problem 1

assignments/hwk02/Problem1/Classify.m

@@ -3,9 +3,20 @@
 % these posterior probabilities are compared using the log odds.
 function [predictions] = Classify(data, m1, m2, S1, S2, pc1, pc2)
 
+  d = 8;
+
   % 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
+  for i = 1:length(data)
+      data(i)
+  end
   
   % TODO: get predictions from log odds calculation
   

assignments/hwk02/Problem1/Param_Est.m

@@ -2,17 +2,32 @@
 % (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, 
 % 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
   if(strcmp(part, '3'))
-  % TODO: compute parameters for model 3
+    S1 = diag(diag(S1));
+    S2 = diag(diag(S2));
 
   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'))
-  % TODO: compute parameters for model 1
   end
   
 end % Function end