epoch_start_usage,epoch_end_usage); fprintf(fpTime,"Epoch %3d Timing: %f user, %f system, %f total.\n",i, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);for(j=0;j<antsPerFeature;j++){oldRm[j]=newRm[j];flag[j]=1;}computeRm(newAnts,data,newRm);updateMemory(newAnts,newRm,Rm);flg=0;for(j=0;j<antsPerFeature;j++){if(newRm[j]!=inf){if(oldRm[j]!=inf){temp=0;temp=newRm[j]-oldRm[j]<0? (oldRm[j]-newRm[j]):(newRm[j]-oldRm[j]);/*printf("\n new %f old %f temp %f ",newRm[j],oldRm[j],temp);*/if(temp/oldRm[j]>minChange){flg=1;break;}}else flg=1;}}i++;printf("\n Epoch %d ",i);}/* Do the final clustering*/finalCluster(newAnts,data,Rm);getrusage(RUSAGE_SELF, &end_usage);perf_times=timing_of(start_usage,end_usage); fprintf(fpTime,"Exp %3d Timing: %f user, %f system, %f total.\n",k, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);/* Compute the error */} /****************************************************************/ void Fcm (objects X1,center V,int flag) { int i,j,l; FILE *ofile,*fpJm; FILE *iniClsCen=fopen("center.clt","a+"); char temp[80]; float jmFinal=0,rmFinal=0; Random_seed=state1[run];/*randseed*/ srand48(Random_seed); for(i=0;i<MAXDATA;i++) for(j=0;j<features;j++)X[i][j]=X1[i][j]; if(iniClsCen==NULL) { printf("\n Unable to open the initial cluster center file\n"); printf("\n choosing random cluster centers....\n"); flag=1; } for (run=0;run<TOTRUNS;run++) { if(flag!=2) { if (EntireHeaps) lCount=heapCount; else lCount=c; for(i=0;i<lCount;i++) for(j=0;j<s;j++) V[i][j]=0; for(i=0;i<lCount;i++) { for(l=0;l<s;l++) { if(flag==1) { rand_index = lrand48() % n; V[i][l] = X[rand_index][l]; } else { fscanf(iniClsCen,"%f ",&V[i][l]); } }//end for }}else lCount=heapCount;eps=0.001; stop = FALSE; /* This loop finds the final cluster center and the corresponding U values */ z = 0; while (stop == FALSE) { z++; UMAT(U,V,X); /** Update the U matrix **/stop = FINISHED(U1,eps); CLUSTER(U,X,V); /** Get new cluster center **/printf("\nIteration: %d \n",z); } /* end while */ printf("Number of iterations: %d\n\n", z);strcpy(temp,""); sprintf(temp,"%sFCM.out",filename);if(flag==1){jmFinal=Compute_Jm(V,X);rmFinal=Compute_Rm(V,X);fpJm=fopen("jm.out","a");fprintf(fpJm," classes %2d Jm: %10.6f Rm: %10.6f\n",lCount,jmFinal,rmFinal);fclose(fpJm);} if(flag==2) Output(temp,V,X); /* output the results */ }/*end for*/ ofile=fopen(temp,"a"); for (i=0; i <= mins; i++) fprintf(ofile, "Min %d of %10.3f occurred %d times\n", i, mintab[i], mincount[i]); fprintf(ofile, "Average iterations %f\n", (float)aveit/(float)TOTRUNS); fclose(ofile); }/* end Main */ /* The NORM function computes the norm of two vectors, x and v, of dimension s. This gives an indication of the distance between them */ float NORM( feature x, feature v) //feature x, v; { /* Parameters : x -- a local copy of an input vector v -- a local copy of a cluster center */ int i,j; /* loop variable */ double sum; /* the square root of sum is the norm; sum is the sum of the differences squared */ float mat1[features]; float mat2[features]; if (maha) { for (i=0; i<features; i++) mat1[i]=x[i]-v[i]; for(i=0;i<features;i++) { sum =0; for(j=0;j<features;j++) sum += mat1[j] * inv_cov[j][i]; mat2[i] = sum; } sum =0; for(j=0;j<features;j++) sum += mat1[j]*mat2[j]; } else { sum = 0; for(j=0;j<features;j++) sum += (x[j] - v[j])* (x[j] - v[j]); } return (float)(sqrt(sum)); } /* end NORM */ /* The UMAT procedure calculates the updated U matrix according to Bezdek's formula. Its side-effect is the update of the global array U1 */ void UMAT( matrix U,center V, objects X) /*matrix U; center V; objects X;*/ { /* Parameters: U, V, and X: same as in main program */ int i, j, k,l ;/* loop variables *///, class /* class whose cluster center //is closest to pattern k */; float d1,d2,d; float sum; int exact; for (k=0;k<MAXDATA;k++) { /*printf("\n k %d ",k);*/ exact=false; for (i=0;i<lCount;i++) { sum=0; for (j=0;j<lCount;j++) { d1=NORM(X[k],V[i]); d2=NORM(X[k],V[j]); if(d2!=0) { d= d1/d2; d= d * d;/* pow(d1/d2,2.0/(m-1.0)); */ sum+=d; } else { exact = true; for(l=0;l<lCount;l++) if (l==j) U1[l][k]=1.0; else U1[l][k]=0.0; break; } } if (exact!=true) U1[i][k]=1.0/sum; } } } /* end UMAT */ /* The function FINISHED determines if a termination occurs. If not it updates U with U1, so its side-effect is the potential modification of U */ BOOLEAN FINISHED( matrix U1,float eps) //matrix U1; float eps; { /* Parameters: U1, eps-- same as in main */ float sum; /* sum of squared differences between corresponding elements of U and U1 */ int i,k; /* loop control variabes */ BOOLEAN finished; /* set to true if all corresponding elements are within epsilon */ finished = TRUE; sum = 0; for (i=0; i<lCount; i++) for (k=0; k<MAXDATA; k++) sum += (U[i][k] - U1[i][k])*(U[i][k]-U1[i][k]); if (sqrt(sum) > eps) { finished = FALSE; for (i=0; i<lCount; i++) for (k=0; k<MAXDATA; k++) U[i][k] = U1[i][k]; } /* end if */ return finished; } /* end FINISHED */ /* The CLUSTER procedure determines cluster centers in V for the c classes being searched for. The global array V is updated */ void CLUSTER( matrix U, objects X,center V) //matrix U; objects X; { /* Parameters: U, X -- as in main */ int i, j, k; /* loop control variables */ feature num; /* numerator in the cluster center computation formula presented by Bezdek */ float denum; /* denumenator in the obove formula */ for (i=0; i<lCount; i++) { denum = 0; for (j=0; j<features; j++) { num[j] = 0; for (k=0; k<MAXDATA; k++) num[j] += U[i][k] * U[i][k] * X[k][j]; /* pow(U[i][k],m) * X[k][j]; */ } /* end for */ for (k=0; k<MAXDATA; k++) denum += U[i][k] * U[i][k]; /* pow(U[i][k],m); */ for (j=0; j<features; j++) V[i][j] = num[j] / denum; } /* end for */ } /* end CLUSTER */ float Compute_Jm(center V, objects X) //center V; //objects X; { int i, k,found; double Jm_value; float u,dik; Jm_value = 0; for (k=0; k<MAXDATA; k++) { found = FALSE; for (i=0; i<lCount; i++) { dik=NORM(X[k],V[i]); dik=dik*dik; if (dik==0.0) { if (found == FALSE) { u=1.0; found = TRUE; printf("found the case with dik=0\n"); } else { printf("\n cant proceed with this initialization"); exit(1); } } else { if (found != TRUE) u=U1[i][k]; /* get_Uvalue(V,X,dik,k); */ else u=0.0; } Jm_value += u * u * dik;/* pow(u, m) * dik; */ } } return (Jm_value); } /* end Compute_Jm */ /* The Output procedure outputs the U matrix which gives the fuzzy measure of each feature belonging to a cluster center. It also outputs the cluster center for each class */ void Output(char fname[],center V,objects X) //char fname[80]; /* Parameters: U, V -- same as in main */ { int i, j, found; /* loop control variables */ float J2c,R2c; FILE *fpJm=fopen("JmRm.out","a"),*fp; if ((fp=fopen(fname,"a"))==NULL) printf("\n CAN'T OPEN %s\n",fname); else { fprintf(fp,"\n \n"); for (i=0; i<lCount; i++) { fprintf(fp,"\n"); for (j=0; j<features; j++) fprintf(fp,"%4.3f ",V[i][j]); } /* end for */ } /* end if */ J2c= Compute_Jm(V,X); R2c=Compute_Rm(V,X); fprintf(fpJm,"Classes: %3d Jm: %10.6f Rm: %10.6f \n",lCount,J2c,R2c); found =0; for (i=0;i<= mins; i++) { if ((found == 0) && ((J2c - mintab[i]) < .001) && ((J2c - mintab[i]) > -.001)){ mincount[i]++; found = 1; } } if (found == 0) { mins++; mintab[mins]=J2c; mincount[mins]=1; } fprintf(fp,"\nJ%2d = %f", m,J2c); fprintf(fp, "Number of iterations: %d\n\n", z); aveit=aveit + z; fclose(fp); fclose(fpJm); } /***************************************************************/ void find_covar( ) { int i,j,k; float mean[features],sum; for (i=0;i<features;i++) mean[i]=0; for (i=0;i<features;i++) for (j=0;j<m;j++) mean[i]+=X[j][i]; for (i=0;i<features;i++) mean[i]/=m; for (i=0;i<features;i++) for (j=0;j<features;j++) { sum=0; for (k=0;k<m;k++) sum+=(X[k][i]-mean[i])*(X[k][j]-mean[j]); Y[i][j]=sum/m; } }float Compute_Rm(center V, objects X){int i=0,j=0,l=0;double Dik=0,temp=0,Rm=0;Rm=0;for(i=0;i<MAXDATA;i++){Dik=0;for(j=0;j<lCount;j++){temp=0;for(l=0;l<features;l++)temp+=(X[i][l]-V[j][l])*(X[i][l]-V[j][l]);Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}return(Rm);}/* End of function *//********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void hardenRm() { FILE *fp=fopen("fcmFCM.out","a"); FILE *fpOut=fopen("ClassDataFCM.out","a"); int i=0,minPosition=-1,position[MAXDATA],j=0,k=0; int cenCount[MAXDATA],classCount[MAXDATA][c],label[c], clsObjects[MAXDATA][MAXDATA]; for(j=0;j<classes;j++) {cenCount[j]=0;for(k=0;k<classes;k++)classCount[j][k]=0; } for(i=0;i<MAXDATA;i++) { minPosition=findPosition(i); position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++; } for(j=0;j<classes;j++) {for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++; } /* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/for(j=0;j<classes;j++){minPosition=0;for(i=1;i<c;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;}}fprintf(fp,"* %d\n",0);for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);}fclose(fpOut);fclose(fp);return; }/*end of function*/ /********************************************************************//* Function Main *//********************************************************************/ int main(int argc, char** argv){int k=0,i=0,j=0;int sum=0;char temp[80];FILE *fpRmOut=fopen("fcmRm.out","w");FILE *fpOut=fopen("ClassData.out","w");FILE *fpCls=fopen("clsCenters.out","w");FILE *fpMinRm=fopen("minRm.out","w");FILE *fpOrgClass=fopen("orgClass.out","w");FILE *fpRm=fopen("Rm.out","w");FILE *fpJmRm=fopen("JmRm.out","w");FILE *fp=fopen("fcm.out","w");FILE *fp1; FILE *fp2=fopen("antAnalysis.out","a");FILE *fp3=fopen("fcmAnalysis.out","a"); FILE *fp4=fopen("errors.out","w");FILE *fp5;FILE *fpJm=fopen("jm.out","w");FILE *fpTime=fopen("time.out","w");FILE *fpFCM=fopen("fcmFCM.out","w");FILE *fpOutFCM=fopen("ClassDataFCM.out","w");fprintf(fpFCM,"# %d\n",EXPRUN);fprintf(fpFCM,"@ %d\n",1);fclose(fpFCM);fclose(fpOutFCM);fclose(fp4);fclose(fpJm);sprintf(temp,"%sObj.out",filename);fp5=fopen(temp,"w");fclose(fp5);strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp5=fopen(temp,"w");fclose(fp5);strcpy(temp,"");sprintf(temp,"%sOp.out",filename);fp1=fopen(temp,"w");fprintf(fp,"\n$%d",EXPRUN);fprintf(fp1,"\n$%d",EXPRUN);fclose(fp);fclose(fp1);fclose(fpJmRm);fprintf(fpRmOut,"# %d\n",EXPRUN);fprintf(fpRmOut,"@ %d\n",antsPerFeature);fclose(fpRmOut);fprintf(fpTime,"Experiments %d\n",EXPRUN);fprintf(fpMinRm,"# %d\n",EXPRUN);fprintf(fpMinRm,"@ %d\n",antsPerFeature);fclose(fpMinRm);fclose(fpOut);fclose(fpCls);fclose(fpOrgClass);fclose(fpRm);fclose(fpTime);/*readInput();*/ if(argc==2)sscanf(argv[1],"%d",&debugLevel); printf("\n Debug level %d\n",debugLevel); for( i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; readInput(); /*readOrg();*/ //Compute the distance of all the points if(maxDist) { Dmax=computeDistance(); //Compute the maximum distance fp5=fopen("maxData.txt","w");fprintf(fp5,"%f",Dmax);fclose(fp5); } else {fp5=fopen("maxData.txt","r");fscanf(fp5,"%f",&Dmax);fclose(fp5); } printf("\n %f \n",Dmax); //Initialise the board for(k=0;k<EXPRUN;k++) { fpRmOut=fopen("fcmRm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpOrgClass=fopen("orgClass.out","a");fpMinRm=fopen("minRm.out","a");fpRm=fopen("Rm.out","a");fpTime=fopen("time.out","a");fpFCM=fopen("fcmFCM.out","a");fpOutFCM=fopen("ClassDataFCM.out","a");fprintf(fpFCM,"$ %d\n",k);fprintf(fpOutFCM,"Experiment %d\n",k);fprintf(fpRm,"Exp: %2d ",k);fclose(fpRm);fprintf(fpCls,"EXP No %2d \n",k);fclose(fpCls);fprintf(fpRmOut,"$ %d\n",k);fprintf(fpMinRm,"$ %d\n",k);fprintf(fpOut,"Experiment %d\n",k);fprintf(fpOrgClass,"Experiment %d\n",k);fprintf(fpTime,"Experiment %d\n",k);fclose(fpRmOut);fclose(fpOut);fclose(fpMinRm);fclose(fpOrgClass);fclose(fpTime); fclose(fpFCM);fclose(fpOutFCM); init(); sum=0; srand48(state1[k]); initialiseBoard(); initialise(1); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(1); printf("\n %d \n",heapCount); //Simulate the working of ants iterate(1); //drop the objects after all the iterations dropFinal(1); //to verify the correctness for(i=0;i<heapCount;i++) sum+=heaps[i].count; //write the cluster centers to the file writeClusterInformation(1); printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); //getchar(); //Run FCM // heapCount=2973;/* To be commented */ printf("\n\n k %d \n\n",k); Fcm(data,V,0); //compute new board size. lCount=heapCount; heapRow=(int)ceil(sqrt(4*heapCount)); newHeapCount=lCount; printf("\n LCount %d newHeapCount %d heap Row %d \n",lCount,newHeapCount,heapRow); //harden the objects harden(newHeaps); //iterate with the heaps as the objects. initialiseBoard(); initialise(0); for(i=0;i<lCount;i++) computeHeapParameters(&heaps[i],0); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(0); printf("\n %d \n",heapCount); //getchar(); //Simulate the working of ants iterate(0); dropFinal(0); writeClusterInformation(0); sum=0; for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); // getchar(); fpJm=fopen("jm.out","a"); fprintf(fpJm,"Experiment %2d: ",k); fclose(fpJm); Fcm(data,V,1); harden1(); RmStage(newHeapCount,V); } fp=fopen("fcm.out","a"); fp1=fopen(temp,"a"); fprintf(fp,"\n"); fprintf(fp1,"\n"); fclose(fp); fclose(fp1); fp=fopen("fcm.out","r"); fp1=fopen(temp,"r"); printf("\n Before read org\n");printf("\nafter read org\n");/*readFCM(fp1,fp2);*/printf("\n After 1st read fcm\n");/*readFCM(fp,fp3);*/printf("\n After 2 fcm read \n");/*analysis(1);analysis(0);*/return(1); }//end of main

Parag Kanade/Combined Algorithm/header.h

/********************************************************************//* Ant Class parameters *//* SPEED # amplitude of moves [1,10] *//* P direction Probability to move in the same direction [.5,1] *//* Max carry : Maximum object carrying time [20,200] *//* P load Probability to pick up a single object [0.4,0.8] *//* P destroy probability to destroy a heap of two objects [0,0.6] *//* T remove min. dissimilarity necessary for removing an object *//* from a heap [.1,.2] *//* T create max dissimilarity permitted for creating a heap[.05,.2]*//********************************************************************/ #include <stdio.h>#include <math.h>#include <string.h>#include <stdlib.h>#include <ctype.h>#include <time.h>#include <sys/time.h>#include "timing_of.c"#define MAXANTS 17#define MAXDATA 50#define MAXANTSFORHEAPS 15#define MAXITERATIONS 1000#define MAXROWS 14#define MAXCOLS 14#define features 4#define filename "british"#define c 5 /* number of clusters */#define EXPRUN 2#define Tremove 3.0#define Tcreate 1.5#define Pdrop 0.2#define Pdestroy 0.3#define Pload 0.3#define normalize 0#define EntireHeaps 1#define TcreateForHeaps 0.10#define errThresh 0.1#define dataDump 1#define maxDist 1#define Prest 0.01#define Pcontinue 0.75#define tContinueCurr 0.20#define Dmin 0.001#define DmaxRm 0.01#define iterations 50#define memory 5#define antsPerFeature 30#define inf 10e100#define m 2#define TRUE 1#define maxEpocs 1000#define minChange 0.00075#define maha 0#define FALSE 0#define true 1#define false 0#define TOTRUNS 1 /* number of V's to write out *//* some type definitions to make C code a little more readable */#define n MAXDATA /* number of input vectors */#define s features /* number of features */#define square 0extern int count;int debugLevel=0;/* To debug the code and verify the correctness of algorithms*/int currHeapCount=0;typedef int BOOLEAN;typedef short TYPE_IN_DATA; /* Type of input binary data */typedef float feature[s]; /* Type of each input; made up of input features */typedef float irisData[s];typedef feature objects[MAXDATA]; /* Type of array holding all inputs */typedef feature center[MAXDATA]; /* Type of array holding cluster centers */typedef float matrix[MAXDATA][MAXDATA];/* Type of the matrix containing membership grades */typedef struct Heap{float Dmax;irisData Ocenter;float Dmean;irisData Odissim;int x;int y;int count;int disPosition;int objects[MAXDATA];int objCount;}heap;typedef struct Ant{int x;int y;int carrying;int speed;int dir;irisData data;}ant;typedef struct a{float Rm[memory];int rest;int direction;float currPos;}antdata;typedef struct HeapInfo{int total;int classObj[c];} heapInfo;antdata *newAnts; /* Ants */int classes;float Rm[antsPerFeature][memory];float orgData[n][s+1];float fcmData[260][n][s];int clsCenCount[260];int currentRow=0;int currentAntCount=0;int updateAfter=50;int heapSum=0;int minAnts=3;int rowMin=5;int antCount=0; /*******************************************************************/ /*** Global Variables ***/ irisData data[MAXDATA]; //To hold the data read from the file int count=0; //Count of the total number of data elements int heapCount=0; // Total number of heaps int heapRow=0; // Rows for the second iteration with heaps int newHeapCount=0; // Count of the heaps for the second iteration heap heaps[MAXDATA]; // Data structure to hold the heap information heap newHeaps[MAXDATA]; // Data Structure to hold the new heap information // For the second iteration int board[MAXROWS][MAXCOLS]; // Board on which the objects are located ant ants[MAXANTS]; // Data structure to hold the ants float Dmax=0; // Distance of the most dissimilar object pair int minHeapCount=100000; // Counter to hold the minimum heap count, used only for academic purpose // no use in the algorithm int minPosition; // the minimum heap iteration , only for academic purpose // no use in the algorithm float clsCen[MAXDATA][MAXDATA][s];//to hold the objects after hardening int cenCount[MAXDATA];//to hold the number of objects after hardening /********************************************************************/ /*** Varaibles to normalize the features between 0 and 1 */ float minInput[features], maxInput[features],scale[features],minForHeaps[features],maxForHeaps[features],scaleForHeaps[features]; /********************************************************************/ /** Variables for the FCM algorithm */ int temp[200]; /* for square matrix tests */ float eps; matrix U, U1; BOOLEAN stop; center V; objects X; char fname[80]; int run; float inv_cov[s+1][s+1]; /* inverse of covariance matrix Y */ float Y[s][s]; long rand_index, Random_seed; long state1[52] = { 9347, 3835, 5194, 8310, 346, 535, 5297, 6711, 77, 3834, 668, 4175, 6868, 5890, 9304, 8462, 5269, 920, 65392, 41600, 70119, 91032, 76220, 26245, 4746, 73608, 32823, 63264, 75641, 99104, 36534, 24704, 98255, 72266, 75336, 65152, 7269, 63163, 88471, 27271, 43641, 76649, 47773, 23777, 27491, 35926, 16651, 48652, 89766, 90921, 6056 }; int z; int mins=0; int aveit =0; float mintab[200]; int mincount[200]; long start,end,total; int lCount;//added by parag/*heapInfo info[MAXDATA];*//********************************************************************//***Function decelarations*//********************************************************************//***For ant based algorithm*/void readInput();float computeDistance();float computeDistanceBetween2(irisData a,irisData b);float findMax(float *distance);float value(irisData a);int found(int a , int b);void initialise(int flag);int findObject(int a,int b);void initialiseAnts(int flag);void moveNext(ant* a,int flag);void computeHeapParameters(heap* data,int flag);void pickObject1(ant *a,int position,int flag);void pickObject2(ant *a , int position,int flag);void pickObjectn(ant *a,int position,int flag);void pick(ant *a,int flag);void dropObject(ant* a,int flag);int findInNewheaps(irisData object);void dropObject1(ant* a, int position,int flag);void drop(ant* a,int flag);void move(ant* a,int flag);void iterate(int flag);void dropFinal(int flag);int partition(heap a[], int low,int high);void quickSort( heap A[], int low,int high ) ;void writeClusterInformation(int flag);void initialiseNewHeaps(heap newHeaps[]);int findPosition(int i);void harden(heap newHeaps[]);void harden1();int findPosition1(int );void writeInfo();void addObject1(int ,int);void readFCM(FILE *,FILE *);void readOrg();int findInData(irisData input,int iflag);void adjustBoard(int flag);void writeIncremental(int iteration,int flag);void initializeAntsRm(antdata* ants,center clsCen);void epoch(antdata *ants);void computeRm(antdata *ants,float data[MAXDATA][features],float newRm[antsPerFeature]);void updateMemory(antdata *ants,float newRm[antsPerFeature], float Rm[antsPerFeature][memory]);void analysis();void GET_INPUTS1(char []);void UMAT( matrix,center,objects);BOOLEAN FINISHED(matrix, float);void CLUSTER( matrix, objects,center);void Output(char[],center V,objects X);void Fcm(objects X,center V,int flag);float Compute_Jm(center V, objects X);float Compute_Rm(center V, objects X);float NORM( feature x, feature v);double *timing_of(); /* Calculate time in seconds */void RmStage(int heapCount,center clsCen);void hardenRm();/********************************************************************//***For FCM algorithm*//*void CLUSTER( matrix, objects);*//*void Output(char[]);*//*void Fcm(irisData data[],int stage);*//********************************************************************/

Parag Kanade/Combined Algorithm/timing_of.c

/* timing_of.c Calculates timing given struct rusage. $Id: timing_of.c,v 1.3 2002/07/12 20:48:49 eschrich Exp $ Steven Eschrich Copyright (C) 2002 University of South Florida This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA*/�#include <stdlib.h>#include <time.h>#include <sys/times.h>#include <sys/resource.h>/* Get time, in seconds */double *timing_of(struct rusage start, struct rusage stop){ long totaluMicroseconds, totalsMicroseconds; static double totalTime[2]; totaluMicroseconds = stop.ru_utime.tv_usec - start.ru_utime.tv_usec; totalsMicroseconds = stop.ru_stime.tv_usec - start.ru_stime.tv_usec; /* If we need to borrow, do so */ if ( totaluMicroseconds < 0 ) { stop.ru_utime.tv_sec--; totaluMicroseconds=1000000L + stop.ru_utime.tv_usec - start.ru_utime.tv_usec; } if ( totalsMicroseconds < 0 ) { stop.ru_stime.tv_sec--; totalsMicroseconds=1000000L + stop.ru_stime.tv_usec - start.ru_stime.tv_usec; } totalTime[0] = (stop.ru_utime.tv_sec - start.ru_utime.tv_sec) + 0.000001 * totaluMicroseconds; totalTime[1] = (stop.ru_stime.tv_sec - start.ru_stime.tv_sec) + 0.000001 * totalsMicroseconds; return totalTime;}

