1 #ifndef _GMM_H
2 #define _GMM_H
3 #include <vector>
4 #include <cmath>
5 using namespace std;
6 class GMM
7 {
8 public:
9 void Init(const vector<double> &inputData,const int clustNum=5,double eps=0.01,double max_steps=20);
10 void train();
11 int predicate(double x);//預(yù)測(cè)輸入的數(shù)據(jù)屬于哪一類(lèi)
12 void print();
13 protected:
14 int clusterNum;
15 vector<double> means;
16 vector<double> means_bkp;
17 vector<double> sigmas;
18 vector<double> sigmas_bkp;
19 vector<double> probilities;
20 vector<double> probilities_bkp;
21 vector<vector<double>> memberships;
22 vector<vector<double>> memberships_bkp;
23 vector<double> data;
24 int dataNum;
25 double epslon;
26 double max_steps;
27 private:
28 double gauss(const double x,const double m,const double sigma);
29 };
30 #endif
1 #include "GMM.h"
2 #include <iostream>
3 #include <fstream>
4 #include <stdlib.h>
5 #include <Windows.h>
6 using namespace std;
7
8 void GMM::Init( const vector<double> &inputData,const int clustNum,double eps,double max_steps )
9 {
10 this->data=inputData;
11 this->dataNum=data.size();
12 this->clusterNum=clustNum;
13 this->epslon=eps;
14 this->max_steps=max_steps;
15 this->means.resize(clusterNum);
16 this->means_bkp.resize(clusterNum);
17 this->sigmas.resize(clusterNum);
18 this->sigmas_bkp.resize(clusterNum);
19 this->memberships.resize(clusterNum);
20 this->memberships_bkp.resize(clusterNum);
21 for (int i=0;i<clusterNum;i++)
22 {
23 memberships[i].resize(data.size());
24 memberships_bkp[i].resize(data.size());
25 }
26 this->probilities.resize(clusterNum);
27 this->probilities_bkp.resize(clusterNum);
28 //initialize mixture probabilities
29 for (int i=0;i<clusterNum;i++)
30 {
31 probilities[i]=probilities_bkp[i]=1.0/(double)clusterNum;
32 //init means
33 means[i]=means_bkp[i]=255.0*i/(clusterNum);
34 //init sigma
35 sigmas[i]=sigmas_bkp[i]=50;
36 }
37 }
38 void GMM::train()
39 {
40 //compute membership probabilities
41 int i,j,k,m;
42 double sum=0,sum2;
43 int steps=0;
44 bool go_on;
45 do
46 {
47 for (k=0;k<clusterNum;k++)
48 {
49 //compute membership probabilities
50 for (j=0;j<data.size();j++)
51 {
52 //計(jì)算p(k|n)
53 sum=0;
54 for (m=0;m<clusterNum;m++)
55 {
56 sum+=probilities[m]*gauss(data[j],means[m],sigmas[m]);
57 }
58 //求分子
59 memberships[k][j]=probilities[k]*gauss(data[j],means[k],sigmas[k])/sum;
60 }
61 //求均值
62 //求條件概率的和
63 sum=0;
64 for (i=0;i<dataNum;i++)
65 {
66 sum+=memberships[k][i];
67 }
68 sum2=0;
69 for (j=0;j<dataNum;j++)
70 {
71 sum2+=memberships[k][j]*data[j];
72 }
73 means[k]=sum2/sum;
74 //求方差
75 sum2=0;
76 for (j=0;j<dataNum;j++)
77 {
78 sum2+=memberships[k][j]*(data[j]-means[k])*(data[j]-means[k]);
79 }
80 sigmas[k]=sqrt(sum2/sum);
81 //求概率
82 probilities[k]=sum/dataNum;
83 }//end for k
84 //check improvement
85 go_on=false;
86 for (k=0;k<clusterNum;k++)
87 {
88 if (means[k]-means_bkp[k]>epslon)
89 {
90 go_on=true;
91 break;
92 }
93 }
94 //back up
95 this->means_bkp=means;
96 this->sigmas_bkp=sigmas;
97 this->probilities_bkp=probilities;
98 } while (go_on&&steps++<max_steps);//end do while
99 }
100
101 double GMM::gauss( const double x,const double m,const double sigma )
102 {
103 return 1.0/(sqrt(2*3.1415926)*sigma)*exp(-0.5*(x-m)*(x-m)/(sigma*sigma));
104 }
105 int GMM::predicate(double x)
106 {
107 double max_p=-100;
108 int i;
109 double current_p;
110 int bestIdx=0;
111 for (i=0;i<clusterNum;i++)
112 {
113 current_p=gauss(x,means[i],sigmas[i]);
114 if (current_p>max_p)
115 {
116 max_p=current_p;
117 bestIdx=i;
118 }
119 }
120 return bestIdx;
121 }
122 void GMM::print()
123 {
124 int i;
125 for (i=0;i<clusterNum;i++)
126 {
127 cout<<"Mean: "<<means[i]<<" Sigma: "<<sigmas[i]<<" Mixture Probability: "<<probilities[i]<<endl;
128 }
129 }
130 //void mainxx()
131 //{
132 // GMM gmm;
133 // gmm.test();
134 //}
135 //void GMM::test()
136 //{
137 // //test guass
138 // for (int j=0;j<10;j++)
139 // {
140 // cout<<gauss(j,0,1)<<endl;
141 // }
142 // srand(GetTickCount());
143 // vector<double> datas;
144 // int i;
145 // double d;
146 // for (i=0;i<20;i++)
147 // {
148 // d=(rand()/(double)RAND_MAX)*255;
149 // cout<<d<<endl;
150 // datas.push_back(d);
151 // }
152 // this->Init(datas,2);
153 // this->train();
154 // this->predicate(100);
155 //}