void bayes_measure(int ss1_mod, int ss2_mod, SampleSet& ss1, SampleSet& ss2) {
cout << "----new Bayes training----" << endl;
vector<vector<double>> samples;
std::vector<int> labels;
int s1_count = 0, s2_count = 0;
for (int i = 0; i < ss1.size(); ++i) {
if (std::rand() % ss1_mod != 0)
continue;
samples.push_back(toBayesSample(ss1[i]));
labels.push_back(+1);
s1_count++;
}
for (int i = 0; i < ss2.size(); ++i) {
if (std::rand() % ss2_mod == 0) {
samples.push_back(toBayesSample(ss2[i]));
labels.push_back(-1);
s2_count++;
}
}
BayesClassifier c;
auto decision_fun = c.train(samples, labels);
// Measure
samples.clear();
labels.clear();
int s1_all_count = 0, s2_all_count = 0;
for (int i = 0; i < ss1.size(); ++i) {
samples.push_back(toBayesSample(ss1[i]));
labels.push_back(+1);
s1_all_count++;
}
for (int i = 0; i < ss2.size(); ++i) {
samples.push_back(toBayesSample(ss2[i]));
labels.push_back(-1);
s2_all_count++;
}
cout << "Train " << s1_count << " + " << s2_count << " measures for " << s1_all_count << " + " << s2_all_count << endl;
cout << "Measure for " << samples.size() << " samples" << endl;
measure_for_dataset(decision_fun, samples, labels);
}
int main( int argc, char** argv )
{
if( argc != 3 )
{
std::cout << "Usage: MLBayesTest <TrainingDataSetFile> <TestDataSetFile>" << std::endl;
return 0;
}
DataSet* data, *test_data;
data = MLLIB->createDataSet();
test_data = MLLIB->createDataSet();
// Load the training dataset
std::string dspath = argv[1];
if( !MLLIB->loadDataSet( dspath + ".arff", data ) )
{
return 0;
}
// Load the test dataset
dspath = argv[2];
if( !MLLIB->loadDataSet( dspath + ".arff", test_data ) )
{
return 0;
}
// Create each classifier, train it and test it
BayesClassifier* bcl = static_cast<BayesClassifier*>( MLLIB->createClassifier(CT_Bayes) );
bcl->setBayesType(BT_Naive);
bcl->train(data);
bcl->test(test_data);
bcl->reset();
bcl->setBayesType(BT_TAN);
bcl->train(data);
bcl->test(test_data);
bcl->reset();
bcl->setBayesType(BT_SparseCandidate);
bcl->train(data);
bcl->test(test_data);
bcl->reset();
return 0;
}