Parag Kanade/Combined Algorithm/iris.c

#include "header.h" /********************************************************************/ /********************************************************************/ /* Function to read the input data set */ /* The file name is assumed to be itest.data */ /* Status: Tested */ /* Output: Data from the file into the "data" (Global) data structure */ /* Output: Count (Global), the count of the number of objects */ /********************************************************************/ void readInput() { FILE *fp; char fname[80]; int i=0,j=0; char ch; sprintf(fname,"%s.data",filename); /* Check if the input file is opened */ if((fp=fopen(fname,"r"))==NULL) { printf("\n Unable to open input file \n"); exit(0); } /*initialise the minimum maximum and the scale */ for(i=0;i<features;i++) { minInput[i]=1000; maxInput[i]=0; minForHeaps[i]=1000; maxForHeaps[i]=0; scale[i]=0; scaleForHeaps[i]=0; } /* Discard leading comments */ ch = 'c'; while (ch != '#') fscanf (fp, "%c", &ch); i=0; /* Parse the file and populate the data structure */ while(!feof(fp)) { for(j=0;j<features;j++) { fscanf(fp,"%f ",&data[i][j]); } fscanf(fp,"\n"); for(j=0;j<features;j++) { if(data[i][j]<minInput[j]) minInput[j]=data[i][j]; if(data[i][j]>maxInput[j]) maxInput[j]=data[i][j]; } i++; } count=i; /* Normailze the feature values in between 0 and 1 */ for(j=0;j<features;j++) { if(minInput[j]!=maxInput[j]) scale[j]=1.0/(maxInput[j]-minInput[j]); else scale[j]=1.0; } for(i=0;i<count;i++) { for(j=0;j<features;j++) data[i][j]=(data[i][j]-minInput[j])*scale[j]; } fclose(fp); return; }//end of function /********************************************************************/ /*Function to compute the eucldiean distance for all input data set */ /* Output : an array conatining the distance of each object with the other */ /********************************************************************/ float computeDistance() { float distance,dist=0; int i=0,j=0,k=0; distance=0; for(j=0;j<count;j++) for(i=0;i<count;i++) { for(k=0;k<features;k++) dist+=sqrt((data[j][k]-data[i][k])*(data[j][k]-data[i][k])); if(distance<dist)distance=dist; dist=0; } return(distance); } /********************************************************************/ /* Function to compute the distance between two objects */ /* Output : Euclidean d istance between the two input objects */ /********************************************************************/ float computeDistanceBetween2(irisData a,irisData b) { float temp=0; int i=0; for(i=0;i<features;i++) temp+=(a[i]-b[i])*(a[i]-b[i]); temp=sqrt(temp); return(temp); } /********************************************************************/ /* Function to compute the value of an object */ /********************************************************************/ float value(irisData a) { int i=0; float temp=0; for(i=0;i<features;i++) temp+=a[i]*a[i]; return(sqrt(temp)); } /********************************************************************/ /* Function to find the object in the data structure */ /* Output : The position of the object in the data */ /* Input : The object */ /********************************************************************/ int findInData(irisData object,int iFlag) { int i=0,lastCount=0,j,position=-1,flag[features],flg;if(iFlag==1)lastCount=MAXDATA;else{}for(i=0;i<lastCount;i++){ for(j=0;j<features;j++) if(fabs(object[j]-data[i][j])<0.00001) flag[j]=1; else { flag[j]=0; break;} flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /* Function to find whether there is an object on the board at a,b */ /* Output : a flag indicating whether an object is found or not */ /* Input : The row and column indicating the board position */ /********************************************************************/ int found(int a , int b) { if(board[a][b]==9) return 1; else return 0; } /********************************************************************/ /* Function to initialize the initial board */ /* Output : None */ /* Global Structures modified : heaps */ /* Input : Flag indicating the iteration 1: First 0: Second */ /********************************************************************/ void initialise(int flag) { int a,b,i=0,k=0; int loopCount,rowCount,colCount; /* Initialize the row, column and number of heaps */ if(flag==1) { loopCount=count; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=lCount; rowCount=heapRow; colCount=heapRow; } /* Initialize the heaps */ for(i=0;i<loopCount;i++) { heaps[i].count=0; heaps[i].x=0; heaps[i].y=0; heaps[i].disPosition=0; heaps[i].Dmax=0; heaps[i].Dmean=0; } for(i=0;i<loopCount;i++) { /*find a board position which is empty*/ do { a=lrand48()%rowCount; b=lrand48()%colCount; } while(found(a,b)); /*Allocate the object to that place and assign a heap to it*/ heaps[i].x=a; heaps[i].y=b; heaps[i].count=1; heaps[i].Dmax=0; heaps[i].Dmean=0; if(flag==1) { heaps[i].objects[0]=i; for(k=0;k<features;k++)heaps[i].Ocenter[k]=data[i][k]; } else { for(k=0;k<features;k++) heaps[i].Ocenter[k]=newHeaps[i].Ocenter[k]; } heaps[i].disPosition=0; board[a][b]=9; if((debugLevel>0)&&(flag==1))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],data[heaps[i].objects[0]][0]); if((debugLevel>0)&&(flag==0))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],newHeaps[i].Ocenter[0]); } /*initialise the initial heap count*/ heapCount=loopCount; currentRow=rowCount; } /********************************************************************/ /* Function to find whether an heap is present at the location a b */ /* Output : Flag indicating the presencs of the heap */ /* Input : Roe and column indicating the board position */ /********************************************************************/ int findObject(int a,int b) { int i; int position=-1; for(i=0;i<heapCount;i++) { if((heaps[i].x==a)&&(heaps[i].y==b)) { position=i; break; } } return(position); } /********************************************************************/ /* Function initialize the ants */ /* Output : None */ /* Input : Flag indicating the iteration */ /* Global Variables Modified : ant, heapCount, heaps, board */ /********************************************************************/ void initialiseAnts(int flag) { int i=0,a=0,b=0,position,j=0,loopCount=0,rowCount,colCount,k=0; if(flag==1) { loopCount=MAXANTS; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=MAXANTSFORHEAPS; rowCount=heapRow; colCount=heapRow; } for(i=0;i<loopCount;i++) { /*generate random positions for the ants*/ a=lrand48()%rowCount; b=lrand48()%colCount; ants[i].x=a; ants[i].y=b; /*Find if an object is present at that position or not*/ if(board[a][b]==9) { ants[i].carrying=1; /*find the heap at that location*/ position=findObject(a,b); if(position==-1) { printf("\n Error in logic: Ant init \n"); //getchar(); break; } else { /*Assign the heap data to the ant data and decrement the heap count*/ if(flag==1){ for(k=0;k<features;k++)ants[i].data[k]=data[heaps[position].objects[0]][k]; }else{for(k=0;k<features;k++)ants[i].data[k]=heaps[position].Ocenter[k];} for(j=position;j<heapCount-1;j++) heaps[j]=heaps[j+1]; heapCount--; } /*Clear the board position */ board[a][b]=-9; } else ants[i].carrying=-1; ants[i].speed=0; ants[i].dir=lrand48()%8; printf("\n Ant %2d X: %2d Y: %2d Carying : %2d ",i,ants[i].x,ants[i].y,ants[i].carrying); } currentAntCount=loopCount; } /********************************************************************/ /* Function to move the ant */ /* Output: None */ /* Input: Ant to be moved and the flag indicating the iteration */ /********************************************************************/ void moveNext(ant* a,int flag) { int rowCount,colCount; rowCount=currentRow;colCount=currentRow; /*Depending upon the direction move the ant*/ switch (a->dir) { case 0: a->y++; if(a->y>=colCount)a->y=0; break; case 1: a->x--; a->y++;; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 2: a->x--; if(a->x<0)a->x=rowCount-1; break; case 3: a->x--; a->y--; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 4: a->y--; if(a->y<0)a->y=colCount-1; break; case 5: a->x++; a->y--; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 6: a->x++; if(a->x>=rowCount) a->x=0; break; case 7: a->x++; a->y++; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break; } }//end of function /********************************************************************/ /* Function to compute the parameters of the heap */ /* Output: None */ /* Input: The heap whose parameters are to be computed iteration flag*/ /********************************************************************/ void computeHeapParameters(heap* heapData,int flag) { int i=0,j=0,k=0,position=0; float distance=0; float Dmean=0.0; float *dis; float featureSum[features]; float max=0; float sum=0; /* init*/ for(k=0;k<features;k++) featureSum[k]=0; /*Compute the distance of each object and find the maximum distance*/ max=0; if(flag==1){ for(j=0;j<heapData->count;j++) for(i=0;i<heapData->count;i++) { for(k=0;k<features;k++) sum+= (data[heapData->objects[j]][k]-data[heapData->objects[i]][k])*(data[heapData->objects[j]][k]-data[heapData->objects[i]][k]); distance=sqrt(sum); if(max<distance) max=distance; sum=0; } /* Assign the maximum distance to the heap */ heapData->Dmax=max;/*Compute the centre object*/for(i=0;i<heapData->count;i++)for(k=0;k<features;k++) featureSum[k]+=data[heapData->objects[i]][k];for(k=0;k<features;k++)heapData->Ocenter[k]=featureSum[k]*1.0/heapData->count; /* Find the mean distance */if((dis=(float *) calloc(heapData->count,sizeof(float)))==NULL){printf("\nUnable to allocate memory in the compute heap parameters procedure\n");exit(0);} for(i=0;i<heapData->count;i++) { Dmean+=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); dis[i]=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); } heapData->Dmean=Dmean*1.0/heapData->count; /*Compute the most dissimilar object*/ max=dis[0]; for(i=1;i<heapData->count;i++) if(max<dis[i]) { max=dis[i]; position=i; } /*Assign the most dissimilar object to the heap variable*/ for(k=0;k<features;k++) heapData->Odissim[k]=data[heapData->objects[position]][k]; heapData->disPosition=position;free(dis); } else{/* Heap Parameters for the second stage */for(k=0;k<features;k++){ heapData->Odissim[k]=heapData->Ocenter[k];}heapData->disPosition=0;heapData->Dmean=0;heapData->Dmax=0;} }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a single object*/ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject1(ant *a,int position,int flag) { int i,k=0; /*Clear the board*/ board[heaps[position].x][heaps[position].y]=-9; /* Assign the data to the ant*/if(flag==1){ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[0]][k];}else {for(k=0;k<features;k++) a->data[k]=heaps[position].Ocenter[k];} a->carrying=1; /*Copy all the heaps remaining heaps in the structure*/ for(i=position;i<heapCount-1;i++) heaps[i]=heaps[i+1]; /*Decrement the heap count*/ heapCount--; }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a two objects */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject2(ant *a , int position,int flag) { float Pdestroy1=-1; int i=0,k=0; /*Compute the probability to destroy the heap*/ Pdestroy1=lrand48()%13*1.0/12.0; /*If the probability is more than Pdestroy then destroy the heap */ if(Pdestroy1<Pdestroy) { /*pick the object randomly*/ i=lrand48()%2; /* Assign the data to the ant */ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[i]][k]; a->carrying=1; /*Correct the heap parameters*/ heaps[position].count=1; heaps[position].objects[0]=heaps[position].objects[1-i]; computeHeapParameters(&heaps[position],flag); } }//end of function /********************************************************************/ /*Function to pick a object from a heap consisting of more than two object */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /********************************************************************/ void pickObjectn(ant *a,int position,int flag) { int i=0,k=0; /*If the criteria is satsfied then pick the object*/ if((computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)*1.0/heaps[position].Dmean)>Tremove) { a->carrying=1; for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[heaps[position].disPosition]][k]; for(i=heaps[position].disPosition;i<heaps[position].count-1;i++) heaps[position].objects[i]=heaps[position].objects[i+1]; heaps[position].count--; computeHeapParameters(&heaps[position],1); } }//end of function /********************************************************************/ /* Function to pick a object */ /********************************************************************/ void pick(ant *a,int flag) { int position=-1,i=0; float Pload1=-1.0; for(i=0;i<heapCount;i++) { /*Check if a heap is present in the eight neighbouring cells*/ if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if Heap is found*/ if(position!=-1) { /* heap or object found.*/ /*Depending upon the number present in the heap call the corresponding routine*/ switch(heaps[i].count) { case 1: Pload1 = lrand48()%151*1.0/150.0; if(Pload1<Pload) { pickObject1(a,position,flag); } break; case 2: pickObject2(a,position,flag); break; default: pickObjectn(a,position,flag); break; } } } /********************************************************************/ /* Function to drop a object to form a new heap */ /* Output: None */ /* Input: Ant, iteration flag */ /* Global Structure modified : heap, heapCount */ /********************************************************************/ void dropObject(ant* a,int flag) { /*Create a new heap*/ int k=0; heaps[heapCount].count=1; heaps[heapCount].x=a->x; heaps[heapCount].y=a->y; heaps[heapCount].Dmax=0; heaps[heapCount].Dmean=0; heaps[heapCount].disPosition=0; for(k=0;k<features;k++) { heaps[heapCount].Odissim[k]=a->data[k]; heaps[heapCount].Ocenter[k]=a->data[k]; } if(flag==1) {heaps[heapCount].objects[0]=findInData(a->data,flag); /*computeHeapParameters(&heaps[heapCount],flag);*/ } computeHeapParameters(&heaps[heapCount],flag); heapCount++; a->carrying=-1; board[a->x][a->y]=9; }//end of function /********************************************************************/ /* Function to find the heap position during the second iteration */ /* Output: position of the heap */ /* Input: object center of the heap that the ant is carrying */ /* Global Structure modified : none */ /********************************************************************/ int findInNewheaps(irisData object) { int position=-1,i,j=0,flag[features],flg; for(i=0;i<newHeapCount;i++) { for(j=0;j<features;j++) { if(fabs(object[j]-newHeaps[i].Ocenter[j])<0.00001) flag[j]=1; else {flag[j]=0;break;} } flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /*Function to drop a object to a heap consisting of a one or more object */ /* Output: none */ /* Input: Ant, position of the heap , iteration flag */ /* Global Structure modified: heaps,newHeaps */ /********************************************************************/ void dropObject1(ant* a, int position,int flag) { int heapPosition=-1,heapPosition1=-1,i,j,k=0; irisData newData,temp; if(flag==1) { heaps[position].objects[heaps[position].count]=findInData(a->data,flag); heaps[position].count++; computeHeapParameters(&heaps[position],flag); } else { for(k=0;k<features;k++)temp[k]=heaps[position].Ocenter[k]; heapPosition=findInNewheaps(a->data); heapPosition1=findInNewheaps(heaps[position].Ocenter); if(heapPosition==-1||heapPosition1==-1) { printf("\n error in logic:new heaps\n"); //getchar(); return; } for(k=0;k<features;k++) {newData[k]=(newHeaps[heapPosition].Ocenter[k]*newHeaps[heapPosition].count+newHeaps[heapPosition1].Ocenter[k]*newHeaps[heapPosition1].count)*1.0/(newHeaps[heapPosition].count+newHeaps[heapPosition1].count);newHeaps[heapPosition].Ocenter[k]=newData[k];heaps[position].Ocenter[k]=newData[k]; } computeHeapParameters(&heaps[position],flag); for(i=newHeaps[heapPosition].count,j=0;j<newHeaps[heapPosition1].count;i++,j++) { newHeaps[heapPosition].objects[i]=newHeaps[heapPosition1].objects[j]; } newHeaps[heapPosition].count+=newHeaps[heapPosition1].count; for(i=heapPosition1;i<newHeapCount-1;i++) newHeaps[i]=newHeaps[i+1]; newHeapCount--; } a->carrying=-1; }//end of function /********************************************************************/ /* Function to drop a object */ /* Output: None */ /* Input: Ant, iteration flag */ /********************************************************************/ void drop(ant* a,int flag) { int i, position=-1; float Pdrop1=0.0; /*Find whether we have a heap in the neighbourhood of the cell*/ for(i=0;i<heapCount;i++) { if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if a heap is found*/ if(position!=-1) { /*heap or object found.*/ /*Depending on the number of objects present take action*/ switch(heaps[position].count) { /*If criteria is satisfied then drop the object*/ case 1: if(flag==1) { if((computeDistanceBetween2(a->data,data[heaps[position].objects[0]])*1.0/Dmax)<Tcreate) dropObject1(a,position,flag); } else { if((computeDistanceBetween2(a->data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) dropObject1(a,position,flag); } break; default: if(computeDistanceBetween2(a->data,heaps[position].Ocenter)<computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)) dropObject1(a,position,flag); break; } } else { /*Compute the probability to drop the object*/ Pdrop1 = lrand48()%151*1.0/150; if(Pdrop1<Pdrop) dropObject(a,flag); } }//end of function /********************************************************************/ /* Function to move the ant */ /* Output: none */ /* Input: Ant, iteration flag */ /********************************************************************/ void move(ant* a,int flag) { float pDir; /*Check the speed */ if (a->speed!=0) { /*move in the same direction*/ a->speed--; moveNext(a,flag); } else { /*generate the new direction*/ pDir=lrand48()%17*1.0/16.0; if(pDir<.85) { a->dir=lrand48()%8; } else if(pDir>.95) { a->speed=2; } moveNext(a,flag); } } /********************************************************************/ /* Function to simulate the movement of the ants */ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified: minHeapCount, minHeapPosition */ /********************************************************************/ void iterate(int flag) { int i=0,j=0,k=0,loopCount=0; if (flag==1) loopCount=MAXANTS; else loopCount=MAXANTSFORHEAPS; while (i<MAXITERATIONS) { i++; for(j=0;j<loopCount;j++) { move(&ants[j],flag); if(ants[j].carrying==1) { drop(&ants[j],flag); } else { pick(&ants[j],flag); } } if(debugLevel>0){ for(k=0;k<heapCount;k++) for(j=0;j<heapCount;j++) if((k!=j)&&(heaps[k].x==heaps[j].x)&&(heaps[k].y==heaps[j].y)) { printf("\nerror two heaps with same co ordinates %d %d ",k,j); //getchar(); } k=0; for(j=0;j<heapCount;j++) k+=heaps[j].count; } if(heapCount<minHeapCount) { minHeapCount=heapCount; minPosition=i; } if(debugLevel>0) printf("\n i:%d Heap count %d sum %d",i,heapCount,k); else printf("\n i:%d Heap count %d",i,heapCount); heapSum+=heapCount; } }//end of function /********************************************************************/ /*Function to drop the objects carried by the ants after all the iterations*/ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified:none */ /********************************************************************/ void dropFinal(int flag) { int i=0,j=0,position=-1,loopCount=0; loopCount=currentAntCount; for(i=0;i<loopCount;i++) { if(ants[i].carrying==1) { if(flag==1) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position,flag); } } else { /*Find if a heap is present */ do { position=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position,flag); position=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position!=-1); } } } }//end of function /********************************************************************/ /* Function for the implementation of QuickSort */ /* Output: none */ /* Input: data structure, low and high indices */ /* Global structure modified:none */ /********************************************************************/ void quickSort( heap A[], int low,int high ) { int mid=0; if(low<high) { mid=partition(A,low,high); quickSort(A,low,mid); quickSort(A,mid+1,high); } } /********************************************************************/ /* Partition routine used by quick sort */ /* Output: partition location */ /* Input: data , low, high */ /* Global structure modified:none */ /********************************************************************/ int partition(heap a[], int low,int high) { heap temp; int x=0,i=0,j=0; i=low; j=high; x=a[low].count; while (1) { while (a[j].count<x) j=j-1; while (a[i].count>x) i=i+1; if(i<j) { temp=a[i]; a[i]=a[j]; a[j--]=temp; } else return(j); } } /********************************************************************/ /* Function used to write the cluster information */ /* Output: none */ /* Input:iteration flag */ /* Global structure modified:none */ /********************************************************************/ void writeClusterInformation(int flag) { FILE *fp=fopen("center.clt","w"); FILE *fp1; FILE *fp2; FILE *fp3; char temp[80]; int i=0,j=0,k=0; int lastCount=0; int rowCount,colCount,antCount; strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp3=fopen(temp,"a"); if(dataDump) {strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp1=fopen(temp,"a");strcpy(temp,"");sprintf(temp,"%sObj.out",filename);fp2=fopen(temp,"a");if(fp1==NULL||fp2==NULL)printf("\n Error in opening file in the writer Cluster Information routine\n"); } rowCount=currentRow; colCount=currentRow; antCount=currentAntCount; if(fp==NULL) { printf("\n Unable to open cluster center output file \n"); /*return;*/ exit(0); } /*sort the array according to the count value and write the value of the top three elements*/ if((flag==1)&&(!EntireHeaps)) quickSort(heaps,0,heapCount); if(EntireHeaps) {if(flag==1) lastCount=heapCount;else lastCount=newHeapCount; } else lastCount=c; for(i=0;i<lastCount;i++) { if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t ",newHeaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",newHeaps[i].Ocenter[k]); fprintf(fp,"\n"); } else { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); fprintf(fp,"\n"); } } if(dataDump) { fprintf(fp1,"\n Information from the Ant Based Algorithm \n"); fprintf(fp1,"\n Important Parameters : Board %d X %d Ants : %d Iterations %d Total Heaps %d ",rowCount,colCount,antCount,MAXITERATIONS,lastCount); } if(flag==0) { if(dataDump) { fprintf(fp1," TCreateForHeaps %f \n",TcreateForHeaps); fprintf(fp2,"\nTotal Heaps : %d\n",heapCount); fprintf(fp2,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); } fprintf(fp3,"\n@ %d\n",lastCount); } else if(dataDump) fprintf(fp1,"\n");if(dataDump) { fprintf(fp1,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); for(i=0;i<lastCount;i++) if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp2,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); } fprintf(fp1," %5d\n",newHeaps[i].count); fprintf(fp2," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp1," %5d\n",heaps[i].count); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]); fprintf(fp1," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]); fprintf(fp1," %5d\n",heaps[i].count); }} if(flag==0) { if(dataDump) fprintf(fp1,"\n Objects in the heaps \n");for(i=0;i<newHeapCount;i++) { if(dataDump) fprintf(fp1,"\n\n Heap %d\n \n",i+1);fprintf(fp3,"# %d %d\n",i ,newHeaps[i].count); for(j=0;j<newHeaps[i].count;j++){ for(k=0;k<features;k++){ fprintf(fp3,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]); if(dataDump) fprintf(fp1,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]);} if(dataDump) fprintf(fp1,"\n"); fprintf(fp3,"\n"); } } } fclose(fp); fclose(fp3); if(dataDump) { fclose(fp1);fclose(fp2); }}//end of flie /********************************************************************/ /* Function to initialise the new heaps */ /* Output: none */ /* Input:new Heap structure */ /* Global structure modified:none */ /********************************************************************/ void initialiseNewHeaps(heap newHeaps[]) { int i=0,j=0; for(i=0;i<lCount;i++) { newHeaps[i].count=0; for(j=0;j<features;j++) newHeaps[i].Ocenter[j]=V[i][j]; newHeaps[i].x=heaps[i].x; newHeaps[i].y=heaps[i].y; } }//end of function /********************************************************************/ /* Function to initialise the new heaps */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ int findPosition(int i) { int j,position=0; for(j=1;j<lCount;j++) { if(U[j][i]>U[position][i]) position=j; } /*printf("\n Position %d ",position);*/ return(position); }//end of function find position /********************************************************************/ /* Function to add object */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ void addObject(int object,heap * heapPosition) { heapPosition->objects[heapPosition->count]=object; heapPosition->count++; }//end of function add object /********************************************************************/ /* Function to harden the outputs obtained from fcm */ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/ void harden(heap newHeaps[]) { int i,heapPosition=-1,sum=0,j=0; /*initialise the new heaps*/ initialiseNewHeaps(newHeaps); for(i=0;i<count;i++) { heapPosition=findPosition(i); addObject(i,&newHeaps[heapPosition]); } /* To check for degenerate partitions*/ for(i=0;i<lCount;i++) { if(newHeaps[i].count==0){for(j=i;j<lCount-1;j++){newHeaps[j]=newHeaps[j+1];}lCount--;i--;} } newHeapCount=lCount; for(i=0;i<lCount;i++) computeHeapParameters(&newHeaps[i],1); if(debugLevel>0) { for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\n sum = %d",sum);} }//end of function/********************************************************************/ /* Function to initialize the initial parameters for the ant algorithm*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void init() { int i=0,j=0; for(i=0;i<MAXROWS;i++) for(j=0;j<MAXCOLS;j++) board[i][j]=-9; heapCount=0; newHeapCount=0; heapRow=0; minPosition=-1; minHeapCount=100000; for(i=0;i<MAXANTS;i++) { ants[i].carrying=-1; ants[i].x=-1; ants[i].y=-1; ants[i].speed=-1; ants[i].dir=-1; } } /********************************************************************/ /* Function to initialize the initial board */ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void initialiseBoard() { int i,j; for(i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; }/********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void harden1() { int i=0,heapPosition=-1; for(i=0;i<heapCount;i++) cenCount[i]=0; for(i=0;i<n;i++) { heapPosition=findPosition1(i); addObject1(i,heapPosition); } writeInfo(); }//end of function/********************************************************************/ /* Function to find to which class the object belongs */ /* Output: Class number */ /* Input: object position */ /* Global Structure Modified: None */ /********************************************************************/int findPosition1(int i) { int j,position=0; for(j=1;j<heapCount;j++) { if(U[j][i]>U[position][i]) position=j; } return(position); }//end of function find position/********************************************************************/ /* Function to Write the output of the hardened objects to file*/ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/void writeInfo(){int i=0,j=0,k=0;FILE *fp=fopen("fcm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}fprintf(fp,"\n@ %d\n",heapCount);for(i=0;i<heapCount;i++){fprintf(fp,"# %d %d\n",i,cenCount[i]);for(j=0;j<cenCount[i];j++){for(k=0;k<s;k++)fprintf(fp,"%8.2f ",clsCen[i][j][k]/scale[k]+minInput[k]);fprintf(fp,"\n");}}fclose(fp);return;}/********************************************************************/ /* Function to add a object to a cluster (2nd stage) */ /* Output: none */ /* Input: object poistion , cluster position */ /* Global Structure Modified: Cluster object and cluster count*/ /*******************************************************************/void addObject1(int object,int heapPosition) { int k=0; for(k=0;k<features;k++) clsCen[heapPosition][cenCount[heapPosition]][k]=X[object][k]; cenCount[heapPosition]++; return; }//end of function add object/********************************************************************/ /* Function to read the original classification of objects */ /* Output: none */ /* Input: none */ /* Global Structure Modified: None */ /*******************************************************************/void readOrg(){int i=0,k=0;char temp[80];FILE *fp;strcpy(temp,"");sprintf(temp,"%sFull.data",filename);fp=fopen(temp,"r");if(fp==NULL){printf("\n Unable to open full data input file ");exit(0);}for(i=0;i<n;i++){fscanf(fp,"%f ",&orgData[i][0]);for(k=1;k<s+1;k++)fscanf(fp,"%f ",&orgData[i][k]);}return;}// end of function/********************************************************************/ /* Function to classify the objects in different clusters */ /* Output: none */ /* Input: the cluster objects, the original classification, */ /* number of objects in the cluster,file to write the data */ /* Global Structure Modified: None *//********************************************************************/int analysisData(float data[][s],float orgData[n][s+1],int count,FILE * fp){int i=0,j=0,k=0,type=1,max=0;heapInfo info;for(i=0;i<c;i++){info.classObj[i]=0;}info.total=0;info.total=count;for(i=0;i<count;i++)for(j=0;j<n;j++){type=1;for(k=0;k<s;k++)if(fabs(data[i][k]-orgData[j][k+1])>errThresh){type=-1;break;}if(type!=-1){info.classObj[(int)orgData[j][0]-1]++;break;}}max=0;for(k=0;k<c;k++){if(info.classObj[k]>info.classObj[max])max=k;fprintf(fp,"\nClass %d Objects % d ",k+1,info.classObj[k]);}j=0;for(k=0;k<c;k++){if(k!=max)j+=info.classObj[k];}fprintf(fp,"\n Total errors : %d \n",j);return(j);}//end of analyse/********************************************************************/ /* Function to read the cluster information */ /* Output: none */ /* Input: input file pointer , output file pointer */ /* Global Structure Modified: None *//********************************************************************/void readFCM(FILE* fp,FILE *fp1){int i=0,j=0,k=0,l=0;int totalRuns=0,heapCount=0,junk=0,sum=0;char ch;FILE *fp2=fopen("errors.out","a");ch = 'c'; while (ch != '$') fscanf (fp, "%c", &ch);fscanf(fp,"%d\n",&totalRuns);printf("\n total runs %d ",totalRuns);for(l=0;l<totalRuns;l++){fscanf(fp,"%c",&ch);fscanf(fp,"%d\n",&heapCount);fprintf(fp1,"\n \nExperiment %d \n ",l+1);sum=0;for(i=0;i<heapCount;i++){fscanf(fp,"%c",&ch);fscanf(fp," %d %d\n",&junk,&clsCenCount[i]);for(j=0;j<clsCenCount[i];j++)for(k=0;k<s;k++)fscanf(fp,"%f",&fcmData[i][j][k]);fscanf(fp,"\n");fprintf(fp1,"\n\n Heap %d Total Objects %d \n",i+1,clsCenCount[i]);sum+=analysisData(fcmData[i],orgData,clsCenCount[i],fp1);}fprintf(fp2," %3d %3d %3d \n",l,sum,heapCount);}fprintf(fp2,"\n --------------------------\n");fclose(fp2);return;}//end of functionvoid initRm(int heapCount) { antCount=heapCount*antsPerFeature*features;classes=heapCount;newAnts=(antdata*) calloc(antCount,sizeof(antdata));if(ants==NULL){printf("\n Cannot allocate memory to ants in Rm stage");printf("\n Exiting ... ");exit(-1);}} /***************************************************************************//* Function to initialise the ants *//* Status: under developement *//* Output: the ant structure is modified with initial random values *//* Input : Ant structure (this is modified) *//***************************************************************************/void initializeAntsRm(antdata* ants,center clsCen){int i,j,k;/* Initialize all the ants with initial values and the rest and direction*/for(i=0;i<antCount;i++){ants[i].currPos=drand48();for(j=0;j<memory;j++){ants[i].Rm[j]=inf;for(k=0;k<antsPerFeature;k++)Rm[k][j]=inf;}ants[i].rest=1;ants[i].direction=drand48()<0.5?1:-1;}for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[j*classes+i].currPos=clsCen[i][j];}}/*end of function*//***************************************************************************//* Function that runs one epoch *//* Status: under developement *//* Output: the ant structure is modified with according to the epoch *//* Input : Ant structure (this is modified) *//***************************************************************************/void epoch(antdata* ants){int i,j;float Trest=0,Tcontinue=0,Tinc=0;float dDiff=0.0;float temp=0;dDiff=DmaxRm-Dmin;for(j=0;j<iterations;j++){for(i=0;i<antCount;i++){if(ants[i].rest!=0){Trest=drand48();if(Trest<Prest){ants[i].rest=0;continue;}Tcontinue=drand48();if(Tcontinue>Pcontinue)ants[i].direction=-1*ants[i].direction;Tinc=drand48();temp=ants[i].currPos+ants[i].direction*(Dmin+Tinc*dDiff);if(temp>1||temp<0)ants[i].direction=-1*ants[i].direction;ants[i].currPos+=ants[i].direction*(Dmin+Tinc*dDiff);}}}for(i=0;i<antCount;i++)ants[i].rest=1;}/*end of function*//***************************************************************************//* Function that computes Rm *//* Status: under developement *//* Output: the ant structure is modified, the worst Rm is removed *//* Input : Ant structure (this is modified) *//***************************************************************************/void computeRm(antdata* ants,float data[MAXDATA][features],float newRm[antsPerFeature]){int i,j,k=0,l=0;float clsCenters[antsPerFeature][MAXDATA][features];double Rm=0.0,temp=0.0,Dik=0;for(k=0;k<antsPerFeature;k++)for(j=0;j<features;j++)for(i=0;i<classes;i++){clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;}for(k=0;k<antsPerFeature;k++){Rm=0;for(i=0;i<count;i++){Dik=0;for(j=0;j<classes;j++){temp=0;for(l=0;l<features;l++)temp+=(data[i][l]-clsCenters[k][j][l])*(data[i][l]-clsCenters[k][j][l]);/*temp=sqrt(temp);*//*temp=NORM(data[i],clsCenters[k][j]);temp=temp*temp;*/Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}newRm[k]=Rm;}return;}/*end of function*//********************************************************************//* Function to update the memory of the ants *//* Output: none *//* Input: ant structure and the new Rm, old memory *//* Global Structure Modified: none *//********************************************************************/void updateMemory(antdata* ants,float newRm[antsPerFeature],float Rm[antsPerFeature][memory]){int i,minPosition[antsPerFeature],maxPosition[antsPerFeature],flag[antsPerFeature],k,j,b[memory],infCount=0;float temp=0,tempRm[memory],tChoose=0,pContinueCurr=0;for(i=0;i<antsPerFeature;i++){minPosition[i]=0;maxPosition[i]=0;flag[i]=1;}for(k=0;k<antsPerFeature;k++){b[0]=0;for(i=1;i<memory;i++){if(Rm[k][i]<Rm[k][minPosition[k]])minPosition[k]=i;if(Rm[k][i]>Rm[k][maxPosition[k]])maxPosition[k]=i;if(Rm[k][i]==newRm[k]){flag[k]=0;break;}temp=(newRm[k]-Rm[k][i])*1.0/Rm[k][i];if(temp<0) temp=-1*temp;if(temp<minChange){flag[k]=0;break;}b[i]=i;}if(flag[k]==1){if(newRm[k]<Rm[k][maxPosition[k]]){for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];}else{/* find min Rm and restore its position*/infCount=0;for(i=0;i<memory;i++){tempRm[i]=Rm[k][i];if(Rm[k][i]==inf) infCount++;}for(i=0;i<memory;i++)for(j=0;j<memory-1-i;j++){if(tempRm[j]>tempRm[j+1]){temp=tempRm[j];tempRm[j]=tempRm[j+1];tempRm[j+1]=temp;temp=b[j];b[j]=b[j+1];b[j+1]=temp;}}tChoose=drand48();if(tChoose<0.6){minPosition[k]=b[0];}else if (tChoose<0.8){minPosition[k]=b[1];}else if(tChoose<0.9){minPosition[k]=b[2];}else if(tChoose<0.975){minPosition[k]=b[3];}else {minPosition[k]=b[4];}pContinueCurr=drand48();if(pContinueCurr<tContinueCurr){/*for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];*/}else if(Rm[k][minPosition[k]]!=inf)for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].currPos=ants[k*classes*features+j*classes+i].Rm[minPosition[k]];}}}}}/* end of function *//********************************************************************//* Function to cluster according to the cluster centroids *//* Output: none *//* Input: ant structure and the data points *//* Global Structure Modified: none *//********************************************************************/void finalCluster(antdata* ants,float data[MAXDATA][features],float Rm[antsPerFeature][memory]){int i,j,position[MAXDATA],minPosition=0,k=0,l=0,minRm=0;float clsCenters[antsPerFeature][MAXDATA][features];float distance[MAXDATA];int cenCount[MAXDATA],classCount[MAXDATA][c];float temp=0;int clsObjects[MAXDATA][MAXDATA],label[MAXDATA];int minPosition1[antsPerFeature],maxPosition1[antsPerFeature];FILE *fp,*fpOut,*fpCls,*fpMinRm,*fpOrgClass,*fpRm;fp=fopen("fcmRm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpMinRm=fopen("minRm.out","a");fpOrgClass=fopen("orgClass.out","a");fpRm=fopen("Rm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}for(k=0;k<antsPerFeature;k++){for(j=0;j<features;j++)for(i=0;i<classes;i++)clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;minPosition1[k]=0;maxPosition1[k]=0;}for(k=0;k<antsPerFeature;k++){for(i=1;i<memory;i++){if(Rm[l][i]<Rm[l][minPosition1[l]])minPosition1[l]=i;if(Rm[l][i]>Rm[l][maxPosition1[l]])maxPosition1[l]=i;}}minRm=0;for(k=1;k<antsPerFeature;k++){if(Rm[k][minPosition1[k]]<Rm[minRm][minPosition1[minRm]])minRm=k;}fprintf(fpRm,"Rm %8.4f\n",Rm[minRm][minPosition1[minRm]]);fclose(fpRm);Fcm(data,clsCenters[minRm],2);hardenRm();for(l=0;l<antsPerFeature;l++){fprintf(fpCls,"Cluster number %2d\n",l);for(j=0;j<classes;j++){distance[j]=inf;cenCount[j]=0;for(k=0;k<c;k++)classCount[j][k]=0;}fprintf(fp,"* %d\n",l);if(l==minRm)fprintf(fpMinRm,"* %d\n",l);for(i=0;i<count;i++){minPosition=0;for(k=0;k<classes;k++){temp=0.0;for(j=0;j<features;j++)temp+= (clsCenters[l][k][j]-data[i][j])*(clsCenters[l][k][j]-data[i][j]);temp=sqrt(temp);distance[k]=temp;if(distance[k]<distance[minPosition])minPosition=k;}position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++;}for(j=0;j<classes;j++){for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++;}/* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/fprintf(fpOrgClass,"\nCluster number: %2d \n",l);for(j=0;j<classes;j++){minPosition=0;fprintf(fpOrgClass,"\n Cluster %2d \n",j+1);fprintf(fpOrgClass,"%2d %2d\n",1,classCount[j][0]);for(i=1;i<c;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;fprintf(fpOrgClass,"%2d %2d\n",i+1,classCount[j][i]);}}for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");fprintf(fpOut,"\nCluster number: %2d \n",l);for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);for(j=0;j<features;j++)fprintf(fpCls,"%8.7f ",clsCenters[l][i][j]/scale[j]+minInput[j]);fprintf(fpCls,"\n");}}/* Write the data to the file */fclose(fp);fclose(fpOut);fclose(fpCls);fclose(fpMinRm);fclose(fpOrgClass);}/* end of function *//********************************************************************//* Function to amnalyze the results *//* Output: none *//* Input: *//* Global Structure Modified: none *//********************************************************************/void analysis(int flag){int i,j,k,temp;char junk;int totalRuns=0,errors[antsPerFeature],epoch=0,position=0,antsperfeature=0,clsNumber=0,min=0,currMinRm;FILE *fp,*fpError,*fpMin,*fpMinRm,*fpMinIp;if(flag==1){fp=fopen("fcmRm.out","r");fpMinIp=fopen("minRm.out","r");fpMinRm=fopen("minRmError.out","w");fpError=fopen("error.out","w");fpMin=fopen("minError.out","w");}else{fp=fopen("fcmFCM.out","r");fpError=fopen("errorFCM.out","w");}if(fp==NULL||fpError==NULL){printf("\n unable to open files in the analysis module \n");return;}fscanf(fp,"%c %d\n",&junk,&totalRuns);fscanf(fp,"%c %d\n",&junk,&antsperfeature);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&temp);}for(i=0;i<totalRuns;i++){for(j=0;j<antsperfeature;j++){errors[j]=0;}fscanf(fp,"%c %d\n",&junk,&epoch);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&currMinRm);}fprintf(fpError,"Experiment %3d ",i);for(k=0;k<antsperfeature;k++){fscanf(fp,"%c %d\n",&junk,&clsNumber);for(j=0;j<count;j++){fscanf(fp,"%d ",&position);if(orgData[j][0]!=position)errors[k]++;}fscanf(fp,"\n");if(flag==1){fprintf(fpError,"\nCluster %3d Errors %5d",k,errors[k]);if(k==currMinRm){fprintf(fpMinRm,"Cluster %2d Errors %5d\n",k,errors[k]);}}else fprintf(fpError," Errors %5d\n",errors[k]);}if(flag==1){min=0;for(j=1;j<antsPerFeature;j++){if(errors[j]<errors[min])min=j;}fprintf(fpMin,"Exp %2d Min Error %5d\n",i,errors[min]);}}fclose(fp);fclose(fpError);if(flag==1){fclose(fpMin);fclose(fpMinRm);fclose(fpMinIp);}}/* end of function */ void RmStage(int heapCount,center clsCen) { FILE *fpTime=fopen("time.out","a");int flag[antsPerFeature],flg;struct rusage start_usage, end_usage,epoch_start_usage, epoch_end_usage;double *perf_times;float newRm[antsPerFeature],oldRm[antsPerFeature],temp;int i=0,j=0,k=0;initRm(heapCount);initializeAntsRm(newAnts,clsCen);for(i=0;i<antsPerFeature;i++){oldRm[i]=inf;newRm[i]=inf;}flg=1;i=0;getrusage(RUSAGE_SELF, &start_usage);while(i<maxEpocs){getrusage(RUSAGE_SELF, &epoch_start_usage);epoch(newAnts);getrusage(RUSAGE_SELF, &epoch_end_usage);perf_times=timing_of(epoch_start_usage,epoch_end_usage); fprintf(fpTime,"Epoch %3d Timing: %f user, %f system, %f total.\n",i, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);for(j=0;j<antsPerFeature;j++){oldRm[j]=newRm[j];flag[j]=1;}computeRm(newAnts,data,newRm);updateMemory(newAnts,newRm,Rm);flg=0;for(j=0;j<antsPerFeature;j++){if(newRm[j]!=inf){if(oldRm[j]!=inf){temp=0;temp=newRm[j]-oldRm[j]<0? (oldRm[j]-newRm[j]):(newRm[j]-oldRm[j]);/*printf("\n new %f old %f temp %f ",newRm[j],oldRm[j],temp);*/if(temp/oldRm[j]>minChange){flg=1;break;}}else flg=1;}}i++;printf("\n Epoch %d ",i);}/* Do the final clustering*/finalCluster(newAnts,data,Rm);getrusage(RUSAGE_SELF, &end_usage);perf_times=timing_of(start_usage,end_usage); fprintf(fpTime,"Exp %3d Timing: %f user, %f system, %f total.\n",k, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);/* Compute the error */} /****************************************************************/ void Fcm (objects X1,center V,int flag) { int i,j,l; FILE *ofile,*fpJm; FILE *iniClsCen=fopen("center.clt","a+"); char temp[80]; float jmFinal=0,rmFinal=0; Random_seed=state1[run];/*randseed*/ srand48(Random_seed); for(i=0;i<MAXDATA;i++) for(j=0;j<features;j++)X[i][j]=X1[i][j]; if(iniClsCen==NULL) { printf("\n Unable to open the initial cluster center file\n"); printf("\n choosing random cluster centers....\n"); flag=1; } for (run=0;run<TOTRUNS;run++) { if(flag!=2) { if (EntireHeaps) lCount=heapCount; else lCount=c; for(i=0;i<lCount;i++) for(j=0;j<s;j++) V[i][j]=0; for(i=0;i<lCount;i++) { for(l=0;l<s;l++) { if(flag==1) { rand_index = lrand48() % n; V[i][l] = X[rand_index][l]; } else { fscanf(iniClsCen,"%f ",&V[i][l]); } }//end for }}else lCount=heapCount;eps=0.001; stop = FALSE; /* This loop finds the final cluster center and the corresponding U values */ z = 0; while (stop == FALSE) { z++; UMAT(U,V,X); /** Update the U matrix **/stop = FINISHED(U1,eps); CLUSTER(U,X,V); /** Get new cluster center **/printf("\nIteration: %d \n",z); } /* end while */ printf("Number of iterations: %d\n\n", z);strcpy(temp,""); sprintf(temp,"%sFCM.out",filename);if(flag==1){jmFinal=Compute_Jm(V,X);rmFinal=Compute_Rm(V,X);fpJm=fopen("jm.out","a");fprintf(fpJm," classes %2d Jm: %10.6f Rm: %10.6f\n",lCount,jmFinal,rmFinal);fclose(fpJm);} if(flag==2) Output(temp,V,X); /* output the results */ }/*end for*/ ofile=fopen(temp,"a"); for (i=0; i <= mins; i++) fprintf(ofile, "Min %d of %10.3f occurred %d times\n", i, mintab[i], mincount[i]); fprintf(ofile, "Average iterations %f\n", (float)aveit/(float)TOTRUNS); fclose(ofile); }/* end Main */ /* The NORM function computes the norm of two vectors, x and v, of dimension s. This gives an indication of the distance between them */ float NORM( feature x, feature v) //feature x, v; { /* Parameters : x -- a local copy of an input vector v -- a local copy of a cluster center */ int i,j; /* loop variable */ double sum; /* the square root of sum is the norm; sum is the sum of the differences squared */ float mat1[features]; float mat2[features]; if (maha) { for (i=0; i<features; i++) mat1[i]=x[i]-v[i]; for(i=0;i<features;i++) { sum =0; for(j=0;j<features;j++) sum += mat1[j] * inv_cov[j][i]; mat2[i] = sum; } sum =0; for(j=0;j<features;j++) sum += mat1[j]*mat2[j]; } else { sum = 0; for(j=0;j<features;j++) sum += (x[j] - v[j])* (x[j] - v[j]); } return (float)(sqrt(sum)); } /* end NORM */ /* The UMAT procedure calculates the updated U matrix according to Bezdek's formula. Its side-effect is the update of the global array U1 */ void UMAT( matrix U,center V, objects X) /*matrix U; center V; objects X;*/ { /* Parameters: U, V, and X: same as in main program */ int i, j, k,l ;/* loop variables *///, class /* class whose cluster center //is closest to pattern k */; float d1,d2,d; float sum; int exact; for (k=0;k<MAXDATA;k++) { /*printf("\n k %d ",k);*/ exact=false; for (i=0;i<lCount;i++) { sum=0; for (j=0;j<lCount;j++) { d1=NORM(X[k],V[i]); d2=NORM(X[k],V[j]); if(d2!=0) { d= d1/d2; d= d * d;/* pow(d1/d2,2.0/(m-1.0)); */ sum+=d; } else { exact = true; for(l=0;l<lCount;l++) if (l==j) U1[l][k]=1.0; else U1[l][k]=0.0; break; } } if (exact!=true) U1[i][k]=1.0/sum; } } } /* end UMAT */ /* The function FINISHED determines if a termination occurs. If not it updates U with U1, so its side-effect is the potential modification of U */ BOOLEAN FINISHED( matrix U1,float eps) //matrix U1; float eps; { /* Parameters: U1, eps-- same as in main */ float sum; /* sum of squared differences between corresponding elements of U and U1 */ int i,k; /* loop control variabes */ BOOLEAN finished; /* set to true if all corresponding elements are within epsilon */ finished = TRUE; sum = 0; for (i=0; i<lCount; i++) for (k=0; k<MAXDATA; k++) sum += (U[i][k] - U1[i][k])*(U[i][k]-U1[i][k]); if (sqrt(sum) > eps) { finished = FALSE; for (i=0; i<lCount; i++) for (k=0; k<MAXDATA; k++) U[i][k] = U1[i][k]; } /* end if */ return finished; } /* end FINISHED */ /* The CLUSTER procedure determines cluster centers in V for the c classes being searched for. The global array V is updated */ void CLUSTER( matrix U, objects X,center V) //matrix U; objects X; { /* Parameters: U, X -- as in main */ int i, j, k; /* loop control variables */ feature num; /* numerator in the cluster center computation formula presented by Bezdek */ float denum; /* denumenator in the obove formula */ for (i=0; i<lCount; i++) { denum = 0; for (j=0; j<features; j++) { num[j] = 0; for (k=0; k<MAXDATA; k++) num[j] += U[i][k] * U[i][k] * X[k][j]; /* pow(U[i][k],m) * X[k][j]; */ } /* end for */ for (k=0; k<MAXDATA; k++) denum += U[i][k] * U[i][k]; /* pow(U[i][k],m); */ for (j=0; j<features; j++) V[i][j] = num[j] / denum; } /* end for */ } /* end CLUSTER */ float Compute_Jm(center V, objects X) //center V; //objects X; { int i, k,found; double Jm_value; float u,dik; Jm_value = 0; for (k=0; k<MAXDATA; k++) { found = FALSE; for (i=0; i<lCount; i++) { dik=NORM(X[k],V[i]); dik=dik*dik; if (dik==0.0) { if (found == FALSE) { u=1.0; found = TRUE; printf("found the case with dik=0\n"); } else { printf("\n cant proceed with this initialization"); exit(1); } } else { if (found != TRUE) u=U1[i][k]; /* get_Uvalue(V,X,dik,k); */ else u=0.0; } Jm_value += u * u * dik;/* pow(u, m) * dik; */ } } return (Jm_value); } /* end Compute_Jm */ /* The Output procedure outputs the U matrix which gives the fuzzy measure of each feature belonging to a cluster center. It also outputs the cluster center for each class */ void Output(char fname[],center V,objects X) //char fname[80]; /* Parameters: U, V -- same as in main */ { int i, j, found; /* loop control variables */ float J2c,R2c; FILE *fpJm=fopen("JmRm.out","a"),*fp; if ((fp=fopen(fname,"a"))==NULL) printf("\n CAN'T OPEN %s\n",fname); else { fprintf(fp,"\n \n"); for (i=0; i<lCount; i++) { fprintf(fp,"\n"); for (j=0; j<features; j++) fprintf(fp,"%4.3f ",V[i][j]); } /* end for */ } /* end if */ J2c= Compute_Jm(V,X); R2c=Compute_Rm(V,X); fprintf(fpJm,"Classes: %3d Jm: %10.6f Rm: %10.6f \n",lCount,J2c,R2c); found =0; for (i=0;i<= mins; i++) { if ((found == 0) && ((J2c - mintab[i]) < .001) && ((J2c - mintab[i]) > -.001)){ mincount[i]++; found = 1; } } if (found == 0) { mins++; mintab[mins]=J2c; mincount[mins]=1; } fprintf(fp,"\nJ%2d = %f", m,J2c); fprintf(fp, "Number of iterations: %d\n\n", z); aveit=aveit + z; fclose(fp); fclose(fpJm); } /***************************************************************/ void find_covar( ) { int i,j,k; float mean[features],sum; for (i=0;i<features;i++) mean[i]=0; for (i=0;i<features;i++) for (j=0;j<m;j++) mean[i]+=X[j][i]; for (i=0;i<features;i++) mean[i]/=m; for (i=0;i<features;i++) for (j=0;j<features;j++) { sum=0; for (k=0;k<m;k++) sum+=(X[k][i]-mean[i])*(X[k][j]-mean[j]); Y[i][j]=sum/m; } }float Compute_Rm(center V, objects X){int i=0,j=0,l=0;double Dik=0,temp=0,Rm=0;Rm=0;for(i=0;i<MAXDATA;i++){Dik=0;for(j=0;j<lCount;j++){temp=0;for(l=0;l<features;l++)temp+=(X[i][l]-V[j][l])*(X[i][l]-V[j][l]);/*temp=sqrt(temp);*//*temp=NORM(X[i],V[j]);temp=temp*temp;*/Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}return(Rm);}/* End of function *//********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void hardenRm() { FILE *fp=fopen("fcmFCM.out","a"); FILE *fpOut=fopen("ClassDataFCM.out","a"); int i=0,minPosition=-1,position[MAXDATA],j=0,k=0; int cenCount[MAXDATA],classCount[MAXDATA][c],label[c], clsObjects[MAXDATA][MAXDATA]; for(j=0;j<classes;j++) {cenCount[j]=0;for(k=0;k<classes;k++)classCount[j][k]=0; } for(i=0;i<MAXDATA;i++) { minPosition=findPosition(i); position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++; } for(j=0;j<classes;j++) {for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++; } /* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/for(j=0;j<classes;j++){minPosition=0;for(i=1;i<c;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;}}fprintf(fp,"* %d\n",0);for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);}fclose(fpOut);fclose(fp);return; }/*end of function*/ /********************************************************************//* Function Main *//********************************************************************/ int main(int argc, char** argv){int k=0,i=0,j=0;int sum=0;char temp[80];FILE *fpRmOut=fopen("fcmRm.out","w");FILE *fpOut=fopen("ClassData.out","w");FILE *fpCls=fopen("clsCenters.out","w");FILE *fpMinRm=fopen("minRm.out","w");FILE *fpOrgClass=fopen("orgClass.out","w");FILE *fpRm=fopen("Rm.out","w");FILE *fpJmRm=fopen("JmRm.out","w");FILE *fp=fopen("fcm.out","w");FILE *fp1; FILE *fp2=fopen("antAnalysis.out","a");FILE *fp3=fopen("fcmAnalysis.out","a"); FILE *fp4=fopen("errors.out","w");FILE *fp5;FILE *fpJm=fopen("jm.out","w");FILE *fpTime=fopen("time.out","w");FILE *fpFCM=fopen("fcmFCM.out","w");FILE *fpOutFCM=fopen("ClassDataFCM.out","w");fprintf(fpFCM,"# %d\n",EXPRUN);fprintf(fpFCM,"@ %d\n",1);fclose(fpFCM);fclose(fpOutFCM);fclose(fp4);fclose(fpJm);sprintf(temp,"%sObj.out",filename);fp5=fopen(temp,"w");fclose(fp5);strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp5=fopen(temp,"w");fclose(fp5);strcpy(temp,"");sprintf(temp,"%sOp.out",filename);fp1=fopen(temp,"w");fprintf(fp,"\n$%d",EXPRUN);fprintf(fp1,"\n$%d",EXPRUN);fclose(fp);fclose(fp1);fclose(fpJmRm);fprintf(fpRmOut,"# %d\n",EXPRUN);fprintf(fpRmOut,"@ %d\n",antsPerFeature);fclose(fpRmOut);fprintf(fpTime,"Experiments %d\n",EXPRUN);fprintf(fpMinRm,"# %d\n",EXPRUN);fprintf(fpMinRm,"@ %d\n",antsPerFeature);fclose(fpMinRm);fclose(fpOut);fclose(fpCls);fclose(fpOrgClass);fclose(fpRm);fclose(fpTime);/*readInput();*/ if(argc==2)sscanf(argv[1],"%d",&debugLevel); printf("\n Debug level %d\n",debugLevel); for( i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; readInput(); readOrg(); //Compute the distance of all the points if(maxDist) { Dmax=computeDistance(); //Compute the maximum distance fp5=fopen("maxData.txt","w");fprintf(fp5,"%f",Dmax);fclose(fp5); } else {fp5=fopen("maxData.txt","r");fscanf(fp5,"%f",&Dmax);fclose(fp5); } printf("\n %f \n",Dmax); //Initialise the board for(k=0;k<EXPRUN;k++) { fpRmOut=fopen("fcmRm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpOrgClass=fopen("orgClass.out","a");fpMinRm=fopen("minRm.out","a");fpRm=fopen("Rm.out","a");fpTime=fopen("time.out","a");fpFCM=fopen("fcmFCM.out","a");fpOutFCM=fopen("ClassDataFCM.out","a");fprintf(fpFCM,"$ %d\n",k);fprintf(fpOutFCM,"Experiment %d\n",k);fprintf(fpRm,"Exp: %2d ",k);fclose(fpRm);fprintf(fpCls,"EXP No %2d \n",k);fclose(fpCls);fprintf(fpRmOut,"$ %d\n",k);fprintf(fpMinRm,"$ %d\n",k);fprintf(fpOut,"Experiment %d\n",k);fprintf(fpOrgClass,"Experiment %d\n",k);fprintf(fpTime,"Experiment %d\n",k);fclose(fpRmOut);fclose(fpOut);fclose(fpMinRm);fclose(fpOrgClass);fclose(fpTime); fclose(fpFCM);fclose(fpOutFCM); init(); sum=0; srand48(state1[k]); initialiseBoard(); initialise(1); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(1); printf("\n %d \n",heapCount); //Simulate the working of ants iterate(1); //drop the objects after all the iterations dropFinal(1); //to verify the correctness for(i=0;i<heapCount;i++) sum+=heaps[i].count; //write the cluster centers to the file writeClusterInformation(1); printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); //getchar(); //Run FCM // heapCount=2973;/* To be commented */ printf("\n\n k %d \n\n",k); Fcm(data,V,0); //compute new board size. lCount=heapCount; heapRow=(int)ceil(sqrt(4*heapCount)); newHeapCount=lCount; printf("\n LCount %d newHeapCount %d heap Row %d \n",lCount,newHeapCount,heapRow); //harden the objects harden(newHeaps); //iterate with the heaps as the objects. initialiseBoard(); initialise(0); for(i=0;i<lCount;i++) computeHeapParameters(&heaps[i],0); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(0); printf("\n %d \n",heapCount); //getchar(); //Simulate the working of ants iterate(0); dropFinal(0); writeClusterInformation(0); sum=0; for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); // getchar(); fpJm=fopen("jm.out","a"); fprintf(fpJm,"Experiment %2d: ",k); fclose(fpJm); Fcm(data,V,1); harden1(); RmStage(newHeapCount,V); } fp=fopen("fcm.out","a"); fp1=fopen(temp,"a"); fprintf(fp,"\n"); fprintf(fp1,"\n"); fclose(fp); fclose(fp1); fp=fopen("fcm.out","r"); fp1=fopen(temp,"r");printf("\n Before read org\n");printf("\nafter read org\n");readFCM(fp1,fp2);printf("\n After 1st read fcm\n");readFCM(fp,fp3);printf("\n After 2 fcm read \n");analysis(1);analysis(0);return(1); }//end of main

