// store the HMM into a file
void HMMState::store(FileOutput &file) {
// phonetic symbol
char strPhone[MAX_PHONETIC_SYMBOL_LENGTH+1];
memset(strPhone,0,MAX_PHONETIC_SYMBOL_LENGTH+1);
strcpy(strPhone,m_phoneSet->getStrPhone(m_iPhone));
IOBase::writeBytes(file.getStream(),reinterpret_cast<char*>(strPhone),MAX_PHONETIC_SYMBOL_LENGTH+1);
// state
IOBase::write(file.getStream(),m_iState);
// within word position (DEPRECATED)
IOBase::write(file.getStream(),m_iPosition);
// Gaussian components
int iGaussianComponents = m_gaussianMixture->getNumberComponents();
IOBase::write(file.getStream(),iGaussianComponents);
for(int iGaussian = 0 ; iGaussian < iGaussianComponents ; ++iGaussian) {
Gaussian *gaussian = (*m_gaussianMixture)(iGaussian);
IOBase::write(file.getStream(),gaussian->weight());
gaussian->mean().writeData(file.getStream());
if (m_iCovarianceModeling == COVARIANCE_MODELLING_TYPE_DIAGONAL) {
gaussian->covarianceDiag().writeData(file.getStream());
} else {
gaussian->covarianceFull().writeData(file.getStream());
}
}
}
// load the HMM from a file
void HMMState::load(FileInput &file, unsigned char iEstimationMethod) {
// phonetic symbol
char strPhone[MAX_PHONETIC_SYMBOL_LENGTH+1];
IOBase::readBytes(file.getStream(),reinterpret_cast<char*>(strPhone),MAX_PHONETIC_SYMBOL_LENGTH+1);
m_iPhone = m_phoneSet->getPhoneIndex(strPhone);
assert(m_iPhone != UCHAR_MAX);
// state
IOBase::read(file.getStream(),&m_iState);
assert(m_iState < NUMBER_HMM_STATES);
// within word position (DEPRECATED)
IOBase::read(file.getStream(),&m_iPosition);
// Gaussian components
int iGaussianComponents = -1;
IOBase::read(file.getStream(),&iGaussianComponents);
for(int iGaussian = 0 ; iGaussian < iGaussianComponents ; ++iGaussian) {
Gaussian *gaussian = new Gaussian(m_iDim,m_iCovarianceModeling);
IOBase::read(file.getStream(),&gaussian->weight());
gaussian->mean().readData(file.getStream());
if (m_iCovarianceModeling == COVARIANCE_MODELLING_TYPE_DIAGONAL) {
gaussian->covarianceDiag().readData(file.getStream());
} else {
gaussian->covarianceFull().readData(file.getStream());
}
m_gaussianMixture->addGaussianComponent(gaussian);
}
}
bool is_approx(const Gaussian<OtherVariate>& other)
{
return fl::are_similar(mean(), other.mean()) &&
fl::are_similar(covariance(), other.covariance());
}
// estimate the parameters of the given HMM-state
void MAPEstimator::estimateParameters(HMMState *hmmState, Accumulator **accumulators, float fPriorKnowledgeWeight) {
// (1) update the mean of each Gaussian component
for(unsigned int g = 0 ; g < hmmState->getMixture().getNumberComponents() ; ++g) {
Gaussian *gaussian = hmmState->getMixture()(g);
Accumulator *accumulator = accumulators[g];
// if there occupation for this component?
if (accumulator == NULL) {
continue;
}
// mean (needs to be computed first in the case of full covariance)
gaussian->mean().mul(fPriorKnowledgeWeight);
gaussian->mean().add(accumulator->getObservation());
gaussian->mean().mul((float)(1.0f/(fPriorKnowledgeWeight+accumulator->getOccupation())));
}
assert(hmmState->getMixture().getNumberComponents() > 0);
}
bool is_approx(const Gaussian<OtherVariate>& other,
Real eps = 1.e-9,
bool scale_with_size = false)
{
Real eps_mean = eps;
Real eps_cov = eps;
if (scale_with_size)
{
eps_mean *= mean().size();
eps_cov *= covariance().size();
}
return fl::are_similar(mean(), other.mean(), eps_mean) &&
fl::are_similar(covariance(), other.covariance(), eps_cov);
}