parzen_smooth.m

来自「用mushrooms数据对模式识别课程讲述的各种模式分类方法[线性分类」· M 代码 · 共 81 行

clc;
clear;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%读取数据，取16个特征
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
samples = textread('data2000.txt');
samples = samples(:,[1:6,9:15,19:22]);  %17列 第1列标号，16列特征

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%对样本进行归一化处理
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[ms ns]=size(samples);
TMax=max(samples);
TMin=min(samples);
% 第一列是样本标签，从第二列开始归一化
for i=2:ns
    samples(:,i)=(samples(:,i)-TMin(i))/(TMax(i)-TMin(i));
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%将样本分为测试样本，第一类训练样本，第二类训练样本
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
p = randperm(2000);%对1：2000的整数随机排序
experiment_test=samples(p(1:100),:);%测试样本
exper_test=experiment_test(:,2:ns);%测试样本,不带标签
experiment_train=samples(p(101:300),:);%训练样本
index1=find(experiment_train(:,1)==1);%找到训练样本中第一类的行号
index2=find(experiment_train(:,1)==2);%找到训练样本中第二类的行号
exper_train_class1=experiment_train(index1,2:ns);%训练样本里属于第一类的样本,不带标签
exper_train_class2=experiment_train(index2,2:ns);%训练样本里属于第二类的样本,不带标签

[m n]=size(exper_test);
[m1 n1]=size(exper_train_class1);
[m2 n2]=size(exper_train_class2);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%计算类条件概率密度
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%构造一个正态函数，以每个训练样本为中心（均值）                                                    
SIGMA=eye(16);
R1=zeros(m,1);%记录待测样本在第一类的测试结果
R2=zeros(m,1);%记录待测样本在第二类的测试结果
%对第一类
Result=zeros(m,1);%记录最后分类结果
for i=1:m
    for j=1:m1
        R1(i)=R1(i)+mvnpdf(exper_test(i,:),exper_train_class1(j,:),SIGMA);
    end
end
R1=R1./m1;
%对第二类
for i=1:m
    for j=1:m2
        R2(i)=R2(i)+mvnpdf(exper_test(i,:),exper_train_class2(j,:),SIGMA);
    end
end
R2=R2./m2;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%画类条件概率密度图，并分类
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%绘图
plot(R1,'-r','LineWidth',2);
hold on;
plot(R2,'-b','LineWidth',2);
xlabel ('待测样本'); 
ylabel ('类条件概率密度 P（x|wi）');
title ('类条件概率密度图');
%分类
for i=1:m
    if R1(i,1)>R2(i,1)
        Result(i,1)=1;
    else
        Result(i,1)=2;
    end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%分析结果
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[correct,error,ROC] = analyse_result(experiment_test,Result);