double InferenceEngineBP::computePartition(FeatureGenerator *fGen, DataSequence *X,
Model *m, int seqLabel, bool bUseStatePerNodes)
{
Beliefs beliefs;
computeBeliefs(beliefs, fGen, X, m, false, seqLabel, bUseStatePerNodes);
return beliefs.partition;
}
void IPFP::applyIPFPstep( std::vector<Message> &actual_marginals, std::vector<Message> &desired_marginals, int idx ){
applyEvidenceAndPropagate( cfg->dv_spectrum_depths[idx] - 1, desired_marginals[idx], actual_marginals[idx] );
double diff = computeBeliefs();
computeMarginals( actual_marginals );
diff_buf[idx] = diff;
}
int IPFP::runGEMA( ){
//Apply GEMA extensions of Iterative Proportional Fitting Procedure
int iter = 0;
targets_modified = false;
IPFP ipfp_copy(*this);
double diff = computeBeliefs();
std::vector<Message> desired_marginals, actual_marginals;
std::vector<Message> gema_marginals(cfg->dv_spectrum_depths.size());
computeMarginals( actual_marginals );
setDesiredMarginals( desired_marginals, actual_marginals );
while( !checkConvergence(false) && iter < MAX_IPFP_ITERATIONS ){
iter++;
//Re-initialise the GEMA target marginals
for( unsigned int idx = 0; idx < cfg->dv_spectrum_depths.size(); idx++ )
gema_marginals[idx].reset(actual_marginals[idx].size());
//Update the GEMA target marginals
for( unsigned int idx = 0; idx < cfg->dv_spectrum_depths.size(); idx++ ){
ipfp_copy = *this; //Create a copy of the IPFP instance so we don't change this one
std::vector<Message> tmp_marginals( actual_marginals );
ipfp_copy.applyIPFPstep( tmp_marginals, desired_marginals, idx );
for( unsigned int j = 0; j < cfg->dv_spectrum_depths.size(); j++ )
gema_marginals[j].addWeightedMessage(tmp_marginals[j], cfg->dv_spectrum_weights[idx]);
}
//Run standard IPFP for number of spectra iterations
for( unsigned int idx = 0; idx < cfg->dv_spectrum_depths.size(); idx++ )
applyIPFPstep( actual_marginals, gema_marginals, idx );
}
return iter;
}
int IPFP::runIPFP( ){
//Apply Iterative Proportional Fitting Procedure
int iter = 0;
targets_modified = false;
double diff = computeBeliefs();
std::vector<Message> desired_marginals, actual_marginals;
computeMarginals( actual_marginals );
setDesiredMarginals( desired_marginals, actual_marginals );
while( !checkConvergence(false) && iter < MAX_IPFP_ITERATIONS ){
iter++;
for( unsigned int idx = 0; idx < cfg->dv_spectrum_depths.size(); idx++ )
applyIPFPstep( actual_marginals, desired_marginals, idx );
}
return iter;
}
int IPFP::runIPFPwithOscAdjust( ){
//Apply Iterative Proportional Fitting Procedure - detecting and adjusting for oscillatory convergence
int iter = 0;
int reverse = 0;
targets_modified = false;
//Store a copy of the initial msgs
std::vector<Message> init_down_msgs = down_msgs;
std::vector<Message> init_up_msgs = up_msgs;
//Initialise the target marginals
std::vector<Message> desired_marginals, actual_marginals, reverse_marginals;
initialiseFactorProbsAndBeliefs();
double diff = computeBeliefs();
computeMarginals( actual_marginals );
setDesiredMarginals( desired_marginals, actual_marginals );
int retries = 0;
while( (retries == 0 || status == OSC_CONVERGE) && retries <= MAX_IPFP_OSC_RETRIES ){
//Run IPFP to convergence (oscillatory or otherwise) in the forward direction
iter = 0;
while( !checkConvergence(true) && iter < MAX_IPFP_ITERATIONS ){
iter++;
//for( int idx = cfg->dv_spectrum_depths.size()-1; idx >= 0; idx-- ){
for( unsigned int idx = 0; idx < cfg->dv_spectrum_depths.size(); idx++ )
applyIPFPstep( actual_marginals, desired_marginals, idx );
}
if( status != OSC_CONVERGE ) break;
//Oscillatory Convergence detected:
// std::cout << "Oscillatory convergence - modifying targets for retry after " << iter << " iterations (" << retries << " retry)" << std::endl;
//Re-Initialise msgs and factor transition/persistence probabilities
down_msgs = init_down_msgs;
up_msgs = init_up_msgs;
initialiseFactorProbsAndBeliefs();
diff = computeBeliefs();
computeMarginals( reverse_marginals );
for( unsigned int i = 0; i < diff_buf.size(); i++){
diff_buf[i] = 100.0;
prev_diff_buf[i] = 1000.0;
}
//Run IPFP to convergence (oscillatory or otherwise) in the reverse direction
iter = 0;
while( !checkConvergence(true) && iter < MAX_IPFP_ITERATIONS ){
iter++;
for( int idx = cfg->dv_spectrum_depths.size()-1; idx >= 0; idx-- )
applyIPFPstep( reverse_marginals, desired_marginals, idx );
}
//Set the target marginals to the average of the forward and reverse marginals
desired_marginals = actual_marginals;
for( unsigned int j = 0; j < cfg->dv_spectrum_depths.size(); j++ )
desired_marginals[j].addWeightedMessage(reverse_marginals[j], 1.0);
targets_modified = true;
//Re-Initialise everything ready to go again
down_msgs = init_down_msgs;
up_msgs = init_up_msgs;
initialiseFactorProbsAndBeliefs();
diff = computeBeliefs();
computeMarginals( actual_marginals );
for( unsigned int i = 0; i < diff_buf.size(); i++){
diff_buf[i] = 100.0;
prev_diff_buf[i] = 1000.0;
}
retries++;
}
return iter;
}
