void LearningInterface::trainCurrent()
{
//Only train if user falls into both classes
if(!m_one_class_only)
{
//Normalize the training set.
m_normalizer.train(m_training_set);
for (unsigned long i = 0; i < m_training_set.size(); ++i)
m_training_set[i] = m_normalizer(m_training_set[i]);
//Perform cross-validation over a range of values of C and gamma
//to determine the best parameters
double C_best = 0;
double gamma_best = 0;
double best_accuracy = 0.0;
//Different values, simple implementation
//Could look into better methods of doing this but this is a nice
//and simple implementation at the cost of speed and efficiency
for(double gamma = 0.0001; gamma < 300; gamma *= 5)
{
for(double C = 0.0001; C < 300; C *= 5)
{
//Set a new kernel
m_trainer.set_kernel(kernel_type(gamma));
//Set a new C
m_trainer.set_c(C);
//Perform 3-fold cross validation and look at the accuracy
matrix<double, 1, 2> accuracy = cross_validate_trainer(m_trainer, m_training_set, m_labels, 3);
DEBUG() << " cross-validation accuracy: " << accuracy(0,0) << " " << accuracy(0,1);
//See if this has the best accuracy so far by multiplying the 0 class accuracy and 1 class accuracy
double curr_accuracy = accuracy(0,0) * accuracy(0,1);
if(curr_accuracy > best_accuracy)
{
best_accuracy = curr_accuracy;
C_best = C;
gamma_best = gamma;
}
}
}
//Set C, epsilon and cache size
m_trainer.set_c(C_best);
//For now just leave defaults
//Set gamma
m_trainer.set_kernel(kernel_type(gamma_best));
m_decision_function.normalizer = m_normalizer;
//Train
m_decision_function.function = m_trainer.train(m_training_set, m_labels);
m_is_trained = true;
}
}
void formula_compiler::normalize() {
// make sure that the assertions are in the right format.
m_normalizer(s.m_assertions);
m_normalizer.cleanup();
}
