Train num_labels one-vs-all logistic regression classifiers

ex3/oneVsAll.m

@@ -1,17 +1,17 @@
 function [all_theta] = oneVsAll(X, y, num_labels, lambda)
 %ONEVSALL trains multiple logistic regression classifiers and returns all
-%the classifiers in a matrix all_theta, where the i-th row of all_theta 
+%the classifiers in a matrix all_theta, where the i-th row of all_theta
 %corresponds to the classifier for label i
 %   [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
 %   logisitc regression classifiers and returns each of these classifiers
-%   in a matrix all_theta, where the i-th row of all_theta corresponds 
+%   in a matrix all_theta, where the i-th row of all_theta corresponds
 %   to the classifier for label i
 
 % Some useful variables
 m = size(X, 1);
 n = size(X, 2);
 
-% You need to return the following variables correctly 
+% You need to return the following variables correctly
 all_theta = zeros(num_labels, n + 1);
 
 % Add ones to the X data matrix
@@ -20,11 +20,11 @@ X = [ones(m, 1) X];
 % ====================== YOUR CODE HERE ======================
 % Instructions: You should complete the following code to train num_labels
 %               logistic regression classifiers with regularization
-%               parameter lambda. 
+%               parameter lambda.
 %
 % Hint: theta(:) will return a column vector.
 %
-% Hint: You can use y == c to obtain a vector of 1's and 0's that tell use 
+% Hint: You can use y == c to obtain a vector of 1's and 0's that tell use
 %       whether the ground truth is true/false for this class.
 %
 % Note: For this assignment, we recommend using fmincg to optimize the cost
@@ -38,29 +38,31 @@ X = [ones(m, 1) X];
 %
 %     % Set Initial theta
 %     initial_theta = zeros(n + 1, 1);
-%     
+%
 %     % Set options for fminunc
 %     options = optimset('GradObj', 'on', 'MaxIter', 50);
-% 
+%
 %     % Run fmincg to obtain the optimal theta
-%     % This function will return theta and the cost 
+%     % This function will return theta and the cost
 %     [theta] = ...
 %         fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
 %                 initial_theta, options);
 %
 
+for c = 1:num_labels
 
+  % Train a one-vs all classifier for this class c
 
+  initial_theta = zeros(n + 1, 1);
+  options = optimset('GradObj', 'on', 'MaxIter', 50);
 
+  [theta] = fmincg(@(t)(lrCostFunction(t, X, (y == c), lambda)),
+                   initial_theta, options);
 
+  all_theta(c,:) = theta';
 
-
-
-
-
-
+end
 
 % =========================================================================
 
-
 end