double
UniformJointPdf<V,M>::lnValue(
const V& domainVector,
const V* domainDirection,
V* gradVector,
M* hessianMatrix,
V* hessianEffect) const
{
if (gradVector ) *gradVector = m_domainSet.vectorSpace().zeroVector();
if (hessianMatrix) *hessianMatrix *= 0.;
if (hessianEffect) *hessianEffect = m_domainSet.vectorSpace().zeroVector();
if (domainVector[0]) {}; // just to remove compiler warning
if (domainDirection) {}; // just to remove compiler warning
double volume = m_domainSet.volume();
if ((queso_isnan(volume)) ||
(volume == -INFINITY ) ||
(volume == INFINITY ) ||
(volume <= 0. ) ||
(m_normalizationStyle != 0 )) {
volume = 1.;
}
return -log(volume);
}
double
BaseJointPdf<V,M>::commonComputeLogOfNormalizationFactor(unsigned int numSamples, bool updateFactorInternally) const
{
double value = 0.;
double volume = m_domainSet.volume();
if ((queso_isnan(volume)) ||
(volume == -INFINITY ) ||
(volume == INFINITY ) ||
(volume <= 0. )) {
// Do nothing
}
else {
const BoxSubset<V,M>* boxSubset = dynamic_cast<const BoxSubset<V,M>* >(&m_domainSet);
if (boxSubset == NULL) {
// Do nothing
}
else {
V tmpVec(m_domainSet.vectorSpace().zeroVector());
double sum = 0.;
for (unsigned int i = 0; i < numSamples; ++i) {
tmpVec.cwSetUniform(boxSubset->minValues(),boxSubset->maxValues());
sum += this->actualValue(tmpVec,NULL,NULL,NULL,NULL);
}
double avgValue = sum/((double) numSamples);
value = -( log(avgValue) + log(volume) );
if (updateFactorInternally) {
m_logOfNormalizationFactor = value;
}
}
}
return value;
}
double
UniformJointPdf<V,M>::actualValue(
const V& domainVector,
const V* domainDirection,
V* gradVector,
M* hessianMatrix,
V* hessianEffect) const
{
queso_require_equal_to_msg(domainVector.sizeLocal(), this->m_domainSet.vectorSpace().dimLocal(), "invalid input");
if (gradVector ) *gradVector = m_domainSet.vectorSpace().zeroVector();
if (hessianMatrix) *hessianMatrix *= 0.;
if (hessianEffect) *hessianEffect = m_domainSet.vectorSpace().zeroVector();
if (domainDirection) {}; // just to remove compiler warning
double volume = m_domainSet.volume();
if ((queso_isnan(volume)) ||
(volume == -INFINITY ) ||
(volume == INFINITY ) ||
(volume <= 0. ) ||
(m_normalizationStyle != 0 )) {
volume = 1.;
}
return 1./volume; // No need to multiply by exp(m_logOfNormalizationFactor) [PDF-04]
}
double
BayesianJointPdf<V,M>::computeLogOfNormalizationFactor(unsigned int numSamples, bool updateFactorInternally) const
{
double value = 0.;
double volume = m_domainSet.volume();
if ((queso_isnan(volume)) ||
(volume == -INFINITY ) ||
(volume == INFINITY ) ||
(volume <= 0. )) {
// Do nothing
}
else {
queso_error_msg("incomplete code for computeLogOfNormalizationFactor()");
}
return value;
}