Parag Kanade/Combined Algorithm/british.c

#include "headerStatic.h" /********************************************************************/ /********************************************************************/ /* Function to read the input data set */ /* The file name is assumed to be itest.data */ /* Status: Tested */ /* Output: Data from the file into the "data" (Global) data structure */ /* Output: Count (Global), the count of the number of objects */ /********************************************************************/ void readInput() { FILE *fp; char fname[80]; int i=0,j=0; char ch; sprintf(fname,"%s.data",filename); /* Check if the input file is opened */ if((fp=fopen(fname,"r"))==NULL) { printf("\n Unable to open input file \n"); exit(0); } /*initialise the minimum maximum and the scale */ for(i=0;i<features;i++) { minInput[i]=1000; maxInput[i]=0; minForHeaps[i]=1000; maxForHeaps[i]=0; scale[i]=0; scaleForHeaps[i]=0; } /* Discard leading comments */ ch = 'c'; while (ch != '#') fscanf (fp, "%c", &ch); i=0; /* Parse the file and populate the data structure */ while(!feof(fp)) { for(j=0;j<features;j++) { fscanf(fp,"%f ",&data[i][j]); } fscanf(fp,"\n"); for(j=0;j<features;j++) { if(data[i][j]<minInput[j]) minInput[j]=data[i][j]; if(data[i][j]>maxInput[j]) maxInput[j]=data[i][j]; } i++; } count=i; /* Normailze the feature values in between 0 and 1 */ for(j=0;j<features;j++) { if(minInput[j]!=maxInput[j]) scale[j]=1.0/(maxInput[j]-minInput[j]); else scale[j]=1.0; } for(i=0;i<count;i++) { for(j=0;j<features;j++) data[i][j]=(data[i][j]-minInput[j])*scale[j]; } fclose(fp); return; }//end of function /********************************************************************/ /*Function to compute the eucldiean distance for all input data set */ /* Output : an array conatining the distance of each object with the other */ /********************************************************************/ float computeDistance() { float distance,dist=0; int i=0,j=0,k=0; distance=0; for(j=0;j<count;j++) for(i=0;i<count;i++) { for(k=0;k<features;k++) dist+=sqrt((data[j][k]-data[i][k])*(data[j][k]-data[i][k])); if(distance<dist)distance=dist; dist=0; } return(distance); } /********************************************************************/ /* Function to compute the distance between two objects */ /* Output : Euclidean d istance between the two input objects */ /********************************************************************/ float computeDistanceBetween2(irisData a,irisData b) { float temp=0; int i=0; for(i=0;i<features;i++) temp+=(a[i]-b[i])*(a[i]-b[i]); temp=sqrt(temp); return(temp); } /********************************************************************/ /* Function to compute the value of an object */ /********************************************************************/ float value(irisData a) { int i=0; float temp=0; for(i=0;i<features;i++) temp+=a[i]*a[i]; return(sqrt(temp)); } /********************************************************************/ /* Function to find the object in the data structure */ /* Output : The position of the object in the data */ /* Input : The object */ /********************************************************************/ int findInData(irisData object,int iFlag) { int i=0,lastCount=0,j,position=-1,flag[features],flg;if(iFlag==1)lastCount=MAXDATA;else{}for(i=0;i<lastCount;i++){ for(j=0;j<features;j++) if(fabs(object[j]-data[i][j])<0.00001) flag[j]=1; else { flag[j]=0; break;} flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /* Function to find whether there is an object on the board at a,b */ /* Output : a flag indicating whether an object is found or not */ /* Input : The row and column indicating the board position */ /********************************************************************/ int found(int a , int b) { if(board[a][b]==9) return 1; else return 0; } /********************************************************************/ /* Function to initialize the initial board */ /* Output : None */ /* Global Structures modified : heaps */ /* Input : Flag indicating the iteration 1: First 0: Second */ /********************************************************************/ void initialise(int flag) { int a,b,i=0,k=0; int loopCount,rowCount,colCount; /* Initialize the row, column and number of heaps */ if(flag==1) { loopCount=count; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=lCount; rowCount=heapRow; colCount=heapRow; } /* Initialize the heaps */ for(i=0;i<loopCount;i++) { heaps[i].count=0; heaps[i].x=0; heaps[i].y=0; heaps[i].disPosition=0; heaps[i].Dmax=0; heaps[i].Dmean=0; } for(i=0;i<loopCount;i++) { /*find a board position which is empty*/ do { a=lrand48()%rowCount; b=lrand48()%colCount; } while(found(a,b)); /*Allocate the object to that place and assign a heap to it*/ heaps[i].x=a; heaps[i].y=b; heaps[i].count=1; heaps[i].Dmax=0; heaps[i].Dmean=0; if(flag==1) { heaps[i].objects[0]=i; for(k=0;k<features;k++)heaps[i].Ocenter[k]=data[i][k]; } else { for(k=0;k<features;k++) heaps[i].Ocenter[k]=newHeaps[i].Ocenter[k]; } heaps[i].disPosition=0; board[a][b]=9; if((debugLevel>0)&&(flag==1))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],data[heaps[i].objects[0]][0]); if((debugLevel>0)&&(flag==0))printf("heap %3d x %3d y %3d centre Ist feature %6.3f Ist Feature %5.3f\n",i,heaps[i].x,heaps[i].y,heaps[i].Ocenter[0],newHeaps[i].Ocenter[0]); } /*initialise the initial heap count*/ heapCount=loopCount; currentRow=rowCount; } /********************************************************************/ /* Function to find whether an heap is present at the location a b */ /* Output : Flag indicating the presencs of the heap */ /* Input : Roe and column indicating the board position */ /********************************************************************/ int findObject(int a,int b) { int i; int position=-1; for(i=0;i<heapCount;i++) { if((heaps[i].x==a)&&(heaps[i].y==b)) { position=i; break; } } return(position); } /********************************************************************/ /* Function initialize the ants */ /* Output : None */ /* Input : Flag indicating the iteration */ /* Global Variables Modified : ant, heapCount, heaps, board */ /********************************************************************/ void initialiseAnts(int flag) { int i=0,a=0,b=0,position,j=0,loopCount=0,rowCount,colCount,k=0; if(flag==1) { loopCount=MAXANTS; rowCount=MAXROWS; colCount=MAXCOLS; } else { loopCount=MAXANTSFORHEAPS; rowCount=heapRow; colCount=heapRow; } for(i=0;i<loopCount;i++) { /*generate random positions for the ants*/ a=lrand48()%rowCount; b=lrand48()%colCount; ants[i].x=a; ants[i].y=b; /*Find if an object is present at that position or not*/ if(board[a][b]==9) { ants[i].carrying=1; /*find the heap at that location*/ position=findObject(a,b); if(position==-1) { printf("\n Error in logic: Ant init \n"); //getchar(); break; } else { /*Assign the heap data to the ant data and decrement the heap count*/ if(flag==1){ for(k=0;k<features;k++)ants[i].data[k]=data[heaps[position].objects[0]][k]; }else{for(k=0;k<features;k++)ants[i].data[k]=heaps[position].Ocenter[k];} for(j=position;j<heapCount-1;j++) heaps[j]=heaps[j+1]; heapCount--; } /*Clear the board position */ board[a][b]=-9; } else ants[i].carrying=-1; ants[i].speed=0; ants[i].dir=lrand48()%8; printf("\n Ant %2d X: %2d Y: %2d Carying : %2d ",i,ants[i].x,ants[i].y,ants[i].carrying); } currentAntCount=loopCount; } /********************************************************************/ /* Function to move the ant */ /* Output: None */ /* Input: Ant to be moved and the flag indicating the iteration */ /********************************************************************/ void moveNext(ant* a,int flag) { int rowCount,colCount; rowCount=currentRow;colCount=currentRow; /*Depending upon the direction move the ant*/ switch (a->dir) { case 0: a->y++; if(a->y>=colCount)a->y=0; break; case 1: a->x--; a->y++;; if(a->x<0)a->x=rowCount-1; if(a->y>=colCount)a->y=0; break; case 2: a->x--; if(a->x<0)a->x=rowCount-1; break; case 3: a->x--; a->y--; if(a->x<0)a->x=rowCount-1; if(a->y<0)a->y=colCount-1; break; case 4: a->y--; if(a->y<0)a->y=colCount-1; break; case 5: a->x++; a->y--; if(a->x>=rowCount)a->x=0; if(a->y<0)a->y=colCount-1; break; case 6: a->x++; if(a->x>=rowCount) a->x=0; break; case 7: a->x++; a->y++; if(a->x>=rowCount)a->x=0; if(a->y>=colCount)a->y=0; break; } }//end of function /********************************************************************/ /* Function to compute the parameters of the heap */ /* Output: None */ /* Input: The heap whose parameters are to be computed iteration flag*/ /********************************************************************/ void computeHeapParameters(heap* heapData,int flag) { int i=0,j=0,k=0,position=0; float distance=0; float Dmean=0.0; float *dis; float featureSum[features]; float max=0; float sum=0; /* init*/ for(k=0;k<features;k++) featureSum[k]=0; /*Compute the distance of each object and find the maximum distance*/ max=0; if(flag==1){ for(j=0;j<heapData->count;j++) for(i=0;i<heapData->count;i++) { for(k=0;k<features;k++) sum+= (data[heapData->objects[j]][k]-data[heapData->objects[i]][k])*(data[heapData->objects[j]][k]-data[heapData->objects[i]][k]); distance=sqrt(sum); if(max<distance) max=distance; sum=0; } /* Assign the maximum distance to the heap */ heapData->Dmax=max;/*Compute the centre object*/for(i=0;i<heapData->count;i++)for(k=0;k<features;k++) featureSum[k]+=data[heapData->objects[i]][k];for(k=0;k<features;k++)heapData->Ocenter[k]=featureSum[k]*1.0/heapData->count; /* Find the mean distance */if((dis=(float *) calloc(heapData->count,sizeof(float)))==NULL){printf("\nUnable to allocate memory in the compute heap parameters procedure\n");exit(0);} for(i=0;i<heapData->count;i++) { Dmean+=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); dis[i]=computeDistanceBetween2(heapData->Ocenter,data[heapData->objects[i]]); } heapData->Dmean=Dmean*1.0/heapData->count; /*Compute the most dissimilar object*/ max=dis[0]; for(i=1;i<heapData->count;i++) if(max<dis[i]) { max=dis[i]; position=i; } /*Assign the most dissimilar object to the heap variable*/ for(k=0;k<features;k++) heapData->Odissim[k]=data[heapData->objects[position]][k]; heapData->disPosition=position;free(dis); } else{/* Heap Parameters for the second stage */for(k=0;k<features;k++){ heapData->Odissim[k]=heapData->Ocenter[k];}heapData->disPosition=0;heapData->Dmean=0;heapData->Dmax=0;} }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a single object*/ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject1(ant *a,int position,int flag) { int i,k=0; /*Clear the board*/ board[heaps[position].x][heaps[position].y]=-9; /* Assign the data to the ant*/if(flag==1){ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[0]][k];}else {for(k=0;k<features;k++) a->data[k]=heaps[position].Ocenter[k];} a->carrying=1; /*Copy all the heaps remaining heaps in the structure*/ for(i=position;i<heapCount-1;i++) heaps[i]=heaps[i+1]; /*Decrement the heap count*/ heapCount--; }//end of function /*********************************************************************/ /*Function to pick a object from a heap consisting of a two objects */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /*********************************************************************/ void pickObject2(ant *a , int position,int flag) { float Pdestroy1=-1; int i=0,k=0; /*Compute the probability to destroy the heap*/ Pdestroy1=lrand48()%13*1.0/12.0; /*If the probability is more than Pdestroy then destroy the heap */ if(Pdestroy1<Pdestroy) { /*pick the object randomly*/ i=lrand48()%2; /* Assign the data to the ant */ for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[i]][k]; a->carrying=1; /*Correct the heap parameters*/ heaps[position].count=1; heaps[position].objects[0]=heaps[position].objects[1-i]; computeHeapParameters(&heaps[position],flag); } }//end of function /********************************************************************/ /*Function to pick a object from a heap consisting of more than two object */ /* Output: None */ /* Input: Ant and the position of the heap */ /* Global Structure Modified: heaps, heapCount, board */ /********************************************************************/ void pickObjectn(ant *a,int position,int flag) { int i=0,k=0; /*If the criteria is satsfied then pick the object*/ if((computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)*1.0/heaps[position].Dmean)>Tremove) { a->carrying=1; for(k=0;k<features;k++) a->data[k]=data[heaps[position].objects[heaps[position].disPosition]][k]; for(i=heaps[position].disPosition;i<heaps[position].count-1;i++) heaps[position].objects[i]=heaps[position].objects[i+1]; heaps[position].count--; computeHeapParameters(&heaps[position],1); } }//end of function /********************************************************************/ /* Function to pick a object */ /********************************************************************/ void pick(ant *a,int flag) { int position=-1,i=0; float Pload1=-1.0; for(i=0;i<heapCount;i++) { /*Check if a heap is present in the eight neighbouring cells*/ if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if Heap is found*/ if(position!=-1) { /* heap or object found.*/ /*Depending upon the number present in the heap call the corresponding routine*/ switch(heaps[i].count) { case 1: Pload1 = lrand48()%151*1.0/150.0; if(Pload1<Pload) { pickObject1(a,position,flag); } break; case 2: pickObject2(a,position,flag); break; default: pickObjectn(a,position,flag); break; } } } /********************************************************************/ /* Function to drop a object to form a new heap */ /* Output: None */ /* Input: Ant, iteration flag */ /* Global Structure modified : heap, heapCount */ /********************************************************************/ void dropObject(ant* a,int flag) { /*Create a new heap*/ int k=0; heaps[heapCount].count=1; heaps[heapCount].x=a->x; heaps[heapCount].y=a->y; heaps[heapCount].Dmax=0; heaps[heapCount].Dmean=0; heaps[heapCount].disPosition=0; for(k=0;k<features;k++) { heaps[heapCount].Odissim[k]=a->data[k]; heaps[heapCount].Ocenter[k]=a->data[k]; } if(flag==1) {heaps[heapCount].objects[0]=findInData(a->data,flag); /*computeHeapParameters(&heaps[heapCount],flag);*/ } computeHeapParameters(&heaps[heapCount],flag); heapCount++; a->carrying=-1; board[a->x][a->y]=9; }//end of function /********************************************************************/ /* Function to find the heap position during the second iteration */ /* Output: position of the heap */ /* Input: object center of the heap that the ant is carrying */ /* Global Structure modified : none */ /********************************************************************/ int findInNewheaps(irisData object) { int position=-1,i,j=0,flag[features],flg; for(i=0;i<newHeapCount;i++) { for(j=0;j<features;j++) { if(fabs(object[j]-newHeaps[i].Ocenter[j])<0.00001) flag[j]=1; else {flag[j]=0;break;} } flg=1; for(j=0;j<features;j++) if(flag[j]==0) { position=-1; flg=0; break; } if(flg==1) { position=i; break; } } return (position); } /********************************************************************/ /*Function to drop a object to a heap consisting of a one or more object */ /* Output: none */ /* Input: Ant, position of the heap , iteration flag */ /* Global Structure modified: heaps,newHeaps */ /********************************************************************/ void dropObject1(ant* a, int position,int flag) { int heapPosition=-1,heapPosition1=-1,i,j,k=0; irisData newData,temp; if(flag==1) { heaps[position].objects[heaps[position].count]=findInData(a->data,flag); heaps[position].count++; computeHeapParameters(&heaps[position],flag); } else { for(k=0;k<features;k++)temp[k]=heaps[position].Ocenter[k]; heapPosition=findInNewheaps(a->data); heapPosition1=findInNewheaps(heaps[position].Ocenter); if(heapPosition==-1||heapPosition1==-1) { printf("\n error in logic:new heaps\n"); //getchar(); return; } for(k=0;k<features;k++) {newData[k]=(newHeaps[heapPosition].Ocenter[k]*newHeaps[heapPosition].count+newHeaps[heapPosition1].Ocenter[k]*newHeaps[heapPosition1].count)*1.0/(newHeaps[heapPosition].count+newHeaps[heapPosition1].count);newHeaps[heapPosition].Ocenter[k]=newData[k];heaps[position].Ocenter[k]=newData[k]; } computeHeapParameters(&heaps[position],flag); for(i=newHeaps[heapPosition].count,j=0;j<newHeaps[heapPosition1].count;i++,j++) { newHeaps[heapPosition].objects[i]=newHeaps[heapPosition1].objects[j]; } newHeaps[heapPosition].count+=newHeaps[heapPosition1].count; for(i=heapPosition1;i<newHeapCount-1;i++) newHeaps[i]=newHeaps[i+1]; newHeapCount--; } a->carrying=-1; }//end of function /********************************************************************/ /* Function to drop a object */ /* Output: None */ /* Input: Ant, iteration flag */ /********************************************************************/ void drop(ant* a,int flag) { int i, position=-1; float Pdrop1=0.0; /*Find whether we have a heap in the neighbourhood of the cell*/ for(i=0;i<heapCount;i++) { if(abs(heaps[i].x-a->x)<=1&&abs(heaps[i].y-a->y)<=1) { position=i; break; } } /*Check if a heap is found*/ if(position!=-1) { /*heap or object found.*/ /*Depending on the number of objects present take action*/ switch(heaps[position].count) { /*If criteria is satisfied then drop the object*/ case 1: if(flag==1) { if((computeDistanceBetween2(a->data,data[heaps[position].objects[0]])*1.0/Dmax)<Tcreate) dropObject1(a,position,flag); } else { if((computeDistanceBetween2(a->data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) dropObject1(a,position,flag); } break; default: if(computeDistanceBetween2(a->data,heaps[position].Ocenter)<computeDistanceBetween2(heaps[position].Odissim,heaps[position].Ocenter)) dropObject1(a,position,flag); break; } } else { /*Compute the probability to drop the object*/ Pdrop1 = lrand48()%151*1.0/150; if(Pdrop1<Pdrop) dropObject(a,flag); } }//end of function /********************************************************************/ /* Function to move the ant */ /* Output: none */ /* Input: Ant, iteration flag */ /********************************************************************/ void move(ant* a,int flag) { float pDir; /*Check the speed */ if (a->speed!=0) { /*move in the same direction*/ a->speed--; moveNext(a,flag); } else { /*generate the new direction*/ pDir=lrand48()%17*1.0/16.0; if(pDir<.85) { a->dir=lrand48()%8; } else if(pDir>.95) { a->speed=2; } moveNext(a,flag); } } /********************************************************************/ /* Function to simulate the movement of the ants */ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified: minHeapCount, minHeapPosition */ /********************************************************************/ void iterate(int flag) { int i=0,j=0,k=0,loopCount=0; if (flag==1) loopCount=MAXANTS; else loopCount=MAXANTSFORHEAPS; while (i<MAXITERATIONS) { i++; for(j=0;j<loopCount;j++) { move(&ants[j],flag); if(ants[j].carrying==1) { drop(&ants[j],flag); } else { pick(&ants[j],flag); } } if(debugLevel>0){ for(k=0;k<heapCount;k++) for(j=0;j<heapCount;j++) if((k!=j)&&(heaps[k].x==heaps[j].x)&&(heaps[k].y==heaps[j].y)) { printf("\nerror two heaps with same co ordinates %d %d ",k,j); //getchar(); } k=0; for(j=0;j<heapCount;j++) k+=heaps[j].count; } if(heapCount<minHeapCount) { minHeapCount=heapCount; minPosition=i; } if(debugLevel>0) printf("\n i:%d Heap count %d sum %d",i,heapCount,k); else printf("\n i:%d Heap count %d",i,heapCount); heapSum+=heapCount; } }//end of function /********************************************************************/ /*Function to drop the objects carried by the ants after all the iterations*/ /* Output: none */ /* Input: Iteration flag */ /* Global structure modified:none */ /********************************************************************/ void dropFinal(int flag) { int i=0,j=0,position=-1,loopCount=0; loopCount=currentAntCount; for(i=0;i<loopCount;i++) { if(ants[i].carrying==1) { if(flag==1) { for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /*drop the object to form a new heap*/ dropObject1(&ants[i],position,flag); } } else { /*Find if a heap is present */ do { position=-1; for(j=0;j<heapCount;j++) { if((heaps[j].x==ants[i].x)&&(heaps[j].y==ants[i].y)) { position=i; break; } } if(position!=-1) { /* if a heap is present then drop the heap if the criteria is satisfied */ if((computeDistanceBetween2(ants[i].data,heaps[position].Ocenter)*1.0/Dmax)<TcreateForHeaps) { dropObject1(&ants[i],position,flag); position=-1; } else { /* Find a new location to drop the heap carried by the ant */ move(&ants[i],flag); } } else { dropObject(&ants[i],flag); } }while (position!=-1); } } } }//end of function /********************************************************************/ /* Function for the implementation of QuickSort */ /* Output: none */ /* Input: data structure, low and high indices */ /* Global structure modified:none */ /********************************************************************/ void quickSort( heap A[], int low,int high ) { int mid=0; if(low<high) { mid=partition(A,low,high); quickSort(A,low,mid); quickSort(A,mid+1,high); } } /********************************************************************/ /* Partition routine used by quick sort */ /* Output: partition location */ /* Input: data , low, high */ /* Global structure modified:none */ /********************************************************************/ int partition(heap a[], int low,int high) { heap temp; int x=0,i=0,j=0; i=low; j=high; x=a[low].count; while (1) { while (a[j].count<x) j=j-1; while (a[i].count>x) i=i+1; if(i<j) { temp=a[i]; a[i]=a[j]; a[j--]=temp; } else return(j); } } /********************************************************************/ /* Function used to write the cluster information */ /* Output: none */ /* Input:iteration flag */ /* Global structure modified:none */ /********************************************************************/ void writeClusterInformation(int flag) { FILE *fp=fopen("center.clt","w"); FILE *fp1; FILE *fp2; FILE *fp3; char temp[80]; int i=0,j=0,k=0; int lastCount=0; int rowCount,colCount,antCount; strcpy(temp,""); sprintf(temp,"%sOp.out",filename); fp3=fopen(temp,"a"); if(dataDump) {strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp1=fopen(temp,"a");strcpy(temp,"");sprintf(temp,"%sObj.out",filename);fp2=fopen(temp,"a");if(fp1==NULL||fp2==NULL)printf("\n Error in opening file in the writer Cluster Information routine\n"); } rowCount=currentRow; colCount=currentRow; antCount=currentAntCount; if(fp==NULL) { printf("\n Unable to open cluster center output file \n"); /*return;*/ exit(0); } /*sort the array according to the count value and write the value of the top three elements*/ if((flag==1)&&(!EntireHeaps)) quickSort(heaps,0,heapCount); if(EntireHeaps) {if(flag==1) lastCount=heapCount;else lastCount=newHeapCount; } else lastCount=c; for(i=0;i<lastCount;i++) { if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t ",newHeaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else { for(k=0;k<features;k++) { fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); } fprintf(fp,"\n"); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",newHeaps[i].Ocenter[k]); fprintf(fp,"\n"); } else { for(k=0;k<features;k++) fprintf(fp,"%5.2f \t",heaps[i].Ocenter[k]); fprintf(fp,"\n"); } } if(dataDump) { fprintf(fp1,"\n Information from the Ant Based Algorithm \n"); fprintf(fp1,"\n Important Parameters : Board %d X %d Ants : %d Iterations %d Total Heaps %d ",rowCount,colCount,antCount,MAXITERATIONS,lastCount); } if(flag==0) { if(dataDump) { fprintf(fp1," TCreateForHeaps %f \n",TcreateForHeaps); fprintf(fp2,"\nTotal Heaps : %d\n",heapCount); fprintf(fp2,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); } fprintf(fp3,"\n@ %d\n",lastCount); } else if(dataDump) fprintf(fp1,"\n");if(dataDump) { fprintf(fp1,"\n Cluster centers \t\t\t\t\t\t\t\t\t\tObjects in Heap \n"); for(i=0;i<lastCount;i++) if(normalize==0) if(flag==0) { for(k=0;k<features;k++) { fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp2,"%8.2f ",newHeaps[i].Ocenter[k]/scale[k]+minInput[k]); } fprintf(fp1," %5d\n",newHeaps[i].count); fprintf(fp2," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]/scale[k]+minInput[k]); fprintf(fp1," %5d\n",heaps[i].count); } else if(flag==0) { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",newHeaps[i].Ocenter[k]); fprintf(fp1," %5d\n",newHeaps[i].count); } else { for(k=0;k<features;k++) fprintf(fp1,"%8.2f ",heaps[i].Ocenter[k]); fprintf(fp1," %5d\n",heaps[i].count); }} if(flag==0) { if(dataDump) fprintf(fp1,"\n Objects in the heaps \n");for(i=0;i<newHeapCount;i++) { if(dataDump) fprintf(fp1,"\n\n Heap %d\n \n",i+1);fprintf(fp3,"# %d %d\n",i ,newHeaps[i].count); for(j=0;j<newHeaps[i].count;j++){ for(k=0;k<features;k++){ fprintf(fp3,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]); if(dataDump) fprintf(fp1,"%8.2f",data[newHeaps[i].objects[j]][k]/scale[k]+minInput[k]);} if(dataDump) fprintf(fp1,"\n"); fprintf(fp3,"\n"); } } } fclose(fp); fclose(fp3); if(dataDump) { fclose(fp1);fclose(fp2); }}//end of flie /********************************************************************/ /* Function to initialise the new heaps */ /* Output: none */ /* Input:new Heap structure */ /* Global structure modified:none */ /********************************************************************/ void initialiseNewHeaps(heap newHeaps[]) { int i=0,j=0; for(i=0;i<lCount;i++) { newHeaps[i].count=0; for(j=0;j<features;j++) newHeaps[i].Ocenter[j]=V[i][j]; newHeaps[i].x=heaps[i].x; newHeaps[i].y=heaps[i].y; } }//end of function /********************************************************************/ /* Function to initialise the new heaps */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ int findPosition(int i) { int j,position=0; for(j=1;j<lCount;j++) { if(U[j][i]>U[position][i]) position=j; } /*printf("\n Position %d ",position);*/ return(position); }//end of function find position /********************************************************************/ /* Function to add object */ /* Output: position of maximum membership */ /* Input:object index */ /* Global structure modified:none */ /********************************************************************/ void addObject(int object,heap * heapPosition) { heapPosition->objects[heapPosition->count]=object; heapPosition->count++; }//end of function add object /********************************************************************/ /* Function to harden the outputs obtained from fcm */ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/ void harden(heap newHeaps[]) { int i,heapPosition=-1,sum=0,j=0; /*initialise the new heaps*/ initialiseNewHeaps(newHeaps); for(i=0;i<count;i++) { heapPosition=findPosition(i); addObject(i,&newHeaps[heapPosition]); } /* To check for degenerate partitions*/ for(i=0;i<lCount;i++) { if(newHeaps[i].count==0){for(j=i;j<lCount-1;j++){newHeaps[j]=newHeaps[j+1];}lCount--;i--;} } newHeapCount=lCount; for(i=0;i<lCount;i++) computeHeapParameters(&newHeaps[i],1); if(debugLevel>0) { for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\n sum = %d",sum);} }//end of function/********************************************************************/ /* Function to initialize the initial parameters for the ant algorithm*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void init() { int i=0,j=0; for(i=0;i<MAXROWS;i++) for(j=0;j<MAXCOLS;j++) board[i][j]=-9; heapCount=0; newHeapCount=0; heapRow=0; minPosition=-1; minHeapCount=100000; for(i=0;i<MAXANTS;i++) { ants[i].carrying=-1; ants[i].x=-1; ants[i].y=-1; ants[i].speed=-1; ants[i].dir=-1; } } /********************************************************************/ /* Function to initialize the initial board */ /* Output: none */ /* Input: None */ /* Global Structure Modified: None */ /********************************************************************/ void initialiseBoard() { int i,j; for(i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; }/********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void harden1() { int i=0,heapPosition=-1; for(i=0;i<heapCount;i++) cenCount[i]=0; for(i=0;i<n;i++) { heapPosition=findPosition1(i); addObject1(i,heapPosition); } writeInfo(); }//end of function/********************************************************************/ /* Function to find to which class the object belongs */ /* Output: Class number */ /* Input: object position */ /* Global Structure Modified: None */ /********************************************************************/int findPosition1(int i) { int j,position=0; for(j=1;j<heapCount;j++) { if(U[j][i]>U[position][i]) position=j; } return(position); }//end of function find position/********************************************************************/ /* Function to Write the output of the hardened objects to file*/ /* Output: none */ /* Input: New heap structure */ /* Global Structure Modified: None */ /********************************************************************/void writeInfo(){int i=0,j=0,k=0;FILE *fp=fopen("fcm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}fprintf(fp,"\n@ %d\n",heapCount);for(i=0;i<heapCount;i++){fprintf(fp,"# %d %d\n",i,cenCount[i]);for(j=0;j<cenCount[i];j++){for(k=0;k<s;k++)fprintf(fp,"%8.2f ",clsCen[i][j][k]/scale[k]+minInput[k]);fprintf(fp,"\n");}}fclose(fp);return;}/********************************************************************/ /* Function to add a object to a cluster (2nd stage) */ /* Output: none */ /* Input: object poistion , cluster position */ /* Global Structure Modified: Cluster object and cluster count*/ /*******************************************************************/void addObject1(int object,int heapPosition) { int k=0; for(k=0;k<features;k++) clsCen[heapPosition][cenCount[heapPosition]][k]=X[object][k]; cenCount[heapPosition]++; return; }//end of function add object/********************************************************************/ /* Function to read the original classification of objects */ /* Output: none */ /* Input: none */ /* Global Structure Modified: None */ /*******************************************************************/void readOrg(){int i=0,k=0;char temp[80];FILE *fp;strcpy(temp,"");sprintf(temp,"%sFull.data",filename);fp=fopen(temp,"r");if(fp==NULL){printf("\n Unable to open full data input file ");exit(0);}for(i=0;i<n;i++){fscanf(fp,"%f ",&orgData[i][0]);for(k=1;k<s+1;k++)fscanf(fp,"%f ",&orgData[i][k]);}return;}// end of function/********************************************************************/ /* Function to classify the objects in different clusters */ /* Output: none */ /* Input: the cluster objects, the original classification, */ /* number of objects in the cluster,file to write the data */ /* Global Structure Modified: None *//********************************************************************/int analysisData(float data[][s],float orgData[n][s+1],int count,FILE * fp){int i=0,j=0,k=0,type=1,max=0;heapInfo info;for(i=0;i<c;i++){info.classObj[i]=0;}info.total=0;info.total=count;for(i=0;i<count;i++)for(j=0;j<n;j++){type=1;for(k=0;k<s;k++)if(fabs(data[i][k]-orgData[j][k+1])>errThresh){type=-1;break;}if(type!=-1){info.classObj[(int)orgData[j][0]-1]++;break;}}max=0;for(k=0;k<c;k++){if(info.classObj[k]>info.classObj[max])max=k;fprintf(fp,"\nClass %d Objects % d ",k+1,info.classObj[k]);}j=0;for(k=0;k<c;k++){if(k!=max)j+=info.classObj[k];}fprintf(fp,"\n Total errors : %d \n",j);return(j);}//end of analyse/********************************************************************/ /* Function to read the cluster information */ /* Output: none */ /* Input: input file pointer , output file pointer */ /* Global Structure Modified: None *//********************************************************************/void readFCM(FILE* fp,FILE *fp1){int i=0,j=0,k=0,l=0;int totalRuns=0,heapCount=0,junk=0,sum=0;char ch;FILE *fp2=fopen("errors.out","a");ch = 'c'; while (ch != '$') fscanf (fp, "%c", &ch);fscanf(fp,"%d\n",&totalRuns);printf("\n total runs %d ",totalRuns);for(l=0;l<totalRuns;l++){fscanf(fp,"%c",&ch);fscanf(fp,"%d\n",&heapCount);fprintf(fp1,"\n \nExperiment %d \n ",l+1);sum=0;for(i=0;i<heapCount;i++){fscanf(fp,"%c",&ch);fscanf(fp," %d %d\n",&junk,&clsCenCount[i]);for(j=0;j<clsCenCount[i];j++)for(k=0;k<s;k++)fscanf(fp,"%f",&fcmData[i][j][k]);fscanf(fp,"\n");fprintf(fp1,"\n\n Heap %d Total Objects %d \n",i+1,clsCenCount[i]);sum+=analysisData(fcmData[i],orgData,clsCenCount[i],fp1);}fprintf(fp2," %3d %3d %3d \n",l,sum,heapCount);}fprintf(fp2,"\n --------------------------\n");fclose(fp2);return;}//end of function void initRm(int heapCount) { antCount=heapCount*antsPerFeature*features;classes=heapCount;newAnts=(antdata*) calloc(antCount,sizeof(antdata));if(ants==NULL){printf("\n Cannot allocate memory to ants in Rm stage");printf("\n Exiting ... ");exit(-1);}} /***************************************************************************//* Function to initialise the ants *//* Status: under developement *//* Output: the ant structure is modified with initial random values *//* Input : Ant structure (this is modified) *//***************************************************************************/void initializeAntsRm(antdata* ants,center clsCen){int i,j,k;/* Initialize all the ants with initial values and the rest and direction*/for(i=0;i<antCount;i++){ants[i].currPos=drand48();for(j=0;j<memory;j++){ants[i].Rm[j]=inf;for(k=0;k<antsPerFeature;k++)Rm[k][j]=inf;}ants[i].rest=1;ants[i].direction=drand48()<0.5?1:-1;}for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[j*classes+i].currPos=clsCen[i][j];}}/*end of function*//***************************************************************************//* Function that runs one epoch *//* Status: under developement *//* Output: the ant structure is modified with according to the epoch *//* Input : Ant structure (this is modified) *//***************************************************************************/void epoch(antdata* ants){int i,j;float Trest=0,Tcontinue=0,Tinc=0;float dDiff=0.0;float temp=0;dDiff=DmaxRm-Dmin;for(j=0;j<iterations;j++){for(i=0;i<antCount;i++){if(ants[i].rest!=0){Trest=drand48();if(Trest<Prest){ants[i].rest=0;continue;}Tcontinue=drand48();if(Tcontinue>Pcontinue)ants[i].direction=-1*ants[i].direction;Tinc=drand48();temp=ants[i].currPos+ants[i].direction*(Dmin+Tinc*dDiff);if(temp>1||temp<0)ants[i].direction=-1*ants[i].direction;ants[i].currPos+=ants[i].direction*(Dmin+Tinc*dDiff);}}}for(i=0;i<antCount;i++)ants[i].rest=1;}/*end of function*//***************************************************************************//* Function that computes Rm *//* Status: under developement *//* Output: the ant structure is modified, the worst Rm is removed *//* Input : Ant structure (this is modified) *//***************************************************************************/void computeRm(antdata* ants,float data[MAXDATA][features],float newRm[antsPerFeature]){int i,j,k=0,l=0;float clsCenters[antsPerFeature][MAXDATA][features];double Rm=0.0,temp=0.0,Dik=0;for(k=0;k<antsPerFeature;k++)for(j=0;j<features;j++)for(i=0;i<classes;i++){clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;}for(k=0;k<antsPerFeature;k++){Rm=0;for(i=0;i<count;i++){Dik=0;for(j=0;j<classes;j++){temp=0;for(l=0;l<features;l++)temp+=(data[i][l]-clsCenters[k][j][l])*(data[i][l]-clsCenters[k][j][l]);/*temp=sqrt(temp);*//*temp=NORM(data[i],clsCenters[k][j]);temp=temp*temp;*/Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}newRm[k]=Rm;}return;}/*end of function*//********************************************************************//* Function to update the memory of the ants *//* Output: none *//* Input: ant structure and the new Rm, old memory *//* Global Structure Modified: none *//********************************************************************/void updateMemory(antdata* ants,float newRm[antsPerFeature],float Rm[antsPerFeature][memory]){int i,minPosition[antsPerFeature],maxPosition[antsPerFeature],flag[antsPerFeature],k,j,b[memory],infCount=0;float temp=0,tempRm[memory],tChoose=0,pContinueCurr=0;for(i=0;i<antsPerFeature;i++){minPosition[i]=0;maxPosition[i]=0;flag[i]=1;}for(k=0;k<antsPerFeature;k++){b[0]=0;for(i=1;i<memory;i++){if(Rm[k][i]<Rm[k][minPosition[k]])minPosition[k]=i;if(Rm[k][i]>Rm[k][maxPosition[k]])maxPosition[k]=i;if(Rm[k][i]==newRm[k]){flag[k]=0;break;}temp=(newRm[k]-Rm[k][i])*1.0/Rm[k][i];if(temp<0) temp=-1*temp;if(temp<minChange){flag[k]=0;break;}b[i]=i;}if(flag[k]==1){if(newRm[k]<Rm[k][maxPosition[k]]){for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];}else{/* find min Rm and restore its position*/infCount=0;for(i=0;i<memory;i++){tempRm[i]=Rm[k][i];if(Rm[k][i]==inf) infCount++;}for(i=0;i<memory;i++)for(j=0;j<memory-1-i;j++){if(tempRm[j]>tempRm[j+1]){temp=tempRm[j];tempRm[j]=tempRm[j+1];tempRm[j+1]=temp;temp=b[j];b[j]=b[j+1];b[j+1]=temp;}}tChoose=drand48();if(tChoose<0.6){minPosition[k]=b[0];}else if (tChoose<0.8){minPosition[k]=b[1];}else if(tChoose<0.9){minPosition[k]=b[2];}else if(tChoose<0.975){minPosition[k]=b[3];}else {minPosition[k]=b[4];}pContinueCurr=drand48();if(pContinueCurr<tContinueCurr){/*for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].Rm[maxPosition[k]]=ants[k*classes*features+j*classes+i].currPos;}Rm[k][maxPosition[k]]=newRm[k];*/}else if(Rm[k][minPosition[k]]!=inf)for(j=0;j<features;j++)for(i=0;i<classes;i++){ants[k*classes*features+j*classes+i].currPos=ants[k*classes*features+j*classes+i].Rm[minPosition[k]];}}}}}/* end of function *//********************************************************************//* Function to cluster according to the cluster centroids *//* Output: none *//* Input: ant structure and the data points *//* Global Structure Modified: none *//********************************************************************/void finalCluster(antdata* ants,float data[MAXDATA][features],float Rm[antsPerFeature][memory]){int i,j,position[MAXDATA],minPosition=0,k=0,l=0,minRm=0;float clsCenters[antsPerFeature][MAXDATA][features];float distance[MAXDATA];int cenCount[MAXDATA],classCount[MAXDATA][c];float temp=0;int clsObjects[MAXDATA][MAXDATA],label[MAXDATA];int minPosition1[antsPerFeature],maxPosition1[antsPerFeature];FILE *fp,*fpOut,*fpCls,*fpMinRm,*fpOrgClass,*fpRm;fp=fopen("fcmRm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpMinRm=fopen("minRm.out","a");fpOrgClass=fopen("orgClass.out","a");fpRm=fopen("Rm.out","a");if(fp==NULL){printf("\n Unable to open fcm output file \n");return;}for(k=0;k<antsPerFeature;k++){for(j=0;j<features;j++)for(i=0;i<classes;i++)clsCenters[k][i][j]=ants[k*classes*features+j*classes+i].currPos;minPosition1[k]=0;maxPosition1[k]=0;}for(k=0;k<antsPerFeature;k++){for(i=1;i<memory;i++){if(Rm[l][i]<Rm[l][minPosition1[l]])minPosition1[l]=i;if(Rm[l][i]>Rm[l][maxPosition1[l]])maxPosition1[l]=i;}}minRm=0;for(k=1;k<antsPerFeature;k++){if(Rm[k][minPosition1[k]]<Rm[minRm][minPosition1[minRm]])minRm=k;}fprintf(fpRm,"Rm %8.4f\n",Rm[minRm][minPosition1[minRm]]);fclose(fpRm);Fcm(data,clsCenters[minRm],2);hardenRm();for(l=0;l<antsPerFeature;l++){fprintf(fpCls,"Cluster number %2d\n",l);for(j=0;j<classes;j++){distance[j]=inf;cenCount[j]=0;for(k=0;k<c;k++)classCount[j][k]=0;}fprintf(fp,"* %d\n",l);if(l==minRm)fprintf(fpMinRm,"* %d\n",l);for(i=0;i<count;i++){minPosition=0;for(k=0;k<classes;k++){temp=0.0;for(j=0;j<features;j++)temp+= (clsCenters[l][k][j]-data[i][j])*(clsCenters[l][k][j]-data[i][j]);temp=sqrt(temp);distance[k]=temp;if(distance[k]<distance[minPosition])minPosition=k;}position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++;}for(j=0;j<classes;j++){for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++;}/* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/fprintf(fpOrgClass,"\nCluster number: %2d \n",l);for(j=0;j<classes;j++){minPosition=0;fprintf(fpOrgClass,"\n Cluster %2d \n",j+1);fprintf(fpOrgClass,"%2d %2d\n",1,classCount[j][0]);for(i=1;i<c;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;fprintf(fpOrgClass,"%2d %2d\n",i+1,classCount[j][i]);}}for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");fprintf(fpOut,"\nCluster number: %2d \n",l);for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);for(j=0;j<features;j++)fprintf(fpCls,"%8.7f ",clsCenters[l][i][j]/scale[j]+minInput[j]);fprintf(fpCls,"\n");}}/* Write the data to the file */fclose(fp);fclose(fpOut);fclose(fpCls);fclose(fpMinRm);fclose(fpOrgClass);}/* end of function *//********************************************************************//* Function to amnalyze the results *//* Output: none *//* Input: *//* Global Structure Modified: none *//********************************************************************/void analysis(int flag){int i,j,k,temp;char junk;int totalRuns=0,errors[antsPerFeature],epoch=0,position=0,antsperfeature=0,clsNumber=0,min=0,currMinRm;FILE *fp,*fpError,*fpMin,*fpMinRm,*fpMinIp;if(flag==1){fp=fopen("fcmRm.out","r");fpMinIp=fopen("minRm.out","r");fpMinRm=fopen("minRmError.out","w");fpError=fopen("error.out","w");fpMin=fopen("minError.out","w");}else{fp=fopen("fcmFCM.out","r");fpError=fopen("errorFCM.out","w");}if(fp==NULL||fpError==NULL){printf("\n unable to open files in the analysis module \n");return;}fscanf(fp,"%c %d\n",&junk,&totalRuns);fscanf(fp,"%c %d\n",&junk,&antsperfeature);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&temp);}for(i=0;i<totalRuns;i++){for(j=0;j<antsperfeature;j++){errors[j]=0;}fscanf(fp,"%c %d\n",&junk,&epoch);if(flag==1){fscanf(fpMinIp,"%c %d\n",&junk,&temp);fscanf(fpMinIp,"%c %d\n",&junk,&currMinRm);}fprintf(fpError,"Experiment %3d ",i);for(k=0;k<antsperfeature;k++){fscanf(fp,"%c %d\n",&junk,&clsNumber);for(j=0;j<count;j++){fscanf(fp,"%d ",&position);if(orgData[j][0]!=position)errors[k]++;}fscanf(fp,"\n");if(flag==1){fprintf(fpError,"\nCluster %3d Errors %5d",k,errors[k]);if(k==currMinRm){fprintf(fpMinRm,"Cluster %2d Errors %5d\n",k,errors[k]);}}else fprintf(fpError," Errors %5d\n",errors[k]);}if(flag==1){min=0;for(j=1;j<antsPerFeature;j++){if(errors[j]<errors[min])min=j;}fprintf(fpMin,"Exp %2d Min Error %5d\n",i,errors[min]);}}fclose(fp);fclose(fpError);if(flag==1){fclose(fpMin);fclose(fpMinRm);fclose(fpMinIp);}}/* end of function */ void RmStage(int heapCount,center clsCen) { FILE *fpTime=fopen("time.out","a");int flag[antsPerFeature],flg;struct rusage start_usage, end_usage,epoch_start_usage, epoch_end_usage;double *perf_times;float newRm[antsPerFeature],oldRm[antsPerFeature],temp;int i=0,j=0,k=0;initRm(heapCount);initializeAntsRm(newAnts,clsCen);for(i=0;i<antsPerFeature;i++){oldRm[i]=inf;newRm[i]=inf;}flg=1;i=0;getrusage(RUSAGE_SELF, &start_usage);while(i<maxEpocs){getrusage(RUSAGE_SELF, &epoch_start_usage);epoch(newAnts);getrusage(RUSAGE_SELF, &epoch_end_usage);perf_times=timing_of(epoch_start_usage,epoch_end_usage); fprintf(fpTime,"Epoch %3d Timing: %f user, %f system, %f total.\n",i, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);for(j=0;j<antsPerFeature;j++){oldRm[j]=newRm[j];flag[j]=1;}computeRm(newAnts,data,newRm);updateMemory(newAnts,newRm,Rm);flg=0;for(j=0;j<antsPerFeature;j++){if(newRm[j]!=inf){if(oldRm[j]!=inf){temp=0;temp=newRm[j]-oldRm[j]<0? (oldRm[j]-newRm[j]):(newRm[j]-oldRm[j]);/*printf("\n new %f old %f temp %f ",newRm[j],oldRm[j],temp);*/if(temp/oldRm[j]>minChange){flg=1;break;}}else flg=1;}}i++;printf("\n Epoch %d ",i);}/* Do the final clustering*/finalCluster(newAnts,data,Rm);getrusage(RUSAGE_SELF, &end_usage);perf_times=timing_of(start_usage,end_usage); fprintf(fpTime,"Exp %3d Timing: %f user, %f system, %f total.\n",k, perf_times[0], perf_times[1], perf_times[0] +perf_times[1]);/* Compute the error */} /****************************************************************/ void Fcm (objects X1,center V,int flag) { int i,j,l; FILE *ofile,*fpJm; FILE *iniClsCen=fopen("center.clt","a+"); char temp[80]; float jmFinal=0,rmFinal=0; Random_seed=state1[run];/*randseed*/ srand48(Random_seed); for(i=0;i<MAXDATA;i++) for(j=0;j<features;j++)X[i][j]=X1[i][j]; if(iniClsCen==NULL) { printf("\n Unable to open the initial cluster center file\n"); printf("\n choosing random cluster centers....\n"); flag=1; } for (run=0;run<TOTRUNS;run++) { if(flag!=2) { if (EntireHeaps) lCount=heapCount; else lCount=c; for(i=0;i<lCount;i++) for(j=0;j<s;j++) V[i][j]=0; for(i=0;i<lCount;i++) { for(l=0;l<s;l++) { if(flag==1) { rand_index = lrand48() % n; V[i][l] = X[rand_index][l]; } else { fscanf(iniClsCen,"%f ",&V[i][l]); } }//end for }}else lCount=heapCount;eps=0.001; stop = FALSE; /* This loop finds the final cluster center and the corresponding U values */ z = 0; while (stop == FALSE) { z++; UMAT(U,V,X); /** Update the U matrix **/stop = FINISHED(U1,eps); CLUSTER(U,X,V); /** Get new cluster center **/printf("\nIteration: %d \n",z); } /* end while */ printf("Number of iterations: %d\n\n", z);strcpy(temp,""); sprintf(temp,"%sFCM.out",filename);if(flag==1){jmFinal=Compute_Jm(V,X);rmFinal=Compute_Rm(V,X);fpJm=fopen("jm.out","a");fprintf(fpJm," classes %2d Jm: %10.6f Rm: %10.6f\n",lCount,jmFinal,rmFinal);fclose(fpJm);} if(flag==2) Output(temp,V,X); /* output the results */ }/*end for*/ ofile=fopen(temp,"a"); for (i=0; i <= mins; i++) fprintf(ofile, "Min %d of %10.3f occurred %d times\n", i, mintab[i], mincount[i]); fprintf(ofile, "Average iterations %f\n", (float)aveit/(float)TOTRUNS); fclose(ofile); }/* end Main */ /* The NORM function computes the norm of two vectors, x and v, of dimension s. This gives an indication of the distance between them */ float NORM( feature x, feature v) //feature x, v; { /* Parameters : x -- a local copy of an input vector v -- a local copy of a cluster center */ int i,j; /* loop variable */ double sum; /* the square root of sum is the norm; sum is the sum of the differences squared */ float mat1[features]; float mat2[features]; if (maha) { for (i=0; i<features; i++) mat1[i]=x[i]-v[i]; for(i=0;i<features;i++) { sum =0; for(j=0;j<features;j++) sum += mat1[j] * inv_cov[j][i]; mat2[i] = sum; } sum =0; for(j=0;j<features;j++) sum += mat1[j]*mat2[j]; } else { sum = 0; for(j=0;j<features;j++) sum += (x[j] - v[j])* (x[j] - v[j]); } return (float)(sqrt(sum)); } /* end NORM */ /* The UMAT procedure calculates the updated U matrix according to Bezdek's formula. Its side-effect is the update of the global array U1 */ void UMAT( matrix U,center V, objects X) /*matrix U; center V; objects X;*/ { /* Parameters: U, V, and X: same as in main program */ int i, j, k,l ;/* loop variables *///, class /* class whose cluster center //is closest to pattern k */; float d1,d2,d; float sum; int exact; for (k=0;k<MAXDATA;k++) { /*printf("\n k %d ",k);*/ exact=false; for (i=0;i<lCount;i++) { sum=0; for (j=0;j<lCount;j++) { d1=NORM(X[k],V[i]); d2=NORM(X[k],V[j]); if(d2!=0) { d= d1/d2; d= d * d;/* pow(d1/d2,2.0/(m-1.0)); */ sum+=d; } else { exact = true; for(l=0;l<lCount;l++) if (l==j) U1[l][k]=1.0; else U1[l][k]=0.0; break; } } if (exact!=true) U1[i][k]=1.0/sum; } } } /* end UMAT */ /* The function FINISHED determines if a termination occurs. If not it updates U with U1, so its side-effect is the potential modification of U */ BOOLEAN FINISHED( matrix U1,float eps) //matrix U1; float eps; { /* Parameters: U1, eps-- same as in main */ float sum; /* sum of squared differences between corresponding elements of U and U1 */ int i,k; /* loop control variabes */ BOOLEAN finished; /* set to true if all corresponding elements are within epsilon */ finished = TRUE; sum = 0; for (i=0; i<lCount; i++) for (k=0; k<MAXDATA; k++) sum += (U[i][k] - U1[i][k])*(U[i][k]-U1[i][k]); if (sqrt(sum) > eps) { finished = FALSE; for (i=0; i<lCount; i++) for (k=0; k<MAXDATA; k++) U[i][k] = U1[i][k]; } /* end if */ return finished; } /* end FINISHED */ /* The CLUSTER procedure determines cluster centers in V for the c classes being searched for. The global array V is updated */ void CLUSTER( matrix U, objects X,center V) //matrix U; objects X; { /* Parameters: U, X -- as in main */ int i, j, k; /* loop control variables */ feature num; /* numerator in the cluster center computation formula presented by Bezdek */ float denum; /* denumenator in the obove formula */ for (i=0; i<lCount; i++) { denum = 0; for (j=0; j<features; j++) { num[j] = 0; for (k=0; k<MAXDATA; k++) num[j] += U[i][k] * U[i][k] * X[k][j]; /* pow(U[i][k],m) * X[k][j]; */ } /* end for */ for (k=0; k<MAXDATA; k++) denum += U[i][k] * U[i][k]; /* pow(U[i][k],m); */ for (j=0; j<features; j++) V[i][j] = num[j] / denum; } /* end for */ } /* end CLUSTER */ float Compute_Jm(center V, objects X) //center V; //objects X; { int i, k,found; double Jm_value; float u,dik; Jm_value = 0; for (k=0; k<MAXDATA; k++) { found = FALSE; for (i=0; i<lCount; i++) { dik=NORM(X[k],V[i]); dik=dik*dik; if (dik==0.0) { if (found == FALSE) { u=1.0; found = TRUE; printf("found the case with dik=0\n"); } else { printf("\n cant proceed with this initialization"); exit(1); } } else { if (found != TRUE) u=U1[i][k]; /* get_Uvalue(V,X,dik,k); */ else u=0.0; } Jm_value += u * u * dik;/* pow(u, m) * dik; */ } } return (Jm_value); } /* end Compute_Jm */ /* The Output procedure outputs the U matrix which gives the fuzzy measure of each feature belonging to a cluster center. It also outputs the cluster center for each class */ void Output(char fname[],center V,objects X) //char fname[80]; /* Parameters: U, V -- same as in main */ { int i, j, found; /* loop control variables */ float J2c,R2c; FILE *fpJm=fopen("JmRm.out","a"),*fp; if ((fp=fopen(fname,"a"))==NULL) printf("\n CAN'T OPEN %s\n",fname); else { fprintf(fp,"\n \n"); for (i=0; i<lCount; i++) { fprintf(fp,"\n"); for (j=0; j<features; j++) fprintf(fp,"%4.3f ",V[i][j]); } /* end for */ } /* end if */ J2c= Compute_Jm(V,X); R2c=Compute_Rm(V,X); fprintf(fpJm,"Classes: %3d Jm: %10.6f Rm: %10.6f \n",lCount,J2c,R2c); found =0; for (i=0;i<= mins; i++) { if ((found == 0) && ((J2c - mintab[i]) < .001) && ((J2c - mintab[i]) > -.001)){ mincount[i]++; found = 1; } } if (found == 0) { mins++; mintab[mins]=J2c; mincount[mins]=1; } fprintf(fp,"\nJ%2d = %f", m,J2c); fprintf(fp, "Number of iterations: %d\n\n", z); aveit=aveit + z; fclose(fp); fclose(fpJm); } /***************************************************************/ void find_covar( ) { int i,j,k; float mean[features],sum; for (i=0;i<features;i++) mean[i]=0; for (i=0;i<features;i++) for (j=0;j<m;j++) mean[i]+=X[j][i]; for (i=0;i<features;i++) mean[i]/=m; for (i=0;i<features;i++) for (j=0;j<features;j++) { sum=0; for (k=0;k<m;k++) sum+=(X[k][i]-mean[i])*(X[k][j]-mean[j]); Y[i][j]=sum/m; } }float Compute_Rm(center V, objects X){int i=0,j=0,l=0;double Dik=0,temp=0,Rm=0;Rm=0;for(i=0;i<MAXDATA;i++){Dik=0;for(j=0;j<lCount;j++){temp=0;for(l=0;l<features;l++)temp+=(X[i][l]-V[j][l])*(X[i][l]-V[j][l]);Dik+=pow(temp,1.0/(1-m));}Rm+=pow(Dik,(1-m));}return(Rm);}/* End of function *//********************************************************************/ /* Function to harden the outputs obtained from fcm(2nd stage)*/ /* Output: none */ /* Input: None */ /* Global Structure Modified: Cluster center , cluster center count*/ /********************************************************************/void hardenRm() { FILE *fp=fopen("fcmFCM.out","a"); FILE *fpOut=fopen("ClassDataFCM.out","a"); int i=0,minPosition=-1,position[MAXDATA],j=0,k=0; int cenCount[MAXDATA],classCount[MAXDATA][c],label[c], clsObjects[MAXDATA][MAXDATA]; for(j=0;j<classes;j++) {cenCount[j]=0;for(k=0;k<classes;k++)classCount[j][k]=0; } for(i=0;i<MAXDATA;i++) { minPosition=findPosition(i); position[i]=minPosition;clsObjects[minPosition][cenCount[minPosition]]=i;cenCount[minPosition]++; } for(j=0;j<classes;j++) {for(i=0;i<cenCount[j];i++)classCount[j][(int)orgData[clsObjects[j][i]][0]-1]++; } /* minPosition varaible re used , now minPosition denotes the maximum class of the cluster*/for(j=0;j<classes;j++){minPosition=0;for(i=1;i<c;i++){if(classCount[j][i]>classCount[j][minPosition]) minPosition=i;label[j]=minPosition;}}fprintf(fp,"* %d\n",0);for(i=0;i<count;i++){for(j=0;j<classes;j++)for(k=0;k<cenCount[j];k++)if(clsObjects[j][k]==i){fprintf(fp,"%d ",label[j]+1);break;}}fprintf(fp,"\n");for(i=0;i<classes;i++){fprintf(fpOut,"%2d %4d\n",label[i]+1,cenCount[i]);}fclose(fpOut);fclose(fp);return; }/*end of function*/ /********************************************************************//* Function Main *//********************************************************************/ int main(int argc, char** argv){int k=0,i=0,j=0;int sum=0;char temp[80];FILE *fpRmOut=fopen("fcmRm.out","w");FILE *fpOut=fopen("ClassData.out","w");FILE *fpCls=fopen("clsCenters.out","w");FILE *fpMinRm=fopen("minRm.out","w");FILE *fpOrgClass=fopen("orgClass.out","w");FILE *fpRm=fopen("Rm.out","w");FILE *fpJmRm=fopen("JmRm.out","w");FILE *fp=fopen("fcm.out","w");FILE *fp1; FILE *fp2=fopen("antAnalysis.out","a");FILE *fp3=fopen("fcmAnalysis.out","a"); FILE *fp4=fopen("errors.out","w");FILE *fp5;FILE *fpJm=fopen("jm.out","w");FILE *fpTime=fopen("time.out","w");FILE *fpFCM=fopen("fcmFCM.out","w");FILE *fpOutFCM=fopen("ClassDataFCM.out","w");fprintf(fpFCM,"# %d\n",EXPRUN);fprintf(fpFCM,"@ %d\n",1);fclose(fpFCM);fclose(fpOutFCM);fclose(fp4);fclose(fpJm);sprintf(temp,"%sObj.out",filename);fp5=fopen(temp,"w");fclose(fp5);strcpy(temp,"");sprintf(temp,"%sTot.out",filename);fp5=fopen(temp,"w");fclose(fp5);strcpy(temp,"");sprintf(temp,"%sOp.out",filename);fp1=fopen(temp,"w");fprintf(fp,"\n$%d",EXPRUN);fprintf(fp1,"\n$%d",EXPRUN);fclose(fp);fclose(fp1);fclose(fpJmRm);fprintf(fpRmOut,"# %d\n",EXPRUN);fprintf(fpRmOut,"@ %d\n",antsPerFeature);fclose(fpRmOut);fprintf(fpTime,"Experiments %d\n",EXPRUN);fprintf(fpMinRm,"# %d\n",EXPRUN);fprintf(fpMinRm,"@ %d\n",antsPerFeature);fclose(fpMinRm);fclose(fpOut);fclose(fpCls);fclose(fpOrgClass);fclose(fpRm);fclose(fpTime);/*readInput();*/ if(argc==2)sscanf(argv[1],"%d",&debugLevel); printf("\n Debug level %d\n",debugLevel); for( i=0;i<MAXROWS;i++) for( j=0;j<MAXCOLS;j++) board[i][j]=-9; readInput(); /*readOrg();*/ //Compute the distance of all the points if(maxDist) { Dmax=computeDistance(); //Compute the maximum distance fp5=fopen("maxData.txt","w");fprintf(fp5,"%f",Dmax);fclose(fp5); } else {fp5=fopen("maxData.txt","r");fscanf(fp5,"%f",&Dmax);fclose(fp5); } printf("\n %f \n",Dmax); //Initialise the board for(k=0;k<EXPRUN;k++) { fpRmOut=fopen("fcmRm.out","a");fpOut=fopen("ClassData.out","a");fpCls=fopen("clsCenters.out","a");fpOrgClass=fopen("orgClass.out","a");fpMinRm=fopen("minRm.out","a");fpRm=fopen("Rm.out","a");fpTime=fopen("time.out","a");fpFCM=fopen("fcmFCM.out","a");fpOutFCM=fopen("ClassDataFCM.out","a");fprintf(fpFCM,"$ %d\n",k);fprintf(fpOutFCM,"Experiment %d\n",k);fprintf(fpRm,"Exp: %2d ",k);fclose(fpRm);fprintf(fpCls,"EXP No %2d \n",k);fclose(fpCls);fprintf(fpRmOut,"$ %d\n",k);fprintf(fpMinRm,"$ %d\n",k);fprintf(fpOut,"Experiment %d\n",k);fprintf(fpOrgClass,"Experiment %d\n",k);fprintf(fpTime,"Experiment %d\n",k);fclose(fpRmOut);fclose(fpOut);fclose(fpMinRm);fclose(fpOrgClass);fclose(fpTime); fclose(fpFCM);fclose(fpOutFCM); init(); sum=0; srand48(state1[k]); initialiseBoard(); initialise(1); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(1); printf("\n %d \n",heapCount); //Simulate the working of ants iterate(1); //drop the objects after all the iterations dropFinal(1); //to verify the correctness for(i=0;i<heapCount;i++) sum+=heaps[i].count; //write the cluster centers to the file writeClusterInformation(1); printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); //getchar(); //Run FCM // heapCount=2973;/* To be commented */ printf("\n\n k %d \n\n",k); Fcm(data,V,0); //compute new board size. lCount=heapCount; heapRow=(int)ceil(sqrt(4*heapCount)); newHeapCount=lCount; printf("\n LCount %d newHeapCount %d heap Row %d \n",lCount,newHeapCount,heapRow); //harden the objects harden(newHeaps); //iterate with the heaps as the objects. initialiseBoard(); initialise(0); for(i=0;i<lCount;i++) computeHeapParameters(&heaps[i],0); printf("\n %d \n",heapCount); //Initialise the ants initialiseAnts(0); printf("\n %d \n",heapCount); //getchar(); //Simulate the working of ants iterate(0); dropFinal(0); writeClusterInformation(0); sum=0; for(i=0;i<newHeapCount;i++) sum+=newHeaps[i].count; printf("\nHeap Count %d sum %d \n",heapCount,sum); printf("\n Min heap count %d iteration %d \n",minHeapCount,minPosition); // getchar(); fpJm=fopen("jm.out","a"); fprintf(fpJm,"Experiment %2d: ",k); fclose(fpJm); Fcm(data,V,1); harden1(); RmStage(newHeapCount,V); } fp=fopen("fcm.out","a"); fp1=fopen(temp,"a"); fprintf(fp,"\n"); fprintf(fp1,"\n"); fclose(fp); fclose(fp1); fp=fopen("fcm.out","r"); fp1=fopen(temp,"r"); printf("\n Before read org\n");printf("\nafter read org\n");/*readFCM(fp1,fp2);*/printf("\n After 1st read fcm\n");/*readFCM(fp,fp3);*/printf("\n After 2 fcm read \n");/*analysis(1);analysis(0);*/return(1); }//end of main

