朴素贝叶斯分类器（MATLAB源代码）

function [predictive, posterior] = predict_nbc(test, priors, likelihood) %PREDICT_NBC uses a naive bayes classifier to predict the class labels of %the test data set. %% [predictive, posterior] = predict_nbc(test, priors, likelihood) %% Input: % test - a struct representing the test data set % test.class - the class of each data % test.features - the feature of each data % priors - a struct representing the priors of each class % priors.class - the class labels % priors.value - the priors of its corresponding classes % likelihood - a struct representing the likelihood % likelihood.matrixColnames - the feature values % likelihood.matrixRownames - the class labels % likelihood.matrix - the likelihood values %% Output: % predictive - the predictive results of the test data set % predictive.class - the predictive class for each data % posterior - a struct representing the posteriors of each class % posterior.class - the class labels % posterior.value - the posteriora of the corresponding classes %% Running these code to get some examples: %nbc_mushroom %% Edited by X. Sun % My homepage: http://pamixsun.github.io/ %% % Check the input arguments if nargin < 3 error(message('MATLAB:UNIQUE:NotEnoughInputs')); end posterior.class = priors.class; % Calculate posteriors for each test data record predictive.class = zeros(length(size(test.features, 1)), 1); posterior.value = zeros(size(test.features, 1), length(priors.class)); for i = 1 : size(test.features, 1) record = test.features(i, :); % Calculate posteriors for each possible class of that record for j = 1 : length(priors.class) class = priors.class(j); % Initialize posterior as the prior value of that class posteriorValue = priors.value(priors.class == class); for k = 1 : length(record) item = record(k); likelihoodValue = ... likelihood.matrix{k}(j, likelihood.matrixColnames{k}(:) == item); posteriorValue = posteriorValue * likelihoodValue; end % Calculate the posteriors posterior.value(i, j) = posteriorValue; end % Get the predictive class predictive.class(i) = ... posterior.class(posterior.value(i, :) == max(posterior.value(i, :))); end predictive.class = char(predictive.class); predictive.class = predictive.class(:); end