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test class header file deleted

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This commit is contained in:
Parikshit Ram
2008-01-25 01:24:26 +00:00
parent f0381cec34
commit d4df76bc71
1 changed files with 0 additions and 84 deletions
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fastlib/u/pram/nbc/test_class_simple_nbc.h
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#include "simple_nbc.h"
class TestClassSimpleNBC{
private:
SimpleNaiveBayesClassifier *nbc_test_;
string filename_train_, filename_test_;
string train_result_, test_result_;
index_t number_of_classes_;
public:
void Init() {
nbc_test_ = new SimpleNaiveBayesClassifier();
filename_train_ = filename_train;
filename_test_ = filename_test;
train_result_ = train_result;
test_result_ = test_result;
number_of_classes_ = number_of_classes;
}
void Destruct() {
delete nbc_test_;
delete filename_train_;
delete filename_test_;
delete number_of_classes_;
delete train_result_;
delete test_result_;
}
void TestInitTrain() {
Matrix train_data, train_res, calc_mat;
data::Load(filename_train_, &train_data);
data::Load(train_result_, &train_res);
// training results will be like means followed by variances followed by class probabilities
nbc_test_->InitTrain(train_data, number_of_classes_);
index_t number_of_features = nbc_test_->means_.n_rows();
calc_mat.Init(2*number_of_features + 1, number_of_classes_);
for(index_t i = 0; i < number_of_features; i++) {
for(index_t j = 0; j < number_of_classes_; j++) {
calc_mat.set(i, j, nbc_test_->means_.get(i, j));
calc_mat.set(i + number_of_features, j, nbc_test_->variances_.get(i, j));
}
}
for(index_t i = 0; i < number_of_classes_; i++) {
calc_mat.set(2 * number_of_features, i, nbc_test_->class_probabilities_[i]);
}
for(index_t i = 0; i < calc_mat.n_rows(); i++) {
for(index_t j = 0; j < number_of_classes_; j++) {
TEST_DOUBLE_EXACT(train_res.get(i, j), calc_mat.get(i, j));
}
}
NONFATAL("Test InitTrain passed...\n");
}
void TestClassify() {
Matrix test_data, test_res;
Vector test_res_vec, calc_vec;
data::Load(filename_test_, &test_data);
data::Load(test_result_, &test_res);
// training results will be like means followed by variances followed by class probabilities
nbc_test_->Classify(test_data, &calc_vec);
index_t number_of_datum = test_data.n_cols();
test_res.MakeColumnVector(0, &test_res_vec);
//for(index_t i = 0; i < number_of_datum; i++) {
// for(index_t j = 0; j < number_of_classes_; j++) {
//calc_mat.set(i, j, nbc_test_->means_.get(i, j));
//calc_mat.set(i + number_of_features, j, nbc_test_->variances_.get(i, j));
//}
//}
// for(index_t i = 0; i < number_of_datum; i++) {
//calc_mat.set(2 * number_of_features, i, nbc_test_->class_probabilities_[i]);
//}
for(index_t i = 0; i < number_of_datum; i++) {
TEST_DOUBLE_EXACT(test_res_vec.get(i), calc_vec.get(i));
}
NONFATAL("Test Classify passed...\n");
}
void TestAll() {
TestInitTrain();
TestClassify();
}
};