//computing p(xn.....xN,zn)
void ocv::DHMM::backwardMatrix(vector<int> &sequence)
{
_beta=MatrixXf(_nstates,sequence.size()+1);
int len=sequence.size()+1;
for(int i=len-1;i>=0;i--)
{
for(int j=0;j<_nstates;j++)
{
if(i==len-1)
{
_beta(j,i)=1;
}
else
{
float s=0;
for(int k=0;k<_nstates;k++)
s=s+_beta(k,i+1)*_emission(k,sequence[i])*_transition(j,k);
_beta(j,i)=s*_scale(0,i+1);
}
}
}
}
GERG2008DepartureFunction::GERG2008DepartureFunction(const std::vector<double> &n,const std::vector<double> &d,const std::vector<double> &t,
const std::vector<double> &eta,const std::vector<double> &epsilon,const std::vector<double> &beta,
const std::vector<double> &gamma, unsigned int Npower)
{
/// Break up into power and gaussian terms
{
std::vector<long double> _n(n.begin(), n.begin()+Npower);
std::vector<long double> _d(d.begin(), d.begin()+Npower);
std::vector<long double> _t(t.begin(), t.begin()+Npower);
std::vector<long double> _l(Npower, 0.0);
phi.add_Power(_n, _d, _t, _l);
}
if (n.size() == Npower)
{
using_gaussian = false;
}
else
{
using_gaussian = true;
std::vector<long double> _n(n.begin()+Npower, n.end());
std::vector<long double> _d(d.begin()+Npower, d.end());
std::vector<long double> _t(t.begin()+Npower, t.end());
std::vector<long double> _eta(eta.begin()+Npower, eta.end());
std::vector<long double> _epsilon(epsilon.begin()+Npower, epsilon.end());
std::vector<long double> _beta(beta.begin()+Npower, beta.end());
std::vector<long double> _gamma(gamma.begin()+Npower, gamma.end());
phi.add_GERG2008Gaussian(_n, _d, _t, _eta, _epsilon, _beta, _gamma);
}
}
