26 lines
929 B
Matlab
26 lines
929 B
Matlab
% implements MLE_Learning, returns the outputs (p1: learned Bernoulli
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% parameters of the first class, p2: learned Bernoulli parameters of the
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% second class; pc1: prior of the first class, pc2: prior of the
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% second class
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function [p1,p2,pc1,pc2] = MLE_Learning(training_data)
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[train_row_size, column_size] = size(training_data); % dimension of training data
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X = training_data(1:train_row_size, 1:column_size-1); %Training data
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y = training_data(:,column_size); % training labels
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% (1) TODO: find label counts of class 1 and class 2
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class1_rows = find(y == 1);
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class1_count = length(class1_rows);
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class2_rows = find(y == 2);
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class2_count = length(class2_rows);
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% (2) TODO: compute priors pc1, pc2
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pc1 = class1_count / train_row_size;
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pc2 = class2_count / train_row_size;
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% (3) TODO: compute maximum likelihood estimate (MLE) p1, p2
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p1 = sum(X(class1_rows, :)) / class1_count;
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p2 = sum(X(class2_rows, :)) / class2_count;
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