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Gauss Data
#
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21.262461	2.9578559
21.268682	3.628921
21.290465	3.2874838
21.293326	4.106079
21.301133	4.1777477
21.302882	3.2121843
21.307465	6.3794084
21.310338	3.0792017
21.377903	3.5628289
21.432628	3.2225798
21.439473	3.385765
21.467547	4.7781926
21.477121	3.6562905
21.501194	2.3480532
21.533824	4.9138983
21.54175	2.9172603
21.548589	4.4224972
21.551281	5.034822
21.580005	2.4249323
21.622936	2.6350977
21.635703	5.0811729
21.635766	0.6839693
21.636906	5.1485335
21.640693	5.0050765
21.644646	2.6398355
21.659895	3.7598802
21.6671	3.0851933
21.672122	1.82188274
21.790655	3.9990614
21.791466	3.9976649
21.823915	5.5021609
21.836958	2.770455
21.849023	5.0410794
21.875795	3.4999431
21.906133	5.8123412
21.929987	6.0171549
21.943218	4.5029935
21.944168	4.8880596
21.973628	5.3975676
21.977883	3.6668553
21.986891	3.3156703
21.990816	6.0919267
21.998018	5.5690895
22.002982	2.9423729
22.003167	3.1783848
22.02293	3.7394457
22.048313	5.7878447
22.04935	3.9196468
22.102964	6.9353623
22.104117	2.3553044
22.112408	4.44293
22.116913	5.7040627
22.117363	5.4379922
22.132975	4.7215931
22.148421	3.8419177
22.157023	3.3189264
22.209926	6.0647142
22.231745	4.1059839
22.243242	5.6659826
22.275244	3.4509242
22.283633	3.0655829
22.31873	3.5765372
22.360228	1.30690857
22.390107	5.4824678
22.391019	5.1110448
22.418397	3.6016607
22.438045	5.2962146
22.502628	2.9129491
22.530678	5.270917
22.531774	3.510775
22.535484	6.3496156
22.555906	2.8812822
22.574879	4.1483826
22.577047	6.5748674
22.698119	5.5684472
22.712666	2.8698348
22.754716	2.8842441
22.840546	5.1758951
22.875637	5.5823763
22.913839	4.7236013
22.921435	4.992112
22.95051	5.507263
22.968837	3.8102658
23.372123	5.1056244
23.570015	7.3520417
23.871862	2.2839561
13.776254	15.9332623
13.828535	17.754768
13.9985099	19.006255
14.0332013	19.171739
14.3495864	18.344629
14.3643301	17.307221
14.6308644	18.729308
14.6742915	18.422276
14.6765912	18.957163
14.7242125	18.603576
14.7309358	18.287858
14.8201135	17.317803
15.04405	18.326776
15.2843895	19.224467
15.3338087	20.315561
15.3864874	19.430293
15.391851	16.6688446
15.4134342	19.813134
15.4887595	18.338325
15.4927375	18.033762
15.5616717	19.041559
15.679285	18.295844
15.6980917	16.8332204
15.7248246	17.813209
15.738379	20.513935
15.7447018	20.612144
15.759245	20.425581
15.8174379	17.387096
15.8318274	20.132314
15.8411981	18.775724
15.8657639	15.18619
15.9045035	16.6292852
15.9060549	17.432955
15.9236652	16.5085818
15.9246185	18.501414
15.9371979	18.538343
15.9424162	17.669786
15.9436611	17.422198
15.954384	16.6210745
16.0048745	18.647301
16.0093275	18.155081
16.0495475	18.4833
16.0627556	18.23776
16.0848806	18.073719
16.0988263	18.650373
16.1219141	17.029351
16.1366616	19.971848
16.1485928	18.236786
16.1559942	16.9274005
16.1784265	16.6347077
16.2007163	20.458792
16.2067843	18.878027
16.2126233	17.627478
16.27771	18.301138
16.339495	22.147247
16.3421613	18.489292
16.3577314	17.460453
16.3724251	17.838472
16.3996649	15.71695
16.4002773	14.6913483
16.4324959	15.5923088
16.4391638	18.273311
16.474673	16.1187519
16.4783074	16.7469574
16.4828174	18.835584
16.5082782	17.96041
16.5131746	16.1271108
16.5226118	20.37399
16.5411115	18.970615
16.5478529	20.084663
16.5593776	18.467168
16.5604301	16.6251063
16.5621066	15.6151074
16.5639823	15.6570201
16.5727543	16.2284542
16.5999234	19.382793
16.6097383	16.6748319
16.6335421	17.659876
16.6346179	19.120215
16.6394592	14.6308142
16.6442026	19.402978
16.6624146	18.518399
16.6824201	19.045062
16.6933922	18.000639
16.6988315	18.716886
16.7180367	18.103054
16.7549039	16.4974324
16.7672336	18.161612
16.7675093	19.396513
16.7926923	18.684571
16.8061956	18.138528
16.8161585	17.744709
16.8442842	16.5564664
16.8517083	18.048076
16.8798602	18.078834
16.894805	17.364323
16.897217	16.2556095
16.9274089	17.295782
16.9540314	17.790538
16.9833606	17.01958
17.010122	16.2967178
17.011117	17.84965
17.02912	18.488563
17.036761	16.4478485
17.037256	18.574155
17.063049	18.151774
17.090068	18.56643
17.094963	18.018778
17.10096	15.8177448
17.122916	17.674656
17.129248	18.158393
17.13386	14.7555909
17.149812	16.2079389
17.152682	20.011784
17.176491	19.079545
17.178535	17.691873
17.183269	18.910638
17.215687	15.8817595
17.235729	15.7945041
17.259081	17.087312
17.264561	17.029223
17.274332	15.8333249
17.330272	16.7431158
17.368219	16.0200739
17.379084	19.194089
17.379872	18.124247
17.385792	18.179336
17.398364	19.606996
17.409342	17.693611
17.437678	17.926307
17.455588	19.845492
17.475238	18.791697
17.508271	16.4602961
17.527687	19.682064
17.536628	17.831121
17.542094	17.563114
17.545504	17.37607
17.547878	17.319933
17.553336	19.569272
17.557667	15.180909
17.582784	19.483361
17.590279	17.149325
17.634664	18.86855
17.639971	21.681327
17.643215	16.5278489
17.650616	18.021068
17.659338	19.160969
17.664585	17.421214
17.67864	13.6651535
17.706178	19.788597
17.743804	18.332319
17.754485	17.600224
17.776854	18.299507
17.788332	18.461131
17.797314	14.9900939
17.812277	16.7877808
17.818708	17.767132
17.825362	17.998126
17.827257	17.411798
17.855049	20.06313
17.895247	15.6882882
17.89677	18.241082
17.90694	16.3827126
17.931778	19.185287
17.935962	17.947167
17.936954	19.25114
17.958115	18.59757
18.026628	19.205066
18.037261	17.780271
18.061187	19.016911
18.097614	18.740769
18.099027	18.797805
18.1145	17.767203
18.131498	17.562132
18.170495	18.460691
18.188678	19.321407
18.191673	15.5556509
18.201821	18.442701
18.237079	15.6778069
18.25179	18.030541
18.270079	19.044006
18.333913	15.3620963
18.35918	17.033823
18.361986	17.354937
18.365055	19.033464
18.365386	16.7474302
18.367864	18.965754
18.432841	19.920374
18.455325	17.293297
18.457648	18.584263
18.482073	16.4698072
18.495794	15.2869317
18.520868	17.981305
18.601644	15.5475595
18.626402	17.237819
18.639048	17.63884
18.664319	18.12114
18.678355	18.234852
18.706041	19.347441
18.946172	18.768173
18.954228	17.480191
19.016013	20.799353
19.025227	18.322595
19.065571	19.06146
19.080158	17.622466
19.260129	17.286994
19.385613	19.526589
19.591116	20.438186
19.774989	19.963467
19.973478	18.539765
20.657494	15.7015363

1	5.1	3.5	1.4	0.2
1	4.9	3	1.4	0.2
1	4.7	3.2	1.3	0.2
1	4.6	3.1	1.5	0.2
1	5	3.6	1.4	0.2
1	5.4	3.9	1.7	0.4
1	4.6	3.4	1.4	0.3
1	5	3.4	1.5	0.2
1	4.4	2.9	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5.4	3.7	1.5	0.2
1	4.8	3.4	1.6	0.2
1	4.8	3	1.4	0.1
1	4.3	3	1.1	0.1
1	5.8	4	1.2	0.2
1	5.7	4.4	1.5	0.4
1	5.4	3.9	1.3	0.4
1	5.1	3.5	1.4	0.3
1	5.7	3.8	1.7	0.3
1	5.1	3.8	1.5	0.3
1	5.4	3.4	1.7	0.2
1	5.1	3.7	1.5	0.4
1	4.6	3.6	1	0.2
1	5.1	3.3	1.7	0.5
1	4.8	3.4	1.9	0.2
1	5	3	1.6	0.2
1	5	3.4	1.6	0.4
1	5.2	3.5	1.5	0.2
1	5.2	3.4	1.4	0.2
1	4.7	3.2	1.6	0.2
1	4.8	3.1	1.6	0.2
1	5.4	3.4	1.5	0.4
1	5.2	4.1	1.5	0.1
1	5.5	4.2	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5	3.2	1.2	0.2
1	5.5	3.5	1.3	0.2
1	4.9	3.1	1.5	0.1
1	4.4	3	1.3	0.2
1	5.1	3.4	1.5	0.2
1	5	3.5	1.3	0.3
1	4.5	2.3	1.3	0.3
1	4.4	3.2	1.3	0.2
1	5	3.5	1.6	0.6
1	5.1	3.8	1.9	0.4
1	4.8	3	1.4	0.3
1	5.1	3.8	1.6	0.2
1	4.6	3.2	1.4	0.2
1	5.3	3.7	1.5	0.2
1	5	3.3	1.4	0.2
2	7	3.2	4.7	1.4
2	6.4	3.2	4.5	1.5
2	6.9	3.1	4.9	1.5
2	5.5	2.3	4	1.3
2	6.5	2.8	4.6	1.5
2	5.7	2.8	4.5	1.3
2	6.3	3.3	4.7	1.6
2	4.9	2.4	3.3	1
2	6.6	2.9	4.6	1.3
2	5.2	2.7	3.9	1.4
2	5	2	3.5	1
2	5.9	3	4.2	1.5
2	6	2.2	4	1
2	6.1	2.9	4.7	1.4
2	5.6	2.9	3.6	1.3
2	6.7	3.1	4.4	1.4
2	5.6	3	4.5	1.5
2	5.8	2.7	4.1	1
2	6.2	2.2	4.5	1.5
2	5.6	2.5	3.9	1.1
2	5.9	3.2	4.8	1.8
2	6.1	2.8	4	1.3
2	6.3	2.5	4.9	1.5
2	6.1	2.8	4.7	1.2
2	6.4	2.9	4.3	1.3
2	6.6	3	4.4	1.4
2	6.8	2.8	4.8	1.4
2	6.7	3	5	1.7
2	6	2.9	4.5	1.5
2	5.7	2.6	3.5	1
2	5.5	2.4	3.8	1.1
2	5.5	2.4	3.7	1
2	5.8	2.7	3.9	1.2
2	6	2.7	5.1	1.6
2	5.4	3	4.5	1.5
2	6	3.4	4.5	1.6
2	6.7	3.1	4.7	1.5
2	6.3	2.3	4.4	1.3
2	5.6	3	4.1	1.3
2	5.5	2.5	4	1.3
2	5.5	2.6	4.4	1.2
2	6.1	3	4.6	1.4
2	5.8	2.6	4	1.2
2	5	2.3	3.3	1
2	5.6	2.7	4.2	1.3
2	5.7	3	4.2	1.2
2	5.7	2.9	4.2	1.3
2	6.2	2.9	4.3	1.3
2	5.1	2.5	3	1.1
2	5.7	2.8	4.1	1.3
3	6.3	3.3	6	2.5
3	5.8	2.7	5.1	1.9
3	7.1	3	5.9	2.1
3	6.3	2.9	5.6	1.8
3	6.5	3	5.8	2.2
3	7.6	3	6.6	2.1
3	4.9	2.5	4.5	1.7
3	7.3	2.9	6.3	1.8
3	6.7	2.5	5.8	1.8
3	7.2	3.6	6.1	2.5
3	6.5	3.2	5.1	2
3	6.4	2.7	5.3	1.9
3	6.8	3	5.5	2.1
3	5.7	2.5	5	2
3	5.8	2.8	5.1	2.4
3	6.4	3.2	5.3	2.3
3	6.5	3	5.5	1.8
3	7.7	3.8	6.7	2.2
3	7.7	2.6	6.9	2.3
3	6	2.2	5	1.5
3	6.9	3.2	5.7	2.3
3	5.6	2.8	4.9	2
3	7.7	2.8	6.7	2
3	6.3	2.7	4.9	1.8
3	6.7	3.3	5.7	2.1
3	7.2	3.2	6	1.8
3	6.2	2.8	4.8	1.8
3	6.1	3	4.9	1.8
3	6.4	2.8	5.6	2.1
3	7.2	3	5.8	1.6
3	7.4	2.8	6.1	1.9
3	7.9	3.8	6.4	2
3	6.4	2.8	5.6	2.2
3	6.3	2.8	5.1	1.5
3	6.1	2.6	5.6	1.4
3	7.7	3	6.1	2.3
3	6.3	3.4	5.6	2.4
3	6.4	3.1	5.5	1.8
3	6	3	4.8	1.8
3	6.9	3.1	5.4	2.1
3	6.7	3.1	5.6	2.4
3	6.9	3.1	5.1	2.3
3	5.8	2.7	5.1	1.9
3	6.8	3.2	5.9	2.3
3	6.7	3.3	5.7	2.5
3	6.7	3	5.2	2.3
3	6.3	2.5	5	1.9
3	6.5	3	5.2	2
3	6.2	3.4	5.4	2.3
3	5.9	3	5.1	1.8

1	5.1	3.5	1.4	0.2
1	4.9	3	1.4	0.2
1	4.7	3.2	1.3	0.2
1	4.6	3.1	1.5	0.2
1	5	3.6	1.4	0.2
1	5.4	3.9	1.7	0.4
1	4.6	3.4	1.4	0.3
1	5	3.4	1.5	0.2
1	4.4	2.9	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5.4	3.7	1.5	0.2
1	4.8	3.4	1.6	0.2
1	4.8	3	1.4	0.1
1	4.3	3	1.1	0.1
1	5.8	4	1.2	0.2
1	5.7	4.4	1.5	0.4
1	5.4	3.9	1.3	0.4
1	5.1	3.5	1.4	0.3
1	5.7	3.8	1.7	0.3
1	5.1	3.8	1.5	0.3
1	5.4	3.4	1.7	0.2
1	5.1	3.7	1.5	0.4
1	4.6	3.6	1	0.2
1	5.1	3.3	1.7	0.5
1	4.8	3.4	1.9	0.2
1	5	3	1.6	0.2
1	5	3.4	1.6	0.4
1	5.2	3.5	1.5	0.2
1	5.2	3.4	1.4	0.2
1	4.7	3.2	1.6	0.2
1	4.8	3.1	1.6	0.2
1	5.4	3.4	1.5	0.4
1	5.2	4.1	1.5	0.1
1	5.5	4.2	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5	3.2	1.2	0.2
1	5.5	3.5	1.3	0.2
1	4.9	3.1	1.5	0.1
1	4.4	3	1.3	0.2
1	5.1	3.4	1.5	0.2
1	5	3.5	1.3	0.3
1	4.5	2.3	1.3	0.3
1	4.4	3.2	1.3	0.2
1	5	3.5	1.6	0.6
1	5.1	3.8	1.9	0.4
1	4.8	3	1.4	0.3
1	5.1	3.8	1.6	0.2
1	4.6	3.2	1.4	0.2
1	5.3	3.7	1.5	0.2
1	5	3.3	1.4	0.2
2	7	3.2	4.7	1.4
2	6.4	3.2	4.5	1.5
2	6.9	3.1	4.9	1.5
2	5.5	2.3	4	1.3
2	6.5	2.8	4.6	1.5
2	5.7	2.8	4.5	1.3
2	6.3	3.3	4.7	1.6
2	4.9	2.4	3.3	1
2	6.6	2.9	4.6	1.3
2	5.2	2.7	3.9	1.4
2	5	2	3.5	1
2	5.9	3	4.2	1.5
2	6	2.2	4	1
2	6.1	2.9	4.7	1.4
2	5.6	2.9	3.6	1.3
2	6.7	3.1	4.4	1.4
2	5.6	3	4.5	1.5
2	5.8	2.7	4.1	1
2	6.2	2.2	4.5	1.5
2	5.6	2.5	3.9	1.1
2	5.9	3.2	4.8	1.8
2	6.1	2.8	4	1.3
2	6.3	2.5	4.9	1.5
2	6.1	2.8	4.7	1.2
2	6.4	2.9	4.3	1.3
2	6.6	3	4.4	1.4
2	6.8	2.8	4.8	1.4
2	6.7	3	5	1.7
2	6	2.9	4.5	1.5
2	5.7	2.6	3.5	1
2	5.5	2.4	3.8	1.1
2	5.5	2.4	3.7	1
2	5.8	2.7	3.9	1.2
2	6	2.7	5.1	1.6
2	5.4	3	4.5	1.5
2	6	3.4	4.5	1.6
2	6.7	3.1	4.7	1.5
2	6.3	2.3	4.4	1.3
2	5.6	3	4.1	1.3
2	5.5	2.5	4	1.3
2	5.5	2.6	4.4	1.2
2	6.1	3	4.6	1.4
2	5.8	2.6	4	1.2
2	5	2.3	3.3	1
2	5.6	2.7	4.2	1.3
2	5.7	3	4.2	1.2
2	5.7	2.9	4.2	1.3
2	6.2	2.9	4.3	1.3
2	5.1	2.5	3	1.1
2	5.7	2.8	4.1	1.3
2	6.3	3.3	6	2.5
2	5.8	2.7	5.1	1.9
2	7.1	3	5.9	2.1
2	6.3	2.9	5.6	1.8
2	6.5	3	5.8	2.2
2	7.6	3	6.6	2.1
2	4.9	2.5	4.5	1.7
2	7.3	2.9	6.3	1.8
2	6.7	2.5	5.8	1.8
2	7.2	3.6	6.1	2.5
2	6.5	3.2	5.1	2
2	6.4	2.7	5.3	1.9
2	6.8	3	5.5	2.1
2	5.7	2.5	5	2
2	5.8	2.8	5.1	2.4
2	6.4	3.2	5.3	2.3
2	6.5	3	5.5	1.8
2	7.7	3.8	6.7	2.2
2	7.7	2.6	6.9	2.3
2	6	2.2	5	1.5
2	6.9	3.2	5.7	2.3
2	5.6	2.8	4.9	2
2	7.7	2.8	6.7	2
2	6.3	2.7	4.9	1.8
2	6.7	3.3	5.7	2.1
2	7.2	3.2	6	1.8
2	6.2	2.8	4.8	1.8
2	6.1	3	4.9	1.8
2	6.4	2.8	5.6	2.1
2	7.2	3	5.8	1.6
2	7.4	2.8	6.1	1.9
2	7.9	3.8	6.4	2
2	6.4	2.8	5.6	2.2
2	6.3	2.8	5.1	1.5
2	6.1	2.6	5.6	1.4
2	7.7	3	6.1	2.3
2	6.3	3.4	5.6	2.4
2	6.4	3.1	5.5	1.8
2	6	3	4.8	1.8
2	6.9	3.1	5.4	2.1
2	6.7	3.1	5.6	2.4
2	6.9	3.1	5.1	2.3
2	5.8	2.7	5.1	1.9
2	6.8	3.2	5.9	2.3
2	6.7	3.3	5.7	2.5
2	6.7	3	5.2	2.3
2	6.3	2.5	5	1.9
2	6.5	3	5.2	2
2	6.2	3.4	5.4	2.3
2	5.9	3	5.1	1.8

