matlab-朴素贝叶斯-鸢尾花数据集

clc clear close all total_data=load('C:\Users\ZAN\Desktop\matalb_drive\machine-learnning\Bayes\iris_data.mat'); len = size(total_data.features,1); rand_num=randperm(len);%disorder sequence ratio=0.8; train_num=ratio*len; test_num=len-train_num; data_mat = total_data.features(rand_num,:);%attribute labels = total_data.classes(rand_num);%class labels % normalization maxV = max(data_mat); minV = min(data_mat); range = maxV-minV; newdataMat = (data_mat-repmat(minV,[len,1]))./(repmat(range,[len,1])); newtrain_dataMat=newdataMat(1:train_num,:); newtest_dataMat=newdataMat(train_num+1:end,:); train_labels=labels(1:train_num); test_labels=labels(train_num+1:end); dataMat_1=newtrain_dataMat(find(train_labels==1),:); dataMat_2=newtrain_dataMat(find(train_labels==2),:); dataMat_3=newtrain_dataMat(find(train_labels==3),:); %compute the mean and stdandard diviation of each class mean_1=mean(dataMat_1); std_1=std(dataMat_1); mean_2=mean(dataMat_2); std_2=std(dataMat_2); mean_3=mean(dataMat_3); std_3=std(dataMat_3); right_pre=0; for i=1:test_num prob_1=prod( normpdf(newtest_dataMat(i,:),mean_1,std_1) ,2); prob_2=prod( normpdf(newtest_dataMat(i,:),mean_2,std_2) ,2); prob_3=prod( normpdf(newtest_dataMat(i,:),mean_3,std_3) ,2); max_prob=max([prob_1,prob_2,prob_3]); if max_prob==prob_1 pre_class=1; elseif max_prob==prob_2 pre_class=2; else pre_class=3; end fprintf('predict class is£º%d true result is£º%d \n',[pre_class , test_labels(i)] ); if pre_class == test_labels(i) right_pre=right_pre+1; end end fprintf('the accuracy of the classifiter is£º%d',right_pre/test_num);