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problem 1

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Michael Zhang 2023-10-13 17:11:18 -05:00
parent e0f6fee834
commit 3b18a625a5
2 changed files with 30 additions and 4 deletions
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11
assignments/hwk02/Problem1/Classify.m
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@ -3,9 +3,20 @@
% these posterior probabilities are compared using the log odds. % these posterior probabilities are compared using the log odds.
function [predictions] = Classify(data, m1, m2, S1, S2, pc1, pc2) function [predictions] = Classify(data, m1, m2, S1, S2, pc1, pc2)
d = 8;
% TODO: calculate P(x|C) * P(C) for both classes % TODO: calculate P(x|C) * P(C) for both classes
pxC1 = exp(-1/2*(data-m1)./S1*(data-m1)') / (power(2*pi,d/2) * sqrt(det(S1)));
pxC2 = exp(-1/2*(data-m2)*(S2\(data-m2).'));
g1 = pxC1 * pc1;
g2 = pxC2 * pc2;
% TODO: calculate log odds, if > 0 then data(i) belongs to class c1, else, c2 % TODO: calculate log odds, if > 0 then data(i) belongs to class c1, else, c2
for i = 1:length(data)
data(i)
end
% TODO: get predictions from log odds calculation % TODO: get predictions from log odds calculation

23
assignments/hwk02/Problem1/Param_Est.m
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@ -2,17 +2,32 @@
% (m1: learned mean of features for class 1, m2: learned mean of features % (m1: learned mean of features for class 1, m2: learned mean of features
% for class 2, S1: learned covariance matrix for features of class 1, % for class 2, S1: learned covariance matrix for features of class 1,
% S2: learned covariance matrix for features of class 2) % S2: learned covariance matrix for features of class 2)
function [m1 m2 S1 S2] = Param_Est(training_data, training_labels, part) function [m1, m2, S1, S2] = Param_Est(training_data, training_labels, part)
[num_rows, num_cols] = size(training_data);
class1_data = training_data(training_labels==1,:);
class2_data = training_data(training_labels==2,:);
m1 = mean(class1_data);
m2 = mean(class2_data);
S1 = cov(class1_data);
S2 = cov(class2_data);
% Parameter estimation for 3 different models described in homework % Parameter estimation for 3 different models described in homework
if(strcmp(part, '3')) if(strcmp(part, '3'))
% TODO: compute parameters for model 3 S1 = diag(diag(S1));
S2 = diag(diag(S2));
elseif(strcmp(part, '2')) elseif(strcmp(part, '2'))
% TODO: compute parameters for model 2 P_C1 = length(class1_data) / num_rows;
P_C2 = length(class2_data) / num_rows;
S = P_C1 * S1 + P_C2 + S2;
S1 = S;
S2 = S;
elseif(strcmp(part, '1')) elseif(strcmp(part, '1'))
% TODO: compute parameters for model 1
end end
end % Function end end % Function end