df: r6 octave

ds/25-1/r/6/mlclass-ex1/gradientDescentMulti.m

@@ -0,0 +1,31 @@
+function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
+%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
+%   theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
+%   taking num_iters gradient steps with learning rate alpha
+
+% Initialize some useful values
+m = length(y); % number of training examples
+J_history = zeros(num_iters, 1);
+
+for iter = 1:num_iters
+
+    % ====================== YOUR CODE HERE ======================
+    % Instructions: Perform a single gradient step on the parameter vector
+    %               theta.
+    %
+    % Hint: While debugging, it can be useful to print out the values
+    %       of the cost function (computeCostMulti) and gradient here.
+    %
+
+    h = X * theta;
+    base = ((h - y)' * X)';
+    theta -= alpha / m * base;
+
+    % ============================================================
+
+    % Save the cost J in every iteration
+    J_history(iter) = computeCostMulti(X, y, theta);
+
+end
+
+end