1	1.52101	13.64	4.49	1.1	71.78	0.06	8.75	0	0
1	1.51761	13.89	3.6	1.36	72.73	0.48	7.83	0	0
1	1.51618	13.53	3.55	1.54	72.99	0.39	7.78	0	0
1	1.51766	13.21	3.69	1.29	72.61	0.57	8.22	0	0
1	1.51742	13.27	3.62	1.24	73.08	0.55	8.07	0	0
1	1.51596	12.79	3.61	1.62	72.97	0.64	8.07	0	0.26
1	1.51743	13.3	3.6	1.14	73.09	0.58	8.17	0	0
1	1.51756	13.15	3.61	1.05	73.24	0.57	8.24	0	0
1	1.51918	14.04	3.58	1.37	72.08	0.56	8.3	0	0
1	1.51755	13	3.6	1.36	72.99	0.57	8.4	0	0.11
1	1.51571	12.72	3.46	1.56	73.2	0.67	8.09	0	0.24
1	1.51763	12.8	3.66	1.27	73.01	0.6	8.56	0	0
1	1.51589	12.88	3.43	1.4	73.28	0.69	8.05	0	0.24
1	1.51748	12.86	3.56	1.27	73.21	0.54	8.38	0	0.17
1	1.51763	12.61	3.59	1.31	73.29	0.58	8.5	0	0
1	1.51761	12.81	3.54	1.23	73.24	0.58	8.39	0	0
1	1.51784	12.68	3.67	1.16	73.11	0.61	8.7	0	0
1	1.52196	14.36	3.85	0.89	71.36	0.15	9.15	0	0
1	1.51911	13.9	3.73	1.18	72.12	0.06	8.89	0	0
1	1.51735	13.02	3.54	1.69	72.73	0.54	8.44	0	0.07
1	1.5175	12.82	3.55	1.49	72.75	0.54	8.52	0	0.19
1	1.51966	14.77	3.75	0.29	72.02	0.03	9	0	0
1	1.51736	12.78	3.62	1.29	72.79	0.59	8.7	0	0
1	1.51751	12.81	3.57	1.35	73.02	0.62	8.59	0	0
1	1.5172	13.38	3.5	1.15	72.85	0.5	8.43	0	0
1	1.51764	12.98	3.54	1.21	73	0.65	8.53	0	0
1	1.51793	13.21	3.48	1.41	72.64	0.59	8.43	0	0
1	1.51721	12.87	3.48	1.33	73.04	0.56	8.43	0	0
1	1.51768	12.56	3.52	1.43	73.15	0.57	8.54	0	0
1	1.51784	13.08	3.49	1.28	72.86	0.6	8.49	0	0
1	1.51768	12.65	3.56	1.3	73.08	0.61	8.69	0	0.14
1	1.51747	12.84	3.5	1.14	73.27	0.56	8.55	0	0
1	1.51775	12.85	3.48	1.23	72.97	0.61	8.56	0.09	0.22
1	1.51753	12.57	3.47	1.38	73.39	0.6	8.55	0	0.06
1	1.51783	12.69	3.54	1.34	72.95	0.57	8.75	0	0
1	1.51567	13.29	3.45	1.21	72.74	0.56	8.57	0	0
1	1.51909	13.89	3.53	1.32	71.81	0.51	8.78	0.11	0
1	1.51797	12.74	3.48	1.35	72.96	0.64	8.68	0	0
1	1.52213	14.21	3.82	0.47	71.77	0.11	9.57	0	0
1	1.52213	14.21	3.82	0.47	71.77	0.11	9.57	0	0
1	1.51793	12.79	3.5	1.12	73.03	0.64	8.77	0	0
1	1.51755	12.71	3.42	1.2	73.2	0.59	8.64	0	0
1	1.51779	13.21	3.39	1.33	72.76	0.59	8.59	0	0
1	1.5221	13.73	3.84	0.72	71.76	0.17	9.74	0	0
1	1.51786	12.73	3.43	1.19	72.95	0.62	8.76	0	0.3
1	1.519		13.49	3.48	1.35	71.95	0.55	9	0	0
1	1.51869	13.19	3.37	1.18	72.72	0.57	8.83	0	0.16
1	1.52667	13.99	3.7	0.71	71.57	0.02	9.82	0	0.1
1	1.52223	13.21	3.77	0.79	71.99	0.13	10.02	0	0
1	1.51898	13.58	3.35	1.23	72.08	0.59	8.91	0	0
1	1.5232	13.72	3.72	0.51	71.75	0.09	10.06	0	0.16
1	1.51926	13.2	3.33	1.28	72.36	0.6	9.14	0	0.11
1	1.51808	13.43	2.87	1.19	72.84	0.55	9.03	0	0
1	1.51837	13.14	2.84	1.28	72.85	0.55	9.07	0	0
1	1.51778	13.21	2.81	1.29	72.98	0.51	9.02	0	0.09
1	1.51769	12.45	2.71	1.29	73.7	0.56	9.06	0	0.24
1	1.51215	12.99	3.47	1.12	72.98	0.62	8.35	0	0.31
1	1.51824	12.87	3.48	1.29	72.95	0.6	8.43	0	0
1	1.51754	13.48	3.74	1.17	72.99	0.59	8.03	0	0
1	1.51754	13.39	3.66	1.19	72.79	0.57	8.27	0	0.11
1	1.51905	13.6	3.62	1.11	72.64	0.14	8.76	0	0
1	1.51977	13.81	3.58	1.32	71.72	0.12	8.67	0.69	0
1	1.52172	13.51	3.86	0.88	71.79	0.23	9.54	0	0.11
1	1.52227	14.17	3.81	0.78	71.35	0	9.69	0	0
1	1.52172	13.48	3.74	0.9	72.01	0.18	9.61	0	0.07
1	1.52099	13.69	3.59	1.12	71.96	0.09	9.4	0	0
1	1.52152	13.05	3.65	0.87	72.22	0.19	9.85	0	0.17
1	1.52152	13.05	3.65	0.87	72.32	0.19	9.85	0	0.17
1	1.52152	13.12	3.58	0.9	72.2	0.23	9.82	0	0.16
1	1.523		13.31	3.58	0.82	71.99	0.12	10.17	0	0.03
2	1.51574	14.86	3.67	1.74	71.87	0.16	7.36	0	0.12
2	1.51848	13.64	3.87	1.27	71.96	0.54	8.32	0	0.32
2	1.51593	13.09	3.59	1.52	73.1	0.67	7.83	0	0
2	1.51631	13.34	3.57	1.57	72.87	0.61	7.89	0	0
2	1.51596	13.02	3.56	1.54	73.11	0.72	7.9	0	0
2	1.5159	13.02	3.58	1.51	73.12	0.69	7.96	0	0
2	1.51645	13.44	3.61	1.54	72.39	0.66	8.03	0	0
2	1.51627	13	3.58	1.54	72.83	0.61	8.04	0	0
2	1.51613	13.92	3.52	1.25	72.88	0.37	7.94	0	0.14
2	1.5159	12.82	3.52	1.9	72.86	0.69	7.97	0	0
2	1.51592	12.86	3.52	2.12	72.66	0.69	7.97	0	0
2	1.51593	13.25	3.45	1.43	73.17	0.61	7.86	0	0
2	1.51646	13.41	3.55	1.25	72.81	0.68	8.1	0	0
2	1.51594	13.09	3.52	1.55	72.87	0.68	8.05	0	0.09
2	1.51409	14.25	3.09	2.08	72.28	1.1	7.08	0	0
2	1.51625	13.36	3.58	1.49	72.72	0.45	8.21	0	0
2	1.51569	13.24	3.49	1.47	73.25	0.38	8.03	0	0
2	1.51645	13.4	3.49	1.52	72.65	0.67	8.08	0	0.1
2	1.51618	13.01	3.5	1.48	72.89	0.6	8.12	0	0
2	1.5164	12.55	3.48	1.87	73.23	0.63	8.08	0	0.09
2	1.51841	12.93	3.74	1.11	72.28	0.64	8.96	0	0.22
2	1.51605	12.9	3.44	1.45	73.06	0.44	8.27	0	0
2	1.51588	13.12	3.41	1.58	73.26	0.07	8.39	0	0.19
2	1.5159	13.24	3.34	1.47	73.1	0.39	8.22	0	0
2	1.51629	12.71	3.33	1.49	73.28	0.67	8.24	0	0
2	1.5186	13.36	3.43	1.43	72.26	0.51	8.6	0	0
2	1.51841	13.02	3.62	1.06	72.34	0.64	9.13	0	0.15
2	1.51743	12.2	3.25	1.16	73.55	0.62	8.9	0	0.24
2	1.51689	12.67	2.88	1.71	73.21	0.73	8.54	0	0
2	1.51811	12.96	2.96	1.43	72.92	0.6	8.79	0.14	0
2	1.51655	12.75	2.85	1.44	73.27	0.57	8.79	0.11	0.22
2	1.5173	12.35	2.72	1.63	72.87	0.7	9.23	0	0
2	1.5182	12.62	2.76	0.83	73.81	0.35	9.42	0	0.2
2	1.52725	13.8	3.15	0.66	70.57	0.08	11.64	0	0
2	1.5241	13.83	2.9	1.17	71.15	0.08	10.79	0	0
2	1.52475	11.45	0	1.88	72.19	0.81	13.24	0	0.34
2	1.53125	10.73	0	2.1	69.81	0.58	13.3	3.15	0.28
2	1.53393	12.3	0	1	70.16	0.12	16.19	0	0.24
2	1.52222	14.43	0	1	72.67	0.1	11.52	0	0.08
2	1.51818	13.72	0	0.56	74.45	0	10.99	0	0
2	1.52664	11.23	0	0.77	73.21	0	14.68	0	0
2	1.52739	11.02	0	0.75	73.08	0	14.96	0	0
2	1.52777	12.64	0	0.67	72.02	0.06	14.4	0	0
2	1.51892	13.46	3.83	1.26	72.55	0.57	8.21	0	0.14
2	1.51847	13.1	3.97	1.19	72.44	0.6	8.43	0	0
2	1.51846	13.41	3.89	1.33	72.38	0.51	8.28	0	0
2	1.51829	13.24	3.9	1.41	72.33	0.55	8.31	0	0.1
2	1.51708	13.72	3.68	1.81	72.06	0.64	7.88	0	0
2	1.51673	13.3	3.64	1.53	72.53	0.65	8.03	0	0.29
2	1.51652	13.56	3.57	1.47	72.45	0.64	7.96	0	0
2	1.51844	13.25	3.76	1.32	72.4	0.58	8.42	0	0
2	1.51663	12.93	3.54	1.62	72.96	0.64	8.03	0	0.21
2	1.51687	13.23	3.54	1.48	72.84	0.56	8.1	0	0
2	1.51707	13.48	3.48	1.71	72.52	0.62	7.99	0	0
2	1.52177	13.2	3.68	1.15	72.75	0.54	8.52	0	0
2	1.51872	12.93	3.66	1.56	72.51	0.58	8.55	0	0.12
2	1.51667	12.94	3.61	1.26	72.75	0.56	8.6	0	0
2	1.52081	13.78	2.28	1.43	71.99	0.49	9.85	0	0.17
2	1.52068	13.55	2.09	1.67	72.18	0.53	9.57	0.27	0.17
2	1.5202	13.98	1.35	1.63	71.76	0.39	10.56	0	0.18
2	1.52177	13.75	1.01	1.36	72.19	0.33	11.14	0	0
2	1.52614	13.7	0	1.36	71.24	0.19	13.44	0	0.1
2	1.51813	13.43	3.98	1.18	72.49	0.58	8.15	0	0
2	1.518		13.71	3.93	1.54	71.81	0.54	8.21	0	0.15
2	1.51811	13.33	3.85	1.25	72.78	0.52	8.12	0	0
2	1.51789	13.19	3.9	1.3	72.33	0.55	8.44	0	0.28
2	1.51806	13	3.8	1.08	73.07	0.56	8.38	0	0.12
2	1.51711	12.89	3.62	1.57	72.96	0.61	8.11	0	0
2	1.51674	12.79	3.52	1.54	73.36	0.66	7.9	0	0
2	1.51674	12.87	3.56	1.64	73.14	0.65	7.99	0	0
2	1.5169	13.33	3.54	1.61	72.54	0.68	8.11	0	0
2	1.51851	13.2	3.63	1.07	72.83	0.57	8.41	0.09	0.17
2	1.51662	12.85	3.51	1.44	73.01	0.68	8.23	0.06	0.25
2	1.51709	13	3.47	1.79	72.72	0.66	8.18	0	0
2	1.5166	12.99	3.18	1.23	72.97	0.58	8.81	0	0.24
2	1.51839	12.85	3.67	1.24	72.57	0.62	8.68	0	0.35
3	1.51769	13.65	3.66	1.11	72.77	0.11	8.6	0	0
3	1.5161	13.33	3.53	1.34	72.67	0.56	8.33	0	0
3	1.5167	13.24	3.57	1.38	72.7	0.56	8.44	0	0.1
3	1.51643	12.16	3.52	1.35	72.89	0.57	8.53	0	0
3	1.51665	13.14	3.45	1.76	72.48	0.6	8.38	0	0.17
3	1.52127	14.32	3.9	0.83	71.5	0	9.49	0	0
3	1.51779	13.64	3.65	0.65	73	0.06	8.93	0	0
3	1.5161	13.42	3.4	1.22	72.69	0.59	8.32	0	0
3	1.51694	12.86	3.58	1.31	72.61	0.61	8.79	0	0
3	1.51646	13.04	3.4	1.26	73.01	0.52	8.58	0	0
3	1.51655	13.41	3.39	1.28	72.64	0.52	8.65	0	0
3	1.52121	14.03	3.76	0.58	71.79	0.11	9.65	0	0
3	1.51776	13.53	3.41	1.52	72.04	0.58	8.79	0	0
3	1.51796	13.5	3.36	1.63	71.94	0.57	8.81	0	0.09
3	1.51832	13.33	3.34	1.54	72.14	0.56	8.99	0	0
3	1.51934	13.64	3.54	0.75	72.65	0.16	8.89	0.15	0.24
3	1.52211	14.19	3.78	0.91	71.36	0.23	9.14	0	0.37
4	1.51514	14.01	2.68	3.5	69.89	1.68	5.87	2.2	0
4	1.51915	12.73	1.85	1.86	72.69	0.6	10.09	0	0
4	1.52171	11.56	1.88	1.56	72.86	0.47	11.41	0	0
4	1.52151	11.03	1.71	1.56	73.44	0.58	11.62	0	0
4	1.51969	12.64	0	1.65	73.75	0.38	11.53	0	0
4	1.51666	12.86	0	1.83	73.88	0.97	10.17	0	0
4	1.51994	13.27	0	1.76	73.03	0.47	11.32	0	0
4	1.52369	13.44	0	1.58	72.22	0.32	12.24	0	0
4	1.51316	13.02	0	3.04	70.48	6.21	6.96	0	0
4	1.51321	13	0	3.02	70.7	6.21	6.93	0	0
4	1.52043	13.38	0	1.4	72.25	0.33	12.5	0	0
4	1.52058	12.85	1.61	2.17	72.18	0.76	9.7	0.24	0.51
4	1.52119	12.97	0.33	1.51	73.39	0.13	11.27	0	0.28
5	1.51905	14	2.39	1.56	72.37	0	9.57	0	0
5	1.51937	13.79	2.41	1.19	72.76	0	9.77	0	0
5	1.51829	14.46	2.24	1.62	72.38	0	9.26	0	0
5	1.51852	14.09	2.19	1.66	72.67	0	9.32	0	0
5	1.51299	14.4	1.74	1.54	74.55	0	7.59	0	0
5	1.51888	14.99	0.78	1.74	72.5	0	9.95	0	0
5	1.51916	14.15	0	2.09	72.74	0	10.88	0	0
5	1.51969	14.56	0	0.56	73.48	0	11.22	0	0
5	1.51115	17.38	0	0.34	75.41	0	6.65	0	0
6	1.51131	13.69	3.2	1.81	72.81	1.76	5.43	1.19	0
6	1.51838	14.32	3.26	2.22	71.25	1.46	5.79	1.63	0
6	1.52315	13.44	3.34	1.23	72.38	0.6	8.83	0	0
6	1.52247	14.86	2.2	2.06	70.26	0.76	9.76	0	0
6	1.52365	15.79	1.83	1.31	70.43	0.31	8.61	1.68	0
6	1.51613	13.88	1.78	1.79	73.1	0	8.67	0.76	0
6	1.51602	14.85	0	2.38	73.28	0	8.76	0.64	0.09
6	1.51623	14.2	0	2.79	73.46	0.04	9.04	0.4	0.09
6	1.51719	14.75	0	2	73.02	0	8.53	1.59	0.08
6	1.51683	14.56	0	1.98	73.29	0	8.52	1.57	0.07
6	1.51545	14.14	0	2.68	73.39	0.08	9.07	0.61	0.05
6	1.51556	13.87	0	2.54	73.23	0.14	9.41	0.81	0.01
6	1.51727	14.7	0	2.34	73.28	0	8.95	0.66	0
6	1.51531	14.38	0	2.66	73.1	0.04	9.08	0.64	0
6	1.51609	15.01	0	2.51	73.05	0.05	8.83	0.53	0
6	1.51508	15.15	0	2.25	73.5	0	8.34	0.63	0
6	1.51653	11.95	0	1.19	75.18	2.7	8.93	0	0
6	1.51514	14.85	0	2.42	73.72	0	8.39	0.56	0
6	1.51658	14.8	0	1.99	73.11	0	8.28	1.71	0
6	1.51617	14.95	0	2.27	73.3	0	8.71	0.67	0
6	1.51732	14.95	0	1.8	72.99	0	8.61	1.55	0
6	1.51645	14.94	0	1.87	73.11	0	8.67	1.38	0
6	1.51831	14.39	0	1.82	72.86	1.41	6.47	2.88	0
6	1.5164	14.37	0	2.74	72.85	0	9.45	0.54	0
6	1.51623	14.14	0	2.88	72.61	0.08	9.18	1.06	0
6	1.51685	14.92	0	1.99	73.06	0	8.4	1.59	0
6	1.52065	14.36	0	2.02	73.42	0	8.44	1.64	0
6	1.51651	14.38	0	1.94	73.61	0	8.48	1.57	0
6	1.51711	14.23	0	2.08	73.36	0	8.62	1.67	0

1	14.23	1.71	2.43	15.6	127	2.8	3.06	0.28	2.29	5.64	1.04	3.92	1065
1	13.2	1.78	2.14	11.2	100	2.65	2.76	0.26	1.28	4.38	1.05	3.4	1050
1	13.16	2.36	2.67	18.6	101	2.8	3.24	0.3	2.81	5.68	1.03	3.17	1185
1	14.37	1.95	2.5	16.8	113	3.85	3.49	0.24	2.18	7.8	0.86	3.45	1480
1	13.24	2.59	2.87	21	118	2.8	2.69	0.39	1.82	4.32	1.04	2.93	735
1	14.2	1.76	2.45	15.2	112	3.27	3.39	0.34	1.97	6.75	1.05	2.85	1450
1	14.39	1.87	2.45	14.6	96	2.5	2.52	0.3	1.98	5.25	1.02	3.58	1290
1	14.06	2.15	2.61	17.6	121	2.6	2.51	0.31	1.25	5.05	1.06	3.58	1295
1	14.83	1.64	2.17	14	97	2.8	2.98	0.29	1.98	5.2	1.08	2.85	1045
1	13.86	1.35	2.27	16	98	2.98	3.15	0.22	1.85	7.22	1.01	3.55	1045
1	14.1	2.16	2.3	18	105	2.95	3.32	0.22	2.38	5.75	1.25	3.17	1510
1	14.12	1.48	2.32	16.8	95	2.2	2.43	0.26	1.57	5	1.17	2.82	1280
1	13.75	1.73	2.41	16	89	2.6	2.76	0.29	1.81	5.6	1.15	2.9	1320
1	14.75	1.73	2.39	11.4	91	3.1	3.69	0.43	2.81	5.4	1.25	2.73	1150
1	14.38	1.87	2.38	12	102	3.3	3.64	0.29	2.96	7.5	1.2	3	1547
1	13.63	1.81	2.7	17.2	112	2.85	2.91	0.3	1.46	7.3	1.28	2.88	1310
1	14.3	1.92	2.72	20	120	2.8	3.14	0.33	1.97	6.2	1.07	2.65	1280
1	13.83	1.57	2.62	20	115	2.95	3.4	0.4	1.72	6.6	1.13	2.57	1130
1	14.19	1.59	2.48	16.5	108	3.3	3.93	0.32	1.86	8.7	1.23	2.82	1680
1	13.64	3.1	2.56	15.2	116	2.7	3.03	0.17	1.66	5.1	0.96	3.36	845
1	14.06	1.63	2.28	16	126	3	3.17	0.24	2.1	5.65	1.09	3.71	780
1	12.93	3.8	2.65	18.6	102	2.41	2.41	0.25	1.98	4.5	1.03	3.52	770
1	13.71	1.86	2.36	16.6	101	2.61	2.88	0.27	1.69	3.8	1.11	4	1035
1	12.85	1.6	2.52	17.8	95	2.48	2.37	0.26	1.46	3.93	1.09	3.63	1015
1	13.5	1.81	2.61	20	96	2.53	2.61	0.28	1.66	3.52	1.12	3.82	845
1	13.05	2.05	3.22	25	124	2.63	2.68	0.47	1.92	3.58	1.13	3.2	830
1	13.39	1.77	2.62	16.1	93	2.85	2.94	0.34	1.45	4.8	0.92	3.22	1195
1	13.3	1.72	2.14	17	94	2.4	2.19	0.27	1.35	3.95	1.02	2.77	1285
1	13.87	1.9	2.8	19.4	107	2.95	2.97	0.37	1.76	4.5	1.25	3.4	915
1	14.02	1.68	2.21	16	96	2.65	2.33	0.26	1.98	4.7	1.04	3.59	1035
1	13.73	1.5	2.7	22.5	101	3	3.25	0.29	2.38	5.7	1.19	2.71	1285
1	13.58	1.66	2.36	19.1	106	2.86	3.19	0.22	1.95	6.9	1.09	2.88	1515
1	13.68	1.83	2.36	17.2	104	2.42	2.69	0.42	1.97	3.84	1.23	2.87	990
1	13.76	1.53	2.7	19.5	132	2.95	2.74	0.5	1.35	5.4	1.25	3	1235
1	13.51	1.8	2.65	19	110	2.35	2.53	0.29	1.54	4.2	1.1	2.87	1095
1	13.48	1.81	2.41	20.5	100	2.7	2.98	0.26	1.86	5.1	1.04	3.47	920
1	13.28	1.64	2.84	15.5	110	2.6	2.68	0.34	1.36	4.6	1.09	2.78	880
1	13.05	1.65	2.55	18	98	2.45	2.43	0.29	1.44	4.25	1.12	2.51	1105
1	13.07	1.5	2.1	15.5	98	2.4	2.64	0.28	1.37	3.7	1.18	2.69	1020
1	14.22	3.99	2.51	13.2	128	3	3.04	0.2	2.08	5.1	0.89	3.53	760
1	13.56	1.71	2.31	16.2	117	3.15	3.29	0.34	2.34	6.13	0.95	3.38	795
1	13.41	3.84	2.12	18.8	90	2.45	2.68	0.27	1.48	4.28	0.91	3	1035
1	13.88	1.89	2.59	15	101	3.25	3.56	0.17	1.7	5.43	0.88	3.56	1095
1	13.24	3.98	2.29	17.5	103	2.64	2.63	0.32	1.66	4.36	0.82	3	680
1	13.05	1.77	2.1	17	107	3	3	0.28	2.03	5.04	0.88	3.35	885
1	14.21	4.04	2.44	18.9	111	2.85	2.65	0.3	1.25	5.24	0.87	3.33	1080
1	14.38	3.59	2.28	16	102	3.25	3.17	0.27	2.19	4.9	1.04	3.44	1065
1	13.9	1.68	2.12	16	101	3.1	3.39	0.21	2.14	6.1	0.91	3.33	985
1	14.1	2.02	2.4	18.8	103	2.75	2.92	0.32	2.38	6.2	1.07	2.75	1060
1	13.94	1.73	2.27	17.4	108	2.88	3.54	0.32	2.08	8.9	1.12	3.1	1260
1	13.05	1.73	2.04	12.4	92	2.72	3.27	0.17	2.91	7.2	1.12	2.91	1150
1	13.83	1.65	2.6	17.2	94	2.45	2.99	0.22	2.29	5.6	1.24	3.37	1265
1	13.82	1.75	2.42	14	111	3.88	3.74	0.32	1.87	7.05	1.01	3.26	1190
1	13.77	1.9	2.68	17.1	115	3	2.79	0.39	1.68	6.3	1.13	2.93	1375
1	13.74	1.67	2.25	16.4	118	2.6	2.9	0.21	1.62	5.85	0.92	3.2	1060
1	13.56	1.73	2.46	20.5	116	2.96	2.78	0.2	2.45	6.25	0.98	3.03	1120
1	14.22	1.7	2.3	16.3	118	3.2	3	0.26	2.03	6.38	0.94	3.31	970
1	13.29	1.97	2.68	16.8	102	3	3.23	0.31	1.66	6	1.07	2.84	1270
1	13.72	1.43	2.5	16.7	108	3.4	3.67	0.19	2.04	6.8	0.89	2.87	1285
2	12.37	0.94	1.36	10.6	88	1.98	0.57	0.28	0.42	1.95	1.05	1.82	520
2	12.33	1.1	2.28	16	101	2.05	1.09	0.63	0.41	3.27	1.25	1.67	680
2	12.64	1.36	2.02	16.8	100	2.02	1.41	0.53	0.62	5.75	0.98	1.59	450
2	13.67	1.25	1.92	18	94	2.1	1.79	0.32	0.73	3.8	1.23	2.46	630
2	12.37	1.13	2.16	19	87	3.5	3.1	0.19	1.87	4.45	1.22	2.87	420
2	12.17	1.45	2.53	19	104	1.89	1.75	0.45	1.03	2.95	1.45	2.23	355
2	12.37	1.21	2.56	18.1	98	2.42	2.65	0.37	2.08	4.6	1.19	2.3	678
2	13.11	1.01	1.7	15	78	2.98	3.18	0.26	2.28	5.3	1.12	3.18	502
2	12.37	1.17	1.92	19.6	78	2.11	2	0.27	1.04	4.68	1.12	3.48	510
2	13.34	0.94	2.36	17	110	2.53	1.3	0.55	0.42	3.17	1.02	1.93	750
2	12.21	1.19	1.75	16.8	151	1.85	1.28	0.14	2.5	2.85	1.28	3.07	718
2	12.29	1.61	2.21	20.4	103	1.1	1.02	0.37	1.46	3.05	0.906	1.82	870
2	13.86	1.51	2.67	25	86	2.95	2.86	0.21	1.87	3.38	1.36	3.16	410
2	13.49	1.66	2.24	24	87	1.88	1.84	0.27	1.03	3.74	0.98	2.78	472
2	12.99	1.67	2.6	30	139	3.3	2.89	0.21	1.96	3.35	1.31	3.5	985
2	11.96	1.09	2.3	21	101	3.38	2.14	0.13	1.65	3.21	0.99	3.13	886
2	11.66	1.88	1.92	16	97	1.61	1.57	0.34	1.15	3.8	1.23	2.14	428
2	13.03	0.9	1.71	16	86	1.95	2.03	0.24	1.46	4.6	1.19	2.48	392
2	11.84	2.89	2.23	18	112	1.72	1.32	0.43	0.95	2.65	0.96	2.52	500
2	12.33	0.99	1.95	14.8	136	1.9	1.85	0.35	2.76	3.4	1.06	2.31	750
2	12.7	3.87	2.4	23	101	2.83	2.55	0.43	1.95	2.57	1.19	3.13	463
2	12	0.92	2	19	86	2.42	2.26	0.3	1.43	2.5	1.38	3.12	278
2	12.72	1.81	2.2	18.8	86	2.2	2.53	0.26	1.77	3.9	1.16	3.14	714
2	12.08	1.13	2.51	24	78	2	1.58	0.4	1.4	2.2	1.31	2.72	630
2	13.05	3.86	2.32	22.5	85	1.65	1.59	0.61	1.62	4.8	0.84	2.01	515
2	11.84	0.89	2.58	18	94	2.2	2.21	0.22	2.35	3.05	0.79	3.08	520
2	12.67	0.98	2.24	18	99	2.2	1.94	0.3	1.46	2.62	1.23	3.16	450
2	12.16	1.61	2.31	22.8	90	1.78	1.69	0.43	1.56	2.45	1.33	2.26	495
2	11.65	1.67	2.62	26	88	1.92	1.61	0.4	1.34	2.6	1.36	3.21	562
2	11.64	2.06	2.46	21.6	84	1.95	1.69	0.48	1.35	2.8	1	2.75	680
2	12.08	1.33	2.3	23.6	70	2.2	1.59	0.42	1.38	1.74	1.07	3.21	625
2	12.08	1.83	2.32	18.5	81	1.6	1.5	0.52	1.64	2.4	1.08	2.27	480
2	12	1.51	2.42	22	86	1.45	1.25	0.5	1.63	3.6	1.05	2.65	450
2	12.69	1.53	2.26	20.7	80	1.38	1.46	0.58	1.62	3.05	0.96	2.06	495
2	12.29	2.83	2.22	18	88	2.45	2.25	0.25	1.99	2.15	1.15	3.3	290
2	11.62	1.99	2.28	18	98	3.02	2.26	0.17	1.35	3.25	1.16	2.96	345
2	12.47	1.52	2.2	19	162	2.5	2.27	0.32	3.28	2.6	1.16	2.63	937
2	11.81	2.12	2.74	21.5	134	1.6	0.99	0.14	1.56	2.5	0.95	2.26	625
2	12.29	1.41	1.98	16	85	2.55	2.5	0.29	1.77	2.9	1.23	2.74	428
2	12.37	1.07	2.1	18.5	88	3.52	3.75	0.24	1.95	4.5	1.04	2.77	660
2	12.29	3.17	2.21	18	88	2.85	2.99	0.45	2.81	2.3	1.42	2.83	406
2	12.08	2.08	1.7	17.5	97	2.23	2.17	0.26	1.4	3.3	1.27	2.96	710
2	12.6	1.34	1.9	18.5	88	1.45	1.36	0.29	1.35	2.45	1.04	2.77	562
2	12.34	2.45	2.46	21	98	2.56	2.11	0.34	1.31	2.8	0.8	3.38	438
2	11.82	1.72	1.88	19.5	86	2.5	1.64	0.37	1.42	2.06	0.94	2.44	415
2	12.51	1.73	1.98	20.5	85	2.2	1.92	0.32	1.48	2.94	1.04	3.57	672
2	12.42	2.55	2.27	22	90	1.68	1.84	0.66	1.42	2.7	0.86	3.3	315
2	12.25	1.73	2.12	19	80	1.65	2.03	0.37	1.63	3.4	1	3.17	510
2	12.72	1.75	2.28	22.5	84	1.38	1.76	0.48	1.63	3.3	0.88	2.42	488
2	12.22	1.29	1.94	19	92	2.36	2.04	0.39	2.08	2.7	0.86	3.02	312
2	11.61	1.35	2.7	20	94	2.74	2.92	0.29	2.49	2.65	0.96	3.26	680
2	11.46	3.74	1.82	19.5	107	3.18	2.58	0.24	3.58	2.9	0.75	2.81	562
2	12.52	2.43	2.17	21	88	2.55	2.27	0.26	1.22	2	0.9	2.78	325
2	11.76	2.68	2.92	20	103	1.75	2.03	0.6	1.05	3.8	1.23	2.5	607
2	11.41	0.74	2.5	21	88	2.48	2.01	0.42	1.44	3.08	1.1	2.31	434
2	12.08	1.39	2.5	22.5	84	2.56	2.29	0.43	1.04	2.9	0.93	3.19	385
2	11.03	1.51	2.2	21.5	85	2.46	2.17	0.52	2.01	1.9	1.71	2.87	407
2	11.82	1.47	1.99	20.8	86	1.98	1.6	0.3	1.53	1.95	0.95	3.33	495
2	12.42	1.61	2.19	22.5	108	2	2.09	0.34	1.61	2.06	1.06	2.96	345
2	12.77	3.43	1.98	16	80	1.63	1.25	0.43	0.83	3.4	0.7	2.12	372
2	12	3.43	2	19	87	2	1.64	0.37	1.87	1.28	0.93	3.05	564
2	11.45	2.4	2.42	20	96	2.9	2.79	0.32	1.83	3.25	0.8	3.39	625
2	11.56	2.05	3.23	28.5	119	3.18	5.08	0.47	1.87	6	0.93	3.69	465
2	12.42	4.43	2.73	26.5	102	2.2	2.13	0.43	1.71	2.08	0.92	3.12	365
2	13.05	5.8	2.13	21.5	86	2.62	2.65	0.3	2.01	2.6	0.73	3.1	380
2	11.87	4.31	2.39	21	82	2.86	3.03	0.21	2.91	2.8	0.75	3.64	380
2	12.07	2.16	2.17	21	85	2.6	2.65	0.37	1.35	2.76	0.86	3.28	378
2	12.43	1.53	2.29	21.5	86	2.74	3.15	0.39	1.77	3.94	0.69	2.84	352
2	11.79	2.13	2.78	28.5	92	2.13	2.24	0.58	1.76	3	0.97	2.44	466
2	12.37	1.63	2.3	24.5	88	2.22	2.45	0.4	1.9	2.12	0.89	2.78	342
2	12.04	4.3	2.38	22	80	2.1	1.75	0.42	1.35	2.6	0.79	2.57	580
3	12.86	1.35	2.32	18	122	1.51	1.25	0.21	0.94	4.1	0.76	1.29	630
3	12.88	2.99	2.4	20	104	1.3	1.22	0.24	0.83	5.4	0.74	1.42	530
3	12.81	2.31	2.4	24	98	1.15	1.09	0.27	0.83	5.7	0.66	1.36	560
3	12.7	3.55	2.36	21.5	106	1.7	1.2	0.17	0.84	5	0.78	1.29	600
3	12.51	1.24	2.25	17.5	85	2	0.58	0.6	1.25	5.45	0.75	1.51	650
3	12.6	2.46	2.2	18.5	94	1.62	0.66	0.63	0.94	7.1	0.73	1.58	695
3	12.25	4.72	2.54	21	89	1.38	0.47	0.53	0.8	3.85	0.75	1.27	720
3	12.53	5.51	2.64	25	96	1.79	0.6	0.63	1.1	5	0.82	1.69	515
3	13.49	3.59	2.19	19.5	88	1.62	0.48	0.58	0.88	5.7	0.81	1.82	580
3	12.84	2.96	2.61	24	101	2.32	0.6	0.53	0.81	4.92	0.89	2.15	590
3	12.93	2.81	2.7	21	96	1.54	0.5	0.53	0.75	4.6	0.77	2.31	600
3	13.36	2.56	2.35	20	89	1.4	0.5	0.37	0.64	5.6	0.7	2.47	780
3	13.52	3.17	2.72	23.5	97	1.55	0.52	0.5	0.55	4.35	0.89	2.06	520
3	13.62	4.95	2.35	20	92	2	0.8	0.47	1.02	4.4	0.91	2.05	550
3	12.25	3.88	2.2	18.5	112	1.38	0.78	0.29	1.14	8.21	0.65	2	855
3	13.16	3.57	2.15	21	102	1.5	0.55	0.43	1.3	4	0.6	1.68	830
3	13.88	5.04	2.23	20	80	0.98	0.34	0.4	0.68	4.9	0.58	1.33	415
3	12.87	4.61	2.48	21.5	86	1.7	0.65	0.47	0.86	7.65	0.54	1.86	625
3	13.32	3.24	2.38	21.5	92	1.93	0.76	0.45	1.25	8.42	0.55	1.62	650
3	13.08	3.9	2.36	21.5	113	1.41	1.39	0.34	1.14	9.4	0.57	1.33	550
3	13.5	3.12	2.62	24	123	1.4	1.57	0.22	1.25	8.6	0.59	1.3	500
3	12.79	2.67	2.48	22	112	1.48	1.36	0.24	1.26	10.8	0.48	1.47	480
3	13.11	1.9	2.75	25.5	116	2.2	1.28	0.26	1.56	7.1	0.61	1.33	425
3	13.23	3.3	2.28	18.5	98	1.8	0.83	0.61	1.87	10.52	0.56	1.51	675
3	12.58	1.29	2.1	20	103	1.48	0.58	0.53	1.4	7.6	0.58	1.55	640
3	13.17	5.19	2.32	22	93	1.74	0.63	0.61	1.55	7.9	0.6	1.48	725
3	13.84	4.12	2.38	19.5	89	1.8	0.83	0.48	1.56	9.01	0.57	1.64	480
3	12.45	3.03	2.64	27	97	1.9	0.58	0.63	1.14	7.5	0.67	1.73	880
3	14.34	1.68	2.7	25	98	2.8	1.31	0.53	2.7	13	0.57	1.96	660
3	13.48	1.67	2.64	22.5	89	2.6	1.1	0.52	2.29	11.75	0.57	1.78	620
3	12.36	3.83	2.38	21	88	2.3	0.92	0.5	1.04	7.65	0.56	1.58	520
3	13.69	3.26	2.54	20	107	1.83	0.56	0.5	0.8	5.88	0.96	1.82	680
3	12.85	3.27	2.58	22	106	1.65	0.6	0.6	0.96	5.58	0.87	2.11	570
3	12.96	3.45	2.35	18.5	106	1.39	0.7	0.4	0.94	5.28	0.68	1.75	675
3	13.78	2.76	2.3	22	90	1.35	0.68	0.41	1.03	9.58	0.7	1.68	615
3	13.73	4.36	2.26	22.5	88	1.28	0.47	0.52	1.15	6.62	0.78	1.75	520
3	13.45	3.7	2.6	23	111	1.7	0.92	0.43	1.46	10.68	0.85	1.56	695
3	12.82	3.37	2.3	19.5	88	1.48	0.66	0.4	0.97	10.26	0.72	1.75	685
3	13.58	2.58	2.69	24.5	105	1.55	0.84	0.39	1.54	8.66	0.74	1.8	750
3	13.4	4.6	2.86	25	112	1.98	0.96	0.27	1.11	8.5	0.67	1.92	630
3	12.2	3.03	2.32	19	96	1.25	0.49	0.4	0.73	5.5	0.66	1.83	510
3	12.77	2.39	2.28	19.5	86	1.39	0.51	0.48	0.64	9.899999	0.57	1.63	470
3	14.16	2.51	2.48	20	91	1.68	0.7	0.44	1.24	9.7	0.62	1.71	660
3	13.71	5.65	2.45	20.5	95	1.68	0.61	0.52	1.06	7.7	0.64	1.74	740
3	13.4	3.91	2.48	23	102	1.8	0.75	0.43	1.41	7.3	0.7	1.56	750
3	13.27	4.28	2.26	20	120	1.59	0.69	0.43	1.35	10.2	0.59	1.56	835
3	13.17	2.59	2.37	20	120	1.65	0.68	0.53	1.46	9.3	0.6	1.62	840
3	14.13	4.1	2.74	24.5	96	2.05	0.76	0.56	1.35	9.2	0.61	1.6	560

