jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
33 lines
1.1 KiB
Matlab
33 lines
1.1 KiB
Matlab
function [f,g] = softmax_regression(theta, X,y)
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%
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% Arguments:
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% theta - A vector containing the parameter values to optimize.
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% In minFunc, theta is reshaped to a long vector. So we need to
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% resize it to an n-by-(num_classes-1) matrix.
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% Recall that we assume theta(:,num_classes) = 0.
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%
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% X - The examples stored in a matrix.
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% X(i,j) is the i'th coordinate of the j'th example.
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% y - The label for each example. y(j) is the j'th example's label.
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%
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m=size(X,2);
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n=size(X,1);
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% theta is a vector; need to reshape to n x num_classes.
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theta=reshape(theta, n, []);
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num_classes=size(theta,2)+1;
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% initialize objective value and gradient.
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f = 0;
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g = zeros(size(theta));
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%
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% TODO: Compute the softmax objective function and gradient using vectorized code.
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% Store the objective function value in 'f', and the gradient in 'g'.
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% Before returning g, make sure you form it back into a vector with g=g(:);
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%
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%%% YOUR CODE HERE %%%
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g=g(:); % make gradient a vector for minFunc
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