% implements Param_Est, returns the parameters for each Multivariate Gaussian
% (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)

  [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'))
    S1 = diag(diag(S1));
    S2 = diag(diag(S2));

  elseif(strcmp(part, '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'))
  end
  
end % Function end