// Implementation of virtual function that determines intersection
double Triangle::firstIntersectionBetween(Ray ray,
double &tMin,
double &tMax)
{
// Setup
computeDummyVariables(ray);
double m = computeM();
// Compute 't'
double t = computeT(m);
if (t < tMin || t > tMax)
return -1;
// Compute gamma
double gamma = computeGamma(m);
if (gamma < 0.0 || gamma > 1.0)
return -1;
// Compute betta
double betta = computeBetta(m);
if (betta < 0.0 || (betta > (1 - gamma)))
return -1;
// Intersected, fill in intersection
tMax = t;
return t;
}
double
HMM::baumWelchCached(const arma::mat & data, const std::vector<GMM> & B, unsigned int seed = 0) {
init(B, seed);
allocateMemory(data.n_cols);
cacheProbabilities(data);
double logprobc = forwardProcedureCached();
double logprobprev = 0.0;
double delta;
do {
backwardProcedureCached();
computeXiCached();
computeGamma();
logprobprev = logprobc;
//update state probabilities pi
pi_ = gamma_.col(0).t();
for (unsigned int i = 0; i < N_; ++i) {
arma::rowvec xi_i = arma::sum(xi_.slice(i));
double shortscale = 1./arma::accu(gamma_.submat(arma::span(i),arma::span(0,T_-2)));
for (unsigned int j = 0; j < N_; ++j) {
A_(i,j) = xi_i(j) * shortscale;
}
// and update distributions
unsigned int numComponents = (unsigned int) BModels_[i].getNumComponents();
arma::mat gamma_lt = arma::mat(T_, numComponents);
gamma_lt = (gamma_.row(i).t()* arma::ones(1, numComponents))% gammaLts_[i];
for(unsigned int t = 0; t < T_ ; ++t) {
double sumProb = B_(t,i);
if (sumProb != 0.) {
gamma_lt.row(t) /= sumProb;
}
}
double scale = 1./arma::accu(gamma_.row(i));
for (unsigned int l = 0; l < numComponents; ++l) {
double sumGammaLt = arma::accu(gamma_lt.col(l));
double newWeight = scale * sumGammaLt;
arma::vec newMu = data * gamma_lt.col(l) / sumGammaLt;
arma::mat tempMat = data- newMu * arma::ones(1,T_);
unsigned int d = data.n_rows;
arma::mat newSigma = arma::zeros(d, d);
for (unsigned int t = 0; t < T_ ; ++t) {
arma::vec diff = data.col(t) - newMu;
newSigma += diff * diff.t() * gamma_lt(t, l)/ sumGammaLt;
}
try{
BModels_[i].updateGM(l, newWeight, newMu, newSigma);
}
catch( const std::runtime_error & e) {
tempMat.print("tempMat");
gamma_lt.col(l).print("gamma_lt");
throw e;
}
}
arma::uvec indices = BModels_[i].cleanupGMs();
gammaLts_[i] = gammaLts_[i].cols(indices);
}
cacheProbabilities(data);
logprobc = forwardProcedureCached();
std::cout << logprobc << " " << logprobprev << std::endl;
delta = logprobc - logprobprev;
}
while (delta >= eps_ * std::abs(logprobprev));
return logprobc;
}
