C++ corr函数代码示例

int HrPwWindow::fit(QVector<double> &Xi,QVector<double> &Yi,int n)
{
        QVector<double> r(5),lnXi(100),lnYi(100),logXi(100),logYi(100),invXi(100);

        //Linear correlation

        r[0]=val_abs(corr(Xi,Yi,n));

        //Exponential correlation

        lnarray(Yi,lnYi,n);
        r[1]=val_abs(corr(Xi,lnYi,n));

        //Power correlation

        logarray(Xi,logXi,n);
        logarray(Yi,logYi,n);
        r[2]=val_abs(corr(logXi,logYi,n));

        //Inverse correlation

        invarray(Xi,invXi,n);
        r[3]=val_abs(corr(invXi,Yi,n));

        //Logarithmic correlation

        lnarray(Xi,lnXi,n);
        r[4]=val_abs(corr(lnXi,Yi,n));

        //Best fit test

        return rmax(r);
}

int
Statistic::ajustement(QVector<double> &Xi,QVector<double> &Yi,int n)
{
        QVector<double> r(5),lnXi(100),lnYi(100),logXi(100),logYi(100),invXi(100);

        //corrélation pour linéaire

        r[0]=val_abs(corr(Xi,Yi,n));

        //corrélation pour exponetielle

        lntab(Yi,lnYi,n);
        r[1]=val_abs(corr(Xi,lnYi,n));

        //corrélation pour puissance

        logtab(Xi,logXi,n);
        logtab(Yi,logYi,n);
        r[2]=val_abs(corr(logXi,logYi,n));

        //corrélation pour inverse

        invtab(Xi,invXi,n);
        r[3]=val_abs(corr(invXi,Yi,n));

        //corrélation pour logarithmique

        lntab(Xi,lnXi,n);
        r[4]=val_abs(corr(lnXi,Yi,n));

        //Test du meilleur ajustement

        return rmax(r);
}

double NCC(const Image<float>& I1,const Image<float>& meanI1,Point m1,const Image<float>& I2,const Image<float>& meanI2,Point m2,int n) {
	if (m1.x<n || m1.x>=I1.width()-n || m1.y<n || m1.y>=I1.height()-n) return -1;
	if (m2.x<n || m2.x>=I2.width()-n || m2.y<n || m2.y>=I2.height()-n) return -1;
	double c1=corr(I1,meanI1,m1,I1,meanI1,m1,n);
	if (c1==0) return -1;
	double c2=corr(I2,meanI2,m2,I2,meanI2,m2,n);
	if (c2==0) return -1;
	return corr(I1,meanI1,m1,I2,meanI2,m2,n)/sqrt(c1*c2);
}

double NCC(const Image<float>& I1,const Image<float>& meanI1,const Image<float>& corrI1,Point m1,const Image<float>& I2,const Image<float>& meanI2,const Image<float>& corrI2,Point m2,int n) {
	if (m1.x<n || m1.x>=I1.width()-n || m1.y<n || m1.y>=I1.height()-n) return -1;
	if (m2.x<n || m2.x>=I2.width()-n || m2.y<n || m2.y>=I2.height()-n) return -1;
	if (corrI1(m1)==0) return -1;
	if (corrI2(m2)==0) return -1;
	return corr(I1,m1,I2,m2,n)/sqrt(corrI1(m1)*corrI2(m2));
}

int
HrPwWindow::findDelay(QVector<double> &wattsArray, QVector<double> &hrArray, int rideTimeSecs)
{
    int delay = 0;
    double maxr = 0;

    if (rideTimeSecs>= 60) {

        for (int a = 10; a <=60; ++a) {

            QVector<double> delayHr(rideTimeSecs);

            for (int j = a; j<rideTimeSecs; ++j) {
                delayHr[j-a] = hrArray[j];
            }
            for (int j = rideTimeSecs-a; j<rideTimeSecs; ++j) {
                delayHr[j] = 0.0;
            }

            double r = corr(wattsArray, delayHr, rideTimeSecs-a);
            //fprintf(stderr, "findDelay %d: %.2f \n", a, r);

            if (r>maxr) {
                maxr = r;
                delay = a;
            }
        }
    } 

    delayEdit->setText(QString("%1").arg(delay));
    rDelayEdit->setText(QString("%1").arg(delay));
    delaySlider->setValue(delay);
    rDelaySlider->setValue(delay);
    return delay;
}

