?? clustering.cpp
字號(hào):
///////////////////////////////////////////////////////////////////////
// File Name: Clustering.cpp
// File Function: 1. Maximun clustering algorithm
// 2. LBG (k-Mean) clustering algorithm
// Date: 2001.10.3
//////////////////////////////////////////////////////////////////////
#include "stdafx.h"
#include "Clustering.h"
#include "stdio.h"
#include "math.h"
/******************************************************************************
/* Name: LBGCluster
/* Function: Clustering input vectors using LBG algorithm
/* Using Euclidean distance
/* Parameter: X -- Input vecters
/* N -- Number of input vectors
/* Y -- Clustering result
/* M -- Number of clustering center
/* Return: 0 -- Correct
/* 1 -- Error
/*
/******************************************************************************/
int LBGCluster(VQ_VECTOR *X, int N, VQ_CENTER *Y, int M)
{
if(N<M) return -1;
int L=1000, m=1, nCenter, i, j, k;//L,迭代的次數(shù)
int nDimension = X[0].nDimension;
double D0, D;
struct VQ_CENTERINFO
{
double* Data;
int nDimension;
double* SumData;
int Num;
};
VQ_CENTERINFO *Center = (VQ_CENTERINFO*)malloc(M*sizeof(VQ_CENTERINFO));
if(Center == NULL) return -1;
double *Distance = (double*)malloc(N*sizeof(double));
if(Distance == NULL) return -1;
for( i=0; i<M; i++)
{
Center[i].nDimension = nDimension;
Center[i].Data = (double*)malloc(sizeof(double)*nDimension);
Center[i].SumData = (double*)malloc(sizeof(double)*nDimension);
if( Center[i].Data == NULL || Center[i].SumData == NULL )
{
AfxMessageBox( "Memory used up!" );
return -1;
}
for( j=0; j<nDimension; j++ )
{
Center[i].Data[j] = X[i*N/M].Data[j];
Center[i].SumData[j] = 0;
}
Center[i].Num = 0;
}
D0=1; D=1e+10;
while(m<L && fabs(D0-D)/D0>1e-5)
{
for(i=0; i<M; i++)
{
for( j=0; j<nDimension; j++ )
Center[i].SumData[j] = 0;
Center[i].Num = 0;
}
D0 = D; D = 0; m++;
for(i=0; i<N; i++)
{
Distance[i] = 1e+10;
for(int j=0; j<M; j++)
{
double Dist = 0;
for( k=0; k<nDimension; k++ )
Dist += (X[i].Data[k]-Center[j].Data[k])*(X[i].Data[k]-Center[j].Data[k]);
if( Dist < Distance[i])
{
nCenter = j;
Distance[i] = Dist;
}
}
X[i].nCluster = nCenter;
for( k=0; k<nDimension; k++ )
Center[nCenter].SumData[k] += X[i].Data[k];
Center[nCenter].Num++;
D += Distance[i];
}
for(i=0; i<M; i++)
{
if(Center[i].Num != 0)
for( k=0; k<nDimension; k++ )
Center[i].Data[k] = Center[i].SumData[k]/Center[i].Num;
else
{
int MaxNum=0;
for( k=1; k<M; k++)
MaxNum = Center[i].Num > Center[MaxNum].Num ? i: MaxNum;
int Num = Center[MaxNum].Num/2;
for( k=0; k<nDimension; k++ )
Center[MaxNum].SumData[k] = 0;
Center[MaxNum].Num = 0;
for(k=0; k<N; k++)
{
if(X[k].nCluster != MaxNum) continue;
if(Center[i].Num < Num)
{
X[k].nCluster = i;
for( m=0; m<nDimension; m++)
Center[i].SumData[m] += X[k].Data[m];
Center[i].Num++;
}
else
{
for( m=0; m<nDimension; m++ )
Center[MaxNum].SumData[m] += X[k].Data[m];
Center[MaxNum].Num++;
}
}
for( m=0; m<nDimension; m++ )
Center[i].Data[m] = Center[i].SumData[m] / Center[i].Num;
if(MaxNum < i)
for( m=0; m<nDimension; m++ )
Center[MaxNum].Data[m] = Center[MaxNum].SumData[m] / Center[MaxNum].Num;
}
}
}
for(i=0; i<M; i++)
{
for( m=0; m<nDimension; m++ )
Y[i].Data[m] = Center[i].Data[m];
Y[i].Num = Center[i].Num;
}
for( i=0; i<M; i++ )
{
free( Center[i].Data );
free( Center[i].SumData );
}
free(Center);
free(Distance);
return 0;
}
/******************************************************************************
/* Name: LBGClusterCor
/* Function: Clustering input vectors using LBG algorithm
/* Using correlation distance
/* Parameter: X -- Input vecters
