float PhaseEstimator::EvaluateParameters(vector<BasecallerRead *>& useful_reads, DPTreephaser& treephaser, const float *parameters, const bool usePIDNorm)
{
float try_cf = parameters[0];
float try_ie = parameters[1];
float try_dr = parameters[2];
if (try_cf < 0 or try_ie < 0 or try_dr < 0 or try_cf > 0.04 or try_ie > 0.04 or try_dr > 0.01)
return 1e10;
treephaser.SetModelParameters(try_cf, try_ie, try_dr);
float metric = 0;
for (vector<BasecallerRead *>::iterator read = useful_reads.begin(); read != useful_reads.end(); ++read) {
treephaser.Simulate(**read, 120);
float normalizer = (usePIDNorm ? treephaser.PIDNormalize(**read, 8, 100) : treephaser.Normalize(**read, 20, 100));
for (unsigned int flow = 20; flow < 100 and flow < (*read)->raw_measurements.size(); flow++) {
if ((*read)->raw_measurements[flow] > 1.2)
continue;
float delta = (*read)->raw_measurements[flow] - (*read)->prediction[flow] * normalizer;
metric += delta * delta;
}
}
return isnan(metric) ? 1e10 : metric;
}
float PhaseEstimator::EvaluateParameters(vector<BasecallerRead *>& useful_reads, DPTreephaser& treephaser, const float *parameters)
{
float try_cf = parameters[0];
float try_ie = parameters[1];
float try_dr = parameters[2];
if (try_cf < 0 or try_ie < 0 or try_dr < 0 or try_cf > 0.04 or try_ie > 0.04 or try_dr > 0.01)
return 1e10;
treephaser.SetModelParameters(try_cf, try_ie, try_dr);
float metric = 0;
for (vector<BasecallerRead *>::iterator read = useful_reads.begin(); read != useful_reads.end(); ++read) {
// Simulate phasing parameter
treephaser.Simulate(**read, phasing_end_flow_+20);
// Optionally determine optimal normalization for this parameter set?
if (norm_during_param_eval_)
NormalizeBasecallerRead(treephaser, **read, phasing_start_flow_, phasing_end_flow_);
// Determine squared distance penalty for this parameter set
for (int flow = phasing_start_flow_; flow < phasing_end_flow_ and flow < (int)(*read)->raw_measurements.size(); ++flow) {
if ((*read)->raw_measurements[flow] > inclusion_threshold_)
continue;
// Keep key normalized raw measurements as a constant and normalize predictions towards key normalized values
float delta = ((*read)->normalized_measurements[flow] - (*read)->prediction[flow]) * (*read)->multiplicative_correction[flow];
metric += delta * delta;
}
}
return isnan(metric) ? 1e10 : metric;
}
int HypothesisEvaluator::EvaluateOneHypothesis(DPTreephaser &working_treephaser, BasecallerRead ¤t_hypothesis, int applyNormalization) {
int last_incorporating_flow = LastIncorporatingFlow(current_hypothesis);
// Simulate sequence
working_treephaser.Simulate(current_hypothesis, nFlows);
// Adaptively normalize each hypothesis
if (applyNormalization>0) {
int window_size= 50;
int steps = last_incorporating_flow / window_size;
working_treephaser.WindowedNormalize(current_hypothesis, steps, window_size);
}
// Solver simulates beginning of the read and then fills in the remaining clipped bases
working_treephaser.Solve(current_hypothesis, nFlows, last_incorporating_flow);
/*cout << "Solved sequence of length: " << hypothesesReadsVector[r].sequence.size() << " ;nFlows = " << nFlows << endl;
cout << "Total read: ";
for (int i=0; i<hypothesesReadsVector[r].sequence.size(); i++)
cout << hypothesesReadsVector[r].sequence[i];
cout << endl;*/
return(last_incorporating_flow);
}
