int main()
{
const int train_sample_count = 300;
//#define LEPIOTA
#ifdef LEPIOTA
const char* filename = "../../../OpenCV_SVN/samples/c/agaricus-lepiota.data";
#else
const char* filename = "../../../OpenCV_SVN/samples/c/waveform.data";
#endif
CvDTree dtree;
CvBoost boost;
CvRTrees rtrees;
CvERTrees ertrees;
CvMLData data;
CvTrainTestSplit spl( train_sample_count );
data.read_csv( filename );
#ifdef LEPIOTA
data.set_response_idx( 0 );
#else
data.set_response_idx( 21 );
data.change_var_type( 21, CV_VAR_CATEGORICAL );
#endif
data.set_train_test_split( &spl );
printf("======DTREE=====\n");
dtree.train( &data, CvDTreeParams( 10, 2, 0, false, 16, 0, false, false, 0 ));
print_result( dtree.calc_error( &data, CV_TRAIN_ERROR), dtree.calc_error( &data ), dtree.get_var_importance() );
#ifdef LEPIOTA
printf("======BOOST=====\n");
boost.train( &data, CvBoostParams(CvBoost::DISCRETE, 100, 0.95, 2, false, 0));
print_result( boost.calc_error( &data, CV_TRAIN_ERROR ), boost.calc_error( &data ), 0 );
#endif
printf("======RTREES=====\n");
rtrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
print_result( rtrees.calc_error( &data, CV_TRAIN_ERROR), rtrees.calc_error( &data ), rtrees.get_var_importance() );
printf("======ERTREES=====\n");
ertrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data ), ertrees.get_var_importance() );
return 0;
}
int main(int argc, char** argv) {
//Read the data from csv file
CvMLData cvml;
cvml.read_csv("char_datasetNM2.csv");
//Indicate which column is the response
cvml.set_response_idx(0);
//Select 50% for the training
CvTrainTestSplit cvtts(0.8f, true);
//Assign the division to the data
cvml.set_train_test_split(&cvtts);
CvBoost boost;
ifstream ifile("./trained_classifierNM2.xml");
if (ifile)
{
//The file exists, so we don't want to train
printf("Found trained_boost_char.xml file, remove it if you want to retrain with new data ... \n");
boost.load("./trained_classifierNM2.xml", "boost");
} else {
//Train with 100 features
printf("Training ... \n");
boost.train(&cvml, CvBoostParams(CvBoost::REAL, 100, 0, 1, false, 0), false);
}
//Calculate the test and train errors
std::vector<float> train_responses, test_responses;
float fl1 = boost.calc_error(&cvml,CV_TRAIN_ERROR,&train_responses);
float fl2 = boost.calc_error(&cvml,CV_TEST_ERROR,&test_responses);
printf("Error train %f \n", fl1);
printf("Error test %f \n", fl2);
//Try a char
static const float arr[] = {0,0.870690,0.096485,2.000000,2.000000,0.137080,1.269940,2.000000};
vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
float prediction = boost.predict( Mat(sample), Mat(), Range::all(), false, false );
float votes = boost.predict( Mat(sample), Mat(), Range::all(), false, true );
printf("\n The char sample is predicted as: %f (with number of votes = %f)\n", prediction,votes);
printf(" Class probability (using Logistic Correction) is P(r|character) = %f\n", (float)1-(float)1/(1+exp(-2*votes)));
//Try a NONchar
//static const float arr2[] = {0,1.500000,0.072162,0.000000,8.000000,0.188095,1.578947,16.000000};
static const float arr2[] = {0,0.565217,0.103749,1.000000,2.000000,0.032258,1.525692,10.000000};
vector<float> sample2 (arr2, arr2 + sizeof(arr2) / sizeof(arr2[0]) );
prediction = boost.predict( Mat(sample2), Mat(), Range::all(), false, false );
votes = boost.predict( Mat(sample2), Mat(), Range::all(), false, true );
printf("\n The non_char sample is predicted as: %f (with number of votes = %f)\n", prediction,votes);
printf(" Class probability (using Logistic Correction) is P(r|character) = %f\n\n", (float)1-(float)1/(1+exp(-2*votes)));
// Save the trained classifier
boost.save("./trained_classifierNM2.xml", "boost");
return EXIT_SUCCESS;
}
int main(int argc, char** argv) {
generateData();
/* STEP 2. Opening the file */
//1. Declare a structure to keep the data
CvMLData cvml;
//2. Read the file
cvml.read_csv("samples.csv");
//3. Indicate which column is the response
cvml.set_response_idx(0);
/* STEP 3. Splitting the samples */
//1. Select 40 for the training
