`k_variance` and `iii` are not defined. about constrained_clustering

[danyaljj]

Here is a slightly older version of the file, probably right before losing the outer loop.

clear all; close all; clc;
pathAll('');
rate = 0.01;
 
for iii = 1:5
    rate = 0.03; 
    p = 1;   
    for k_variance = -3:3
    %for rate = [0.01 0.03 0.05]
        %for p = [0.8 0.9 0.95 1]
            %     p = 1;
            if rate == 0.01 && iii == 1
                continue;   
            end
 
            [X,Y] = readUCIData('iris' );
            %[X,Y] = gaussians(200);
            size1 = size(X,1);
            order = randperm(size1);
            X = X(order,:);
            Y = Y(order,:);
            k = 3;
            kprime = k-k_variance;
            if kprime > 0 
                experiment_1(X,Y,kprime, ['Variable_k_iris_p=' num2str(p) .... 
                    '_rate=' num2str(rate) '_i=' num2str(iii) '_k_variance='  num2str(k_variance) ], rate, p);
            end
            %     p = 1;
            [X,Y] = readUCIData('wine');
            %[X,Y] = gaussians(200);
            size1 = size(X,1);
            order = randperm(size1);
            X = X(order,:);
            Y = Y(order,:);
            k = 3;
            kprime = k-k_variance;
            if kprime > 0 
                experiment_1(X,Y,kprime, ['Variable_k_wine_p=' num2str(p) '_rate=' num2str(rate) '_i=' num2str(iii)], rate, p);                
            end
 
            %     p = 1;
            [X,Y] = readUCIData('ecoli');
            %[X,Y] = gaussians(200);
            size1 = size(X,1);
            order = randperm(size1);
            X = X(order,:);
            Y = Y(order,:);
            k = 8;
            kprime = k-k_variance;
            if kprime > 0 
                experiment_1(X,Y,kprime, ['Variable_k_eclli_p=' num2str(p) '_rate=' num2str(rate) '_i=' num2str(iii)], rate, p);
            end
 
            %     p = 1;
            [X,Y] = readUCIData('glass');
            %[X,Y] = gaussians(200);
            size1 = size(X,1);
            order = randperm(size1);
            X = X(order,:);
            Y = Y(order,:);
            k = 7;
            kprime = k-k_variance;
            if kprime > 0 
                experiment_1(X,Y,kprime, ['Variable_k_glass_p=' num2str(p) '_rate=' num2str(rate) '_i=' num2str(iii) ], rate, p);
            end
 
            %     p = 1;
            [X,Y] = readUCIData('isomosphere');
            %[X,Y] = gaussians(200);
            size1 = size(X,1);
            order = randperm(size1);
            X = X(order,:);
            Y = Y(order,:);
            k = 2;
            kprime = k-k_variance;
            if kprime > 0 
                experiment_1(X,Y,kprime, ['Variable_k_isomosphere_p=' num2str(p) '_rate=' num2str(rate) '_i=' num2str(iii)], rate, p);
            end
 
            %     p = 1;
            [X,Y] = readUCIData('balance');
            %[X,Y] = gaussians(200);
            size1 = size(X,1);
            order = randperm(size1);
            X = X(order,:);
            Y = Y(order,:);
            k = 3;
            kprime = k-k_variance;
            if kprime > 0 
                experiment_1(X,Y,kprime, ['Variable_k_balance_p=' num2str(p) '_rate=' num2str(rate) '_i=' num2str(iii)], rate, p);
            end
    end
end

[se4u]

Ah okay so k_variance is just the range of values of k that we are going to try, and iii is just the number of trials.