double
gsl_ran_binomial_pdf (const unsigned int k, const double p,
const unsigned int n)
{
if (k > n)
{
return 0;
}
else
{
double P;
if (p == 0)
{
P = (k == 0) ? 1 : 0;
}
else if (p == 1)
{
P = (k == n) ? 1 : 0;
}
else
{
double ln_Cnk = gsl_sf_lnchoose (n, k);
P = ln_Cnk + k * log (p) + (n - k) * log1p (-p);
P = exp (P);
}
return P;
}
}
double calcEIndex(int nX1, int nY1, int nX2, int nY2, int nSplit, int nLenI)
{
int i = 0, nM = 0, nN = 0, nL = 0, nH = 0, nO = 0;
double dLN1 = 0.0, dLN2 = 0.0, dLD = 0.0;
if(nX1 <= nX2){
nM = nX1 - nY1;
nH = nY1;
nN = nX1;
nL = nLenI - nSplit - 1;
nO = nX1 + nY1;
}
else{
nM = nX2 - nY2;
nH = nY2;
nN = nX2;
nL = nSplit + 1;
nO = nX2 + nY2;
}
if(nL - nO >= 0 && nH <= nL - nO){
dLN1 = gsl_sf_lnchoose(nL - nO, nH);
}
else{
dLN1 = 0.0;
}
dLN2 = gsl_sf_lnchoose(nO, nM);
if(nL >= nN){
dLD = gsl_sf_lnchoose(nL, nN);
}
else{
dLD = 0.0;
}
return dLD - dLN1 - dLN2;
}
/*
Calculates the minimum number of inliers as a function of the number of
putative correspondences. Based on equation (7) in
Chum, O. and Matas, J. Matching with PROSAC -- Progressive Sample Consensus.
In <EM>Conference on Computer Vision and Pattern Recognition (CVPR)</EM>,
(2005), pp. 220--226.
@param n number of putative correspondences
@param m min number of correspondences to compute the model in question
@param p_badsupp prob. that a bad model is supported by a correspondence
@param p_badxform desired prob. that the final transformation returned is bad
@return Returns the minimum number of inliers required to guarantee, based
on p_badsupp, that the probability that the final transformation returned
by RANSAC is less than p_badxform
*/
int calc_min_inliers( int n, int m, double p_badsupp, double p_badxform )
{
double pi, sum;
int i, j;
for( j = m+1; j <= n; j++ )
{
sum = 0;
for( i = j; i <= n; i++ )
{
pi = ( i - m ) * log( p_badsupp ) + ( n - i + m ) * log( 1.0 - p_badsupp ) +
gsl_sf_lnchoose( n - m, i - m );
sum += exp( pi );
}
if( sum < p_badxform )
break;
}
return j;
}
double
gsl_ran_binomial_pdf (const unsigned int k, const double p,
const unsigned int n)
{
if (k > n)
{
return 0;
}
else
{
double P;
double ln_Cnk = gsl_sf_lnchoose (n, k);
P = ln_Cnk + k * log (p) + (n - k) * log (1 - p);
P = exp (P);
return P;
}
}
static VALUE rb_gsl_sf_lnchoose(VALUE obj, VALUE n, VALUE m)
{
CHECK_FIXNUM(n); CHECK_FIXNUM(m);
return rb_float_new(gsl_sf_lnchoose(FIX2INT(n), FIX2INT(m)));
}
