BPNet.rar_BP_BPNet C++

#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,迭代的次数 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 --