47 lines
1.4 KiB
Matlab
47 lines
1.4 KiB
Matlab
% implements Param_Est, returns the parameters for each Multivariate Gaussian
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% (m1: learned mean of features for class 1, m2: learned mean of features
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% for class 2, S1: learned covariance matrix for features of class 1,
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% S2: learned covariance matrix for features of class 2)
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function [m1, m2, S1, S2] = Param_Est(training_data, training_labels, part)
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[num_rows, ~] = size(training_data);
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class1_data = training_data(training_labels==1,:);
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class2_data = training_data(training_labels==2,:);
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m1 = mean(class1_data);
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m2 = mean(class2_data);
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S1 = cov(class1_data);
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S2 = cov(class2_data);
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% Model 1.
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% Assume independent 𝑆1 and 𝑆2 (the discriminant function is as equation (5.17) in the textbook).
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if (strcmp(part, '1'))
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% Already calculated above so nothing to be done here
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% Model 2.
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% Assume 𝑆1 = 𝑆2. In other words, shared S between two classes
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% (the discriminant function is as equation (5.21) and (5.22) in the textbook).
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elseif (strcmp(part, '2'))
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P_C1 = length(class1_data) / num_rows;
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P_C2 = length(class2_data) / num_rows;
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S = P_C1 * S1 + P_C2 * S2;
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S1 = S;
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S2 = S;
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% Model 3.
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% Assume 𝑆1 and 𝑆2 are diagonal (the Naive Bayes model in equation (5.24)).
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elseif (strcmp(part, '3'))
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% pull diagonals into vector -> turn vector into diagonal matrix
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S1 = diag(diag(S1));
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S2 = diag(diag(S2));
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end
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end % Function end
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