/* N -- Number of input vectors
/* Y -- Clustering result
/* M -- Number of clustering center
/* Return: 0 -- Correct
/* 1 -- Error
/*
/******************************************************************************/
int LBGClusterCor(VQ_VECTOR *X, int N, VQ_CENTER *Y, int M)
{
if(N<M) return -1;
int L=1000, m=1, nCenter, i, j, k;
int nDimension = X[0].nDimension;
double SumCor0, SumCor;
struct VQ_CENTERINFO
{
double* Data;
int nDimension;
double* SumData;
int Num;
};
VQ_CENTERINFO *Center = (VQ_CENTERINFO*)malloc(M*sizeof(VQ_CENTERINFO));
if(Center == NULL) return -1;
double *Correlation = (double*)malloc(N*sizeof(double));
if(Correlation == NULL) return -1;
for( i=0; i<M; i++)
{
Center[i].nDimension = nDimension;
Center[i].Data = (double*)malloc(sizeof(double)*nDimension);
Center[i].SumData = (double*)malloc(sizeof(double)*nDimension);
if( Center[i].Data == NULL || Center[i].SumData == NULL )
{
AfxMessageBox( "Memory used up!" );
return -1;
}
for( j=0; j<nDimension; j++ )
{
Center[i].Data[j] = X[i*N/M].Data[j];
Center[i].SumData[j] = 0;
}
Center[i].Num = 0;
}
SumCor0=0.001; SumCor=0.1;
while(m<L && fabs(SumCor0-SumCor)/SumCor0>1e-20)
{
for(i=0; i<M; i++)
{
for( j=0; j<nDimension; j++ )
Center[i].SumData[j] = 0;
Center[i].Num = 0;
}
SumCor0 = SumCor; SumCor = 0; m++;
for(i=0; i<N; i++)
{
Correlation[i] = 0;
for(int j=0; j<M; j++)
{
double Cor = GetCorrelation( X[i].Data, Center[j].Data, nDimension);
if( Cor > Correlation[i])
{
nCenter = j;
Correlation[i] = Cor;
}
}
X[i].nCluster = nCenter;
for( k=0; k<nDimension; k++ )
Center[nCenter].SumData[k] += X[i].Data[k];
Center[nCenter].Num++;
SumCor += Correlation[i];
}
for(i=0; i<M; i++)
{
if(Center[i].Num != 0)
for( k=0; k<nDimension; k++ )
Center[i].Data[k] = Center[i].SumData[k]/Center[i].Num;
else
{
int MaxNum=0;
for( k=1; k<M; k++)
MaxNum = Center[i].Num > Center[MaxNum].Num ? i: MaxNum;
int Num = Center[MaxNum].Num/2;
for( k=0; k<nDimension; k++ )
Center[MaxNum].SumData[k] = 0;
Center[MaxNum].Num = 0;
for(k=0; k<N; k++)
{
if(X[k].nCluster != MaxNum) continue;
if(Center[i].Num < Num)
{
X[k].nCluster = i;
for( m=0; m<nDimension; m++)
Center[i].SumData[m] += X[k].Data[m];
Center[i].Num++;
}
else
{
for( m=0; m<nDimension; m++ )
Center[MaxNum].SumData[m] += X[k].Data[m];
Center[MaxNum].Num++;
}
}
for( m=0; m<nDimension; m++ )
Center[i].Data[m] = Center[i].SumData[m] / Center[i].Num;
if(MaxNum < i)
for( m=0; m<nDimension; m++ )
Center[MaxNum].Data[m] = Center[MaxNum].SumData[m] / Center[MaxNum].Num;
}
}
}
for(i=0; i<M; i++)
{
for( m=0; m<nDimension; m++ )
Y[i].Data[m] = Center[i].Data[m];
Y[i].Num = Center[i].Num;
}
for( i=0; i<M; i++ )
{
free( Center[i].Data );
free( Center[i].SumData );
}
free(Center);
free(Correlation);
return 0;
}
/*********************************************************************
/* Name: GetCorrelation
/* Function: Calculate correlation of two vectors
/* Parameter: X -- Vector one
/* Y -- Vector two
/* nDimension -- Dimension of the vectors
/* Return: Correlation of two vectors
/*
/*********************************************************************/
double GetCorrelation( double* X, double* Y, int nDimension )
{
double Correlation=0, DX=0, DY=0;
for(int i=0; i<nDimension; i++ )
{
Correlation += X[i]*Y[i];
DX += X[i]*X[i];
DY += Y[i]*Y[i];
}
return Correlation/sqrt( DX*DY );
}
/******************************************************************************
/* Name: MaxCluster
/* Function: Clustering input vectors using Maximum algorithm
/* Parameter: X -- Input vecters
/* N -- Number of input vectors
/* Y -- Clustering result
