-
Notifications
You must be signed in to change notification settings - Fork 0
/
main_incremental_CVIs_example.m
276 lines (261 loc) · 10.4 KB
/
main_incremental_CVIs_example.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
%% This is an example of the iCVI toolbox usage.
%
% PROGRAM DESCRIPTION
% This program exemplifies the usage of the iCVI toolbox provided. The
% latter includes code for batch and incremental versions of:
% 1. Calinski-Harabasz
% 2. I index
% Additional Parameter: p>=1 (default = 2)
% Note: The default reduces the I index to Pakhira-Bandyopadhyay-Maulik
% 3. WB index
% 4. Silhouette
% 5. Xie-Beni
% 6. Davies-Bouldin
% 7. Generalized Dunn's Index 43
% 8. Generalized Dunn's Index 53
% 9. Partition Separation
% 10. Negentropy Increment
% Additional Parameter: epsilon (default = 12)
% 11. Representative Cross Information Potential
% Additional Parameter: epsilon (default = 12)
% 12. Representative Cross Entropy
% Additional Parameter: epsilon (default = 12)
% 13. Conn_Index
% Additional Parameters: condition 'CONN' or 'CADJ' (default = 'CONN')
% ART-A vigilance parameter rho_A
% Note: In this example rho_A = 0.9
%
% REFERENCES
% [1] L. E. Brito da Silva, N. M. Melton, and D. C. Wunsch II, "Incremental
% Cluster Validity Indices for Hard Partitions: Extensions and Comparative
% Study," ArXiv e-prints, Feb 2019, arXiv:1902.06711v1 [cs.LG].
%
% Code written by Leonardo Enzo Brito da Silva
% under the supervision of Dr. Donald C. Wunsch II
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Clean up
clear variables; close all; fclose all; echo off; clc;
%% Add path
addpath('classes', 'functions', 'data', 'inputs');
%% Inputs
fprintf('************************************************************************************\n')
fprintf('************************************************************************************\n')
fprintf('Cluster Validity Indices (CVIs) available:\n')
fprintf('\t1. Calinski-Harabasz: for this CVI type CH\n')
fprintf('\t2. I index: for this CVI type I\n')
fprintf('\t3. WB index: for this CVI type WB\n')
fprintf('\t4. Silhouette: for this CVI type SIL\n')
fprintf('\t5. Xie-Beni: for this CVI type XB\n')
fprintf('\t6. Davies-Bouldin: for this CVI type DB\n')
fprintf('\t7. Generalized Dunn''s Index 43: for this CVI type GD43\n')
fprintf('\t8. Generalized Dunn''s Index 53: for this CVI type GD53\n')
fprintf('\t9. Partition Separation: for this CVI type PS\n')
fprintf('\t10. Negentropy Increment: for this CVI type NI\n')
fprintf('\t11. Representative Cross Information Potential: for this CVI type rCIP\n')
fprintf('\t12. Representative Cross Entropy: for this CVI type rH\n')
fprintf('\t13. Conn_Index: for this CVI type CONN\n')
set_icvis_names();
opts_CVI = get_icvis_names();
CVI = input('Please select an iCVI to continue...\n', 's');
ind_CVI = ismember(opts_CVI, CVI);
if any(ind_CVI)
ind2 = find(ind_CVI);
switch ind2
case 1
CVI_name = 'Calinski-Harabasz';
case 2
CVI_name = 'Silhouette';
case 3
CVI_name = 'Pakhira-Bandyopadhyay-Maulik';
case 4
CVI_name = 'WB';
case 5
CVI_name = 'Xie-Beni';
case 6
CVI_name = 'Davies-Bouldin';
case 7
CVI_name = 'Generalized Dunn''s Index 43';
case 8
CVI_name = 'Generalized Dunn''s Index 53';
case 9
CVI_name = 'Partition Separation';
case 10
CVI_name = 'Conn_Index';
case 11
CVI_name = 'Negentropy Increment';
case 12
CVI_name = 'Representative Cross Information Potential';
case 13
CVI_name = 'Representative Cross Entropy';
end
fprintf('\n\tIncremental CVI selected: %s\n\n', CVI_name);
else
error('\nThe input provided was: %s. \nThe input (CVI) must be one of the following:\nCH, SIL, I, WB, XB, DB, GD43, GD53, PS, CONN, NI, rCIP or rH.', CVI)
end
fprintf('Experiments available:\n')
fprintf('\t1. Correct-partitioning: for this experiment type cp\n')
fprintf('\t2. Under-partitioning: for this experiment type up\n')
fprintf('\t3. Over-partitioning: for this experiment type op\n')
input_experiment = input('Please select an experiment type to continue...\n', 's');
opts_exp = {'cp', 'up', 'op'};
ind_exp = ismember(opts_exp, input_experiment);
if any(ind_exp)
ind2 = find(ind_exp);
switch ind2
case 1
experiment = 'correct-partition';
case 2
experiment = 'under-partition';
case 3
experiment = 'over-partition';
end
fprintf('\n\tExperiment selected: %s\n\n', experiment);
else
error('\nThe input provided was: %s. \nThe input (experiment) must be one of the following:\ncp, up or op.', input_experiment)
end
fprintf('************************************************************************************\n')
fprintf('************************************************************************************\n')
