remove binary and temporary file

src/Algorithms/bet/math/#linfit.cpp#

-/* linfit.C
-   linear fitting routines
-
-   NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5)
-
-   Revision history
-   Date          Description                                     Programmer
-   ------------  --------------------------------------------    ------------
-   30/04/1992    Copied from "numerical recipies"                Rene Bakker
-
-*/
-
-#include <stdlib.h>
-#include <stdio.h>
-#include <math.h>
-
-#include "Algorithms/bet/error.h"
-#include "linfit.h"
-
-#include <math.h>
-/* Internal functions
-   ========================================================================= */
-
-static double sqrarg;
-#define SQR(a) ((sqrarg=(a)) == 0.0 ? 0.0 : sqrarg*sqrarg)
-
-/* gcf()
- */
-#define ITMAX 100
-#define EPS 3.0e-7
-#define FPMIN 1.0e-30
-
-/* gammln()
-   Returns the ln-value of the gamma function ln[G(xx)] for xx > 0.
-*/
-static double gammln(double xx)
-{
-  double x,y,tmp,ser;
-  static double cof[6]={76.18009172947146,-86.50532032941677,
-			24.01409824083091,-1.231739572450155,
-			0.1208650973866179e-2,-0.5395239384953e-5};
-  int j;
-  
-  y=x=xx;
-  tmp  =x+5.5;
-  tmp -= (x+0.5)*log(tmp);
-  ser  =1.000000000190015;
-  for (j=0;j<=5;j++) ser += cof[j]/++y;
-  return -tmp+log(2.5066282746310005*ser/x);
-} /* gammln */
-
-
-/* gcf()
-
-   Returns the incomplete gamma function Q(a, x) evaluated by its
-   continued fraction representation as gammcf. Also returns Gamma(a) as
-   gln.
-*/
-
-static void gcf(double *gammcf, double a, double x, double *gln)
-{
-  int i;
-  double an,b,c,d,del,h;
-  
-  *gln=gammln(a);
-  b=x+1.0-a;
-  c=1.0/FPMIN;
-  d=1.0/b;
-  h=d;
-  for (i=1;i<=ITMAX;i++) {
-    an = -i*(i-a);
-    b += 2.0;
-    d=an*d+b;
-    if (fabs(d) < FPMIN) d=FPMIN;
-    c=b+an/c;
-    if (fabs(c) < FPMIN) c=FPMIN;
-    d=1.0/d;
-    del=d*c;
-    h *= del;
-    if (fabs(del-1.0) < EPS) break;
-  }
-  if (i > ITMAX) {
-    writeError(errModeAll,errFatal,
-	       "linfit-->gfc(): a (%le) too large, ITMAX (%d) too small",
-	       a,ITMAX);
-  }
-  *gammcf=exp(-x+a*log(x)-(*gln))*h;
-} /* gcf */
-
-
-/* gser() Returns the incomplete gamma function P(a,x) evaluated by
-   its series representation as gamser.  Also returns ln Gamma(a) as gln.
-*/
-static void gser(double *gamser, double a, double x, double *gln)
-{
-  int n;
-  double sum,del,ap;
-  
-  *gln=gammln(a);
-  if (x <= 0.0) {
-    if (x < 0.0) {
-      writeError(errModeAll,errFatal,
-		 "linfit-->gser(): x (%lf) less than 0",
-		 x);
-    }
-    *gamser=0.0;
-    return;
-  } else {
-    ap=a;
-    del=sum=1.0/a;
-    for (n=1;n<=ITMAX;n++) {
-      ++ap;
-      del *= x/ap;
-      sum += del;
-      if (fabs(del) < fabs(sum)*EPS) {
-	*gamser=sum*exp(-x+a*log(x)-(*gln));
-	return;
-      }
-    }
-    writeError(errModeAll,errFatal,
-	       "linfit-->gser(): a (%le) too large, ITMAX (%d) too small",
-	       a,ITMAX);
-    return;
-	}
-} /* gser */
-
-#undef ITMAX
-#undef EPS
-#undef FPMIN
-
-
-/* gammq()
-   Returns the incomplete gamma function Q(a, x) = 1 - P(a, x). */
-
-static double gammq(double a, double x)
-{
-  double gamser,gammcf,gln;
-  
-  if (x < 0.0 || a <= 0.0) {
-    writeError(errModeAll,errFatal,
-	       "lintfit-->gammq(): Invalid arguments in routine gammq");
-  }
-  if (x < (a+1.0)) {
-    gser(&gamser,a,x,&gln);
-    return 1.0-gamser;
-  } else {
-    gcf(&gammcf,a,x,&gln);
-    return gammcf;
-  }
-} /* gammq */
-
-/* external functions
-   ========================================================================= */
-
-/* linfit() Given a set of data points x[0..ndata-1],y[0..ndata-1] with
-   individual standard deviations sig[0..ndata-1], fit them to a
-   straight line y = a + bx by minimizing chi2. Returned are a,b and
-   their respective probable uncertainties siga and sigb, the
-   chi-square chi2, and the goodness-of-fit probability q (that the
-   fit would have chi2 this large or larger). If mwt=0 on input, then
-   the standard deviations are assumed to be unavailable: q is
-   returned as 1.0 and the normalization of chi2 is to unit standard
-   deviation on all points.  