/* M -- Number of clustering center
/* Return: 0 -- Correct
/* 1 -- Error
/*
/******************************************************************************/
int MaxCluster(VQ_VECTOR *X, int N, VQ_CENTER *Y, int M)
{
if(N<M) return -1;
int m=1;
struct VQ_CENTERINFO
{
double* Data;
int nDimension;
double* SumData;
int Num;
};
VQ_CENTERINFO *Center = (VQ_CENTERINFO*)malloc(M*sizeof(VQ_CENTERINFO));
if(Center == NULL) return -1;
double *Distance = (double*)malloc(N*sizeof(double));
if(Distance == NULL) return -1;
int nDimension = X[0].nDimension;
int i, j, k;
for(i=0; i<M; i++)
{
for( k=0; k<nDimension; k++ )
Center[i].SumData[k] = 0;
Center[i].Num = 0;
}
double* SumData = (double*)malloc(sizeof(double)*nDimension);
for( k=0; k<nDimension; k++ )
SumData[k]= 0;
for(i=0; i<N; i++)
for( k=0; k<nDimension; k++ )
SumData[k] += X[i].Data[k];
for( k=0; k<nDimension; k++ )
SumData[k] /= N;
int l=0;
double L=0, Dist;
for(i=0; i<N; i++)
{
Dist = 0;
for( k=0; k<nDimension; k++ )
Dist += (X[i].Data[k]-SumData[k])*(X[i].Data[k]-SumData[k]);
if(Dist > L)
{
L = Dist;
l = i;
}
}
for( k=0; k<nDimension; k++ )
Center[0].Data[k] = X[l].Data[k];
for(m=1; m<M; m++)
{
double MaxMinDist = -1;
int MaxMin = -1;
for( j=0; j<N; j++)
{
double MinDist = 1e+10;
for(int k=0; k<m; k++)
{
Dist = 0;
for( k=0; k<nDimension; k++ )
Dist += (X[j].Data[k] - Center[k].Data[k])*(X[j].Data[k] - Center[k].Data[k]);
MinDist = min(Dist , MinDist);
}
if(MinDist > MaxMinDist)
{
MaxMinDist = MinDist;
MaxMin = j;
}
}
for( k=0; k<nDimension; k++ )
Center[m].Data[k] = X[MaxMin].Data[k];
}
for(i=0; i<N; i++)
{ int Min = -1;
double MinDist = 1e+10;
for(m=0; m<M; m++)
{
Dist = 0;
for( k=0; k<nDimension; k++ )
Dist += (X[i].Data[k] - Center[m].Data[k])*(X[i].Data[k] - Center[m].Data[k]);
if(Dist < MinDist)
{
MinDist = Dist;
Min = m;
}
}
X[i].nCluster = Min;
if(Min<0 || Min>=M)
return -1;
for( k=0; k<nDimension; k++ )
Center[Min].SumData[k] += X[i].Data[k];
Center[Min].Num++;
}
for(m=0; m<M; m++)
for( k=0; k<nDimension; k++ )
Center[m].Data[k] = Center[m].SumData[k] / Center[m].Num;
for(i=0; i<M; i++)
{
for( k=0; k<nDimension; k++ )
Y[i].Data[k] = Center[i].Data[k];
Y[i].Num = Center[i].Num;
}
free( SumData );
for( i=0; i<M; i++ )
{
free( Center[i].Data );
free( Center[i].SumData );
}
free(Center);
free(Distance);
return 0;
}
/******************************************************************************
/* Name: DumpClusterData
/* Function: Dump clustering result to a text file for debugging
/* Parameter: FileName -- Dump text file name
/* X -- Input vecters
/* N -- Number of input vectors
/* Y -- Clustering result
/* M -- Number of clustering center
/* Return: 0 -- Correct
/* 1 -- Error
/*
/******************************************************************************/
int DumpClusterData(CString FileName, VQ_VECTOR *X, int N, VQ_CENTER *Y, int M)
{
int i, j, k;
int nDimension = X[0].nDimension;
FILE *fp = fopen(FileName, "wt");
for( i=0; i<M; i++)
{
fprintf(fp, "Center%02d: ", i);
for( k=0; k<nDimension; k++ )
fprintf( fp, "%5.1f ", Y[i].Data[k] );
fprintf( fp, "Num=%03d\n", Y[i].Num);
for( j=0; j<N; j++)
{ if(X[j].nCluster != i) continue;
double Distance = 0;
for( k=0; k<nDimension; k++)
Distance += (X[j].Data[k]- Y[i].Data[k])*(X[j].Data[k]-Y[i].Data[k]);
Distance = sqrt( Distance );
for( k=0; k<nDimension; k++ )
fprintf(fp, " %03d ", (int)X[j].Data[k] );
fprintf( fp, " D=%5.1f\n", Distance);
}
}
fclose(fp);
return 0;
}
?? 快捷鍵說明
復(fù)制代碼
Ctrl + C
搜索代碼
Ctrl + F
全屏模式
F11
切換主題
Ctrl + Shift + D
顯示快捷鍵
?
增大字號(hào)
Ctrl + =
減小字號(hào)
Ctrl + -