int kh_training_test3() {
int* counts_array;
cout << "\n\nKH-TRAINING-TEST-3" << endl;
string s[] = { ".", ".", ".", ".", ".", "." };
counts_array = new int[9];
clean_int_array(counts_array, 9);
for (int i = 0; i < 6; i++)
process_structure(&(s[i]), counts_array);
cout << endl << "13, 6, 19, 19, 7, 26, 7, 1, 8," << endl;
string disp = "";
double probs[3];
double perc[3];
get_production_probabilities(counts_array, probs);
get_state_probabilities(counts_array, perc);
set_production_probabilities_string(&disp, probs);
set_state_probabilities_string(&disp, perc);
cout << endl << disp;
delete[] counts_array;
return 0;
}
/**
* **Note: Overloaded to accept vectors<string>.
*
* Trains the Knudson-Hein grammar given a vector of string representing
* structures. The calculated probabilities and percentages will fill the
* corresponding provided arrays.
*
* @param set - Every structures in dot-bracket notation in the training set
* @param cardinality - Size of the training set.
* @param probs - Array to be set with the production probabilities. (Length = 3)
* @param probs - Array to be set with the state production percentages. (Length = 3)
*/
void train_grammar(vector<string>* vec, double* probs, double* perc, bool verbose){
DEBUG = false;
int counts[9];
clean_int_array(counts, 9);
for (int i = 0; i < vec->size(); i++)
process_structure(&((*vec)[i]), counts);
get_production_probabilities(counts, probs);
get_state_probabilities(counts, perc);
if (verbose) {
string s = "";
set_production_probabilities_string(&s, probs);
set_state_probabilities_string(&s, perc);
cout << endl << s << endl;
}
}
/**
* Trains the Knudson-Hein grammar given a string array of structures. The
* calculated probabilities and percentages will fill the corresponding
* provided arrays.
*
* @param set - Every structures in dot-bracket notation in the training set
* @param cardinality - Size of the training set.
* @param probs - Array to be set with the production probabilities. (Length = 3)
* @param probs - Array to be set with the state production percentages. (Length = 3)
*/
void train_grammar(string* set, int cardinality, double* probs, double* perc,
bool verbose) {
DEBUG = false;
int counts[9];
clean_int_array(counts, 9);
for (int i = 0; i < cardinality; i++)
process_structure(&(set[i]), counts);
get_production_probabilities(counts, probs);
get_state_probabilities(counts, perc);
if (verbose) {
string s = "";
set_production_probabilities_string(&s, probs);
set_state_probabilities_string(&s, perc);
cout << endl << s << endl;
}
}
void EnsembleGenerator::output(Ensemble& ensemble,
const Vector<Vector<saxs::WeightedFitParameters> >& fps) const {
if(ensemble.size() == 0) return;
// calculate z-score
Vector<double> scores(ensemble.size());
for(unsigned int i=0; i<ensemble.size(); i++) scores[i] = ensemble[i].get_score();
std::pair<double, double> average_and_std = get_average_and_stdev(scores);
for(unsigned int i=0; i<ensemble.size(); i++) {
double zscore = (ensemble[i].get_score()-average_and_std.first) /
average_and_std.second;
ensemble[i].set_zscore(zscore);
}
// calculate frequency of each state
Vector<double> state_prob;
get_state_probabilities(ensemble, state_prob);
// calculate weights average and variance
Vector<Vector<double> > weights_average(scorers_.size()),
weights_variance(scorers_.size());
for(unsigned int i=0; i<scorers_.size(); i++) {
get_weights_average_and_std(ensemble, fps[i], weights_average[i],
weights_variance[i]);
}
// output file
unsigned int number_of_states = ensemble[0].size();
std::string out_file_name = "ensembles_size_" +
std::string(boost::lexical_cast<std::string>(number_of_states)) + ".txt";
std::ofstream s(out_file_name.c_str());
std::cout << "multi_state_model_size " << ensemble.size ()
<< " number_of_states " << number_of_states << std::endl;
for(unsigned int i=0; i<ensemble.size(); i++) {
// output ensemble scores
s.setf(std::ios::fixed, std::ios::floatfield);
s << i+1 << " | " << std::setw(5) << std::setprecision(2)
<< ensemble[i].get_score(); // << " | " << ensemble[i].get_zscore();
// output scores for each scorer
for(unsigned int j=0; j<scorers_.size(); j++) {
const saxs::WeightedFitParameters& p = fps[j][i];
s << " | x" << std::string(boost::lexical_cast<std::string>(j+1))
//scorers_[j]->get_dataset_name() << ": "
<< " " << std::setprecision(2) << p.get_chi()
<< " (" << p.get_c1() << ", " << p.get_c2() << ")";
}
s << std::endl;
// output states and their probabilities
const Vector<unsigned int>& states = ensemble[i].get_states();
for(unsigned int k=0; k<states.size(); k++) {
s << std::setw(5) << states[k];
// output weights
for(unsigned int j=0; j<scorers_.size(); j++) {
const saxs::WeightedFitParameters& p = fps[j][i];
if(p.get_weights().size() > k) {
s << std::setw(5) << std::setprecision(3) << " | "
<< p.get_weights()[k] << " ("
<< weights_average[j][states[k]] << ", "
<< weights_variance[j][states[k]] << ")";
}
}
s << " | " << scorers_[0]->get_state_name(states[k])
<< " (" << state_prob[states[k]] << ")" << std::endl;
}
// output fit file
if(i<10) { // TODO: add parameter
for(unsigned int j=0; j<scorers_.size(); j++) {
std::string fit_file_name = "multi_state_model_" +
std::string(boost::lexical_cast<std::string>(number_of_states)) + "_" +
std::string(boost::lexical_cast<std::string>(i+1));
if(scorers_.size() > 0) {
fit_file_name += "_" + std::string(boost::lexical_cast<std::string>(j+1));
}
fit_file_name += ".dat";
scorers_[j]->write_fit_file(ensemble[i], fps[j][i], fit_file_name);
}
}
}
s.close();
}
