Real BBPCostFunction::evaluate( const InfAlg &ia, const vector<size_t> *stateP ) const {
Real cf = 0.0;
const FactorGraph &fg = ia.fg();
switch( (size_t)(*this) ) {
case CFN_BETHE_ENT: // ignores state
cf = -ia.logZ();
break;
case CFN_VAR_ENT: // ignores state
for( size_t i = 0; i < fg.nrVars(); i++ )
cf += -ia.beliefV(i).entropy();
break;
case CFN_FACTOR_ENT: // ignores state
for( size_t I = 0; I < fg.nrFactors(); I++ )
cf += -ia.beliefF(I).entropy();
break;
case CFN_GIBBS_B:
case CFN_GIBBS_B2:
case CFN_GIBBS_EXP: {
DAI_ASSERT( stateP != NULL );
vector<size_t> state = *stateP;
DAI_ASSERT( state.size() == fg.nrVars() );
for( size_t i = 0; i < fg.nrVars(); i++ ) {
Real b = ia.beliefV(i)[state[i]];
switch( (size_t)(*this) ) {
case CFN_GIBBS_B:
cf += b;
break;
case CFN_GIBBS_B2:
cf += b * b / 2.0;
break;
case CFN_GIBBS_EXP:
cf += exp( b );
break;
default:
DAI_THROW(UNKNOWN_ENUM_VALUE);
}
}
break;
} case CFN_GIBBS_B_FACTOR:
case CFN_GIBBS_B2_FACTOR:
case CFN_GIBBS_EXP_FACTOR: {
DAI_ASSERT( stateP != NULL );
vector<size_t> state = *stateP;
DAI_ASSERT( state.size() == fg.nrVars() );
for( size_t I = 0; I < fg.nrFactors(); I++ ) {
size_t x_I = getFactorEntryForState( fg, I, state );
Real b = ia.beliefF(I)[x_I];
switch( (size_t)(*this) ) {
case CFN_GIBBS_B_FACTOR:
cf += b;
break;
case CFN_GIBBS_B2_FACTOR:
cf += b * b / 2.0;
break;
case CFN_GIBBS_EXP_FACTOR:
cf += exp( b );
break;
default:
DAI_THROW(UNKNOWN_ENUM_VALUE);
}
}
break;
} default:
DAI_THROWE(UNKNOWN_ENUM_VALUE, "Unknown cost function " + std::string(*this));
}
return cf;
}
/// Run the algorithm and store its results
void doDAI() {
double tic = toc();
if( obj != NULL ) {
// Initialize
obj->init();
// Run
obj->run();
// Record the time
time += toc() - tic;
// Store logarithm of the partition sum (if supported)
try {
logZ = obj->logZ();
has_logZ = true;
} catch( Exception &e ) {
if( e.getCode() == Exception::NOT_IMPLEMENTED )
has_logZ = false;
else
throw;
}
// Store maximum difference encountered in last iteration (if supported)
try {
maxdiff = obj->maxDiff();
has_maxdiff = true;
} catch( Exception &e ) {
if( e.getCode() == Exception::NOT_IMPLEMENTED )
has_maxdiff = false;
else
throw;
}
// Store number of iterations needed (if supported)
try {
iters = obj->Iterations();
has_iters = true;
} catch( Exception &e ) {
if( e.getCode() == Exception::NOT_IMPLEMENTED )
has_iters = false;
else
throw;
}
// Store variable marginals
varMarginals.clear();
for( size_t i = 0; i < obj->fg().nrVars(); i++ )
varMarginals.push_back( obj->beliefV( i ) );
// Store factor marginals
facMarginals.clear();
for( size_t I = 0; I < obj->fg().nrFactors(); I++ )
try {
facMarginals.push_back( obj->beliefF( I ) );
} catch( Exception &e ) {
if( e.getCode() == Exception::BELIEF_NOT_AVAILABLE )
facMarginals.push_back( Factor( obj->fg().factor(I).vars(), INFINITY ) );
else
throw;
}
// Store all marginals calculated by the method
allMarginals = obj->beliefs();
};
}
