40 lines
No EOL
1.4 KiB
Matlab
40 lines
No EOL
1.4 KiB
Matlab
% implements Classify, return the predicted class for each row (we'll call each row x) in data
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% by computing the posterior probability that x is in class 1 vs. class 2 then
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% these posterior probabilities are compared using the log odds.
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function [predictions] = Classify(data, m1, m2, S1, S2, pc1, pc2)
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[num_rows, d] = size(data);
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% calculate P(x|C) * P(C) for both classes
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% pxC1 = 1/(power(2*pi, d/2) * power(det(S1), 1/2)) * exp(-1/2 * (data-m1) * inv(S1) * (data-m1)');
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% pxC2 = 1/(power(2*pi, d/2) * power(det(S2), 1/2)) * exp(-1/2 * (data-m2) * inv(S2) * (data-m2)');
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pxC1 = zeros(num_rows,1);
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pxC2 = zeros(num_rows,1);
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for i = 1:num_rows
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x = data(i,:);
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pxC1(i) = 1/(power(2*pi, d/2) * power(det(S1), 1/2)) * exp(-1/2 * (x-m1) * inv(S1) * (x-m1)');
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pxC2(i) = 1/(power(2*pi, d/2) * power(det(S2), 1/2)) * exp(-1/2 * (x-m2) * inv(S2) * (x-m2)');
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end
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% pxC1 = mvnpdf(data, m1, S1);
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% pxC2 = mvnpdf(data, m2, S2);
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% P(C|x) = (P(x|C) * P(C)) / common factor
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pC1x = pxC1 * pc1;
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pC2x = pxC2 * pc2;
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% calculate log odds, if > 0 then data(i) belongs to class c1, else, c2
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log_odds = log(pC1x) - log(pC2x);
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% get predictions from log odds calculation
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predictions = zeros(num_rows,1);
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for i = 1:num_rows
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if log_odds(i) > 0
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predictions(i) = 1;
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else
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predictions(i) = 2;
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end
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end
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end % Function end |