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Train num_labels one-vs-all logistic regression classifiers

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neingeist 2014-10-23 21:17:20 +02:00
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ex3/oneVsAll.m
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@ -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