最简单的贝叶斯分类器演示Matlab程序

% % Liscense Notification 2013 % % Redistributions in source and binary forms, with or without % modification, are totally FREE provided that you keep this keep this % notification % % Descritption: This package demonstrates the discrete adaboost with % stump(decision tree with only the root node) weak classifers. % Author: Ranch Lai (ranchlai@hotmail.com) % Release date: Aug 17, 2013 %/ %% clc; clear; close all; %% generate random data shift =3.0; n = 2;%2 dim %y = 1./exp(-w'*x+b) sigma = 1; N = 500; x = [randn(n,N/2)-shift, randn(n,N/2)*sigma+shift]; y = [ones(N/2,1);-ones(N/2,1)]; %show the data figure; plot(x(1,1:N/2),x(2,1:N/2),'rs'); hold on; plot(x(1,1+N/2:N),x(2,1+N/2:N),'go'); title('2d training data'); legend('Positve samples','Negative samples','Location','SouthEast'); % model fitting using maximum likelihood [model_pos,model_neg] = FindGuassianModel(x,y); %% test on new dataset, same distribution n = 2;%2 dim %y = 1./exp(-w'*x+b) sigma = 2; N = 500; x = [randn(n,N/2)-shift, randn(n,N/2)*sigma+shift]; y = [ones(N/2,1);-ones(N/2,1)]; figure; plot(x(1,1:N/2),x(2,1:N/2),'rs'); hold on; plot(x(1,1+N/2:N),x(2,1+N/2:N),'go'); title('2d testing data'); hold on; %% gaussian model as a baseline [err,h] = FindModelError(model_pos,model_neg,x,y); fprintf('Baysian error on test data set: %f\n',err/N); x_pos = x(:,h==1); x_neg = x(:,h~=1); plot(x_pos(1,:),x_pos(2,:),'r.'); hold on; plot(x_neg(1,:),x_neg(2,:),'g.'); legend('Positve samples','Negative samples','Positve samples as predicted','Negative samples as predicted','Location','SouthEast');