%% Load Data
fprintf('Data set selected: D4\n');
fprintf('Loading data...\n');
load D4.mat
[nSamples, dim] = size(data);
fprintf('Done.\n');
fprintf('************************************************************************************\n')
fprintf('************************************************************************************\n')
%% CVI
fprintf('Setting up CVI...\n');
SFAM = [];
switch CVI
case 'CH'
valind_inc = CVI_CH();
case 'SIL'
valind_inc = CVI_cSIL();
case 'I'
valind_inc = CVI_I();
valind_inc.p = 2;
case 'WB'
valind_inc = CVI_WB();
CVI_name = 'WB';
case 'XB'
valind_inc = CVI_XB();
case 'DB'
valind_inc = CVI_DB();
case 'GD43'
valind_inc = CVI_GD43();
case 'GD53'
valind_inc = CVI_GD53();
case 'NI'
valind_inc = CVI_NI();
case 'rCIP'
valind_inc = CVI_rCIP();
case 'rH'
valind_inc = CVI_rH();
case 'PS'
valind_inc = CVI_PS();
case 'CONN'
valind_inc = CVI_CONN();
valind_inc.condition = 'CONN';
% Modified Simplified Fuzzy ARTMAP
settings = struct();
settings.ART_A = struct();
settings.ART_A.rho = 0.9;
settings.ART_A.alpha = 1e-3;
settings.ART_A.beta = 1;
SFAM = SFuzzyARTMAPMod(settings);
SFAM.display = false;
SFAM.ART_A.find_bmu2 = true;
end
valind_inc.dim = dim;
if strcmp(CVI, 'rH') || strcmp(CVI, 'rCIP') || strcmp(CVI, 'NI')
epsilon = 12;
delta = 10^(-epsilon/dim);
valind_inc.delta_term = delta.*eye(dim, dim);
end
fprintf('Done.\n');
fprintf('************************************************************************************\n')
fprintf('************************************************************************************\n')
%% Experimental example
fprintf('Starting experiment...\n');
rng(0, 'twister'); % Set seed for reproducibility
switch experiment
case 'correct-partition'
fprintf('\t\tSetting up correct partition...\n');
[x, y, x_axis_ticks, nClasses, class_order] = setup_correct_partition(data, classes);
case 'under-partition'
fprintf('\t\tSetting up under-partition...\n');
[x, y, y_under, x_axis_ticks, classes_merged, nClasses_under, nClasses, class_order] = setup_under_partition(data, classes);
case 'over-partition'
fprintf('\t\tSetting up over-partition...\n');
[x, y, y_over, x_axis_ticks, class_selected, nClasses_over, nClasses, class_order] = setup_over_partition(data, classes);
end
fprintf('\t\tDone.\n');
% Allocate Variables
CriterionValue_inc = zeros(size(x, 1), 1);
nClusters_inc = zeros(size(x, 1), 1);
labels = zeros(size(x, 1), 1);
protos = zeros(size(x, 1), 2);
switch experiment
case 'correct-partition'
labels_tracking = zeros(1, nClasses);
y_hat = y;
case 'under-partition'
labels_tracking = zeros(1, nClasses_under);
y_hat = y_under;
case 'over-partition'
labels_tracking = zeros(1, nClasses_over);
y_hat = y_over;
end
% Experiment
maxEpochs = 1;
counter = 1;
fprintf('\t\tStreaming samples...\n');
for ix = 1:nSamples
% Update iCVI parameters (Clustering Algorithm Agnostic)
if ix==1
labels(ix) = 1;
labels_tracking(counter) = y_hat(ix);
else
if ~ismember(y_hat(ix), labels_tracking) % new cluster
labels(ix) = sum(labels_tracking>0) + 1;
counter = counter + 1;
labels_tracking(counter) = y_hat(ix);
else
labels(ix) = find(labels_tracking == y_hat(ix)) ;
end
end
% Update iCVI parameters
if ~strcmp(CVI, 'CONN')
valind_inc = valind_inc.param_inc(x(ix, :), labels(ix));
else % ART
xcc = [x(ix, :) 1 - x(ix, :)]; % Complemenent-code
SFAM = SFAM.train(xcc, maxEpochs, labels(ix));
protos(ix, :) = [SFAM.ART_A.labels, SFAM.ART_A.labels2];
valind_inc = valind_inc.param_inc(protos(ix, 1), protos(ix, 2), labels(ix));
end
% Track variables 01
nClusters_inc(ix) = valind_inc.nClusters;
% Compute iCVI if there are at least 2 clusters
if nClusters_inc(ix) > 1
valind_inc = valind_inc.evaluate();
else
valind_inc.CriterionValue = 0;
end
% Track variables 02
CriterionValue_inc(ix) = valind_inc.CriterionValue;
end
fprintf('\t\tDone.\n');
%% Plot behaviors/partitions
fprintf('\t\tPlotting outputs...\n');
switch experiment
case 'correct-partition'
plot_behavior_correct_partition(x, labels, CVI, CriterionValue_inc, nClusters_inc, valind_inc, SFAM, x_axis_ticks, CVI_name)
case 'under-partition'
plot_behavior_under_partition(x, labels, CVI, CriterionValue_inc, nClusters_inc, valind_inc, SFAM, x_axis_ticks, CVI_name, class_order, classes_merged, y)
case 'over-partition'
plot_behavior_over_partition(x, labels, CVI, CriterionValue_inc, nClusters_inc, valind_inc, SFAM, x_axis_ticks, CVI_name, class_selected, y)
end
fprintf('\t\tDone.\n');
fprintf('Done.\n');
fprintf('************************************************************************************\n')
fprintf('************************************************************************************\n')