Gauss data
#
-0.46551197	2.3396181
-0.14154019	2.6467355
-0.06147374	2.910021
-0.00960664	2.6959237
0.13522751	3.3371545
0.15816343	2.74101
0.16902868	2.7145902
0.20321506	2.3230994
0.24094079	2.3069846
0.25245225	3.1839526
0.26993324	3.5483323
0.28680391	2.6052432
0.3192367	3.2276855
0.36114833	3.8009784
0.4244067	2.8634449
0.46037797	2.4532789
0.46098959	3.3555875
0.48936047	2.8126198
0.493417	1.52432961
0.5579586	3.0069093
0.59517099	1.74266104
0.601286	3.447746
0.64249205	1.22137058
0.64603926	3.098612
0.68936857	3.3436606
0.70125847	2.9881431
0.70954162	2.8340884
0.71271143	2.1911658
0.73828501	3.9157625
0.76121259	3.887774
0.76300537	4.36236
0.78715506	2.7998882
0.79603202	2.3837631
0.81552587	1.7659359
0.81671088	3.0235945
0.83646559	2.7292928
0.84905869	1.90959966
0.87280583	2.9185033
0.93911004	3.2377149
0.94658989	2.8099329
0.95071896	4.3593771
0.95477185	1.82422933
1.0475351	4.7358953
1.0483555	3.6222619
1.0663274	3.0772458
1.0719789	3.3051114
1.0841659	3.8982847
1.0917331	2.4965835
1.1268271	2.7541776
1.1308673	3.1353612
1.1428466	3.0972579
1.1493513	2.8227118
1.1819349	2.8738619
1.1900377	2.5835831
1.2101874	2.8796963
1.2181118	4.0111068
1.2277972	3.0808183
1.2335498	3.9573163
1.2448562	2.6017201
1.2555436	2.9472067
1.2597352	1.95874592
1.2601195	1.81798378
1.2715242	1.46185734
1.2749423	4.2592336
1.2888759	3.4162943
1.319464	3.1180977
1.3266557	2.9249356
1.3269461	3.8783508
1.3458998	2.6047048
1.3471548	3.0660325
1.3753515	3.4089999
1.3793406	3.1089793
1.3872928	4.3455047
1.3979966	3.3829163
1.4001717	4.0482038
1.4054732	2.6215221
1.4312392	1.79191521
1.4354818	4.0271297
1.4410139	3.7986203
1.4641202	1.58155858
1.4646153	4.9791516
1.5055329	2.0990125
1.5286615	3.6943944
1.5534019	4.741445
1.5735908	2.5571016
1.5856639	1.99127551
1.6083411	3.3154722
1.6167515	4.4641362
1.6315791	3.9447703
1.6485825	2.9459715
1.6558902	2.1154736
1.6603318	3.4801616
1.6657018	2.9762646
1.6740867	3.9751515
1.7109312	2.5716093
1.7172934	2.9542265
1.7245997	3.387059
1.7280016	4.2168608
1.7407356	2.6770587
1.7446611	4.8440441
1.7479167	2.3913499
1.7507312	3.769599
1.7567404	2.4447784
1.7591262	4.9952283
1.7610234	4.5877581
1.7736913	2.4399875
1.7826518	4.1827802
1.7882331	3.7558682
1.8048077	2.4894358
1.806578	3.2980184
1.8105734	4.0728863
1.8132452	1.11900227
1.8581162	3.3816176
1.8735926	3.1120087
1.8771577	2.9606585
1.8791867	1.81713304
1.8866894	2.5761363
1.8881009	1.12698201
1.8980516	3.4640572
1.911659	1.92031474
1.9163593	3.2659011
1.9217044	2.9429349
1.9240625	2.2683148
1.933498	1.99443935
1.9366692	4.0426166
1.9572064	4.2689621
1.987931	5.1287767
1.9907116	2.6489055
1.9930155	3.6846483
2.0011578	3.3236036
2.0107086	3.1802041
2.0650972	1.62687304
2.0760993	2.8564104
2.076595	4.141211
2.0797088	3.6444816
2.0828643	3.4026682
2.0948837	2.8648059
2.0985622	4.7026465
2.0994979	5.0382142
2.1023642	4.2814187
2.1102441	4.3805769
2.1522589	2.6625872
2.1613626	3.6416719
2.1760954	1.66759943
2.1805424	3.0442384
2.1842685	3.5954692
2.1891405	3.9582391
2.1963915	3.0430046
2.2020264	3.176707
2.204684	1.83693931
2.2052541	2.7499996
2.2066926	3.7208274
2.2109871	1.60017807
2.2146996	2.399128
2.2174554	1.72364346
2.2304159	1.82961734
2.2473506	2.2875368
2.2490397	3.1734422
2.2519844	2.2969987
2.2604619	1.7826934
2.2688021	3.0460717
2.3082186	4.4655713
2.337285	3.3586476
2.3616657	3.9431843
2.372691	2.7305975
2.3783937	2.3535819
2.3816021	2.9007384
2.3967371	2.1569486
2.409683	3.3595594
2.4193469	2.0856312
2.4234583	1.51782552
2.4392568	4.1463499
2.440037	4.2618835
2.4499319	1.4923003
2.4582683	2.3588849
2.4769531	4.5814207
2.4844316	2.9984511
2.4845173	2.8737064
2.4932187	2.66146
2.5161047	3.2298843
2.5216744	3.9607432
2.5229139	2.492989
2.5270772	0.93612531
2.5353904	1.28615059
2.5781225	3.5085023
2.5789043	3.6564247
2.5913078	4.4678316
2.6017795	3.3329228
2.6083691	2.7219107
2.609068	3.4104897
2.6328854	2.4587599
2.6469687	2.7014709
2.6920039	2.295654
2.6979838	3.9314267
2.7126542	3.1619493
2.7176177	3.6184841
2.720342	3.7590754
2.7288016	2.8601606
2.7729759	3.3649024
2.8041777	2.8947211
2.8297566	3.1058471
2.8568165	4.2398304
2.8708006	3.5365312
2.8884565	3.4983996
2.8940135	1.80718637
2.9270099	2.4705192
2.933241	1.61062
2.9722306	2.9105811
2.9772854	1.05260017
2.9773797	3.4382292
2.980513	1.98434561
3.005612	3.8833924
3.0089706	1.48283347
3.0261818	3.8696189
3.0400688	3.2351968
3.0647613	2.3867858
3.0958156	5.7977043
3.0974361	1.61238889
3.123407	4.2756611
3.140026	2.9104996
3.1401996	4.0088845
3.1743328	3.9899578
3.1808794	3.408549
3.1832203	2.0703815
3.2276428	3.180782
3.2372055	2.4413745
3.2952069	3.9016304
3.3387983	4.2365316
3.3562441	4.3595853
3.4416423	5.7357497
3.521928	3.8651324
3.5249241	3.4056552
3.6396121	1.74341452
3.6525806	5.0596418
3.6773415	1.65264229
3.7089812	4.4749576
3.7570425	3.2789297
3.7990812	2.8031089
3.8079212	4.2604948
3.8635523	4.3065351
3.8826769	3.4434805
3.9105801	1.0730681
4.0275824	3.9110729
4.2610977	4.5150765
4.2654032	3.5862794
4.3772226	4.6939538
4.3862929	2.8272327
4.6736332	2.4853571
8.9435583	9.4739571
8.9739184	7.87866
9.187397	9.348852
9.2407109	8.5518535
9.2436521	9.4541242
9.2551838	7.5564442
9.285243	10.2071673
9.3299446	9.0000361
9.3319237	10.0936765
9.373557	9.8826912
9.464094	9.0581829
9.4906742	9.3292611
9.5115338	9.8535287
9.5659971	10.0486414
9.6023175	7.9585361
9.6234813	8.3907157
9.6445082	9.6998504
9.649045	9.8849591
9.6584125	9.0152807
9.6665508	9.4282592
9.6730605	10.5942431
9.763729	8.2835604
9.7755229	10.407463
9.8076254	9.658945
9.8276345	8.4644596
9.8479234	7.756862
9.856574	8.503814
9.8622591	9.9170009
9.9363758	7.2319598
9.943909	8.4875439
9.9889206	10.611449
10.0165059	9.5475354
10.0419705	10.8528666
10.102277	11.0164613
10.1288555	8.7255618
10.1296474	9.9023458
10.1553296	10.365873
10.1730311	8.742746
10.1820635	8.0384904
10.2560427	10.2301575
10.2981906	11.2181469
10.3011218	5.9657148
10.301961	9.6504424
10.3145947	8.2175059
10.3361955	11.0435696
10.3803912	9.9176344
10.4078596	9.5672229
10.40994	9.6915476
10.4850186	8.672043
10.4965347	10.4085449
10.5192183	9.1697117
10.5647357	10.0321895
10.5648727	10.5589759
10.6015189	6.0994906
10.603581	10.3790253
10.6099817	7.1638026
10.6206807	9.6087325
10.6345994	9.1531639
10.6429483	10.3542054
10.6724078	8.4637002
10.7092349	8.7151763
10.714601	7.8089865
10.7182087	7.8359403
10.7245	8.2597278
10.7385045	8.4247141
10.770265	9.7400903
10.8110738	8.4712292
10.8312662	7.5418477
10.8319838	8.9931978
10.8578799	9.0934209
10.8853276	9.0234515
10.913638	9.5415647
10.9267408	8.7133325
11.018074	9.6140025
11.0184744	9.045611
11.0412225	8.2239493
11.0485496	9.0632895
11.0518117	8.0080764
11.0664894	9.1414022
11.0949584	9.125697
11.101395	9.8750952
11.1235131	8.6977035
11.1394266	8.110125
11.1673507	8.731084
11.1958179	9.1986208
11.2270168	10.334217
11.2319251	9.5158861
11.2327407	9.0726365
11.2746275	8.693953
11.2802248	6.3492874
11.307973	8.5744575
11.4089124	8.1652054
11.4206061	7.1979654
11.4332237	9.4580566
11.4413669	8.0914636
11.4696749	8.8581128
11.4830634	11.2508174
11.4864111	7.7290216
11.4928201	10.746028
11.4952661	7.12129
11.4990939	10.0108213
11.5146056	9.6430751
11.5521972	8.328596
11.574167	9.0679398
11.5811656	7.0079888
11.5818258	9.1019273
11.6091367	8.5752111
11.6246849	8.5365028
11.6642143	8.4458003
11.6691018	10.7915455
11.7002922	10.6383879
11.7120615	8.794039
11.7644048	8.8808288
11.7934826	7.4950836
11.9684594	10.0053487
11.9714406	7.2756851
11.9788712	9.9465767
12.0029676	7.9771513
12.024769	6.9830776
12.108955	10.0022074
12.1667502	7.1966235
12.2008513	11.2806987
12.2237865	9.3287031
12.3157701	7.5950945
12.3606768	12.1086731
12.5844491	9.865713
12.6733525	7.6230164
12.682216	9.4233843
12.6856208	9.310157
12.6906946	7.328908
12.7297457	10.1547281
15.382204	9.6953397
1.46197526	19.8670258
1.49022974	19.5352805
1.56415542	20.831295
1.95117941	18.9503718
2.05835481	20.557332
2.10288324	19.5935174
2.17447523	18.0540215
2.19810974	19.12201
2.21934452	19.0784394
2.23439904	19.844791
2.26788561	20.209558
2.47250581	18.6123102
2.5142698	18.4614009
2.54996643	19.2768358
2.55418227	18.884939
2.60602417	20.936468
2.63375978	20.206995
2.63673117	18.7844843
2.7274187	19.0976652
2.78672127	18.6171725
2.84069897	18.7650607
2.885701	20.137431
2.91613686	20.233336
2.92942006	18.5713626
2.95596944	20.535309
2.96854857	19.8038184
3.0145628	18.2879164
3.0177927	20.84433
3.0215634	20.765331
3.0319656	19.6683734
3.0682963	19.1762061
3.0797411	20.497518
3.0890363	19.7297415
3.1020139	19.1104753
3.1111965	20.974068
3.1118762	20.730051
3.1190241	20.626021
3.1290053	20.708113
3.1567038	18.9859388
3.1642717	20.86776
3.1877287	19.0277225
3.2417761	19.9239422
3.2697233	20.58267
3.2805359	20.839587
3.315033	17.800678
3.3374848	19.8847305
3.3436787	21.160946
3.3601605	19.9220547
3.3804341	19.5464206
3.419576	18.5611838
3.44766	19.7976419
3.4497856	20.992422
3.4515913	19.246366
3.462917	19.0683491
3.469379	21.277371
3.476635	19.7049207
3.4789768	18.7587035
3.4864838	17.8367824
3.494347	18.4188009
3.4977578	20.235316
3.5150408	21.469438
3.5465376	20.102797
3.5481756	20.500124
3.5623819	21.11233
3.5657523	19.8653065
3.5680518	19.7992902
3.5787906	19.4795982
3.580711	20.634115
3.5882776	21.154661
3.6154553	19.5561227
3.6240277	18.1513374
3.6271007	19.9594745
3.6423654	19.5207591
3.6532214	18.9213972
3.6708971	19.0647823
3.6816109	20.307332
3.6831495	20.753881
3.6909234	18.3056743
3.694486	18.4031309
3.7029568	18.7852089
3.7209245	21.160009
3.7554475	19.5635625
3.7561105	19.1486694
3.7591644	21.053899
3.7816041	21.754704
3.7876631	20.692215
3.8234422	19.7212479
3.8262062	20.727142
3.8288627	20.673864
3.8297793	20.458916
3.832603	21.074711
3.8353628	19.0803144
3.8556235	19.6185811
3.8563045	20.950317
3.8567533	20.230949
3.8755811	19.7954
3.8808773	21.261579
3.8832988	18.9781784
3.8843075	19.8027541
3.8905587	22.469766
3.8975282	20.28635
3.9109833	20.301225
3.9144448	18.974059
3.9165083	21.676352
3.9209176	19.7131755
3.9781659	21.664901
3.9788063	17.9605311
3.9795792	20.604285
3.9988977	19.3873
4.0085747	20.703644
4.0196867	22.969208
4.025619	20.081437
4.0534619	20.705229
4.0547586	20.305228
4.0547586	20.816871
4.0755392	18.3552001
4.075569	19.5328423
4.1066715	18.9310227
4.1219421	18.0786952
4.1325375	20.278667
4.1346469	19.7929784
4.1377228	19.3141014
4.1424518	19.0621782
4.163728	18.0126414
4.1789909	21.545179
4.1910496	22.74184
4.192648	21.567828
4.2120042	19.5149355
4.2236802	19.7026045
4.235359	21.291386
4.2504888	20.255275
4.2849944	19.6670767
4.2936236	20.04094
4.2943962	18.6098342
4.2972952	21.044405
4.3159775	18.8025205
4.3183388	19.7124052
4.3312717	19.1945021
4.3332788	19.858556
4.3436751	20.278825
4.3510198	19.4443378
4.3536828	19.6918611
4.3762487	20.001178
4.3873331	19.944966
4.4018079	21.336344
4.4049931	20.354974
4.4069312	20.229606
4.4145946	21.865367
4.4298175	20.177013
4.4304013	21.295831
4.4350526	19.2814343
4.4365224	20.607422
4.4385972	20.113629
4.4395695	20.287594
4.4433867	19.5672236
4.4581051	19.3005269
4.484958	19.6122817
4.4855137	19.7430907
4.4961756	19.0852748
4.4961815	19.9774792
4.5060367	20.626502
4.5111277	20.048125
4.5353022	21.006037
4.5477455	19.5218191
4.5646723	18.5753288
4.6069566	19.4928334
4.610661	19.2333245
4.6110635	21.161409
4.6156599	22.24306
4.6157363	19.6247648
4.6231143	21.467851
4.6303499	21.166585
4.6479363	19.4927011
4.649848	19.9999997
4.6726846	21.196404
4.6956556	21.338031
4.7507975	21.35678
4.8039113	20.381728
4.8669884	21.926908
4.8952958	19.2367016
4.9323323	19.425336
4.9469818	20.622926
4.9524754	19.5783952
4.9781864	20.542842
4.9871194	18.4396185
5.0319823	20.890154
5.0549366	19.0793781
5.0848657	20.441317
5.0908691	20.080454
5.1299154	19.4593151
5.1662752	21.241106
5.1816155	18.5673625
5.2062795	19.3081385
5.2424753	20.28896
5.2425295	20.158123
5.2906467	21.072758
5.297312	19.8472714
5.2975145	20.757499
5.3062086	20.421346
5.3323506	20.513092
5.3448124	19.1347046
5.3474952	20.536695
5.4494709	21.900892
5.4986571	20.600885
5.5130076	21.136178
5.5275875	21.167535
5.5458203	20.827132
5.6357217	21.777843
5.6418671	20.280765
5.642627	19.750893
5.766496	20.256047
5.8495488	18.0793218
6.1037454	20.818065
6.2250822	22.607042
6.3813155	20.092317
6.634051	21.15266
7.0207123	20.895823
17.2951741	5.950383
17.6426889	4.026425
18.3783284	2.6677586
18.6610826	2.2858431
18.7658667	2.2502372
18.8735189	4.0070031
18.9202878	3.3794615
19.2157376	5.0456349
19.2828371	5.7614267
19.3416173	5.7342967
19.372867	5.071573
19.3793344	4.4288482
19.4471513	2.0290971
19.5507429	4.1520108
19.5796525	3.3137553
19.6592429	2.9047836
19.7027051	3.391466
19.7066826	3.8575872
19.7665198	4.6534996
19.8166826	5.0681208
19.8309236	4.4234537
19.8705837	1.95144045
19.8706565	2.4984245
19.8761654	4.5287529
19.9085948	1.91336718
19.9163915	5.4980526
19.9321293	5.6884465
19.9635774	5.1519153
19.9774131	1.71576669
20.005386	4.9725339
20.03108	3.1899709
20.031303	5.3222825
20.03259	5.1497509
20.03428	5.0453932
20.080447	4.3698057
20.083517	6.3154411
20.109498	1.6949377
20.12784	3.1077324
20.143328	5.2932866
20.153527	1.14745808
20.185165	5.6840752
20.186108	3.0095029
20.218486	1.86943064
20.225788	4.6446198
20.230586	3.5665214
20.242446	3.0842829
20.250058	4.6674981
20.256518	5.1025102
20.265683	2.0045272
20.282189	3.5401449
20.302099	3.6052738
20.308668	6.0612829
20.351454	4.9648817
20.353287	2.9206918
20.36641	2.0461857
20.372119	4.0134533
20.373874	4.3479411
20.385863	5.8749841
20.409758	2.4770249
20.429302	4.1856019
20.584737	5.5193866
20.607137	3.7815047
20.612883	1.24133727
20.62282	3.8538072
20.626709	5.3176161
20.631416	4.6234233
20.637825	1.13733134
20.650459	5.6764957
20.65138	5.4412149
20.663191	5.1562683
20.671364	3.1726842
20.676551	3.3428428
20.689668	1.11834644
20.693096	5.0325472
20.696343	4.5175537
20.699593	2.8767087
20.703645	4.631295
20.795686	2.7721686
20.809542	6.5642634
20.825847	3.1371149
20.842786	4.3904774
20.84874	6.3513859
20.860288	6.4784907
20.86037	2.8584966
20.869171	5.2307423
20.872055	4.0749112
20.87257	1.23464118
20.910788	5.2690191
20.928634	3.6359366
20.970335	3.8242789
21.036661	2.7005158
21.049342	3.7664064
21.060166	4.2008833
21.060814	4.9944114
21.068511	4.9511485
21.075475	3.162381
21.121069	4.024253
21.135204	4.2472877
21.137853	3.0020879
21.173174	4.1480361
21.186386	7.2251656
21.200991	2.1901514
21.208839	4.4279441
21.218948	3.7864309
21.221807	3.0719514
21.246152	5.4355796
21.262461	2.9578559
21.268682	3.628921
21.290465	3.2874838
21.293326	4.106079
21.301133	4.1777477
21.302882	3.2121843
21.307465	6.3794084
21.310338	3.0792017
21.377903	3.5628289
21.432628	3.2225798
21.439473	3.385765
21.467547	4.7781926
21.477121	3.6562905
21.501194	2.3480532
21.533824	4.9138983
21.54175	2.9172603
21.548589	4.4224972
21.551281	5.034822
21.580005	2.4249323
21.622936	2.6350977
21.635703	5.0811729
21.635766	0.6839693
21.636906	5.1485335
21.640693	5.0050765
21.644646	2.6398355
21.659895	3.7598802
21.6671	3.0851933
21.672122	1.82188274
21.790655	3.9990614
21.791466	3.9976649
21.823915	5.5021609
21.836958	2.770455
21.849023	5.0410794
21.875795	3.4999431
21.906133	5.8123412
21.929987	6.0171549
21.943218	4.5029935
21.944168	4.8880596
21.973628	5.3975676
21.977883	3.6668553
21.986891	3.3156703
21.990816	6.0919267
21.998018	5.5690895
22.002982	2.9423729
22.003167	3.1783848
22.02293	3.7394457
22.048313	5.7878447
22.04935	3.9196468
22.102964	6.9353623
22.104117	2.3553044
22.112408	4.44293
22.116913	5.7040627
22.117363	5.4379922
22.132975	4.7215931
22.148421	3.8419177
22.157023	3.3189264
22.209926	6.0647142
22.231745	4.1059839
22.243242	5.6659826
22.275244	3.4509242
22.283633	3.0655829
22.31873	3.5765372
22.360228	1.30690857
22.390107	5.4824678
22.391019	5.1110448
22.418397	3.6016607
22.438045	5.2962146
22.502628	2.9129491
22.530678	5.270917
22.531774	3.510775
22.535484	6.3496156
22.555906	2.8812822
22.574879	4.1483826
22.577047	6.5748674
22.698119	5.5684472
22.712666	2.8698348
22.754716	2.8842441
22.840546	5.1758951
22.875637	5.5823763
22.913839	4.7236013
22.921435	4.992112
22.95051	5.507263
22.968837	3.8102658
23.372123	5.1056244
23.570015	7.3520417
23.871862	2.2839561
14.776254	15.9332623
14.828535	17.754768
14.9985099	19.006255
15.0332013	19.171739
15.3495864	18.344629
15.3643301	17.307221
15.6308644	18.729308
15.6742915	18.422276
15.6765912	18.957163
15.7242125	18.603576
15.7309358	18.287858
15.8201135	17.317803
16.04405	18.326776
16.2843895	19.224467
16.3338087	20.315561
16.3864874	19.430293
16.391851	16.6688446
16.4134342	19.813134
16.4887595	18.338325
16.4927375	18.033762
16.5616717	19.041559
16.679285	18.295844
16.6980917	16.8332204
16.7248246	17.813209
16.738379	20.513935
16.7447018	20.612144
16.759245	20.425581
16.8174379	17.387096
16.8318274	20.132314
16.8411981	18.775724
16.8657639	15.18619
16.9045035	16.6292852
16.9060549	17.432955
16.9236652	16.5085818
16.9246185	18.501414
16.9371979	18.538343
16.9424162	17.669786
16.9436611	17.422198
16.954384	16.6210745
17.0048745	18.647301
17.0093275	18.155081
17.0495475	18.4833
17.0627556	18.23776
17.0848806	18.073719
17.0988263	18.650373
17.1219141	17.029351
17.1366616	19.971848
17.1485928	18.236786
17.1559942	16.9274005
17.1784265	16.6347077
17.2007163	20.458792
17.2067843	18.878027
17.2126233	17.627478
17.27771	18.301138
17.339495	22.147247
17.3421613	18.489292
17.3577314	17.460453
17.3724251	17.838472
17.3996649	15.71695
17.4002773	14.6913483
17.4324959	15.5923088
17.4391638	18.273311
17.474673	16.1187519
17.4783074	16.7469574
17.4828174	18.835584
17.5082782	17.96041
17.5131746	16.1271108
17.5226118	20.37399
17.5411115	18.970615
17.5478529	20.084663
17.5593776	18.467168
17.5604301	16.6251063
17.5621066	15.6151074
17.5639823	15.6570201
17.5727543	16.2284542
17.5999234	19.382793
17.6097383	16.6748319
17.6335421	17.659876
17.6346179	19.120215
17.6394592	14.6308142
17.6442026	19.402978
17.6624146	18.518399
17.6824201	19.045062
17.6933922	18.000639
17.6988315	18.716886
17.7180367	18.103054
17.7549039	16.4974324
17.7672336	18.161612
17.7675093	19.396513
17.7926923	18.684571
17.8061956	18.138528
17.8161585	17.744709
17.8442842	16.5564664
17.8517083	18.048076
17.8798602	18.078834
17.894805	17.364323
17.897217	16.2556095
17.9274089	17.295782
17.9540314	17.790538
17.9833606	17.01958
18.010122	16.2967178
18.011117	17.84965
18.02912	18.488563
18.036761	16.4478485
18.037256	18.574155
18.063049	18.151774
18.090068	18.56643
18.094963	18.018778
18.10096	15.8177448
18.122916	17.674656
18.129248	18.158393
18.13386	14.7555909
18.149812	16.2079389
18.152682	20.011784
18.176491	19.079545
18.178535	17.691873
18.183269	18.910638
18.215687	15.8817595
18.235729	15.7945041
18.259081	17.087312
18.264561	17.029223
18.274332	15.8333249
18.330272	16.7431158
18.368219	16.0200739
18.379084	19.194089
18.379872	18.124247
18.385792	18.179336
18.398364	19.606996
18.409342	17.693611
18.437678	17.926307
18.455588	19.845492
18.475238	18.791697
18.508271	16.4602961
18.527687	19.682064
18.536628	17.831121
18.542094	17.563114
18.545504	17.37607
18.547878	17.319933
18.553336	19.569272
18.557667	15.180909
18.582784	19.483361
18.590279	17.149325
18.634664	18.86855
18.639971	21.681327
18.643215	16.5278489
18.650616	18.021068
18.659338	19.160969
18.664585	17.421214
18.67864	13.6651535
18.706178	19.788597
18.743804	18.332319
18.754485	17.600224
18.776854	18.299507
18.788332	18.461131
18.797314	14.9900939
18.812277	16.7877808
18.818708	17.767132
18.825362	17.998126
18.827257	17.411798
18.855049	20.06313
18.895247	15.6882882
18.89677	18.241082
18.90694	16.3827126
18.931778	19.185287
18.935962	17.947167
18.936954	19.25114
18.958115	18.59757
19.026628	19.205066
19.037261	17.780271
19.061187	19.016911
19.097614	18.740769
19.099027	18.797805
19.1145	17.767203
19.131498	17.562132
19.170495	18.460691
19.188678	19.321407
19.191673	15.5556509
19.201821	18.442701
19.237079	15.6778069
19.25179	18.030541
19.270079	19.044006
19.333913	15.3620963
19.35918	17.033823
19.361986	17.354937
19.365055	19.033464
19.365386	16.7474302
19.367864	18.965754
19.432841	19.920374
19.455325	17.293297
19.457648	18.584263
19.482073	16.4698072
19.495794	15.2869317
19.520868	17.981305
19.601644	15.5475595
19.626402	17.237819
19.639048	17.63884
19.664319	18.12114
19.678355	18.234852
19.706041	19.347441
19.946172	18.768173
19.954228	17.480191
20.016013	20.799353
20.025227	18.322595
20.065571	19.06146
20.080158	17.622466
20.260129	17.286994
20.385613	19.526589
20.591116	20.438186
20.774989	19.963467
20.973478	18.539765
21.657494	15.7015363

Gauss Data
#
0.53448803	2.3396181
0.85845981	2.6467355
0.93852626	2.910021
0.99039336	2.6959237
1.13522751	3.3371545
1.15816343	2.74101
1.16902868	2.7145902
1.20321506	2.3230994
1.24094079	2.3069846
1.25245225	3.1839526
1.26993324	3.5483323
1.28680391	2.6052432
1.3192367	3.2276855
1.36114833	3.8009784
1.4244067	2.8634449
1.46037797	2.4532789
1.46098959	3.3555875
1.48936047	2.8126198
1.493417	1.52432961
1.5579586	3.0069093
1.59517099	1.74266104
1.601286	3.447746
1.64249205	1.22137058
1.64603926	3.098612
1.68936857	3.3436606
1.70125847	2.9881431
1.70954162	2.8340884
1.71271143	2.1911658
1.73828501	3.9157625
1.76121259	3.887774
1.76300537	4.36236
1.78715506	2.7998882
1.79603202	2.3837631
1.81552587	1.7659359
1.81671088	3.0235945
1.83646559	2.7292928
1.84905869	1.90959966
1.87280583	2.9185033
1.93911004	3.2377149
1.94658989	2.8099329
1.95071896	4.3593771
1.95477185	1.82422933
2.0475351	4.7358953
2.0483555	3.6222619
2.0663274	3.0772458
2.0719789	3.3051114
2.0841659	3.8982847
2.0917331	2.4965835
2.1268271	2.7541776
2.1308673	3.1353612
2.1428466	3.0972579
2.1493513	2.8227118
2.1819349	2.8738619
2.1900377	2.5835831
2.2101874	2.8796963
2.2181118	4.0111068
2.2277972	3.0808183
2.2335498	3.9573163
2.2448562	2.6017201
2.2555436	2.9472067
2.2597352	1.95874592
2.2601195	1.81798378
2.2715242	1.46185734
2.2749423	4.2592336
2.2888759	3.4162943
2.319464	3.1180977
2.3266557	2.9249356
2.3269461	3.8783508
2.3458998	2.6047048
2.3471548	3.0660325
2.3753515	3.4089999
2.3793406	3.1089793
2.3872928	4.3455047
2.3979966	3.3829163
2.4001717	4.0482038
2.4054732	2.6215221
2.4312392	1.79191521
2.4354818	4.0271297
2.4410139	3.7986203
2.4641202	1.58155858
2.4646153	4.9791516
2.5055329	2.0990125
2.5286615	3.6943944
2.5534019	4.741445
2.5735908	2.5571016
2.5856639	1.99127551
2.6083411	3.3154722
2.6167515	4.4641362
2.6315791	3.9447703
2.6485825	2.9459715
2.6558902	2.1154736
2.6603318	3.4801616
2.6657018	2.9762646
2.6740867	3.9751515
2.7109312	2.5716093
2.7172934	2.9542265
2.7245997	3.387059
2.7280016	4.2168608
2.7407356	2.6770587
2.7446611	4.8440441
2.7479167	2.3913499
2.7507312	3.769599
2.7567404	2.4447784
2.7591262	4.9952283
2.7610234	4.5877581
2.7736913	2.4399875
2.7826518	4.1827802
2.7882331	3.7558682
2.8048077	2.4894358
2.806578	3.2980184
2.8105734	4.0728863
2.8132452	1.11900227
2.8581162	3.3816176
2.8735926	3.1120087
2.8771577	2.9606585
2.8791867	1.81713304
2.8866894	2.5761363
2.8881009	1.12698201
2.8980516	3.4640572
2.911659	1.92031474
2.9163593	3.2659011
2.9217044	2.9429349
2.9240625	2.2683148
2.933498	1.99443935
2.9366692	4.0426166
2.9572064	4.2689621
2.987931	5.1287767
2.9907116	2.6489055
2.9930155	3.6846483
3.0011578	3.3236036
3.0107086	3.1802041
3.0650972	1.62687304
3.0760993	2.8564104
3.076595	4.141211
3.0797088	3.6444816
3.0828643	3.4026682
3.0948837	2.8648059
3.0985622	4.7026465
3.0994979	5.0382142
3.1023642	4.2814187
3.1102441	4.3805769
3.1522589	2.6625872
3.1613626	3.6416719
3.1760954	1.66759943
3.1805424	3.0442384
3.1842685	3.5954692
3.1891405	3.9582391
3.1963915	3.0430046
3.2020264	3.176707
3.204684	1.83693931
3.2052541	2.7499996
3.2066926	3.7208274
3.2109871	1.60017807
3.2146996	2.399128
3.2174554	1.72364346
3.2304159	1.82961734
3.2473506	2.2875368
3.2490397	3.1734422
3.2519844	2.2969987
3.2604619	1.7826934
3.2688021	3.0460717
3.3082186	4.4655713
3.337285	3.3586476
3.3616657	3.9431843
3.372691	2.7305975
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16.2843895	34.224467
16.3338087	35.315561
16.3864874	34.430293
16.391851	31.6688446
16.4134342	34.813134
16.4887595	33.338325
16.4927375	33.033762
16.5616717	34.041559
16.679285	33.295844
16.6980917	31.8332204
16.7248246	32.813209
16.738379	35.513935
16.7447018	35.612144
16.759245	35.425581
16.8174379	32.387096
16.8318274	35.132314
16.8411981	33.775724
16.8657639	30.18619
16.9045035	31.6292852
16.9060549	32.432955
16.9236652	31.5085818
16.9246185	33.501414
16.9371979	33.538343
16.9424162	32.669786
16.9436611	32.422198
16.954384	31.6210745
17.0048745	33.647301
17.0093275	33.155081
17.0495475	33.4833
17.0627556	33.23776
17.0848806	33.073719
17.0988263	33.650373
17.1219141	32.029351
17.1366616	34.971848
17.1485928	33.236786
17.1559942	31.9274005
17.1784265	31.6347077
17.2007163	35.458792
17.2067843	33.878027
17.2126233	32.627478
17.27771	33.301138
17.339495	37.147247
17.3421613	33.489292
17.3577314	32.460453
17.3724251	32.838472
17.3996649	30.71695
17.4002773	29.6913483
17.4324959	30.5923088
17.4391638	33.273311
17.474673	31.1187519
17.4783074	31.7469574
17.4828174	33.835584
17.5082782	32.96041
17.5131746	31.1271108
17.5226118	35.37399
17.5411115	33.970615
17.5478529	35.084663
17.5593776	33.467168
17.5604301	31.6251063
17.5621066	30.6151074
17.5639823	30.6570201
17.5727543	31.2284542
17.5999234	34.382793
17.6097383	31.6748319
17.6335421	32.659876
17.6346179	34.120215
17.6394592	29.6308142
17.6442026	34.402978
17.6624146	33.518399
17.6824201	34.045062
17.6933922	33.000639
17.6988315	33.716886
17.7180367	33.103054
17.7549039	31.4974324
17.7672336	33.161612
17.7675093	34.396513
17.7926923	33.684571
17.8061956	33.138528
17.8161585	32.744709
17.8442842	31.5564664
17.8517083	33.048076
17.8798602	33.078834
17.894805	32.364323
17.897217	31.2556095
17.9274089	32.295782
17.9540314	32.790538
17.9833606	32.01958
18.010122	31.2967178
18.011117	32.84965
18.02912	33.488563
18.036761	31.4478485
18.037256	33.574155
18.063049	33.151774
18.090068	33.56643
18.094963	33.018778
18.10096	30.8177448
18.122916	32.674656
18.129248	33.158393
18.13386	29.7555909
18.149812	31.2079389
18.152682	35.011784
18.176491	34.079545
18.178535	32.691873
18.183269	33.910638
18.215687	30.8817595
18.235729	30.7945041
18.259081	32.087312
18.264561	32.029223
18.274332	30.8333249
18.330272	31.7431158
18.368219	31.0200739
18.379084	34.194089
18.379872	33.124247
18.385792	33.179336
18.398364	34.606996
18.409342	32.693611
18.437678	32.926307
18.455588	34.845492
18.475238	33.791697
18.508271	31.4602961
18.527687	34.682064
18.536628	32.831121
18.542094	32.563114
18.545504	32.37607
18.547878	32.319933
18.553336	34.569272
18.557667	30.180909
18.582784	34.483361
18.590279	32.149325
18.634664	33.86855
18.639971	36.681327
18.643215	31.5278489
18.650616	33.021068
18.659338	34.160969
18.664585	32.421214
18.67864	28.6651535
18.706178	34.788597
18.743804	33.332319
18.754485	32.600224
18.776854	33.299507
18.788332	33.461131
18.797314	29.9900939
18.812277	31.7877808
18.818708	32.767132
18.825362	32.998126
18.827257	32.411798
18.855049	35.06313
18.895247	30.6882882
18.89677	33.241082
18.90694	31.3827126
18.931778	34.185287
18.935962	32.947167
18.936954	34.25114
18.958115	33.59757
19.026628	34.205066
19.037261	32.780271
19.061187	34.016911
19.097614	33.740769
19.099027	33.797805
19.1145	32.767203
19.131498	32.562132
19.170495	33.460691
19.188678	34.321407
19.191673	30.5556509
19.201821	33.442701
19.237079	30.6778069
19.25179	33.030541
19.270079	34.044006
19.333913	30.3620963
19.35918	32.033823
19.361986	32.354937
19.365055	34.033464
19.365386	31.7474302
19.367864	33.965754
19.432841	34.920374
19.455325	32.293297
19.457648	33.584263
19.482073	31.4698072
19.495794	30.2869317
19.520868	32.981305
19.601644	30.5475595
19.626402	32.237819
19.639048	32.63884
19.664319	33.12114
19.678355	33.234852
19.706041	34.347441
19.946172	33.768173
19.954228	32.480191
20.016013	35.799353
20.025227	33.322595
20.065571	34.06146
20.080158	32.622466
20.260129	32.286994
20.385613	34.526589
20.591116	35.438186
20.774989	34.963467
20.973478	33.539765
21.657494	30.7015363