void bmfm_fancy(float *disp,         // output disparities image (dx, dy)
		float *errc,         // output error image
		float *a,            // input image A
		float *b,            // input image B
		int w,               // width
		int h,               // height
		int pd,              // pixel dimension
		double fm[9],        // fundamental matrix
		float *disp_init,    // initialization (optional)
		float *search_radius // optional, w.r.t. initialization
		)
{
	int maxpoints = 2 * (w+h), (*p)[2] = xmalloc(maxpoints*sizeof*p);
	for (int j = 0; j < h; j++)
	for (int i = 0; i < w; i++) {
		float rad = NAN, ini[2] = {0, 0};
		if (search_radius) rad = search_radius[j*w+i];
		if (disp_init) ini[0] = disp_init[2*(j*w+i)+0];
		if (disp_init) ini[1] = disp_init[2*(j*w+i)+1];
		int np = plot_epipolar_fancy(p, fm, w, h, i, j, ini, rad);
		float mincorr = INFINITY;
		int minidx = 0;
		for (int k = 0; k < np; k++) {
			float c = corr(a, b, w, h, pd, i, j, p[k][0], p[k][1]);
			if (c < mincorr) {
				mincorr = c;
				minidx = k;
			}
		}
		if (disp) disp[2*(j*w+i) + 0] = p[minidx][0] - i;
		if (disp) disp[2*(j*w+i) + 1] = p[minidx][1] - j;
		if (errc) errc[j*w+i] = mincorr;
	}
	free(p);
}

void shot_detector::compare(pcl::PointCloud<DescriptorType>::Ptr model_descriptions, pcl::PointCloud<DescriptorType>::Ptr scene_descriptions)
{
    model_scene_corrs->clear();
    pcl::KdTreeFLANN<DescriptorType> match_search;
    match_search.setInputCloud (model_descriptions);
    std::cerr << scene_descriptions->size() << " and  " << model_descriptions->size() << std::endl;
    model_good_keypoints_indices.clear();
    scene_good_keypoints_indices.clear();

    //  For each scene keypoint descriptor, find nearest neighbor into the model keypoints descriptor cloud and add it to the correspondences vector.
    for (size_t i = 0; i < scene_descriptions->size (); ++i) {
        std::vector<int> neigh_indices (1);
        std::vector<float> neigh_sqr_dists (1);
        if (!pcl_isfinite (scene_descriptors->at (i).descriptor[0])) { //skipping NaNs
            continue;
        }
        int found_neighs = match_search.nearestKSearch (scene_descriptors->at (i), 1, neigh_indices, neigh_sqr_dists);
        if(found_neighs == 1 && neigh_sqr_dists[0] < corr_dist_) { //  add match only if the squared descriptor distance is less than 0.25 (SHOT descriptor distances are between 0 and 1 by design)
            pcl::Correspondence corr (neigh_indices[0], static_cast<int> (i), neigh_sqr_dists[0]);
            model_scene_corrs->push_back (corr);
        }
    }
    pcl::copyPointCloud (*model_keypoints, model_good_keypoints_indices, *model_good_kp);
    pcl::copyPointCloud (*scene_keypoints, scene_good_keypoints_indices, *scene_good_kp);
    std::cerr << "Correspondences found: " << model_scene_corrs->size () << std::endl;

}

 double operator()(configuration const& c) const {
  auto delta = (rhs - kern(c)) / error_bars;
  int M = first_dim(delta);
  double kappa = 0;
  for(int i = 1; i < M; ++i) kappa += corr(delta(i), delta(i-1));
  kappa /= M - 1;
  return kappa;
 }

// Renvoit la matrice avec toutes les auto-correlations
Image<float> corrImage(const Image<float>& I, const Image<float>& meanI, int n){
	Image<float> corrI(I.width(), I.height(),CV_32F);
	for(int i=n+1; i<I.height()-n-1; i++){
		for(int j=n+1; j<I.width()-n-1; j++){
			//cout<< i << " " << j << endl;
			corrI.at<float>(i,j) = (float)(corr(I, meanI, Point(j,i), I, meanI, Point(j,i),n));
		}
	}
	return corrI;
}

int InitialGuess (double *s, double *p, int nphase, int nchn, int *chn) 
{
	int index;
	double frac_off = 0.05;  // set to be 0.05

	double ptemp[nphase];
	double temp[nchn];
	int i, h;
	for (i = 0; i < nchn; i++)
	{
		for (h = 0; h < nphase; h++)
		{
			ptemp[h] = p[i*nphase+h];
		}
		int x;
		x = def_off_pulse (nphase, ptemp, frac_off);

		double ptemp_out[nphase];
		pre_diff (ptemp, nphase, x, frac_off, ptemp_out);

		temp[i] = find_peak_value(nphase,ptemp_out);
	}

	int peak;
	find_peak (0, nchn, temp, &peak);
	//printf ("%d\n",peak);
	(*chn) = peak;

	double p_use[nphase];
	double s_use[nphase];
	for (h = 0; h < nphase; h++)
	{
		p_use[h] = p[peak*nphase+h];
		s_use[h] = s[peak*nphase+h];
	}

