hist_2_conj.rar_conj_histogram

#include <iostream> #include <math.h> #include <stdlib.h> using namespace std; double PX[601][601] , PR[601][601] , X[200 * 200], X1[200 * 200], X2[200 * 200], R1[200 * 200], R2[200 * 200], R[200 * 200], DKL[50]; int M[200*200]; double GenerateWhiteGaussianNoise(double average,double sigma) ; double quantif(double x,double Dt); int sign(double x); int i1 , l1 ,i2 , l2 , i3, l3 , i4 , l4, i5, l5 , i6 , l6 , i7 , l7 ; int m1 , n1 , k1, N , k; double sigmaw, alpha=1.0 , p = 0.2 ; double er, Dt, varx,L = 600; int compt1=0 , compt2=0 ; main() { cout << "donnez N, m et n : \n" << endl; cin >> N; cin >> m1; cin >> n1; //Initialisation for (k=0;k<40;k++) { DKL[k]=0.0; } for (k1=0;k1<40;k1++) { cout << k1 << endl; for(i1=0;i1<=L;i1++) { for(l1=0;l1<=L;l1++) { PX[i1][l1]=0.0 ; PR[i1][l1]=0.0 ; } } for(i2=0;i2<N;i2++) { //Generation du signal gaussien for(i3=0;i3 < m1*n1-1;i3++) { double interm; { sigmaw=20*10^(-k1/10); X[i3] = GenerateWhiteGaussianNoise(0.0, 20) ; interm = GenerateWhiteGaussianNoise(0.0, 1.0); M[i3]=(sign(interm)+1)/2; Dt=sqrt(12*sigmaw); er=quantif(X[i3]-M[i3]*Dt/2,Dt)-(X[i3]-M[i3]*Dt/2); R[i3]=X[i3]+alpha*er; if (i3 %2==0) { X1[compt1] = X[i3]; R1[compt1] = R[i3]; compt1=compt1++; cout << compt1 <<endl; } else { X2[compt2] = X[i3]; R2[compt2] = R[i3]; compt2=compt2++; cout << compt2 <<endl; } } } //Calcul de l'histograme for(i4=0;i4<L;i4++) { for(l4=0;l4<L;l4++) { for(i5=0;i5<m1*n1-1;i5++) { if (((i4-1-L/2)*p<=X1[i5])&&(X1[i5]<(i4-L/2)*p)&&((l4-1-L/2)*p<=X2[i5])&&(X2[i5]<(l4-L/2)*p)) { PX[i4][l4] = PX[i4][l4] + 2.0/(m1*n1*p); } if (((i4-1-L/2)*p<=R1[i5])&&(R1[i5]<(i4-L/2)*p)&&((l4-1-L/2)*p<=R2[i5])&&(R2[i5]<(l4-L/2)*p)) { PR[i4][l4] = PR[i4][l4] + 2.0/(m1*n1*p); // PR[i4+601*l4] = PR[i4+601*l4] + 2.0/(m1); } } } } //Calcul de la KLD_2D for(l6=0;l6<=L;l6++) { if ((PX[i4][l6]!=0.0)&&(PR[i4][l6]!=0.0)) { DKL[k1]=DKL[k1]+PX[i4][l6]*log(PX[i4][l6]/PR[i4][l6])/log(2.0); } } //Affichage des r�sultats // for(i7=0;i7<L;i7++) // { // for(l7=0;l7<L;l7++) // { // if( PX[i7][l7]!=1.0e-21 ) // { // cout << "PR[" <<i7<< "]["<<l7<<"]=" << PR[i7][l7] << "\n PX["<<i7<<"]["<<l7<<"]=" << PX[i7][l7]<<endl; // } // } // } } cout << DKL[k1] << endl; } return 0; } //*******Fonction qui genere l'echantillon gaussien******* double GenerateWhiteGaussianNoise(double average,double sigma) { double M_PI=3.14; double x,y; double g,h; do { x = (double)rand() / RAND_MAX; y = (double)rand() / RAND_MAX; } while(x == 0.0 || y == 0.0 || x == 1.0 || y == 1.0); g = sqrt(-log(x)); h = sqrt(2.0) * cos(2.0 * M_PI * y); return (average + sigma * g * h); } //****************Fonction de quantification********************** double quantif(double x,double Dt) {double Q; Q=Dt*floor(x/Dt+0.5); return Q; } //**************fonction calcul sign*************************** int sign(double x) {int y; if (x==0) y=0; else {if (x>0) y=1; else y=-1; } return y; }