Week 3

Finishes all mandatory exercises

W3/machine-learning-ex2/ex2/costFunction.m

@@ -31,7 +31,7 @@ J = 1 / m * sum(part_1 - part_2);
 % Calculating g
 temp_1 = sigmoid(X*theta) - y;
 temp_2 = repmat(temp_1, 1, size(X, 2));
-g = 1/m * sum(X .* temp_2);
+grad = 1/m * sum(X .* temp_2);
 
 % =============================================================
 

W3/machine-learning-ex2/ex2/costFunctionReg.m

@@ -17,10 +17,24 @@ grad = zeros(size(theta));
 %               Compute the partial derivatives and set grad to the partial
 %               derivatives of the cost w.r.t. each parameter in theta
 
+% Calculating J
+theta_t_x = X*theta;
+h_theta = sigmoid(theta_t_x);
 
+part_1 = -y'*log(h_theta);
+part_2 = (1 - y)' * log(1 - h_theta);
+part_3 =  sum(theta(2:length(theta)).^2);
+%part_3 = sum(theta(2:length(theta)).*theta(2:length(theta)));
 
+J = ((1 / m) * sum(part_1 - part_2)) + ((lambda/(2*m)) * part_3);
 
+% Calculating g
+temp_1 = sigmoid(X*theta) - y;
+temp_2 = repmat(temp_1, 1, size(X, 2));
 
+theta_vector = (lambda/m) * theta;
+theta_vector(1) = 0;
+grad = (1/m * sum(X .* temp_2))' + theta_vector;
 
 % =============================================================
 

W3/machine-learning-ex2/ex2/predict.m

@@ -15,11 +15,7 @@ p = zeros(m, 1);
 %               You should set p to a vector of 0's and 1's
 %
 
-
-
-
-
-
+p = sigmoid(X*theta) >= 0.5;
 
 % =========================================================================