4iii

assignments/hwk01/Bayes_Learning.m

@@ -8,11 +8,14 @@ function [p1,p2,pc1,pc2] = Bayes_Learning(training_data, validation_data)
 [train_row_size, column_size] = size (training_data); % dimension of training data
 [valid_row_size, ~] = size (validation_data); % dimension of validation data
 X = training_data(1:train_row_size, 1:column_size-1); %Training data
+y = training_data(:,column_size); % training labels
+Xvalid = validation_data(1:valid_row_size, 1:column_size-1); %Training data
+yvalid = validation_data(:,column_size); % training labels
 
 % (1) TODO: find label counts of class 1 and class 2
 
 % (2) TODO: get MLE p1, p2
-
+[p1,p2] = MLE_Learning(training_data);
 
 % Use different P(C_1) and P(C_2) on validation set
 % We compute g(x) = based on priors P(C_1), P(C_2), MLE estimator p1, p2, and x_{1*D}
@@ -25,9 +28,29 @@ for sigma = [0.00001,0.0001,0.001,0.01,0.1,1,2,3,4,5,6]
     error_count = 0; % total number of errors to be count
     % (3) TODO: compute likelihood for class1 and class2 , then compute the posterior
     % probability for both classes (posterior = prior x likelihood). 
-    % Classify each validation sample as whichever class has the higher posterior probability. 
-    % If the sample is misclassified, increment the error count (error_count = error_count + 1);
+    
+    for i = 1:valid_row_size
 
+        x = Xvalid(i, :);
+        correct_label = yvalid(i);
+    
+        postc1 = prod(p1 .^ x .* (1 - p1) .^ (1 - x)) * P_C1;
+        postc2 = prod(p2 .^ x .* (1 - p2) .^ (1 - x)) * P_C2;
+    
+        % Classify each validation sample as whichever class has the higher posterior probability.
+    
+        if postc1 > postc2
+            lab = 1;
+        else
+            lab = 2;
+        end
+    
+        % If the sample is misclassified, increment the error count (error_count = error_count + 1);
+    
+        if lab ~= correct_label
+            error_count = error_count + 1;
+        end
+    end
 
     error_table(index,1) = sigma;
     error_table(index,2) = P_C1;