Adding my solutions to ex1.

ex1/computeCost.m

@@ -14,7 +14,10 @@
 %               You should set J to the cost.
 
 
+predictions = X*theta;
+sqrerrors = (predictions-y).^2;
 
+J = 1/(2*m) * sum(sqrerrors);
 
 
 % =========================================================================

ex1/computeCostMulti.m

@@ -14,7 +14,10 @@
 %               You should set J to the cost.
 
 
+predictions = X*theta;
+sqrerrors = (predictions-y).^2;
 
+J = 1/(2*m) * sum(sqrerrors);
 
 
 % =========================================================================

ex1/ex1.m

@@ -30,10 +30,10 @@
 % Complete warmUpExercise.m 
 fprintf('Running warmUpExercise ... \n');
 fprintf('5x5 Identity Matrix: \n');
-warmUpExercise()
+%warmUpExercise()
 
-fprintf('Program paused. Press enter to continue.\n');
-pause;
+%fprintf('Program paused. Press enter to continue.\n');
+%pause;
 
 
 %% ======================= Part 2: Plotting =======================
@@ -46,8 +46,8 @@
 % Note: You have to complete the code in plotData.m
 plotData(X, y);
 
-fprintf('Program paused. Press enter to continue.\n');
-pause;
+%fprintf('Program paused. Press enter to continue.\n');
+%pause;
 
 %% =================== Part 3: Gradient descent ===================
 fprintf('Running Gradient Descent ...\n')

ex1/ex1_multi.m

@@ -82,7 +82,7 @@
 fprintf('Running gradient descent ...\n');
 
 % Choose some alpha value
-alpha = 0.01;
+alpha = 0.1;
 num_iters = 100;
 
 % Init Theta and Run Gradient Descent 
@@ -104,8 +104,15 @@
 % ====================== YOUR CODE HERE ======================
 % Recall that the first column of X is all-ones. Thus, it does
 % not need to be normalized.
-price = 0; % You should change this
 
+params = [1650 3];
+
+params = (params - mu) ./ sigma;
+
+params = [1 params];
+
+%price = theta(1) + ((1650 - mu(1)) * ((theta(2) * sigma(1)) + mu(1))) + (3 * ((theta(3) * sigma(2)) + mu(2)));
+price = params * theta;
 
 % ============================================================
 
@@ -149,8 +156,8 @@
 
 % Estimate the price of a 1650 sq-ft, 3 br house
 % ====================== YOUR CODE HERE ======================
-price = 0; % You should change this
 
+price = theta(1) + (1650 * theta(2)) + (3 * theta(3));
 
 % ============================================================
 

ex1/featureNormalize.m

@@ -6,6 +6,7 @@
 %   working with learning algorithms.
 
 % You need to set these values correctly
+m = size(X, 2);
 X_norm = X;
 mu = zeros(1, size(X, 2));
 sigma = zeros(1, size(X, 2));
@@ -28,6 +29,14 @@
 
 
 
+for dim = 1:m
+
+	feature = X(:, dim);
+	feature -= (mu(1, dim) = mean(feature));
+	feature /= (sigma(1, dim) = std(feature));
+	X_norm(:, dim) = feature;
+
+end
 
 
 

ex1/gradientDescent.m

@@ -7,6 +7,8 @@
 m = length(y); % number of training examples
 J_history = zeros(num_iters, 1);
 
+aa = alpha * (1/m);
+
 for iter = 1:num_iters
 
     % ====================== YOUR CODE HERE ======================
@@ -18,9 +20,14 @@
     %
 
 
+	deviation = (X*theta)-y;
 
+	theta -= aa * [
+		sum(deviation);
+		sum(deviation .* X(:,2))
+	];
 
-
+    computeCost(X, y, theta)
 
 
     % ============================================================

ex1/gradientDescentMulti.m

@@ -5,8 +5,12 @@
 
 % Initialize some useful values
 m = length(y); % number of training examples
+n = length(X(1, :));
+
 J_history = zeros(num_iters, 1);
 
+aa = alpha * (1/m);
+
 for iter = 1:num_iters
 
     % ====================== YOUR CODE HERE ======================
@@ -17,12 +21,18 @@
     %       of the cost function (computeCostMulti) and gradient here.
     %
 
+	deviation = (X*theta)-y;
+	tmp = zeros(n, 1);
 
+	for feature = 1:n
+		tmp(feature) =sum(deviation .* X(:, feature));
+	end
 
-
-
-
-
+	theta -= aa * tmp;
+	%theta -= aa * [
+		%sum(deviation);
+		%sum(deviation .* X(:,2))
+	%];
 
 
 

ex1/normalEqn.m

@@ -13,6 +13,7 @@
 % ---------------------- Sample Solution ----------------------
 
 
+theta = pinv(X' * X) * X' * y;
 
 
 % -------------------------------------------------------------

ex1/plotData.m

@@ -14,11 +14,14 @@ function plotData(x, y)
 %       appear as red crosses. Furthermore, you can make the
 %       markers larger by using plot(..., 'rx', 'MarkerSize', 10);
 
-figure; % open a new figure window
 
 
+plot(x, y, 'rx', 'MarkerSize', 10);
+ylabel('Profit in $10,000s');
+xlabel('Population of City in 10,000s');
 
 
+figure; % open a new figure window
 
 
 % ============================================================

ex1/warmUpExercise.m

@@ -2,15 +2,15 @@
 %WARMUPEXERCISE Example function in octave
 %   A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix
 
-A = [];
+%A = [];
 % ============= YOUR CODE HERE ==============
 % Instructions: Return the 5x5 identity matrix 
 %               In octave, we return values by defining which variables
 %               represent the return values (at the top of the file)
 %               and then set them accordingly. 
 
 
-
+A = eye(5);