gauss Data
#
1.20790910	3.72105890
1.22318330	4.62444200
1.27242740	3.72119890
1.39058610	2.85282570
1.52632060	3.19027090
1.54465150	2.90923660
2.08176410	2.29323270
2.13295880	2.79096560
2.20619080	2.55876980
2.22945410	3.70946740
2.23116050	2.18429310
2.37294590	2.61465910
2.45178300	4.09422380
2.46312140	1.97760290
2.46411810	3.99186090
2.50921260	3.20285370
2.52496350	1.88579450
2.54084840	3.09384190
2.55385850	4.13531910
2.58099800	3.17036370
2.62216610	4.05325020
2.66759260	3.50787480
2.66931780	3.02588030
2.67570580	1.53863590
2.68751670	4.39556030
2.81365690	4.25504410
2.83307330	4.75222820
2.83880630	4.80213920
2.83944630	4.19682650
2.85643290	1.77432030
2.86941540	2.24156170
2.88384730	3.88449670
2.91513020	2.51188730
2.91979180	4.40812900
2.95465960	4.20401090
2.97074170	2.43121040
3.01351070	3.94685690
3.04563820	3.69689970
3.06625130	3.90264770
3.07317570	5.06072370
3.09401840	5.38469030
3.09980030	3.01968540
3.10625880	3.86655360
3.11135740	2.74664260
3.14015020	2.32282300
3.16450400	2.37724910
3.16772100	4.49169200
3.17923370	3.19213120
3.18903400	3.92090930
3.19233550	5.02698590
3.24202770	2.68666690
3.25633940	2.63186020
3.26466530	3.36216560
3.27783900	3.59039120
3.28408270	2.39489040
3.31341280	3.74656750
3.34329660	2.30249760
3.35886880	2.63951690
3.37731540	3.50969100
3.39236820	3.77047540
3.39283960	3.93073540
3.40973750	3.02436640
3.42509050	4.29578070
3.42564670	5.19090880
3.42985900	4.63892740
3.43833830	4.59459460
3.44998160	3.34065440
3.46489990	3.76339640
3.51018710	3.09220030
3.58142780	4.47539960
3.58831510	2.20499400
3.60654970	3.73048950
3.61092180	4.41089130
3.68002160	3.65956820
3.68657310	2.87362660
3.73646180	3.11096490
3.78348040	4.44785110
3.80693700	2.21548770
3.85050150	2.74956110
3.88946560	3.58457720
3.89807100	4.44213340
3.91916380	4.13792570
3.92325950	2.36911640
3.92630710	2.34994830
3.93368750	3.54306570
3.95610750	2.01434500
3.95678700	2.29137210
3.95973750	3.92336320
3.96472580	3.55419380
4.00146640	1.59593080
4.09676130	3.34609180
4.11136190	4.95612900
4.13931330	3.12903690
4.24274510	4.32936230
4.24473240	2.40552780
4.28089580	4.18497110
4.29582480	4.52221000
4.33338810	4.89267550
4.37706670	4.14826300
4.38728110	3.08349770
4.39438060	1.20470410
4.42311330	5.14285450
4.42424630	4.74370290
4.47371880	5.19608090
4.54348320	2.95401650
4.55423010	4.65144570
4.55661300	3.67922140
4.55693430	4.35570380
4.56818780	2.73349690
4.61544290	4.99191540
4.65732870	3.63231810
4.75235850	4.67648360
4.75303290	1.69703220
4.88558470	3.10924050
4.92029430	3.68786210
5.06929180	4.46140430
5.09425100	4.99866570
5.22136030	2.88596540
5.29025000	4.12315510
5.30474090	4.05297750
5.38075760	2.88971840
5.40225010	4.21574110
5.52586830	4.60779350
5.56029020	4.02022760
5.59284930	5.94734880
5.63379210	2.04773990
4.56496330	8.81990280
4.94924760	9.24178310
4.96398430	8.13018410
5.22679600	7.08964560
5.51604770	8.33961800
5.54949660	9.31870340
5.73213920	7.30918760
5.78872000	7.96719220
5.80742230	8.16041300
5.80772180	9.28296680
5.84010640	8.43407780
5.88878260	9.18261600
5.93854940	7.08269680
6.01732790	10.01593400
6.13331750	9.06412020
6.14938460	5.58131310
6.32476160	8.50081580
6.35965520	8.43000210
6.44987570	8.46609640
6.50398860	9.12919810
6.55778340	6.13529350
6.62439690	7.18625970
6.66533610	7.89935120
6.67529210	7.25224350
6.69788670	7.91823120
6.70500830	10.97463400
6.74736090	7.24850780
6.74938060	7.51299370
6.78877990	7.85880770
6.83635100	7.21746830
6.87264510	8.08347420
6.89035080	6.72500990
6.89074810	8.72978990
6.89556320	7.21410970
6.91331220	10.33794100
6.98172740	7.63455400
6.99855530	6.02362240
7.09174110	7.55206390
7.11107720	8.55369310
7.13839200	7.24159980
7.16242050	7.74177850
7.16753040	8.34244030
7.19596080	7.67605230
7.19600340	8.48065380
7.20138350	9.69803770
7.21410180	8.68375570
7.23488380	6.43974970
7.24224830	7.34937430
7.24308230	7.76448210
7.26136760	8.86718760
7.29467750	7.15935710
7.33502880	8.46004700
7.33583100	9.45610450
7.34010160	9.13983420
7.37391630	7.92165490
7.43575000	7.36740770
7.47351160	7.36219690
7.50943180	8.74251930
7.51927650	7.09198750
7.52303360	7.31811160
7.53891530	8.37066250
7.56902300	9.69571210
7.68146310	7.04343990
7.71449540	5.19183500
7.71988930	9.08293360
7.78037680	7.88613490
7.93262710	8.18044940
7.97323520	7.99187600
7.98495340	7.88536950
8.11242610	7.37063310
8.11544250	8.90069810
8.12542490	6.09283890
8.27950630	8.55703860
8.29758040	7.17683620
8.38008940	7.23224310
9.14977290	8.41239770
0.14794639	9.97961100
0.87296376	12.25967700
0.95057589	10.25391200
1.17749740	10.07844500
1.22125760	11.18073600
1.33193920	12.25034600
1.34172490	10.92280400
1.36668230	11.05772600
1.43430450	10.84407300
1.46370020	11.50454500
1.57309490	12.12008300
1.69603260	10.71967200
1.71769480	10.68105200
1.73597100	12.28138600
1.79071270	10.97072200
1.89341280	12.57715300
1.90672830	11.33818500
1.96209270	12.09142100
1.99673980	12.70860600
2.00474310	11.37161400
2.04421500	10.68913300
2.08722420	12.30532500
2.08879850	11.69753500
2.20018500	12.66379400
2.20085780	10.88587900
2.25572220	10.88979800
2.28927610	9.60072350
2.30283070	10.63739200
2.34192850	10.22409200
2.34228770	10.20432700
2.35837570	12.04876100
2.38938590	11.91840600
2.41693470	12.29231400
2.45767500	11.09708100
2.45786590	11.97433900
2.47888920	12.15292500
2.50464770	12.38179100
2.50590350	12.73737100
2.56210210	11.85764700
2.67207820	9.41642710
2.69590010	12.99519600
2.69928480	11.47312700
2.70662150	10.62388600
2.72956480	11.27896300
2.74608850	11.57901500
2.86995370	13.05209000
2.87165180	13.41363400
2.88735720	12.96273800
2.90458980	12.71584900
2.94569430	11.24886600
2.97652460	12.88339300
2.98191090	13.26836000
2.99856800	12.24334200
3.04330960	13.34705800
3.05960440	13.05621500
3.07944220	11.75840700
3.11295220	11.22429300
3.14139420	11.92967100
3.16274410	13.15897700
3.17671860	11.18468300
3.17819640	12.23026600
3.24316480	12.96259800
3.26183990	11.53375400
3.38296950	11.58900800
3.39006150	12.29883300
3.40722920	11.45213100
3.46309720	13.51363000
3.48651150	11.16625900
3.48980800	11.30236500
3.49857810	11.17645400
3.49956030	12.33953700
3.50158880	12.54662300
3.51269070	13.18131800
3.51868070	11.24392300
3.52004940	12.60635900
3.55460590	12.52347500
3.55935130	13.20819000
3.62011300	13.04117400
3.75619870	12.16167200
3.77773560	11.77477900
3.79934290	12.86226800
3.81080360	13.17230500
3.81384880	14.48241200
3.97179070	12.81386100
3.99469290	13.56421500
4.00593370	12.62987100
4.01141640	12.94734500
4.03763150	11.08670200
4.13606590	11.97887100
4.19763770	13.45016000
4.37603220	11.31880000
4.42905310	12.54053600
4.44971780	12.76788900
4.45278030	12.62002400
4.63880080	12.74506600
4.64736980	12.45052900
4.75663770	13.14369800
4.79004530	12.38207400
4.87588930	12.90501000
4.90238840	12.81827300
4.97880720	11.95991400
5.03241930	12.25283300
5.34790270	11.87561800
8.46521070	2.68311350
8.52442550	1.11853270
8.67661500	2.13651330
8.70603570	4.88009440
9.05078220	4.93069960
9.09261750	5.54696160
9.16203610	3.43624720
9.52865900	4.96536840
9.68202000	4.93236340
9.81131050	5.04474820
9.83952930	3.28463710
9.89044360	3.97566880
9.90190200	4.05961210
10.00957000	3.83878480
10.04175700	3.29727220
10.07012400	3.59750180
10.12569800	5.21450270
10.13737000	5.06962500
10.17977800	2.71180740
10.30071400	2.81169900
10.30258900	4.68952940
10.31004400	3.39047430
10.36094800	4.04840500
10.38777600	2.65069700
10.40494800	2.36508410
10.41509700	4.01420430
10.41704700	3.46878260
10.44274500	5.03026400
10.44372100	4.14097060
10.44794100	5.89888690
10.47658200	3.74385900
10.51515800	5.10607600
10.59575200	3.86267340
10.61560200	3.20222610
10.61961000	5.76297450
10.63234700	3.37483350
10.65750300	3.27475530
10.70975800	5.02876970
10.74794400	5.01521760
10.75237100	4.71894070
10.81876900	3.78021970
10.82088800	3.12724520
10.83051500	5.60152800
10.86365600	2.65339460
10.88482000	5.26577010
10.90414800	4.89656370
10.93918700	3.15856750
10.95262800	2.27413580
10.97361300	4.86119440
10.97606000	2.95091260
11.00025100	4.87971710
11.00443500	2.87298020
11.00487400	4.99616980
11.00894900	4.45823450
11.02545500	4.38707080
11.04502200	4.99164180
11.09629500	2.97304400
11.19527800	2.96275470
11.21192000	4.38513520
11.21560300	4.15389300
11.36223500	2.94083170
11.48733800	3.45545070
11.50706100	4.90595590
11.54495400	3.07483510
11.56112500	3.31124010
11.57547800	3.28234880
11.61449000	4.98729330
11.61803700	3.91622200
11.63065000	4.32804290
11.66666800	4.04761060
11.69318900	4.54265920
11.71092000	2.57448550
11.71747900	3.53853580
11.72360300	4.82693130
11.74095600	4.53624170
11.81034200	3.33452410
11.88485200	5.29660640
11.88918000	2.40882330
11.90812400	4.46929530
11.92066900	5.10469960
12.06688200	4.24435140
12.11959600	3.81954270
12.13463800	4.05018190
12.15121000	5.09391660
12.17471200	1.77235590
12.23597000	5.82360270
12.27071900	3.93579920
12.41840200	5.01047690
12.42309100	4.65551600
12.58717000	5.48074750
12.60821600	5.59588540
12.62136500	5.10096210
12.63983700	5.78542180
12.67532200	3.08836940
12.88580600	4.53693810
13.12371200	3.13710230
13.31430800	4.26492890
8.12719180	13.29639700
9.04106330	13.24608600
9.07657040	12.28166900
9.15794410	12.82111200
9.20492930	12.13289400
9.25094630	11.36860000
9.29959740	13.31582100
9.44880220	11.79787900
9.47187580	13.40053700
9.60374990	13.16798100
9.62998540	10.91674400
9.63856090	13.13439400
9.72228300	10.34876500
9.87687880	10.79534100
9.88051540	11.42727800
9.92456650	12.17199200
9.94197530	10.11674000
9.94583880	11.57250200
9.99630910	11.91016200
9.99776370	10.26438300
10.12010800	12.29802800
10.16164200	10.23556100
10.25961900	12.53857900
10.27886500	11.90784800
10.28485100	12.78127000
10.30212600	11.55107700
10.41908200	11.56086200
10.45122900	12.87717000
10.48102200	12.63129500
10.48728900	11.29968300
10.57084400	11.07369500
10.58737300	12.50664700
10.62594400	11.49938600
10.64176100	11.15718600
10.64511000	10.94285200
10.66789800	13.98203800
10.67048500	11.95564100
10.70635600	11.36171500
10.71106100	12.23674400
10.77142800	10.76609500
10.77560700	14.05258700
10.88680500	11.02355400
10.92819600	9.22391410
10.95017900	12.18759200
10.96660900	11.02039600
10.98409700	11.00589000
10.99799700	12.66918500
11.08956900	12.39886800
11.10849400	11.91033400
11.14779800	11.72434200
11.15511500	14.12141600
11.15875800	12.71864300
11.21317900	10.73132600
11.22268900	11.89486600
11.23743900	12.72826900
11.24774200	13.20666200
11.25684200	12.44251400
11.26917800	11.62872800
11.35049800	11.56381600
11.46988300	12.18857100
11.49420000	12.38967000
11.52686000	10.77942800
11.55350500	12.53957200
11.55941800	13.57071200
11.56770700	12.90194800
11.63469200	10.07494800
11.63483700	12.23668400
11.64180700	12.72672800
11.67708900	12.33253800
11.76721500	13.09220900
11.78101600	10.59577900
11.87888600	12.32909800
11.89113700	11.18777000
11.89519300	9.65998290
11.94527300	11.15652100
11.94926100	10.94432800
11.99813800	11.18681500
12.01272000	12.27014800
12.02110800	13.49550800
12.02660200	11.86160900
12.05616400	11.72890500
12.06579400	11.84558600
12.08769700	13.52384800
12.10513600	11.63889300
12.19760400	12.02264500
12.29602500	13.50911500
12.34908800	12.00290200
12.37035300	14.05121500
12.42581800	10.55447000
12.46564200	10.74513400
12.47088300	11.28020800
12.47389700	11.42576900
12.51639400	11.22290100
12.64667100	11.88991900
12.65157200	13.06442800
12.88839300	10.70949500
13.07564100	12.57658800
14.00985600	12.54814700

1	1.2079091	3.7210589
1	1.2231833	4.624442
1	1.2724274	3.7211989
1	1.3905861	2.8528257
1	1.5263206	3.1902709
1	1.5446515	2.9092366
1	2.0817641	2.2932327
1	2.1329588	2.7909656
1	2.2061908	2.5587698
1	2.2294541	3.7094674
1	2.2311605	2.1842931
1	2.3729459	2.6146591
1	2.451783	4.0942238
1	2.4631214	1.9776029
1	2.4641181	3.9918609
1	2.5092126	3.2028537
1	2.5249635	1.8857945
1	2.5408484	3.0938419
1	2.5538585	4.1353191
1	2.580998	3.1703637
1	2.6221661	4.0532502
1	2.6675926	3.5078748
1	2.6693178	3.0258803
1	2.6757058	1.5386359
1	2.6875167	4.3955603
1	2.8136569	4.2550441
1	2.8330733	4.7522282
1	2.8388063	4.8021392
1	2.8394463	4.1968265
1	2.8564329	1.7743203
1	2.8694154	2.2415617
1	2.8838473	3.8844967
1	2.9151302	2.5118873
1	2.9197918	4.408129
1	2.9546596	4.2040109
1	2.9707417	2.4312104
1	3.0135107	3.9468569
1	3.0456382	3.6968997
1	3.0662513	3.9026477
1	3.0731757	5.0607237
1	3.0940184	5.3846903
1	3.0998003	3.0196854
1	3.1062588	3.8665536
1	3.1113574	2.7466426
1	3.1401502	2.322823
1	3.164504	2.3772491
1	3.167721	4.491692
1	3.1792337	3.1921312
1	3.189034	3.9209093
1	3.1923355	5.0269859
1	3.2420277	2.6866669
1	3.2563394	2.6318602
1	3.2646653	3.3621656
1	3.277839	3.5903912
1	3.2840827	2.3948904
1	3.3134128	3.7465675
1	3.3432966	2.3024976
1	3.3588688	2.6395169
1	3.3773154	3.509691
1	3.3923682	3.7704754
1	3.3928396	3.9307354
1	3.4097375	3.0243664
1	3.4250905	4.2957807
1	3.4256467	5.1909088
1	3.429859	4.6389274
1	3.4383383	4.5945946
1	3.4499816	3.3406544
1	3.4648999	3.7633964
1	3.5101871	3.0922003
1	3.5814278	4.4753996
1	3.5883151	2.204994
1	3.6065497	3.7304895
1	3.6109218	4.4108913
1	3.6800216	3.6595682
1	3.6865731	2.8736266
1	3.7364618	3.1109649
1	3.7834804	4.4478511
1	3.806937	2.2154877
1	3.8505015	2.7495611
1	3.8894656	3.5845772
1	3.898071	4.4421334
1	3.9191638	4.1379257
1	3.9232595	2.3691164
1	3.9263071	2.3499483
1	3.9336875	3.5430657
1	3.9561075	2.014345
1	3.956787	2.2913721
1	3.9597375	3.9233632
1	3.9647258	3.5541938
1	4.0014664	1.5959308
1	4.0967613	3.3460918
1	4.1113619	4.956129
1	4.1393133	3.1290369
1	4.2427451	4.3293623
1	4.2447324	2.4055278
1	4.2808958	4.1849711
1	4.2958248	4.52221
1	4.3333881	4.8926755
1	4.3770667	4.148263
1	4.3872811	3.0834977
1	4.3943806	1.2047041
1	4.4231133	5.1428545
1	4.4242463	4.7437029
1	4.4737188	5.1960809
1	4.5434832	2.9540165
1	4.5542301	4.6514457
1	4.556613	3.6792214
1	4.5569343	4.3557038
1	4.5681878	2.7334969
1	4.6154429	4.9919154
1	4.6573287	3.6323181
1	4.7523585	4.6764836
1	4.7530329	1.6970322
1	4.8855847	3.1092405
1	4.9202943	3.6878621
1	5.0692918	4.4614043
1	5.094251	4.9986657
1	5.2213603	2.8859654
1	5.29025	4.1231551
1	5.3047409	4.0529775
1	5.3807576	2.8897184
1	5.4022501	4.2157411
1	5.5258683	4.6077935
1	5.5602902	4.0202276
1	5.5928493	5.9473488
1	5.6337921	2.0477399
2	4.5649633	8.8199028
2	4.9492476	9.2417831
2	4.9639843	8.1301841
2	5.226796	7.0896456
2	5.5160477	8.339618
2	5.5494966	9.3187034
2	5.7321392	7.3091876
2	5.78872	7.9671922
2	5.8074223	8.160413
2	5.8077218	9.2829668
2	5.8401064	8.4340778
2	5.8887826	9.182616
2	5.9385494	7.0826968
2	6.0173279	10.015934
2	6.1333175	9.0641202
2	6.1493846	5.5813131
2	6.3247616	8.5008158
2	6.3596552	8.4300021
2	6.4498757	8.4660964
2	6.5039886	9.1291981
2	6.5577834	6.1352935
2	6.6243969	7.1862597
2	6.6653361	7.8993512
2	6.6752921	7.2522435
2	6.6978867	7.9182312
2	6.7050083	10.974634
2	6.7473609	7.2485078
2	6.7493806	7.5129937
2	6.7887799	7.8588077
2	6.836351	7.2174683
2	6.8726451	8.0834742
2	6.8903508	6.7250099
2	6.8907481	8.7297899
2	6.8955632	7.2141097
2	6.9133122	10.337941
2	6.9817274	7.634554
2	6.9985553	6.0236224
2	7.0917411	7.5520639
2	7.1110772	8.5536931
2	7.138392	7.2415998
2	7.1624205	7.7417785
2	7.1675304	8.3424403
2	7.1959608	7.6760523
2	7.1960034	8.4806538
2	7.2013835	9.6980377
2	7.2141018	8.6837557
2	7.2348838	6.4397497
2	7.2422483	7.3493743
2	7.2430823	7.7644821
2	7.2613676	8.8671876
2	7.2946775	7.1593571
2	7.3350288	8.460047
2	7.335831	9.4561045
2	7.3401016	9.1398342
2	7.3739163	7.9216549
2	7.43575	7.3674077
2	7.4735116	7.3621969
2	7.5094318	8.7425193
2	7.5192765	7.0919875
2	7.5230336	7.3181116
2	7.5389153	8.3706625
2	7.569023	9.6957121
2	7.6814631	7.0434399
2	7.7144954	5.191835
2	7.7198893	9.0829336
2	7.7803768	7.8861349
2	7.9326271	8.1804494
2	7.9732352	7.991876
2	7.9849534	7.8853695
2	8.1124261	7.3706331
2	8.1154425	8.9006981
2	8.1254249	6.0928389
2	8.2795063	8.5570386
2	8.2975804	7.1768362
2	8.3800894	7.2322431
2	9.1497729	8.4123977
3	0.14794639	9.979611
3	0.87296376	12.259677
3	0.95057589	10.253912
3	1.1774974	10.078445
3	1.2212576	11.180736
3	1.3319392	12.250346
3	1.3417249	10.922804
3	1.3666823	11.057726
3	1.4343045	10.844073
3	1.4637002	11.504545
3	1.5730949	12.120083
3	1.6960326	10.719672
3	1.7176948	10.681052
3	1.735971	12.281386
3	1.7907127	10.970722
3	1.8934128	12.577153
3	1.9067283	11.338185
3	1.9620927	12.091421
3	1.9967398	12.708606
3	2.0047431	11.371614
3	2.044215	10.689133
3	2.0872242	12.305325
3	2.0887985	11.697535
3	2.200185	12.663794
3	2.2008578	10.885879
3	2.2557222	10.889798
3	2.2892761	9.6007235
3	2.3028307	10.637392
3	2.3419285	10.224092
3	2.3422877	10.204327
3	2.3583757	12.048761
3	2.3893859	11.918406
3	2.4169347	12.292314
3	2.457675	11.097081
3	2.4578659	11.974339
3	2.4788892	12.152925
3	2.5046477	12.381791
3	2.5059035	12.737371
3	2.5621021	11.857647
3	2.6720782	9.4164271
3	2.6959001	12.995196
3	2.6992848	11.473127
3	2.7066215	10.623886
3	2.7295648	11.278963
3	2.7460885	11.579015
3	2.8699537	13.05209
3	2.8716518	13.413634
3	2.8873572	12.962738
3	2.9045898	12.715849
3	2.9456943	11.248866
3	2.9765246	12.883393
3	2.9819109	13.26836
3	2.998568	12.243342
3	3.0433096	13.347058
3	3.0596044	13.056215
3	3.0794422	11.758407
3	3.1129522	11.224293
3	3.1413942	11.929671
3	3.1627441	13.158977
3	3.1767186	11.184683
3	3.1781964	12.230266
3	3.2431648	12.962598
3	3.2618399	11.533754
3	3.3829695	11.589008
3	3.3900615	12.298833
3	3.4072292	11.452131
3	3.4630972	13.51363
3	3.4865115	11.166259
3	3.489808	11.302365
3	3.4985781	11.176454
3	3.4995603	12.339537
3	3.5015888	12.546623
3	3.5126907	13.181318
3	3.5186807	11.243923
3	3.5200494	12.606359
3	3.5546059	12.523475
3	3.5593513	13.20819
3	3.620113	13.041174
3	3.7561987	12.161672
3	3.7777356	11.774779
3	3.7993429	12.862268
3	3.8108036	13.172305
3	3.8138488	14.482412
3	3.9717907	12.813861
3	3.9946929	13.564215
3	4.0059337	12.629871
3	4.0114164	12.947345
3	4.0376315	11.086702
3	4.1360659	11.978871
3	4.1976377	13.45016
3	4.3760322	11.3188
3	4.4290531	12.540536
3	4.4497178	12.767889
3	4.4527803	12.620024
3	4.6388008	12.745066
3	4.6473698	12.450529
3	4.7566377	13.143698
3	4.7900453	12.382074
3	4.8758893	12.90501
3	4.9023884	12.818273
3	4.9788072	11.959914
3	5.0324193	12.252833
3	5.3479027	11.875618
4	8.4652107	2.6831135
4	8.5244255	1.1185327
4	8.676615	2.1365133
4	8.7060357	4.8800944
4	9.0507822	4.9306996
4	9.0926175	5.5469616
4	9.1620361	3.4362472
4	9.528659	4.9653684
4	9.68202	4.9323634
4	9.8113105	5.0447482
4	9.8395293	3.2846371
4	9.8904436	3.9756688
4	9.901902	4.0596121
4	10.00957	3.8387848
4	10.041757	3.2972722
4	10.070124	3.5975018
4	10.125698	5.2145027
4	10.13737	5.069625
4	10.179778	2.7118074
4	10.300714	2.811699
4	10.302589	4.6895294
4	10.310044	3.3904743
4	10.360948	4.048405
4	10.387776	2.650697
4	10.404948	2.3650841
4	10.415097	4.0142043
4	10.417047	3.4687826
4	10.442745	5.030264
4	10.443721	4.1409706
4	10.447941	5.8988869
4	10.476582	3.743859
4	10.515158	5.106076
4	10.595752	3.8626734
4	10.615602	3.2022261
4	10.61961	5.7629745
4	10.632347	3.3748335
4	10.657503	3.2747553
4	10.709758	5.0287697
4	10.747944	5.0152176
4	10.752371	4.7189407
4	10.818769	3.7802197
4	10.820888	3.1272452
4	10.830515	5.601528
4	10.863656	2.6533946
4	10.88482	5.2657701
4	10.904148	4.8965637
4	10.939187	3.1585675
4	10.952628	2.2741358
4	10.973613	4.8611944
4	10.97606	2.9509126
4	11.000251	4.8797171
4	11.004435	2.8729802
4	11.004874	4.9961698
4	11.008949	4.4582345
4	11.025455	4.3870708
4	11.045022	4.9916418
4	11.096295	2.973044
4	11.195278	2.9627547
4	11.21192	4.3851352
4	11.215603	4.153893
4	11.362235	2.9408317
4	11.487338	3.4554507
4	11.507061	4.9059559
4	11.544954	3.0748351
4	11.561125	3.3112401
4	11.575478	3.2823488
4	11.61449	4.9872933
4	11.618037	3.916222
4	11.63065	4.3280429
4	11.666668	4.0476106
4	11.693189	4.5426592
4	11.71092	2.5744855
4	11.717479	3.5385358
4	11.723603	4.8269313
4	11.740956	4.5362417
4	11.810342	3.3345241
4	11.884852	5.2966064
4	11.88918	2.4088233
4	11.908124	4.4692953
4	11.920669	5.1046996
4	12.066882	4.2443514
4	12.119596	3.8195427
4	12.134638	4.0501819
4	12.15121	5.0939166
4	12.174712	1.7723559
4	12.23597	5.8236027
4	12.270719	3.9357992
4	12.418402	5.0104769
4	12.423091	4.655516
4	12.58717	5.4807475
4	12.608216	5.5958854
4	12.621365	5.1009621
4	12.639837	5.7854218
4	12.675322	3.0883694
4	12.885806	4.5369381
4	13.123712	3.1371023
4	13.314308	4.2649289
5	8.1271918	13.296397
5	9.0410633	13.246086
5	9.0765704	12.281669
5	9.1579441	12.821112
5	9.2049293	12.132894
5	9.2509463	11.3686
5	9.2995974	13.315821
5	9.4488022	11.797879
5	9.4718758	13.400537
5	9.6037499	13.167981
5	9.6299854	10.916744
5	9.6385609	13.134394
5	9.722283	10.348765
5	9.8768788	10.795341
5	9.8805154	11.427278
5	9.9245665	12.171992
5	9.9419753	10.11674
5	9.9458388	11.572502
5	9.9963091	11.910162
5	9.9977637	10.264383
5	10.120108	12.298028
5	10.161642	10.235561
5	10.259619	12.538579
5	10.278865	11.907848
5	10.284851	12.78127
5	10.302126	11.551077
5	10.419082	11.560862
5	10.451229	12.87717
5	10.481022	12.631295
5	10.487289	11.299683
5	10.570844	11.073695
5	10.587373	12.506647
5	10.625944	11.499386
5	10.641761	11.157186
5	10.64511	10.942852
5	10.667898	13.982038
5	10.670485	11.955641
5	10.706356	11.361715
5	10.711061	12.236744
5	10.771428	10.766095
5	10.775607	14.052587
5	10.886805	11.023554
5	10.928196	9.2239141
5	10.950179	12.187592
5	10.966609	11.020396
5	10.984097	11.00589
5	10.997997	12.669185
5	11.089569	12.398868
5	11.108494	11.910334
5	11.147798	11.724342
5	11.155115	14.121416
5	11.158758	12.718643
5	11.213179	10.731326
5	11.222689	11.894866
5	11.237439	12.728269
5	11.247742	13.206662
5	11.256842	12.442514
5	11.269178	11.628728
5	11.350498	11.563816
5	11.469883	12.188571
5	11.4942	12.38967
5	11.52686	10.779428
5	11.553505	12.539572
5	11.559418	13.570712
5	11.567707	12.901948
5	11.634692	10.074948
5	11.634837	12.236684
5	11.641807	12.726728
5	11.677089	12.332538
5	11.767215	13.092209
5	11.781016	10.595779
5	11.878886	12.329098
5	11.891137	11.18777
5	11.895193	9.6599829
5	11.945273	11.156521
5	11.949261	10.944328
5	11.998138	11.186815
5	12.01272	12.270148
5	12.021108	13.495508
5	12.026602	11.861609
5	12.056164	11.728905
5	12.065794	11.845586
5	12.087697	13.523848
5	12.105136	11.638893
5	12.197604	12.022645
5	12.296025	13.509115
5	12.349088	12.002902
5	12.370353	14.051215
5	12.425818	10.55447
5	12.465642	10.745134
5	12.470883	11.280208
5	12.473897	11.425769
5	12.516394	11.222901
5	12.646671	11.889919
5	12.651572	13.064428
5	12.888393	10.709495
5	13.075641	12.576588
5	14.009856	12.548147

gauss Data
#
1.2079091	5.7210589
1.2231833	6.624442
1.2724274	5.7211989
1.3905861	4.8528257
1.5263206	5.1902709
1.5446515	4.9092366
2.0817641	4.2932327
2.1329588	4.7909656
2.2061908	4.5587698
2.2294541	5.7094674
2.2311605	4.1842931
2.3729459	4.6146591
2.451783	6.0942238
2.4631214	3.9776029
2.4641181	5.9918609
2.5092126	5.2028537
2.5249635	3.8857945
2.5408484	5.0938419
2.5538585	6.1353191
2.580998	5.1703637
2.6221661	6.0532502
2.6675926	5.5078748
2.6693178	5.0258803
2.6757058	3.5386359
2.6875167	6.3955603
2.8136569	6.2550441
2.8330733	6.7522282
2.8388063	6.8021392
2.8394463	6.1968265
2.8564329	3.7743203
2.8694154	4.2415617
2.8838473	5.8844967
2.9151302	4.5118873
2.9197918	6.408129
2.9546596	6.2040109
2.9707417	4.4312104
3.0135107	5.9468569
3.0456382	5.6968997
3.0662513	5.9026477
3.0731757	7.0607237
3.0940184	7.3846903
3.0998003	5.0196854
3.1062588	5.8665536
3.1113574	4.7466426
3.1401502	4.322823
3.164504	4.3772491
3.167721	6.491692
3.1792337	5.1921312
3.189034	5.9209093
3.1923355	7.0269859
3.2420277	4.6866669
3.2563394	4.6318602
3.2646653	5.3621656
3.277839	5.5903912
3.2840827	4.3948904
3.3134128	5.7465675
3.3432966	4.3024976
3.3588688	4.6395169
3.3773154	5.509691
3.3923682	5.7704754
3.3928396	5.9307354
3.4097375	5.0243664
3.4250905	6.2957807
3.4256467	7.1909088
3.429859	6.6389274
3.4383383	6.5945946
3.4499816	5.3406544
3.4648999	5.7633964
3.5101871	5.0922003
3.5814278	6.4753996
3.5883151	4.204994
3.6065497	5.7304895
3.6109218	6.4108913
3.6800216	5.6595682
3.6865731	4.8736266
3.7364618	5.1109649
3.7834804	6.4478511
3.806937	4.2154877
3.8505015	4.7495611
3.8894656	5.5845772
3.898071	6.4421334
3.9191638	6.1379257
3.9232595	4.3691164
3.9263071	4.3499483
3.9336875	5.5430657
3.9561075	4.014345
3.956787	4.2913721
3.9597375	5.9233632
3.9647258	5.5541938
4.0014664	3.5959308
4.0967613	5.3460918
4.1113619	6.956129
4.1393133	5.1290369
4.2427451	6.3293623
4.2447324	4.4055278
4.2808958	6.1849711
4.2958248	6.52221
4.3333881	6.8926755
4.3770667	6.148263
4.3872811	5.0834977
4.3943806	3.2047041
4.4231133	7.1428545
4.4242463	6.7437029
4.4737188	7.1960809
4.5434832	4.9540165
4.5542301	6.6514457
4.556613	5.6792214
4.5569343	6.3557038
4.5681878	4.7334969
4.6154429	6.9919154
4.6573287	5.6323181
4.7523585	6.6764836
4.7530329	3.6970322
4.8855847	5.1092405
4.9202943	5.6878621
5.0692918	6.4614043
5.094251	6.9986657
5.2213603	4.8859654
5.29025	6.1231551
5.3047409	6.0529775
5.3807576	4.8897184
5.4022501	6.2157411
5.5258683	6.6077935
5.5602902	6.0202276
5.5928493	7.9473488
5.6337921	4.0477399
9.5649633	15.8199028
9.9492476	16.2417831
9.9639843	15.1301841
10.226796	14.0896456
10.5160477	15.339618
10.5494966	16.3187034
10.7321392	14.3091876
10.78872	14.9671922
10.8074223	15.160413
10.8077218	16.2829668
10.8401064	15.4340778
10.8887826	16.182616
10.9385494	14.0826968
11.0173279	17.015934
11.1333175	16.0641202
11.1493846	12.5813131
11.3247616	15.5008158
11.3596552	15.4300021
11.4498757	15.4660964
11.5039886	16.1291981
11.5577834	13.1352935
11.6243969	14.1862597
11.6653361	14.8993512
11.6752921	14.2522435
11.6978867	14.9182312
11.7050083	17.974634
11.7473609	14.2485078
11.7493806	14.5129937
11.7887799	14.8588077
11.836351	14.2174683
11.8726451	15.0834742
11.8903508	13.7250099
11.8907481	15.7297899
11.8955632	14.2141097
11.9133122	17.337941
11.9817274	14.634554
11.9985553	13.0236224
12.0917411	14.5520639
12.1110772	15.5536931
12.138392	14.2415998
12.1624205	14.7417785
12.1675304	15.3424403
12.1959608	14.6760523
12.1960034	15.4806538
12.2013835	16.6980377
12.2141018	15.6837557
12.2348838	13.4397497
12.2422483	14.3493743
12.2430823	14.7644821
12.2613676	15.8671876
12.2946775	14.1593571
12.3350288	15.460047
12.335831	16.4561045
12.3401016	16.1398342
12.3739163	14.9216549
12.43575	14.3674077
12.4735116	14.3621969
12.5094318	15.7425193
12.5192765	14.0919875
12.5230336	14.3181116
12.5389153	15.3706625
12.569023	16.6957121
12.6814631	14.0434399
12.7144954	12.191835
12.7198893	16.0829336
12.7803768	14.8861349
12.9326271	15.1804494
12.9732352	14.991876
12.9849534	14.8853695
13.1124261	14.3706331
13.1154425	15.9006981
13.1254249	13.0928389
13.2795063	15.5570386
13.2975804	14.1768362
13.3800894	14.2322431
14.1497729	15.4123977
1.14794639	18.979611
1.87296376	21.259677
1.95057589	19.253912
2.1774974	19.078445
2.2212576	20.180736
2.3319392	21.250346
2.3417249	19.922804
2.3666823	20.057726
2.4343045	19.844073
2.4637002	20.504545
2.5730949	21.120083
2.6960326	19.719672
2.7176948	19.681052
2.735971	21.281386
2.7907127	19.970722
2.8934128	21.577153
2.9067283	20.338185
2.9620927	21.091421
2.9967398	21.708606
3.0047431	20.371614
3.044215	19.689133
3.0872242	21.305325
3.0887985	20.697535
3.200185	21.663794
3.2008578	19.885879
3.2557222	19.889798
3.2892761	18.6007235
3.3028307	19.637392
3.3419285	19.224092
3.3422877	19.204327
3.3583757	21.048761
3.3893859	20.918406
3.4169347	21.292314
3.457675	20.097081
3.4578659	20.974339
3.4788892	21.152925
3.5046477	21.381791
3.5059035	21.737371
3.5621021	20.857647
3.6720782	18.4164271
3.6959001	21.995196
3.6992848	20.473127
3.7066215	19.623886
3.7295648	20.278963
3.7460885	20.579015
3.8699537	22.05209
3.8716518	22.413634
3.8873572	21.962738
3.9045898	21.715849
3.9456943	20.248866
3.9765246	21.883393
3.9819109	22.26836
3.998568	21.243342
4.0433096	22.347058
4.0596044	22.056215
4.0794422	20.758407
4.1129522	20.224293
4.1413942	20.929671
4.1627441	22.158977
4.1767186	20.184683
4.1781964	21.230266
4.2431648	21.962598
4.2618399	20.533754
4.3829695	20.589008
4.3900615	21.298833
4.4072292	20.452131
4.4630972	22.51363
4.4865115	20.166259
4.489808	20.302365
4.4985781	20.176454
4.4995603	21.339537
4.5015888	21.546623
4.5126907	22.181318
4.5186807	20.243923
4.5200494	21.606359
4.5546059	21.523475
4.5593513	22.20819
4.620113	22.041174
4.7561987	21.161672
4.7777356	20.774779
4.7993429	21.862268
4.8108036	22.172305
4.8138488	23.482412
4.9717907	21.813861
4.9946929	22.564215
5.0059337	21.629871
5.0114164	21.947345
5.0376315	20.086702
5.1360659	20.978871
5.1976377	22.45016
5.3760322	20.3188
5.4290531	21.540536
5.4497178	21.767889
5.4527803	21.620024
5.6388008	21.745066
5.6473698	21.450529
5.7566377	22.143698
5.7900453	21.382074
5.8758893	21.90501
5.9023884	21.818273
5.9788072	20.959914
6.0324193	21.252833
6.3479027	20.875618
10.4652107	2.6831135
10.5244255	1.1185327
10.676615	2.1365133
10.7060357	4.8800944
11.0507822	4.9306996
11.0926175	5.5469616
11.1620361	3.4362472
11.528659	4.9653684
11.68202	4.9323634
11.8113105	5.0447482
11.8395293	3.2846371
11.8904436	3.9756688
11.901902	4.0596121
12.00957	3.8387848
12.041757	3.2972722
12.070124	3.5975018
12.125698	5.2145027
12.13737	5.069625
12.179778	2.7118074
12.300714	2.811699
12.302589	4.6895294
12.310044	3.3904743
12.360948	4.048405
12.387776	2.650697
12.404948	2.3650841
12.415097	4.0142043
12.417047	3.4687826
12.442745	5.030264
12.443721	4.1409706
12.447941	5.8988869
12.476582	3.743859
12.515158	5.106076
12.595752	3.8626734
12.615602	3.2022261
12.61961	5.7629745
12.632347	3.3748335
12.657503	3.2747553
12.709758	5.0287697
12.747944	5.0152176
12.752371	4.7189407
12.818769	3.7802197
12.820888	3.1272452
12.830515	5.601528
12.863656	2.6533946
12.88482	5.2657701
12.904148	4.8965637
12.939187	3.1585675
12.952628	2.2741358
12.973613	4.8611944
12.97606	2.9509126
13.000251	4.8797171
13.004435	2.8729802
13.004874	4.9961698
13.008949	4.4582345
13.025455	4.3870708
13.045022	4.9916418
13.096295	2.973044
13.195278	2.9627547
13.21192	4.3851352
13.215603	4.153893
13.362235	2.9408317
13.487338	3.4554507
13.507061	4.9059559
13.544954	3.0748351
13.561125	3.3112401
13.575478	3.2823488
13.61449	4.9872933
13.618037	3.916222
13.63065	4.3280429
13.666668	4.0476106
13.693189	4.5426592
13.71092	2.5744855
13.717479	3.5385358
13.723603	4.8269313
13.740956	4.5362417
13.810342	3.3345241
13.884852	5.2966064
13.88918	2.4088233
13.908124	4.4692953
13.920669	5.1046996
14.066882	4.2443514
14.119596	3.8195427
14.134638	4.0501819
14.15121	5.0939166
14.174712	1.7723559
14.23597	5.8236027
14.270719	3.9357992
14.418402	5.0104769
14.423091	4.655516
14.58717	5.4807475
14.608216	5.5958854
14.621365	5.1009621
14.639837	5.7854218
14.675322	3.0883694
14.885806	4.5369381
15.123712	3.1371023
15.314308	4.2649289
15.1271918	15.296397
16.0410633	15.246086
16.0765704	14.281669
16.1579441	14.821112
16.2049293	14.132894
16.2509463	13.3686
16.2995974	15.315821
16.4488022	13.797879
16.4718758	15.400537
16.6037499	15.167981
16.6299854	12.916744
16.6385609	15.134394
16.722283	12.348765
16.8768788	12.795341
16.8805154	13.427278
16.9245665	14.171992
16.9419753	12.11674
16.9458388	13.572502
16.9963091	13.910162
16.9977637	12.264383
17.120108	14.298028
17.161642	12.235561
17.259619	14.538579
17.278865	13.907848
17.284851	14.78127
17.302126	13.551077
17.419082	13.560862
17.451229	14.87717
17.481022	14.631295
17.487289	13.299683
17.570844	13.073695
17.587373	14.506647
17.625944	13.499386
17.641761	13.157186
17.64511	12.942852
17.667898	15.982038
17.670485	13.955641
17.706356	13.361715
17.711061	14.236744
17.771428	12.766095
17.775607	16.052587
17.886805	13.023554
17.928196	11.2239141
17.950179	14.187592
17.966609	13.020396
17.984097	13.00589
17.997997	14.669185
18.089569	14.398868
18.108494	13.910334
18.147798	13.724342
18.155115	16.121416
18.158758	14.718643
18.213179	12.731326
18.222689	13.894866
18.237439	14.728269
18.247742	15.206662
18.256842	14.442514
18.269178	13.628728
18.350498	13.563816
18.469883	14.188571
18.4942	14.38967
18.52686	12.779428
18.553505	14.539572
18.559418	15.570712
18.567707	14.901948
18.634692	12.074948
18.634837	14.236684
18.641807	14.726728
18.677089	14.332538
18.767215	15.092209
18.781016	12.595779
18.878886	14.329098
18.891137	13.18777
18.895193	11.6599829
18.945273	13.156521
18.949261	12.944328
18.998138	13.186815
19.01272	14.270148
19.021108	15.495508
19.026602	13.861609
19.056164	13.728905
19.065794	13.845586
19.087697	15.523848
19.105136	13.638893
19.197604	14.022645
19.296025	15.509115
19.349088	14.002902
19.370353	16.051215
19.425818	12.55447
19.465642	12.745134
19.470883	13.280208
19.473897	13.425769
19.516394	13.222901
19.646671	13.889919
19.651572	15.064428
19.888393	12.709495
20.075641	14.576588
21.009856	14.548147

