int CStochastic::UniformSampling(int R)
{
int Index=(double)UniformSampling()*R;
if(Index>R-1)
Index=R-1;
return Index;
}
double CStochastic::GaussianSampling()
{
int i;
double result=0;
for (i=0;i<12;i++)
result+=UniformSampling();
result-=6;
return result;
}
int CStochastic::Sampling(double* Density,int NumSamples)
{
double RandNumber=UniformSampling();
int i;
double sum=0;
for(i=0;i<NumSamples;i++)
{
sum+=Density[i];
if(sum>=RandNumber)
return i;
}
return NumSamples-1;
}
double _findCentroid(SegBox *Sx, SegBox *s0, SegBox *s1, int nsamples, int seedlen){
int iter1, iter2;
double costMin = INF;
/// keep best seeds
int s0stB, s1stB, s0lenB, s1lenB; //best
int s0stC, s1stC, s0lenC, s1lenC; //current
/// make sample set
UniformSet(Sx, seedlen, nsamples, &ws->U);
/// start uniform sampling
for(iter1=0;iter1<ws->U.nSb;iter1++)
for(iter2=iter1+1;iter2<ws->U.nSb;iter2++){
UniformSampling(Sx, s0, s1, seedlen, iter1, iter2, &ws->U);
if(s0->nSb==0 || s1->nSb==0) continue; // not sufficient
// copy positions
s0stC=s0->Sb[0].st; s0lenC=s0->Sb[0].len;
s1stC=s1->Sb[0].st; s1lenC=s1->Sb[0].len;
// estimate HMM
_estimateHMM_k(s0, MINK);
_estimateHMM_k(s1, MINK);
// cut point search
_cps(Sx, s0, s1, YES);
if(s0->nSb==0 || s1->nSb==0) continue;
if(costMin > s0->costT + s1->costT){
// update best seeds
costMin = s0->costT+s1->costT;
s0stB=s0stC; s0lenB=s0lenC;
s1stB=s1stC; s1lenB=s1lenC;
}//if
}//iter1,2
if(costMin==INF){
//fprintf(stderr, "couldn't find good seeds...\n");
FixedSampling(Sx, s0, s1, seedlen);
return INF;
}
ResetStEd(s0); ResetStEd(s1);
AddStEd(s0, s0stB, s0lenB);
AddStEd(s1, s1stB, s1lenB);
return costMin;
}