Matrix<double> Camera::getView()
{
this->direction.normalize();
this->up = Vector::cross(this->direction, this->right);
this->up.normalize();
this->right = Vector::cross(this->up, this->direction);
this->right.normalize();
double x = -Vector::scalar(this->right, this->position);
double y = -Vector::scalar(this->up, this->position);
double z = -Vector::scalar(this->direction, this->position);
Matrix<double> tmp(4, 4);
tmp.setColumn(0, this->right.getArray());
tmp.setColumn(1, this->up.getArray());
tmp.setColumn(2, this->direction.getArray());
IArray<double> itmp(tmp[3]);
itmp[0].updatedir(x);
itmp[1].updatedir(y);
itmp[2].updatedir(z);
itmp[3].updatedir(1);
return tmp;
}
/**
* Description not yet available.
* \param
*/
void laplace_approximation_calculator::generate_antithetical_rvs()
{
// number of random vectors
const ivector & itmp=(*num_local_re_array)(1,num_separable_calls);
//const ivector & itmpf=(*num_local_fixed_array)(1,num_separable_calls);
for (int i=2;i<=num_separable_calls;i++)
{
if (itmp(i) != itmp(i-1))
{
cerr << "At present can only use antithetical rv's when "
"all separable calls are the same size" << endl;
ad_exit(1);
}
}
int n=itmp(1);
int samplesize=num_importance_samples;
// mesh size
double delta=0.01;
// maximum of distribution is near here
double mid=sqrt(double(n-1));
dmatrix weights(1,2*n,1,2);
double spread=15;
if (mid-spread<=0.001)
spread=mid-0.1;
double ssum=0.0;
double x=0.0;
double tmax=(n-1)*log(mid)-0.5*mid*mid;
for (x=mid-spread;x<=mid+spread;x+=delta)
{
ssum+=exp((n-1)*log(x)-0.5*x*x-tmax);
}
double tsum=0;
dvector dist(1,samplesize+1);
dist.initialize();
int is=0;
int ii;
for (x=mid-spread;x<=mid+spread;x+=delta)
{
tsum+=exp((n-1)*log(x)-0.5*x*x-tmax)/ssum*samplesize;
int ns=int(tsum);
for (ii=1;ii<=ns;ii++)
{
dist(++is)=x;
}
tsum-=ns;
}
if (is==samplesize-1)
{
dist(samplesize)=mid;
}
else if (is<samplesize-1)
{
cerr << "This can't happen" << endl;
exit(1);
}
// get random numbers
random_number_generator rng(rseed);
if (antiepsilon)
{
if (allocated(*antiepsilon))
{
delete antiepsilon;
antiepsilon=0;
}
}
antiepsilon=new dmatrix(1,samplesize,1,n);
dmatrix & M=*antiepsilon;
M.fill_randn(rng);
for (int i=1;i<=samplesize;i++)
{
M(i)=M(i)/norm(M(i));
}
int nvar=(samplesize-1)*n;
independent_variables xx(1,nvar);
ii=0;
for (int i=2;i<=samplesize;i++)
{
for (int j=1;j<=n;j++)
{
xx(++ii)=M(i,j);
}
}
fmmt1 fmc(nvar,5);
//fmm fmc(nvar,5);
fmc.noprintx=1;
fmc.iprint=10;
fmc.maxfn=2500;
fmc.crit=1.e-6;
double f;
double fbest=1.e+50;;
dvector g(1,nvar);
dvector gbest(1,nvar);
dvector xbest(1,nvar);
gbest.fill_seqadd(1.e+50,0.);
{
while (fmc.ireturn>=0)
{
//int badflag=0;
fmc.fmin(f,xx,g);
if (fmc.ihang)
{
//int hang_flag=fmc.ihang;
//double maxg=max(g);
//double ccrit=fmc.crit;
//int current_ifn=fmc.ifn;
}
if (fmc.ireturn>0)
{
f=fcomp1(xx,dist,samplesize,n,g,M);
if (f < fbest)
{
fbest=f;
gbest=g;
xbest=xx;
}
}
}
xx=xbest;
}
ii=0;
for (int i=2;i<=samplesize;i++)
{
for (int j=1;j<=n;j++)
{
M(i,j)=xx(++ii);
}
}
for (int i=1;i<=samplesize;i++)
{
M(i)*=dist(i)/norm(M(i));
}
}