	// remove the baseline of template
	index = def_off_pulse (nphase, s_use, frac_off);

	double s_out[nphase];
	pre_diff (s_use, nphase, index, frac_off, s_out);

	// remove the baseline of profile
	index = def_off_pulse (nphase, p_use, frac_off);

	double p_out[nphase];
	pre_diff (p_use, nphase, index, frac_off, p_out);

	// Guess the phase shift
	int d;
	d = corr (s_out, p_out, nphase);

	return d;
}

int main()
{
	
	int a, i, j;
	gets(string);
	a = 1;
	i = 0;
	for(j = 0; string[j] != '\0'; j++);
	j--;
	while(j - i >= 1)
	{
		while(corr(string[i])) i++;
		while(corr(string[j]) && (j >= 1)) j--;
		if ((reg(string[i]) != reg(string[j])) && (j - i >= 1)) a = 0; 
		i++;
		j--;
	}
	if (a) printf("Yes");
	else printf("No");
	return 0;
}       

static void roguejoin(struct level *lev, int x1, int y1, int x2, int y2, int horiz)
{
	int x,y,middle;
#ifndef MAX
#define MAX(a,b) (((a) > (b)) ? (a) : (b))
#endif
#ifndef MIN
#define MIN(a,b) (((a) < (b)) ? (a) : (b))
#endif
	if (horiz) {
		middle = x1 + rn2(x2-x1+1);
		for (x=MIN(x1,middle); x<=MAX(x1,middle); x++)
			corr(lev, x, y1);
		for (y=MIN(y1,y2); y<=MAX(y1,y2); y++)
			corr(lev, middle,y);
		for (x=MIN(middle,x2); x<=MAX(middle,x2); x++)
			corr(lev, x, y2);
	} else {
		middle = y1 + rn2(y2-y1+1);
		for (y=MIN(y1,middle); y<=MAX(y1,middle); y++)
			corr(lev, x1, y);
		for (x=MIN(x1,x2); x<=MAX(x1,x2); x++)
			corr(lev, x, middle);
		for (y=MIN(middle,y2); y<=MAX(middle,y2); y++)
			corr(lev, x2,y);
	}
}

int fit() {
  
  cout << "start of fit procedure" << endl;

  readsource("run369/CRS/CRS_QDC_01!qdc01.dat",7214);
  readbackgr("run371/CRS/CRS_QDC_01!qdc01.dat",56948);
  corr();


  TCanvas* c1 = new TCanvas("c1","c1",800,600);



  int maxbin=-1;
  Double_t maxvalue=-1;
  Double_t tempbin=0;

  TString text="";

  hcorr->GetXaxis()->SetRange(200,4000);
  maxbin=hcorr->GetMaximumBin();
  maxvalue=hcorr->GetBinContent(maxbin);
  hcorr->GetXaxis()->SetRange(0,4095);

  TF1* f1 = new TF1("f1","[0]*exp(-((x-[1])^2)/[2])");
  f1->SetParameter(0,maxvalue);
  f1->SetParameter(1,maxbin);
  f1->SetParameter(2,1);

  f1->SetLineColor(2);

  hcorr->SetMinimum(0);
  hcorr->SetMaximum(1.2*maxvalue);

  hcorr->Fit("f1","","",maxbin-maxbin/6,4095);

  tempbin=sqrt(-log(maxvalue*2/3/(f1->GetParameter(0)))*(f1->GetParameter(2)))+f1->GetParameter(1);

  cout << "2/3 of maximum height is in bin " << tempbin << "->" <<int(tempbin+0.5) << endl;



  
  cout << endl << "max bin = " << maxbin << " with content = " << maxvalue << endl;



}

static void
spatial_mat(double *par, double *dist, longint *n, longint *nug,
	    double (*corr)(double ), double *mat)
{
    longint i, j, np1 = *n + 1;
    double aux, *sdist, ratio = 1.0;
    sdist = dist;
    if (*nug) ratio = par[1];
    for(i = 0; i < *n; i++) {
	mat[i * np1] = 1.0;
	for(j = i + 1; j < *n; j++, sdist++) {
	    aux = *sdist / *par;
	    *(mat + i + j * (*n)) = *(mat + j + i * (*n)) = ratio * corr(aux);
	}
    }
}

std::vector<Joystick::CalibrationData>
Joystick::get_calibration()
{
  std::vector<struct js_corr> corr(get_axis_count());

  if (ioctl(fd, JSIOCGCORR, &*corr.begin()) < 0)
    {
      std::ostringstream str;
      str << filename << ": " << strerror(errno);
      throw std::runtime_error(str.str());
    }
  else
    {
      std::vector<CalibrationData> data;
      std::transform(corr.begin(), corr.end(), std::back_inserter(data), corr2cal);
      return data;
    }
}