//normal random could accerlate the speed of convergence
void MFBiasApp() {
if (!FLAGS_flag_mfbias) {
return;
}
SpMat train;
SpMat test;
std::pair<int, int> p = ReadData(FLAGS_train_path, &train);
ReadData(FLAGS_test_path, &test);
MF mf;
RandomInit(p.second, p.first, FLAGS_k, &mf);
SGD(FLAGS_it_num, FLAGS_eta, FLAGS_lambda, train, test, &mf);
}
void network::learning(const train_couple* training_data, int data_count, int batch_size, double eta, int epoch_number)
{
train_couple* data_copy=new train_couple[data_count]; //data to shuffle later in order to have no affect on original training_data
for (int i=0; i<data_count; i++)
data_copy[i]=training_data[i];
int nbatches=data_count/batch_size;
for (int i=0; i<epoch_number; i++) {
shuffle(data_copy,data_count);
for (int batch_number=0; batch_number<nbatches; batch_number++){
train_couple* mini_batch=get_batch(data_copy,batch_size,batch_number);
SGD(mini_batch,batch_size,eta,data_count);
delete [] mini_batch;
}
cout<<"epoch # "<<i<<" has been finished\n";
}
delete [] data_copy;
}
fmat SGD(const fmat& X, const fmat& Y) {
return SGD(X, Y, ALPHA);
}
