clc
clear
close?all

total_data=load（‘C:\Users\ZAN\Desktop\matalb_drive\machine-learnning\Bayes\iris_data.mat‘）;
len?=?size（total_data.features1）;
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[len1]））./（repmat（range[len1]））;

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_1std_1）?2）;
????????prob_2=prod（?normpdf（newtest_dataMat（i:）mean_2std_2）?2）;
????????prob_3=prod（?normpdf（newtest_dataMat（i:）mean_3std_3）?2）;
????????max_prob=max（[prob_1prob_2prob_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：%d??true?result?is：%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：%d‘right_pre/test_num）;

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????1105??2010-10-17?14:51??iris_data.mat
?????文件????????1762??2018-08-31?09:37??my_bayes.m