lrCostFunction.m
J = 1./m*(-y'*log(sigmoid(X*theta))-(1-y')*log(1-sigmoid(X*theta))); J = J + lambda/(2*m)*(sum(theta.^2)-theta(1).^2); grad = 1./m*X'*(sigmoid(X*theta)-y); grad = grad + lambda/m*theta; grad(1) = grad(1) - lambda/m*theta(1);
oneVsAll.m
initial_theta = zeros(n+1,1); options = optimset('GradObj', 'on', 'MaxIter', 50); for c = 1:num_labels [theta] = fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)),initial_theta, options); all_theta(c,:) = theta'; end
predictOneVsAll.m
[c,p] = max(sigmoid(X*all_theta'),[],2);
predict.m
X = [ones(m,1) X]; a2 = sigmoid(X*Theta1'); a2 = [ones(size(a2,1),1) a2]; a3 = (sigmoid(a2*Theta2')); [c, p] = max(a3, [], 2);
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