-*/
-
-void linfit(double x[], double y[], int ndata, 
-	    double sig[], int mwt, double *a, double *b, double *siga,
-	    double *sigb, double *chi2, double *q) { 
-
-  int 
-    i; 
-  double
-    wt,t,sxoss,sx=0.0,sy=0.0,st2=0.0,ss,sigdat;
-  
-  *b=0.0;
-  if (mwt) {
-    ss=0.0;
-    for (i=0;i<ndata;i++) {
-      wt=1.0/SQR(sig[i]);
-      ss += wt;
-      sx += x[i]*wt;
-      sy += y[i]*wt;
-    }
-  } else {
-    for (i=0;i<ndata;i++) {
-      sx += x[i];
-      sy += y[i];
-    }
-    ss=ndata;
-  }
-  sxoss=sx/ss;
-  if (mwt) {
-    for (i=0;i<ndata;i++) {
-      t=(x[i]-sxoss)/sig[i];
-      st2 += t*t;
-      *b += t*y[i]/sig[i];
-    }
-  } else {
-    for (i=0;i<ndata;i++) {
-      t=x[i]-sxoss;
-      st2 += t*t;
-      *b += t*y[i];
-    }
-  }
-  *b /= st2;
-  *a=(sy-sx*(*b))/ss;
-  *siga=sqrt((1.0+sx*sx/(ss*st2))/ss);
-  *sigb=sqrt(1.0/st2);
-  *chi2=0.0;
-  if (mwt == 0) {
-    for (i=0;i<ndata;i++)
-      *chi2 += SQR(y[i]-(*a)-(*b)*x[i]);
-    *q=1.0;
-    sigdat=sqrt((*chi2)/(ndata-2));
-    *siga *= sigdat;
-    *sigb *= sigdat;
-  } else {
-    for (i=0;i<ndata;i++)
-      *chi2 += SQR((y[i]-(*a)-(*b)*x[i])/sig[i]);
-    *q=gammq(0.5*(ndata-2),0.5*(*chi2));
-  }
-} /* linfit */
-
-/* linfit() 
-   Given a set of data points x[0..ndata-1],y[0..ndata-1] with, fit
-   them to a straight line y = a + bx by minimizing chi2. Returned are
-   a,b and their respective probable uncertainties siga and sigb, and
-   the chi-square chi2.
-*/
-void linfit(double x[], double y[], int ndata, 
-	    double *a, double *b, double *siga,
-	    double *sigb, double *chi2) { 
-
-  int 
-    i; 
-  double
-    t,sxoss,sx,sy,st2,sigdat;
-  
-  *b=0.0;
-  sx = 0.0;
-  sy = 0.0;
-  for (i=0;i<ndata;i++) {
-    sx += x[i];
-    sy += y[i];
-  }
-
-  sxoss=sx/ndata;
-
-  st2 = 0.0;
-  for (i=0;i<ndata;i++) {
-    t=x[i]-sxoss;
-    st2 += t*t;
-    *b += t*y[i];
-  }
-  *b /= st2;
-  *a=(sy-sx*(*b))/ndata;
-  *siga=sqrt((1.0+sx*sx/(ndata*st2))/ndata);
-  *sigb=sqrt(1.0/st2);
-  *chi2=0.0;
-
-  for (i=0;i<ndata;i++)
-    *chi2 += SQR(y[i]-(*a)-(*b)*x[i]);
-
-  sigdat=sqrt((*chi2)/(ndata-2));
-  *siga *= sigdat;
-  *sigb *= sigdat;
-} /* linfit */
-
-