CvTrainTestSplit cvtts(15, true);
//2. Assign the division to the data
cvml.set_train_test_split(&cvtts);
printf("Training ... ");
/* STEP 4. The training */
//1. Declare the classifier
CvBoost boost;
//2. Train it with 100 features
boost.train(&cvml, CvBoostParams(CvBoost::REAL, 100, 0, 1, false, 0), false);
/* STEP 5. Calculating the testing and training error */
// 1. Declare a couple of vectors to save the predictions of each sample
vector<float> train_responses;
vector<float> test_responses;
// 2. Calculate the training error
float fl1 = boost.calc_error(&cvml,CV_TRAIN_ERROR,&train_responses);
// 3. Calculate the test error
float fl2 = boost.calc_error(&cvml,CV_TEST_ERROR,&test_responses);
printf("Error train %f \n", fl1);
printf("Error test %f \n", fl2);
/* STEP 6. Save your classifier */
// Save the trained classifier
boost.save("./trained_boost.xml", "boost");
return EXIT_SUCCESS;
}
int main(int argc, char** argv) {
/* STEP 2. Opening the file */
//1. Declare a structure to keep the data
CvMLData cvml;
//2. Read the file
cvml.read_csv("groups_dataset.csv");
//cvml.read_csv("strokes_dataset_noresized.csv");
//3. Indicate which column is the response
cvml.set_response_idx(0);
/* STEP 3. Splitting the samples */
//1. Select 50% for the training (an integer value is also allowed here)
CvTrainTestSplit cvtts(0.9f, true);
//2. Assign the division to the data
cvml.set_train_test_split(&cvtts);
/* STEP 4. The training */
//1. Declare the classifier
CvBoost boost;
ifstream ifile("./trained_boost_groups.xml");
if (ifile)
{
// The file exists, so we don't need to train
boost.load("./trained_boost_groups.xml", "boost");
} else {
//2. Train it with 100 features
printf("Training ... \n");
boost.train(&cvml, CvBoostParams(CvBoost::REAL, 500, 0, 1, false, 0), false);
}
/* STEP 5. Calculating the testing and training error */
// 1. Declare a couple of vectors to save the predictions of each sample
std::vector<float> train_responses, test_responses;
// 2. Calculate the training error
float fl1 = boost.calc_error(&cvml,CV_TRAIN_ERROR,&train_responses);
// 3. Calculate the test error
float fl2 = boost.calc_error(&cvml,CV_TEST_ERROR,&test_responses);
printf("Error train %f \n", fl1);
printf("Error test %f \n", fl2);
static const float arr[] = {0,-1.980394,1.249858,-0.631116,2.819193,0.305448,0.108346,0.801116,0.104873,0.130908,0.559806,0.255053,0.455610,0.294118,0.455645,1.549193,0.087770,0.144896,1.650866};
vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
float prediction = boost.predict( Mat(sample), Mat(), Range::all(), false, false );
float votes = boost.predict( Mat(sample), Mat(), Range::all(), false, true );
printf("\n The group sample is predicted as: %f (with number of votes = %f)\n", prediction,votes);
//static const float arr2[] = {0,0.911369,1.052156,1.154478,3.321924,0.829768,0.249785,0.616930,0.246637,0.399782,0.337159,0.103893,0.308142,0.666667,0.745356,1.118034,0.009747,0.011016,1.130162};
static const float arr2[] = {0,1.14335,3.00412,2.62747,3.26428,2.32749,0.713018,0.47244,0.289846,0.613508,0.40514,0.216716,0.53305,0.878788,3.21698,3.6607,0.0422318,0.114392,2.70868};
vector<float> sample2 (arr2, arr2 + sizeof(arr2) / sizeof(arr2[0]) );
float prediction2 = boost.predict( Mat(sample2), Mat(), Range::all(), false, false );
float votes2 = boost.predict( Mat(sample2), Mat(), Range::all(), false, true );
printf("\n The group sample is predicted as: %f (with number of votes = %f)\n", prediction2,votes2);
/* STEP 6. Save your classifier */
// Save the trained classifier
boost.save("./trained_boost_groups.xml", "boost");
return EXIT_SUCCESS;
}
int CV_GBTreesTest::TestTrainPredict(int test_num)
{
int code = cvtest::TS::OK;
int weak_count = 200;
float shrinkage = 0.1f;
float subsample_portion = 0.5f;
int max_depth = 5;
bool use_surrogates = false;
int loss_function_type = 0;
switch (test_num)
{
case (1) : loss_function_type = CvGBTrees::SQUARED_LOSS; break;
case (2) : loss_function_type = CvGBTrees::ABSOLUTE_LOSS; break;