1	5.1	3.5	1.4	0.2
1	4.9	3	1.4	0.2
1	4.7	3.2	1.3	0.2
1	4.6	3.1	1.5	0.2
1	5	3.6	1.4	0.2
1	5.4	3.9	1.7	0.4
1	4.6	3.4	1.4	0.3
1	5	3.4	1.5	0.2
1	4.4	2.9	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5.4	3.7	1.5	0.2
1	4.8	3.4	1.6	0.2
1	4.8	3	1.4	0.1
1	4.3	3	1.1	0.1
1	5.8	4	1.2	0.2
1	5.7	4.4	1.5	0.4
1	5.4	3.9	1.3	0.4
1	5.1	3.5	1.4	0.3
1	5.7	3.8	1.7	0.3
1	5.1	3.8	1.5	0.3
1	5.4	3.4	1.7	0.2
1	5.1	3.7	1.5	0.4
1	4.6	3.6	1	0.2
1	5.1	3.3	1.7	0.5
1	4.8	3.4	1.9	0.2
1	5	3	1.6	0.2
1	5	3.4	1.6	0.4
1	5.2	3.5	1.5	0.2
1	5.2	3.4	1.4	0.2
1	4.7	3.2	1.6	0.2
1	4.8	3.1	1.6	0.2
1	5.4	3.4	1.5	0.4
1	5.2	4.1	1.5	0.1
1	5.5	4.2	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5	3.2	1.2	0.2
1	5.5	3.5	1.3	0.2
1	4.9	3.1	1.5	0.1
1	4.4	3	1.3	0.2
1	5.1	3.4	1.5	0.2
1	5	3.5	1.3	0.3
1	4.5	2.3	1.3	0.3
1	4.4	3.2	1.3	0.2
1	5	3.5	1.6	0.6
1	5.1	3.8	1.9	0.4
1	4.8	3	1.4	0.3
1	5.1	3.8	1.6	0.2
1	4.6	3.2	1.4	0.2
1	5.3	3.7	1.5	0.2
1	5	3.3	1.4	0.2
2	7	3.2	4.7	1.4
2	6.4	3.2	4.5	1.5
2	6.9	3.1	4.9	1.5
2	5.5	2.3	4	1.3
2	6.5	2.8	4.6	1.5
2	5.7	2.8	4.5	1.3
2	6.3	3.3	4.7	1.6
2	4.9	2.4	3.3	1
2	6.6	2.9	4.6	1.3
2	5.2	2.7	3.9	1.4
2	5	2	3.5	1
2	5.9	3	4.2	1.5
2	6	2.2	4	1
2	6.1	2.9	4.7	1.4
2	5.6	2.9	3.6	1.3
2	6.7	3.1	4.4	1.4
2	5.6	3	4.5	1.5
2	5.8	2.7	4.1	1
2	6.2	2.2	4.5	1.5
2	5.6	2.5	3.9	1.1
2	5.9	3.2	4.8	1.8
2	6.1	2.8	4	1.3
2	6.3	2.5	4.9	1.5
2	6.1	2.8	4.7	1.2
2	6.4	2.9	4.3	1.3
2	6.6	3	4.4	1.4
2	6.8	2.8	4.8	1.4
2	6.7	3	5	1.7
2	6	2.9	4.5	1.5
2	5.7	2.6	3.5	1
2	5.5	2.4	3.8	1.1
2	5.5	2.4	3.7	1
2	5.8	2.7	3.9	1.2
2	6	2.7	5.1	1.6
2	5.4	3	4.5	1.5
2	6	3.4	4.5	1.6
2	6.7	3.1	4.7	1.5
2	6.3	2.3	4.4	1.3
2	5.6	3	4.1	1.3
2	5.5	2.5	4	1.3
2	5.5	2.6	4.4	1.2
2	6.1	3	4.6	1.4
2	5.8	2.6	4	1.2
2	5	2.3	3.3	1
2	5.6	2.7	4.2	1.3
2	5.7	3	4.2	1.2
2	5.7	2.9	4.2	1.3
2	6.2	2.9	4.3	1.3
2	5.1	2.5	3	1.1
2	5.7	2.8	4.1	1.3
3	6.3	3.3	6	2.5
3	5.8	2.7	5.1	1.9
3	7.1	3	5.9	2.1
3	6.3	2.9	5.6	1.8
3	6.5	3	5.8	2.2
3	7.6	3	6.6	2.1
3	4.9	2.5	4.5	1.7
3	7.3	2.9	6.3	1.8
3	6.7	2.5	5.8	1.8
3	7.2	3.6	6.1	2.5
3	6.5	3.2	5.1	2
3	6.4	2.7	5.3	1.9
3	6.8	3	5.5	2.1
3	5.7	2.5	5	2
3	5.8	2.8	5.1	2.4
3	6.4	3.2	5.3	2.3
3	6.5	3	5.5	1.8
3	7.7	3.8	6.7	2.2
3	7.7	2.6	6.9	2.3
3	6	2.2	5	1.5
3	6.9	3.2	5.7	2.3
3	5.6	2.8	4.9	2
3	7.7	2.8	6.7	2
3	6.3	2.7	4.9	1.8
3	6.7	3.3	5.7	2.1
3	7.2	3.2	6	1.8
3	6.2	2.8	4.8	1.8
3	6.1	3	4.9	1.8
3	6.4	2.8	5.6	2.1
3	7.2	3	5.8	1.6
3	7.4	2.8	6.1	1.9
3	7.9	3.8	6.4	2
3	6.4	2.8	5.6	2.2
3	6.3	2.8	5.1	1.5
3	6.1	2.6	5.6	1.4
3	7.7	3	6.1	2.3
3	6.3	3.4	5.6	2.4
3	6.4	3.1	5.5	1.8
3	6	3	4.8	1.8
3	6.9	3.1	5.4	2.1
3	6.7	3.1	5.6	2.4
3	6.9	3.1	5.1	2.3
3	5.8	2.7	5.1	1.9
3	6.8	3.2	5.9	2.3
3	6.7	3.3	5.7	2.5
3	6.7	3	5.2	2.3
3	6.3	2.5	5	1.9
3	6.5	3	5.2	2
3	6.2	3.4	5.4	2.3
3	5.9	3	5.1	1.8

1	5.1	3.5	1.4	0.2
1	4.9	3	1.4	0.2
1	4.7	3.2	1.3	0.2
1	4.6	3.1	1.5	0.2
1	5	3.6	1.4	0.2
1	5.4	3.9	1.7	0.4
1	4.6	3.4	1.4	0.3
1	5	3.4	1.5	0.2
1	4.4	2.9	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5.4	3.7	1.5	0.2
1	4.8	3.4	1.6	0.2
1	4.8	3	1.4	0.1
1	4.3	3	1.1	0.1
1	5.8	4	1.2	0.2
1	5.7	4.4	1.5	0.4
1	5.4	3.9	1.3	0.4
1	5.1	3.5	1.4	0.3
1	5.7	3.8	1.7	0.3
1	5.1	3.8	1.5	0.3
1	5.4	3.4	1.7	0.2
1	5.1	3.7	1.5	0.4
1	4.6	3.6	1	0.2
1	5.1	3.3	1.7	0.5
1	4.8	3.4	1.9	0.2
1	5	3	1.6	0.2
1	5	3.4	1.6	0.4
1	5.2	3.5	1.5	0.2
1	5.2	3.4	1.4	0.2
1	4.7	3.2	1.6	0.2
1	4.8	3.1	1.6	0.2
1	5.4	3.4	1.5	0.4
1	5.2	4.1	1.5	0.1
1	5.5	4.2	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5	3.2	1.2	0.2
1	5.5	3.5	1.3	0.2
1	4.9	3.1	1.5	0.1
1	4.4	3	1.3	0.2
1	5.1	3.4	1.5	0.2
1	5	3.5	1.3	0.3
1	4.5	2.3	1.3	0.3
1	4.4	3.2	1.3	0.2
1	5	3.5	1.6	0.6
1	5.1	3.8	1.9	0.4
1	4.8	3	1.4	0.3
1	5.1	3.8	1.6	0.2
1	4.6	3.2	1.4	0.2
1	5.3	3.7	1.5	0.2
1	5	3.3	1.4	0.2
2	7	3.2	4.7	1.4
2	6.4	3.2	4.5	1.5
2	6.9	3.1	4.9	1.5
2	5.5	2.3	4	1.3
2	6.5	2.8	4.6	1.5
2	5.7	2.8	4.5	1.3
2	6.3	3.3	4.7	1.6
2	4.9	2.4	3.3	1
2	6.6	2.9	4.6	1.3
2	5.2	2.7	3.9	1.4
2	5	2	3.5	1
2	5.9	3	4.2	1.5
2	6	2.2	4	1
2	6.1	2.9	4.7	1.4
2	5.6	2.9	3.6	1.3
2	6.7	3.1	4.4	1.4
2	5.6	3	4.5	1.5
2	5.8	2.7	4.1	1
2	6.2	2.2	4.5	1.5
2	5.6	2.5	3.9	1.1
2	5.9	3.2	4.8	1.8
2	6.1	2.8	4	1.3
2	6.3	2.5	4.9	1.5
2	6.1	2.8	4.7	1.2
2	6.4	2.9	4.3	1.3
2	6.6	3	4.4	1.4
2	6.8	2.8	4.8	1.4
2	6.7	3	5	1.7
2	6	2.9	4.5	1.5
2	5.7	2.6	3.5	1
2	5.5	2.4	3.8	1.1
2	5.5	2.4	3.7	1
2	5.8	2.7	3.9	1.2
2	6	2.7	5.1	1.6
2	5.4	3	4.5	1.5
2	6	3.4	4.5	1.6
2	6.7	3.1	4.7	1.5
2	6.3	2.3	4.4	1.3
2	5.6	3	4.1	1.3
2	5.5	2.5	4	1.3
2	5.5	2.6	4.4	1.2
2	6.1	3	4.6	1.4
2	5.8	2.6	4	1.2
2	5	2.3	3.3	1
2	5.6	2.7	4.2	1.3
2	5.7	3	4.2	1.2
2	5.7	2.9	4.2	1.3
2	6.2	2.9	4.3	1.3
2	5.1	2.5	3	1.1
2	5.7	2.8	4.1	1.3
2	6.3	3.3	6	2.5
2	5.8	2.7	5.1	1.9
2	7.1	3	5.9	2.1
2	6.3	2.9	5.6	1.8
2	6.5	3	5.8	2.2
2	7.6	3	6.6	2.1
2	4.9	2.5	4.5	1.7
2	7.3	2.9	6.3	1.8
2	6.7	2.5	5.8	1.8
2	7.2	3.6	6.1	2.5
2	6.5	3.2	5.1	2
2	6.4	2.7	5.3	1.9
2	6.8	3	5.5	2.1
2	5.7	2.5	5	2
2	5.8	2.8	5.1	2.4
2	6.4	3.2	5.3	2.3
2	6.5	3	5.5	1.8
2	7.7	3.8	6.7	2.2
2	7.7	2.6	6.9	2.3
2	6	2.2	5	1.5
2	6.9	3.2	5.7	2.3
2	5.6	2.8	4.9	2
2	7.7	2.8	6.7	2
2	6.3	2.7	4.9	1.8
2	6.7	3.3	5.7	2.1
2	7.2	3.2	6	1.8
2	6.2	2.8	4.8	1.8
2	6.1	3	4.9	1.8
2	6.4	2.8	5.6	2.1
2	7.2	3	5.8	1.6
2	7.4	2.8	6.1	1.9
2	7.9	3.8	6.4	2
2	6.4	2.8	5.6	2.2
2	6.3	2.8	5.1	1.5
2	6.1	2.6	5.6	1.4
2	7.7	3	6.1	2.3
2	6.3	3.4	5.6	2.4
2	6.4	3.1	5.5	1.8
2	6	3	4.8	1.8
2	6.9	3.1	5.4	2.1
2	6.7	3.1	5.6	2.4
2	6.9	3.1	5.1	2.3
2	5.8	2.7	5.1	1.9
2	6.8	3.2	5.9	2.3
2	6.7	3.3	5.7	2.5
2	6.7	3	5.2	2.3
2	6.3	2.5	5	1.9
2	6.5	3	5.2	2
2	6.2	3.4	5.4	2.3
2	5.9	3	5.1	1.8

gauss Data
#
3.2079091	7.7210589
3.2231833	8.624442
3.2724274	7.7211989
3.3905861	6.8528257
3.5263206	7.1902709
3.5446515	6.9092366
4.0817641	6.2932327
4.1329588	6.7909656
4.2061908	6.5587698
4.2294541	7.7094674
4.2311605	6.1842931
4.3729459	6.6146591
4.451783	8.0942238
4.4631214	5.9776029
4.4641181	7.9918609
4.5092126	7.2028537
4.5249635	5.8857945
4.5408484	7.0938419
4.5538585	8.1353191
4.580998	7.1703637
4.6221661	8.0532502
4.6675926	7.5078748
4.6693178	7.0258803
4.6757058	5.5386359
4.6875167	8.3955603
4.8136569	8.2550441
4.8330733	8.7522282
4.8388063	8.8021392
4.8394463	8.1968265
4.8564329	5.7743203
4.8694154	6.2415617
4.8838473	7.8844967
4.9151302	6.5118873
4.9197918	8.408129
4.9546596	8.2040109
4.9707417	6.4312104
5.0135107	7.9468569
5.0456382	7.6968997
5.0662513	7.9026477
5.0731757	9.0607237
5.0940184	9.3846903
5.0998003	7.0196854
5.1062588	7.8665536
5.1113574	6.7466426
5.1401502	6.322823
5.164504	6.3772491
5.167721	8.491692
5.1792337	7.1921312
5.189034	7.9209093
5.1923355	9.0269859
5.2420277	6.6866669
5.2563394	6.6318602
5.2646653	7.3621656
5.277839	7.5903912
5.2840827	6.3948904
5.3134128	7.7465675
5.3432966	6.3024976
5.3588688	6.6395169
5.3773154	7.509691
5.3923682	7.7704754
5.3928396	7.9307354
5.4097375	7.0243664
5.4250905	8.2957807
5.4256467	9.1909088
5.429859	8.6389274
5.4383383	8.5945946
5.4499816	7.3406544
5.4648999	7.7633964
5.5101871	7.0922003
5.5814278	8.4753996
5.5883151	6.204994
5.6065497	7.7304895
5.6109218	8.4108913
5.6800216	7.6595682
5.6865731	6.8736266
5.7364618	7.1109649
5.7834804	8.4478511
5.806937	6.2154877
5.8505015	6.7495611
5.8894656	7.5845772
5.898071	8.4421334
5.9191638	8.1379257
5.9232595	6.3691164
5.9263071	6.3499483
5.9336875	7.5430657
5.9561075	6.014345
5.956787	6.2913721
5.9597375	7.9233632
5.9647258	7.5541938
6.0014664	5.5959308
6.0967613	7.3460918
6.1113619	8.956129
6.1393133	7.1290369
6.2427451	8.3293623
6.2447324	6.4055278
6.2808958	8.1849711
6.2958248	8.52221
6.3333881	8.8926755
6.3770667	8.148263
6.3872811	7.0834977
6.3943806	5.2047041
6.4231133	9.1428545
6.4242463	8.7437029
6.4737188	9.1960809
6.5434832	6.9540165
6.5542301	8.6514457
6.556613	7.6792214
6.5569343	8.3557038
6.5681878	6.7334969
6.6154429	8.9919154
6.6573287	7.6323181
6.7523585	8.6764836
6.7530329	5.6970322
6.8855847	7.1092405
6.9202943	7.6878621
7.0692918	8.4614043
7.094251	8.9986657
7.2213603	6.8859654
7.29025	8.1231551
7.3047409	8.0529775
7.3807576	6.8897184
7.4022501	8.2157411
7.5258683	8.6077935
7.5602902	8.0202276
7.5928493	9.9473488
7.6337921	6.0477399
12.5649633	14.8199028
12.9492476	15.2417831
12.9639843	14.1301841
13.226796	13.0896456
13.5160477	14.339618
13.5494966	15.3187034
13.7321392	13.3091876
13.78872	13.9671922
13.8074223	14.160413
13.8077218	15.2829668
13.8401064	14.4340778
13.8887826	15.182616
13.9385494	13.0826968
14.0173279	16.015934
14.1333175	15.0641202
14.1493846	11.5813131
14.3247616	14.5008158
14.3596552	14.4300021
14.4498757	14.4660964
14.5039886	15.1291981
14.5577834	12.1352935
14.6243969	13.1862597
14.6653361	13.8993512
14.6752921	13.2522435
14.6978867	13.9182312
14.7050083	16.974634
14.7473609	13.2485078
14.7493806	13.5129937
14.7887799	13.8588077
14.836351	13.2174683
14.8726451	14.0834742
14.8903508	12.7250099
14.8907481	14.7297899
14.8955632	13.2141097
14.9133122	16.337941
14.9817274	13.634554
14.9985553	12.0236224
15.0917411	13.5520639
15.1110772	14.5536931
15.138392	13.2415998
15.1624205	13.7417785
15.1675304	14.3424403
15.1959608	13.6760523
15.1960034	14.4806538
15.2013835	15.6980377
15.2141018	14.6837557
15.2348838	12.4397497
15.2422483	13.3493743
15.2430823	13.7644821
15.2613676	14.8671876
15.2946775	13.1593571
15.3350288	14.460047
15.335831	15.4561045
15.3401016	15.1398342
15.3739163	13.9216549
15.43575	13.3674077
15.4735116	13.3621969
15.5094318	14.7425193
15.5192765	13.0919875
15.5230336	13.3181116
15.5389153	14.3706625
15.569023	15.6957121
15.6814631	13.0434399
15.7144954	11.191835
15.7198893	15.0829336
15.7803768	13.8861349
15.9326271	14.1804494
15.9732352	13.991876
15.9849534	13.8853695
16.1124261	13.3706331
16.1154425	14.9006981
16.1254249	12.0928389
16.2795063	14.5570386
16.2975804	13.1768362
16.3800894	13.2322431
17.1497729	14.4123977
6.14794639	18.979611
6.87296376	21.259677
6.95057589	19.253912
7.1774974	19.078445
7.2212576	20.180736
7.3319392	21.250346
7.3417249	19.922804
7.3666823	20.057726
7.4343045	19.844073
7.4637002	20.504545
7.5730949	21.120083
7.6960326	19.719672
7.7176948	19.681052
7.735971	21.281386
7.7907127	19.970722
7.8934128	21.577153
7.9067283	20.338185
7.9620927	21.091421
7.9967398	21.708606
8.0047431	20.371614
8.044215	19.689133
8.0872242	21.305325
8.0887985	20.697535
8.200185	21.663794
8.2008578	19.885879
8.2557222	19.889798
8.2892761	18.6007235
8.3028307	19.637392
8.3419285	19.224092
8.3422877	19.204327
8.3583757	21.048761
8.3893859	20.918406
8.4169347	21.292314
8.457675	20.097081
8.4578659	20.974339
8.4788892	21.152925
8.5046477	21.381791
8.5059035	21.737371
8.5621021	20.857647
8.6720782	18.4164271
8.6959001	21.995196
8.6992848	20.473127
8.7066215	19.623886
8.7295648	20.278963
8.7460885	20.579015
8.8699537	22.05209
8.8716518	22.413634
8.8873572	21.962738
8.9045898	21.715849
8.9456943	20.248866
8.9765246	21.883393
8.9819109	22.26836
8.998568	21.243342
9.0433096	22.347058
9.0596044	22.056215
9.0794422	20.758407
9.1129522	20.224293
9.1413942	20.929671
9.1627441	22.158977
9.1767186	20.184683
9.1781964	21.230266
9.2431648	21.962598
9.2618399	20.533754
9.3829695	20.589008
9.3900615	21.298833
9.4072292	20.452131
9.4630972	22.51363
9.4865115	20.166259
9.489808	20.302365
9.4985781	20.176454
9.4995603	21.339537
9.5015888	21.546623
9.5126907	22.181318
9.5186807	20.243923
9.5200494	21.606359
9.5546059	21.523475
9.5593513	22.20819
9.620113	22.041174
9.7561987	21.161672
9.7777356	20.774779
9.7993429	21.862268
9.8108036	22.172305
9.8138488	23.482412
9.9717907	21.813861
9.9946929	22.564215
10.0059337	21.629871
10.0114164	21.947345
10.0376315	20.086702
10.1360659	20.978871
10.1976377	22.45016
10.3760322	20.3188
10.4290531	21.540536
10.4497178	21.767889
10.4527803	21.620024
10.6388008	21.745066
10.6473698	21.450529
10.7566377	22.143698
10.7900453	21.382074
10.8758893	21.90501
10.9023884	21.818273
10.9788072	20.959914
11.0324193	21.252833
11.3479027	20.875618
13.4652107	2.6831135
13.5244255	1.1185327
13.676615	2.1365133
13.7060357	4.8800944
14.0507822	4.9306996
14.0926175	5.5469616
14.1620361	3.4362472
14.528659	4.9653684
14.68202	4.9323634
14.8113105	5.0447482
14.8395293	3.2846371
14.8904436	3.9756688
14.901902	4.0596121
15.00957	3.8387848
15.041757	3.2972722
15.070124	3.5975018
15.125698	5.2145027
15.13737	5.069625
15.179778	2.7118074
15.300714	2.811699
15.302589	4.6895294
15.310044	3.3904743
15.360948	4.048405
15.387776	2.650697
15.404948	2.3650841
15.415097	4.0142043
15.417047	3.4687826
15.442745	5.030264
15.443721	4.1409706
15.447941	5.8988869
15.476582	3.743859
15.515158	5.106076
15.595752	3.8626734
15.615602	3.2022261
15.61961	5.7629745
15.632347	3.3748335
15.657503	3.2747553
15.709758	5.0287697
15.747944	5.0152176
15.752371	4.7189407
15.818769	3.7802197
15.820888	3.1272452
15.830515	5.601528
15.863656	2.6533946
15.88482	5.2657701
15.904148	4.8965637
15.939187	3.1585675
15.952628	2.2741358
15.973613	4.8611944
15.97606	2.9509126
16.000251	4.8797171
16.004435	2.8729802
16.004874	4.9961698
16.008949	4.4582345
16.025455	4.3870708
16.045022	4.9916418
16.096295	2.973044
16.195278	2.9627547
16.21192	4.3851352
16.215603	4.153893
16.362235	2.9408317
16.487338	3.4554507
16.507061	4.9059559
16.544954	3.0748351
16.561125	3.3112401
16.575478	3.2823488
16.61449	4.9872933
16.618037	3.916222
16.63065	4.3280429
16.666668	4.0476106
16.693189	4.5426592
16.71092	2.5744855
16.717479	3.5385358
16.723603	4.8269313
16.740956	4.5362417
16.810342	3.3345241
16.884852	5.2966064
16.88918	2.4088233
16.908124	4.4692953
16.920669	5.1046996
17.066882	4.2443514
17.119596	3.8195427
17.134638	4.0501819
17.15121	5.0939166
17.174712	1.7723559
17.23597	5.8236027
17.270719	3.9357992
17.418402	5.0104769
17.423091	4.655516
17.58717	5.4807475
17.608216	5.5958854
17.621365	5.1009621
17.639837	5.7854218
17.675322	3.0883694
17.885806	4.5369381
18.123712	3.1371023
18.314308	4.2649289
24.1271918	19.296397
25.0410633	19.246086
25.0765704	18.281669
25.1579441	18.821112
25.2049293	18.132894
25.2509463	17.3686
25.2995974	19.315821
25.4488022	17.797879
25.4718758	19.400537
25.6037499	19.167981
25.6299854	16.916744
25.6385609	19.134394
25.722283	16.348765
25.8768788	16.795341
25.8805154	17.427278
25.9245665	18.171992
25.9419753	16.11674
25.9458388	17.572502
25.9963091	17.910162
25.9977637	16.264383
26.120108	18.298028
26.161642	16.235561
26.259619	18.538579
26.278865	17.907848
26.284851	18.78127
26.302126	17.551077
26.419082	17.560862
26.451229	18.87717
26.481022	18.631295
26.487289	17.299683
26.570844	17.073695
26.587373	18.506647
26.625944	17.499386
26.641761	17.157186
26.64511	16.942852
26.667898	19.982038
26.670485	17.955641
26.706356	17.361715
26.711061	18.236744
26.771428	16.766095
26.775607	20.052587
26.886805	17.023554
26.928196	15.2239141
26.950179	18.187592
26.966609	17.020396
26.984097	17.00589
26.997997	18.669185
27.089569	18.398868
27.108494	17.910334
27.147798	17.724342
27.155115	20.121416
27.158758	18.718643
27.213179	16.731326
27.222689	17.894866
27.237439	18.728269
27.247742	19.206662
27.256842	18.442514
27.269178	17.628728
27.350498	17.563816
27.469883	18.188571
27.4942	18.38967
27.52686	16.779428
27.553505	18.539572
27.559418	19.570712
27.567707	18.901948
27.634692	16.074948
27.634837	18.236684
27.641807	18.726728
27.677089	18.332538
27.767215	19.092209
27.781016	16.595779
27.878886	18.329098
27.891137	17.18777
27.895193	15.6599829
27.945273	17.156521
27.949261	16.944328
27.998138	17.186815
28.01272	18.270148
28.021108	19.495508
28.026602	17.861609
28.056164	17.728905
28.065794	17.845586
28.087697	19.523848
28.105136	17.638893
28.197604	18.022645
28.296025	19.509115
28.349088	18.002902
28.370353	20.051215
28.425818	16.55447
28.465642	16.745134
28.470883	17.280208
28.473897	17.425769
28.516394	17.222901
28.646671	17.889919
28.651572	19.064428
28.888393	16.709495
29.075641	18.576588
30.009856	18.548147

Fuzzy Ants as a Clustering Concept

Documents

Parag Kanade/fuzzy ants/Gauss2/gauss02.data

Parag Kanade/fuzzy ants/Gauss2/gauss02Full.data

Parag Kanade/fuzzy ants/Gauss2/gauss.c

Parag Kanade/fuzzy ants/Gauss2/headerStatic.h

Parag Kanade/fuzzy ants/Gauss2/maxData.txt

Parag Kanade/fuzzy ants/Iris/iris.c

Parag Kanade/fuzzy ants/Iris/iris.data

Parag Kanade/fuzzy ants/Iris/irisFull.data

Parag Kanade/fuzzy ants/Iris/2class/iris.c

Parag Kanade/fuzzy ants/Iris/2class/maxData.txt

Parag Kanade/fuzzy ants/Iris/2class/iris.data

Parag Kanade/fuzzy ants/Iris/2class/irisFull.data

Parag Kanade/fuzzy ants/Iris/2class/headerStatic.h

Parag Kanade/fuzzy ants/Iris/headerStatic.h

Parag Kanade/fuzzy ants/Iris/maxData.txt

Parag Kanade/fuzzy ants/Glass/headerStatic.h

Parag Kanade/fuzzy ants/Glass/maxData.txt

Parag Kanade/fuzzy ants/Glass/glass.data

Parag Kanade/fuzzy ants/Glass/glassFull.data

Parag Kanade/fuzzy ants/Glass/2class/maxData.txt

Parag Kanade/fuzzy ants/Glass/2class/glassFull.data

Parag Kanade/fuzzy ants/Glass/2class/headerStatic.h

Parag Kanade/fuzzy ants/Glass/2class/glass.data

Parag Kanade/fuzzy ants/Glass/2class/glass.c

Parag Kanade/fuzzy ants/Glass/glass.c

Parag Kanade/fuzzy ants/wine/maxData.txt

Parag Kanade/fuzzy ants/wine/headerStatic.h

Parag Kanade/fuzzy ants/wine/wineFull.data

Parag Kanade/fuzzy ants/wine/wine.data

Parag Kanade/fuzzy ants/Gauss1/maxData.txt

Parag Kanade/fuzzy ants/Gauss1/gauss01Full.data

Parag Kanade/fuzzy ants/Gauss1/headerStatic.h

Parag Kanade/fuzzy ants/Gauss1/gauss01.data

Parag Kanade/fuzzy ants/Gauss1/gauss.c

Parag Kanade/fuzzy ants/3D Board/header_z3.h

Parag Kanade/fuzzy ants/3D Board/ant_z3.c

Parag Kanade/fuzzy ants/3D Board/ant_xyz.c

Parag Kanade/fuzzy ants/3D Board/header_xyz.h

Parag Kanade/fuzzy ants/Gauss3/gauss03.data

Parag Kanade/fuzzy ants/Gauss3/gauss03Full.data

Parag Kanade/fuzzy ants/Gauss3/gauss.c

Parag Kanade/fuzzy ants/Gauss3/headerStatic.h

Parag Kanade/fuzzy ants/Gauss3/maxData.txt

Parag Kanade/fuzzy ants/Gauss4/gauss04.data

Parag Kanade/fuzzy ants/Gauss4/gauss04Full.data

Parag Kanade/fuzzy ants/Gauss4/gauss.c

Parag Kanade/fuzzy ants/Gauss4/headerStatic.h

Parag Kanade/fuzzy ants/Gauss4/maxData.txt

Parag Kanade/fuzzy ants/Gauss5/gauss05.data

Parag Kanade/fuzzy ants/Gauss5/gauss05Full.data

Parag Kanade/fuzzy ants/Gauss5/headerStatic.h

Parag Kanade/fuzzy ants/Gauss5/maxData.txt

Parag Kanade/fuzzy ants/Gauss5/gauss.c

Parag Kanade/fuzzy ants/Gauss500-1/gauss500_01.data

Parag Kanade/fuzzy ants/Gauss500-1/gauss500_01Full.data

Parag Kanade/fuzzy ants/Gauss500-1/gauss.c

Parag Kanade/fuzzy ants/Gauss500-1/headerStatic.h

Parag Kanade/fuzzy ants/Gauss500-1/maxData.txt

Parag Kanade/fuzzy ants/Gauss500-2/gauss500_02.data

Parag Kanade/fuzzy ants/Gauss500-2/gauss500_02Full.data

Parag Kanade/fuzzy ants/Gauss500-2/gauss.c

Parag Kanade/fuzzy ants/Gauss500-2/headerStatic.h

Parag Kanade/fuzzy ants/Gauss500-2/maxData.txt

Parag Kanade/fuzzy ants/Gauss500-3/gauss500_03.data

Parag Kanade/fuzzy ants/Gauss500-3/gauss500_03Full.data

Parag Kanade/fuzzy ants/Gauss500-3/gauss.c

Parag Kanade/fuzzy ants/Gauss500-3/headerStatic.h

Parag Kanade/fuzzy ants/Gauss500-3/maxData.txt

Parag Kanade/fuzzy ants/Gauss500-4/gauss500_04.data

Parag Kanade/fuzzy ants/Gauss500-4/gauss500_04Full.data

Parag Kanade/fuzzy ants/Gauss500-4/gauss.c

Parag Kanade/fuzzy ants/Gauss500-4/headerStatic.h

Parag Kanade/fuzzy ants/Gauss500-4/maxData.txt

Parag Kanade/fuzzy ants/Gauss500-5/gauss500_05.data

Parag Kanade/fuzzy ants/Gauss500-5/gauss500_05Full.data

Parag Kanade/fuzzy ants/Gauss500-5/gauss.c

Parag Kanade/fuzzy ants/Gauss500-5/headerStatic.h

Parag Kanade/fuzzy ants/Gauss500-5/maxData.txt

1	5.1	3.5	1.4	0.2
1	4.9	3	1.4	0.2
1	4.7	3.2	1.3	0.2
1	4.6	3.1	1.5	0.2
1	5	3.6	1.4	0.2
1	5.4	3.9	1.7	0.4
1	4.6	3.4	1.4	0.3
1	5	3.4	1.5	0.2
1	4.4	2.9	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5.4	3.7	1.5	0.2
1	4.8	3.4	1.6	0.2
1	4.8	3	1.4	0.1
1	4.3	3	1.1	0.1
1	5.8	4	1.2	0.2
1	5.7	4.4	1.5	0.4
1	5.4	3.9	1.3	0.4
1	5.1	3.5	1.4	0.3
1	5.7	3.8	1.7	0.3
1	5.1	3.8	1.5	0.3
1	5.4	3.4	1.7	0.2
1	5.1	3.7	1.5	0.4
1	4.6	3.6	1	0.2
1	5.1	3.3	1.7	0.5
1	4.8	3.4	1.9	0.2
1	5	3	1.6	0.2
1	5	3.4	1.6	0.4
1	5.2	3.5	1.5	0.2
1	5.2	3.4	1.4	0.2
1	4.7	3.2	1.6	0.2
1	4.8	3.1	1.6	0.2
1	5.4	3.4	1.5	0.4
1	5.2	4.1	1.5	0.1
1	5.5	4.2	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5	3.2	1.2	0.2
1	5.5	3.5	1.3	0.2
1	4.9	3.1	1.5	0.1
1	4.4	3	1.3	0.2
1	5.1	3.4	1.5	0.2
1	5	3.5	1.3	0.3
1	4.5	2.3	1.3	0.3
1	4.4	3.2	1.3	0.2
1	5	3.5	1.6	0.6
1	5.1	3.8	1.9	0.4
1	4.8	3	1.4	0.3
1	5.1	3.8	1.6	0.2
1	4.6	3.2	1.4	0.2
1	5.3	3.7	1.5	0.2
1	5	3.3	1.4	0.2
2	7	3.2	4.7	1.4
2	6.4	3.2	4.5	1.5
2	6.9	3.1	4.9	1.5
2	5.5	2.3	4	1.3
2	6.5	2.8	4.6	1.5
2	5.7	2.8	4.5	1.3
2	6.3	3.3	4.7	1.6
2	4.9	2.4	3.3	1
2	6.6	2.9	4.6	1.3
2	5.2	2.7	3.9	1.4
2	5	2	3.5	1
2	5.9	3	4.2	1.5
2	6	2.2	4	1
2	6.1	2.9	4.7	1.4
2	5.6	2.9	3.6	1.3
2	6.7	3.1	4.4	1.4
2	5.6	3	4.5	1.5
2	5.8	2.7	4.1	1
2	6.2	2.2	4.5	1.5
2	5.6	2.5	3.9	1.1
2	5.9	3.2	4.8	1.8
2	6.1	2.8	4	1.3
2	6.3	2.5	4.9	1.5
2	6.1	2.8	4.7	1.2
2	6.4	2.9	4.3	1.3
2	6.6	3	4.4	1.4
2	6.8	2.8	4.8	1.4
2	6.7	3	5	1.7
2	6	2.9	4.5	1.5
2	5.7	2.6	3.5	1
2	5.5	2.4	3.8	1.1
2	5.5	2.4	3.7	1
2	5.8	2.7	3.9	1.2
2	6	2.7	5.1	1.6
2	5.4	3	4.5	1.5
2	6	3.4	4.5	1.6
2	6.7	3.1	4.7	1.5
2	6.3	2.3	4.4	1.3
2	5.6	3	4.1	1.3
2	5.5	2.5	4	1.3
2	5.5	2.6	4.4	1.2
2	6.1	3	4.6	1.4
2	5.8	2.6	4	1.2
2	5	2.3	3.3	1
2	5.6	2.7	4.2	1.3
2	5.7	3	4.2	1.2
2	5.7	2.9	4.2	1.3
2	6.2	2.9	4.3	1.3
2	5.1	2.5	3	1.1
2	5.7	2.8	4.1	1.3
3	6.3	3.3	6	2.5
3	5.8	2.7	5.1	1.9
3	7.1	3	5.9	2.1
3	6.3	2.9	5.6	1.8
3	6.5	3	5.8	2.2
3	7.6	3	6.6	2.1
3	4.9	2.5	4.5	1.7
3	7.3	2.9	6.3	1.8
3	6.7	2.5	5.8	1.8
3	7.2	3.6	6.1	2.5
3	6.5	3.2	5.1	2
3	6.4	2.7	5.3	1.9
3	6.8	3	5.5	2.1
3	5.7	2.5	5	2
3	5.8	2.8	5.1	2.4
3	6.4	3.2	5.3	2.3
3	6.5	3	5.5	1.8
3	7.7	3.8	6.7	2.2
3	7.7	2.6	6.9	2.3
3	6	2.2	5	1.5
3	6.9	3.2	5.7	2.3
3	5.6	2.8	4.9	2
3	7.7	2.8	6.7	2
3	6.3	2.7	4.9	1.8
3	6.7	3.3	5.7	2.1
3	7.2	3.2	6	1.8
3	6.2	2.8	4.8	1.8
3	6.1	3	4.9	1.8
3	6.4	2.8	5.6	2.1
3	7.2	3	5.8	1.6
3	7.4	2.8	6.1	1.9
3	7.9	3.8	6.4	2
3	6.4	2.8	5.6	2.2
3	6.3	2.8	5.1	1.5
3	6.1	2.6	5.6	1.4
3	7.7	3	6.1	2.3
3	6.3	3.4	5.6	2.4
3	6.4	3.1	5.5	1.8
3	6	3	4.8	1.8
3	6.9	3.1	5.4	2.1
3	6.7	3.1	5.6	2.4
3	6.9	3.1	5.1	2.3
3	5.8	2.7	5.1	1.9
3	6.8	3.2	5.9	2.3
3	6.7	3.3	5.7	2.5
3	6.7	3	5.2	2.3
3	6.3	2.5	5	1.9
3	6.5	3	5.2	2
3	6.2	3.4	5.4	2.3
3	5.9	3	5.1	1.8

1	5.1	3.5	1.4	0.2
1	4.9	3	1.4	0.2
1	4.7	3.2	1.3	0.2
1	4.6	3.1	1.5	0.2
1	5	3.6	1.4	0.2
1	5.4	3.9	1.7	0.4
1	4.6	3.4	1.4	0.3
1	5	3.4	1.5	0.2
1	4.4	2.9	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5.4	3.7	1.5	0.2
1	4.8	3.4	1.6	0.2
1	4.8	3	1.4	0.1
1	4.3	3	1.1	0.1
1	5.8	4	1.2	0.2
1	5.7	4.4	1.5	0.4
1	5.4	3.9	1.3	0.4
1	5.1	3.5	1.4	0.3
1	5.7	3.8	1.7	0.3
1	5.1	3.8	1.5	0.3
1	5.4	3.4	1.7	0.2
1	5.1	3.7	1.5	0.4
1	4.6	3.6	1	0.2
1	5.1	3.3	1.7	0.5
1	4.8	3.4	1.9	0.2
1	5	3	1.6	0.2
1	5	3.4	1.6	0.4
1	5.2	3.5	1.5	0.2
1	5.2	3.4	1.4	0.2
1	4.7	3.2	1.6	0.2
1	4.8	3.1	1.6	0.2
1	5.4	3.4	1.5	0.4
1	5.2	4.1	1.5	0.1
1	5.5	4.2	1.4	0.2
1	4.9	3.1	1.5	0.1
1	5	3.2	1.2	0.2
1	5.5	3.5	1.3	0.2
1	4.9	3.1	1.5	0.1
1	4.4	3	1.3	0.2
1	5.1	3.4	1.5	0.2
1	5	3.5	1.3	0.3
1	4.5	2.3	1.3	0.3
1	4.4	3.2	1.3	0.2
1	5	3.5	1.6	0.6
1	5.1	3.8	1.9	0.4
1	4.8	3	1.4	0.3
1	5.1	3.8	1.6	0.2
1	4.6	3.2	1.4	0.2
1	5.3	3.7	1.5	0.2
1	5	3.3	1.4	0.2
2	7	3.2	4.7	1.4
2	6.4	3.2	4.5	1.5
2	6.9	3.1	4.9	1.5
2	5.5	2.3	4	1.3
2	6.5	2.8	4.6	1.5
2	5.7	2.8	4.5	1.3
2	6.3	3.3	4.7	1.6
2	4.9	2.4	3.3	1
2	6.6	2.9	4.6	1.3
2	5.2	2.7	3.9	1.4
2	5	2	3.5	1
2	5.9	3	4.2	1.5
2	6	2.2	4	1
2	6.1	2.9	4.7	1.4
2	5.6	2.9	3.6	1.3
2	6.7	3.1	4.4	1.4
2	5.6	3	4.5	1.5
2	5.8	2.7	4.1	1
2	6.2	2.2	4.5	1.5
2	5.6	2.5	3.9	1.1
2	5.9	3.2	4.8	1.8
2	6.1	2.8	4	1.3
2	6.3	2.5	4.9	1.5
2	6.1	2.8	4.7	1.2
2	6.4	2.9	4.3	1.3
2	6.6	3	4.4	1.4
2	6.8	2.8	4.8	1.4
2	6.7	3	5	1.7
2	6	2.9	4.5	1.5
2	5.7	2.6	3.5	1
2	5.5	2.4	3.8	1.1
2	5.5	2.4	3.7	1
2	5.8	2.7	3.9	1.2
2	6	2.7	5.1	1.6
2	5.4	3	4.5	1.5
2	6	3.4	4.5	1.6
2	6.7	3.1	4.7	1.5
2	6.3	2.3	4.4	1.3
2	5.6	3	4.1	1.3
2	5.5	2.5	4	1.3
2	5.5	2.6	4.4	1.2
2	6.1	3	4.6	1.4
2	5.8	2.6	4	1.2
2	5	2.3	3.3	1
2	5.6	2.7	4.2	1.3
2	5.7	3	4.2	1.2
2	5.7	2.9	4.2	1.3
2	6.2	2.9	4.3	1.3
2	5.1	2.5	3	1.1
2	5.7	2.8	4.1	1.3
2	6.3	3.3	6	2.5
2	5.8	2.7	5.1	1.9
2	7.1	3	5.9	2.1
2	6.3	2.9	5.6	1.8
2	6.5	3	5.8	2.2
2	7.6	3	6.6	2.1
2	4.9	2.5	4.5	1.7
2	7.3	2.9	6.3	1.8
2	6.7	2.5	5.8	1.8
2	7.2	3.6	6.1	2.5
2	6.5	3.2	5.1	2
2	6.4	2.7	5.3	1.9
2	6.8	3	5.5	2.1
2	5.7	2.5	5	2
2	5.8	2.8	5.1	2.4
2	6.4	3.2	5.3	2.3
2	6.5	3	5.5	1.8
2	7.7	3.8	6.7	2.2
2	7.7	2.6	6.9	2.3
2	6	2.2	5	1.5
2	6.9	3.2	5.7	2.3
2	5.6	2.8	4.9	2
2	7.7	2.8	6.7	2
2	6.3	2.7	4.9	1.8
2	6.7	3.3	5.7	2.1
2	7.2	3.2	6	1.8
2	6.2	2.8	4.8	1.8
2	6.1	3	4.9	1.8
2	6.4	2.8	5.6	2.1
2	7.2	3	5.8	1.6
2	7.4	2.8	6.1	1.9
2	7.9	3.8	6.4	2
2	6.4	2.8	5.6	2.2
2	6.3	2.8	5.1	1.5
2	6.1	2.6	5.6	1.4
2	7.7	3	6.1	2.3
2	6.3	3.4	5.6	2.4
2	6.4	3.1	5.5	1.8
2	6	3	4.8	1.8
2	6.9	3.1	5.4	2.1
2	6.7	3.1	5.6	2.4
2	6.9	3.1	5.1	2.3
2	5.8	2.7	5.1	1.9
2	6.8	3.2	5.9	2.3
2	6.7	3.3	5.7	2.5
2	6.7	3	5.2	2.3
2	6.3	2.5	5	1.9
2	6.5	3	5.2	2
2	6.2	3.4	5.4	2.3
2	5.9	3	5.1	1.8