Generalized linear regression

calculate_hypothesis.m

@@ -1,9 +1,9 @@
 function hypothesis = calculate_hypothesis(X, theta, training_example)
     %CALCULATE_HYPOTHESIS This calculates the hypothesis for a given X,
     %theta and specified training example
-    x0 = X(training_example, 1);
-    x1 = X(training_example, 2);
+    x = X(training_example, 1:length(theta));
 
-    hypothesis = theta(1)*x0+theta(2)*x1;
+    hypothesis = sum(x.*theta);
+    %hypothesis = theta(1)*x0+theta(2)*x1+theta(3)*x2;
 end
 

gradient_descent.m

@@ -19,34 +19,23 @@ function theta = gradient_descent(X, y, theta, alpha, iterations, do_plot)
         % gradient descent
         %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-        theta_0 = theta(1);
-        theta_1 = theta(2);
-
+        % fill temporary array with values of theta
+        theta_temp = theta;
+        for ind = 1:length(theta_temp)
             %update theta(1) and store in temporary variable theta_0
             sigma = 0.0;
 
             for i = 1:m
                 hypothesis = calculate_hypothesis(X, theta, i);
                 output = y(i);
-            sigma = sigma + (hypothesis - output);
+                sigma = sigma + (hypothesis - output) * X(i, ind);
             end
 
-        theta_0 = theta_0 - ((alpha * 1.0) / m) * sigma;
-
-
-        %update theta(2) and store in temporary variable theta_1
-        sigma = 0.0;
-
-        for i = 1:m
-            hypothesis = calculate_hypothesis(X, theta, i);
-            output = y(i);
-            sigma = sigma + (hypothesis - output) * X(i, 2);
+            theta_temp(ind) = theta_temp(ind) - ((alpha * 1.0) / m) * sigma;
         end
 
-        theta_1 = theta_1 - ((alpha * 1.0) / m) * sigma;
-
         %update theta
-        theta = [theta_0, theta_1];
+        theta = theta_temp;
 
         %update cost_vector
         cost_vector = [cost_vector; compute_cost(X, y, theta)];

mllab1.m

@@ -4,7 +4,7 @@
 %% initialize
 theta = [0.0, 0.0]; %The weights of our model.
 
-alpha = 0.01; %The step size for gradient descent.
+alpha = 0.001; %The step size for gradient descent.
 iterations = 50;
 
 %do plotting