case (3) : loss_function_type = CvGBTrees::HUBER_LOSS; break;
case (0) : loss_function_type = CvGBTrees::DEVIANCE_LOSS; break;
default :
{
ts->printf( cvtest::TS::LOG, "Bad test_num value in CV_GBTreesTest::TestTrainPredict(..) function." );
return cvtest::TS::FAIL_BAD_ARG_CHECK;
}
}
int dataset_num = test_num == 0 ? 0 : 1;
if (!data)
{
data = new CvMLData();
data->set_delimiter(',');
if (data->read_csv(datasets[dataset_num].c_str()))
{
ts->printf( cvtest::TS::LOG, "File reading error." );
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
if (test_num == 0)
{
data->set_response_idx(57);
data->set_var_types("ord[0-56],cat[57]");
}
else
{
data->set_response_idx(13);
data->set_var_types("ord[0-2,4-13],cat[3]");
subsample_portion = 0.7f;
}
int train_sample_count = cvFloor(_get_len(data->get_responses())*0.5f);
CvTrainTestSplit spl( train_sample_count );
data->set_train_test_split( &spl );
}
data->mix_train_and_test_idx();
if (gtb) delete gtb;
gtb = new CvGBTrees();
bool tmp_code = true;
tmp_code = gtb->train(data, CvGBTreesParams(loss_function_type, weak_count,
shrinkage, subsample_portion,
max_depth, use_surrogates));
if (!tmp_code)
{
ts->printf( cvtest::TS::LOG, "Model training was failed.");
return cvtest::TS::FAIL_INVALID_OUTPUT;
}
code = checkPredictError(test_num);
return code;
}
int main()
{
const int train_sample_count = 300;
bool is_regression = false;
const char* filename = "data/waveform.data";
int response_idx = 21;
CvMLData data;
CvTrainTestSplit spl( train_sample_count );
if(data.read_csv(filename) != 0)
{
printf("couldn't read %s\n", filename);
exit(0);
}
data.set_response_idx(response_idx);
data.change_var_type(response_idx, CV_VAR_CATEGORICAL);
data.set_train_test_split( &spl );
const CvMat* values = data.get_values();
const CvMat* response = data.get_responses();
const CvMat* missing = data.get_missing();
const CvMat* var_types = data.get_var_types();
const CvMat* train_sidx = data.get_train_sample_idx();
const CvMat* var_idx = data.get_var_idx();
CvMat*response_map;
CvMat*ordered_response = cv_preprocess_categories(response, var_idx, response->rows, &response_map, NULL);
int num_classes = response_map->cols;
CvDTree dtree;
printf("======DTREE=====\n");
CvDTreeParams cvd_params( 10, 1, 0, false, 16, 0, false, false, 0);
dtree.train( &data, cvd_params);
print_result( dtree.calc_error( &data, CV_TRAIN_ERROR), dtree.calc_error( &data, CV_TEST_ERROR ), dtree.get_var_importance() );
#if 0
/* boosted trees are only implemented for two classes */
printf("======BOOST=====\n");
CvBoost boost;
boost.train( &data, CvBoostParams(CvBoost::DISCRETE, 100, 0.95, 2, false, 0));
print_result( boost.calc_error( &data, CV_TRAIN_ERROR ), boost.calc_error( &data, CV_TEST_ERROR), 0 );
#endif
printf("======RTREES=====\n");
CvRTrees rtrees;
rtrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
print_result( rtrees.calc_error( &data, CV_TRAIN_ERROR), rtrees.calc_error( &data, CV_TEST_ERROR ), rtrees.get_var_importance() );
printf("======ERTREES=====\n");
CvERTrees ertrees;
ertrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() );
printf("======GBTREES=====\n");
CvGBTrees gbtrees;
CvGBTreesParams gbparams;
gbparams.loss_function_type = CvGBTrees::DEVIANCE_LOSS; // classification, not regression
gbtrees.train( &data, gbparams);
//gbt_print_error(&gbtrees, values, response, response_idx, train_sidx);
print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0);
printf("======KNEAREST=====\n");
CvKNearest knearest;
//bool CvKNearest::train( const Mat& _train_data, const Mat& _responses,
// const Mat& _sample_idx, bool _is_regression,
// int _max_k, bool _update_base )
bool is_classifier = var_types->data.ptr[var_types->cols-1] == CV_VAR_CATEGORICAL;
assert(is_classifier);
int max_k = 10;
knearest.train(values, response, train_sidx, is_regression, max_k, false);
CvMat* new_response = cvCreateMat(response->rows, 1, values->type);
//print_types();
//const CvMat* train_sidx = data.get_train_sample_idx();
