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67 lines
1.9 KiB
Matlab
67 lines
1.9 KiB
Matlab
function [all_theta] = oneVsAll(X, y, num_labels, lambda)
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%ONEVSALL trains multiple logistic regression classifiers and returns all
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%the classifiers in a matrix all_theta, where the i-th row of all_theta
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%corresponds to the classifier for label i
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% [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
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% logisitc regression classifiers and returns each of these classifiers
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% in a matrix all_theta, where the i-th row of all_theta corresponds
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% to the classifier for label i
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% Some useful variables
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m = size(X, 1);
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n = size(X, 2);
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% You need to return the following variables correctly
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all_theta = zeros(num_labels, n + 1);
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% Add ones to the X data matrix
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X = [ones(m, 1) X];
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% ====================== YOUR CODE HERE ======================
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% Instructions: You should complete the following code to train num_labels
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% logistic regression classifiers with regularization
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% parameter lambda.
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%
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% Hint: theta(:) will return a column vector.
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%
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% Hint: You can use y == c to obtain a vector of 1's and 0's that tell use
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% whether the ground truth is true/false for this class.
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%
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% Note: For this assignment, we recommend using fmincg to optimize the cost
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% function. It is okay to use a for-loop (for c = 1:num_labels) to
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% loop over the different classes.
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%
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% fmincg works similarly to fminunc, but is more efficient when we
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% are dealing with large number of parameters.
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%
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% Example Code for fmincg:
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%
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% % Set Initial theta
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% initial_theta = zeros(n + 1, 1);
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%
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% % Set options for fminunc
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% options = optimset('GradObj', 'on', 'MaxIter', 50);
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%
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% % Run fmincg to obtain the optimal theta
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% % This function will return theta and the cost
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% [theta] = ...
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% fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
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% initial_theta, options);
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%
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% =========================================================================
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end
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