Sample use In Octave:

X = [m rows of training data, where each row is a vector of n input values.] y = [is a vector of m correct values between 0 and num_labels] num_labels =the number of possible different lables to find for the datalambda = 0.1; m = size(X, 1); n = size(X, 2); class_thetas = zeros(num_labels, n + 1); X = [ones(m, 1) X]; %add a column of ones. options = optimset('MaxIter', 50); guess = zeros(n+1, 1); for k = 1:num_labels; [theta] = fmincg (@(t)(Cost(t, X, (y==k), lambda)), guess, options); class_thetas(k,:)=theta'; end

At this point, we have a set of thetas to classify each label. To use those, given XX; a new matrix of test data, with rows of n input values:

[confidence, label] = max(sigmoid( XX * class_thetas'), [], 2);

This example uses the standard Logistic Cost function, with Regularization.

function [J, S] = cost(theta, X, y) m = length(y); hyp = sigmoid(X*theta); %make a guess based on the sigmoid of our training data times our current paramaters. costs = -y' * log(hyp) - (1-y)' * log(1-hyp); %cost with sigmoid function J = sum(costs)/m + (lambda * sum(theta(2:end).^2) / (2*m)); %mean cost + regularization err = (hyp .- y); %actual error.

%Note this happens to be the derivative of our cost function. S = (X' * err)./m + (lambda .* [0;theta(2:end)] ./ m ); %slope of the error + regularization end

file: /Techref/method/ai/LogisticClassifier.htm, 1KB, , updated: 2015/8/29 12:50, local time: 2024/4/17 12:41, |

©2024 These pages are served without commercial sponsorship. (No popup ads, etc...).Bandwidth abuse increases hosting cost forcing sponsorship or shutdown. This server aggressively defends against automated copying for any reason including offline viewing, duplication, etc... Please respect this requirement and DO NOT RIP THIS SITE. Questions?<A HREF="http://www.massmind.org/techref/method/ai/LogisticClassifier.htm"> Machine Learning Method Logistic Classifier</A> |

Did you find what you needed? |

## Welcome to massmind.org! |

## Welcome to www.massmind.org! |

.