knearest.find_nearest(values, max_k, new_response, 0, 0, 0);
print_result(knearest_calc_error(values, response, new_response, train_sidx, is_regression, CV_TRAIN_ERROR),
knearest_calc_error(values, response, new_response, train_sidx, is_regression, CV_TEST_ERROR), 0);
printf("======== RBF SVM =======\n");
//printf("indexes: %d / %d, responses: %d\n", train_sidx->cols, var_idx->cols, values->rows);
CvMySVM svm1;
CvSVMParams params1 = CvSVMParams(CvSVM::C_SVC, CvSVM::RBF,
/*degree*/0, /*gamma*/1, /*coef0*/0, /*C*/1,
/*nu*/0, /*p*/0, /*class_weights*/0,
cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON));
//svm1.train(values, response, train_sidx, var_idx, params1);
svm1.train_auto(values, response, var_idx, train_sidx, params1);
svm_print_error(&svm1, values, response, response_idx, train_sidx);
printf("======== Linear SVM =======\n");
CvMySVM svm2;
CvSVMParams params2 = CvSVMParams(CvSVM::C_SVC, CvSVM::LINEAR,
/*degree*/0, /*gamma*/1, /*coef0*/0, /*C*/1,
/*nu*/0, /*p*/0, /*class_weights*/0,
cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON));
//svm2.train(values, response, train_sidx, var_idx, params2);
svm2.train_auto(values, response, var_idx, train_sidx, params2);
svm_print_error(&svm2, values, response, response_idx, train_sidx);
printf("======NEURONAL NETWORK=====\n");
int num_layers = 3;
CvMat layers = cvMat(1, num_layers, CV_32SC1, calloc(1, sizeof(double)*num_layers*1));
cvmSetI(&layers, 0, 0, values->cols-1);
cvmSetI(&layers, 0, 1, num_classes);
cvmSetI(&layers, 0, 2, num_classes);
CvANN_MLP ann(&layers, CvANN_MLP::SIGMOID_SYM, 0.0, 0.0);
CvANN_MLP_TrainParams ann_params;
//ann_params.train_method = CvANN_MLP_TrainParams::BACKPROP;
CvMat ann_response = cvmat_make_boolean_class_columns(response, num_classes);
CvMat values2 = cvmat_remove_column(values, response_idx);
ann.train(&values2, &ann_response, NULL, train_sidx, ann_params, 0x0000);
//ann.train(values, &ann_response, NULL, train_sidx, ann_params, 0x0000);
ann_print_error(&ann, values, num_classes, &ann_response, response, response_idx, train_sidx);
#if 0 /* slow */
printf("======== Polygonal SVM =======\n");
//printf("indexes: %d / %d, responses: %d\n", train_sidx->cols, var_idx->cols, values->rows);
CvMySVM svm3;
CvSVMParams params3 = CvSVMParams(CvSVM::C_SVC, CvSVM::POLY,
/*degree*/2, /*gamma*/1, /*coef0*/0, /*C*/1,
/*nu*/0, /*p*/0, /*class_weights*/0,
cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON));
//svm3.train(values, response, train_sidx, var_idx, params3);
svm3.train_auto(values, response, var_idx, train_sidx, params3);
svm_print_error(&svm3, values, response, response_idx, train_sidx);
#endif
return 0;
}
int main(int argc, char** argv) {
/* STEP 2. Opening the file */
//1. Declare a structure to keep the data
CvMLData cvml;
//2. Read the file
cvml.read_csv("char_dataset.csv");
//cvml.read_csv("strokes_dataset_noresized.csv");
//3. Indicate which column is the response
cvml.set_response_idx(0);
/* STEP 3. Splitting the samples */
//1. Select 50% for the training (an integer value is also allowed here)
CvTrainTestSplit cvtts(0.9f, true);
//2. Assign the division to the data
cvml.set_train_test_split(&cvtts);
/* STEP 4. The training */
//1. Declare the classifier
CvBoost boost;
ifstream ifile("./trained_boost_char.xml");
if (ifile)
{
// The file exists, so we don't need to train
boost.load("./trained_boost_char.xml", "boost");
} else {
//2. Train it with 100 features
printf("Training ... \n");
boost.train(&cvml, CvBoostParams(CvBoost::REAL, 2, 0, 1, false, 0), false);
}
cout<<"after train"<<endl;
/* STEP 5. Calculating the testing and training error */
// 1. Declare a couple of vectors to save the predictions of each sample
std::vector<float> train_responses, test_responses;
// 2. Calculate the training error
float fl1 = boost.calc_error(&cvml,CV_TRAIN_ERROR,&train_responses);
// 3. Calculate the test error
float fl2 = boost.calc_error(&cvml,CV_TEST_ERROR,&test_responses);
printf("Error train %f \n", fl1);
printf("Error test %f \n", fl2);
//Try a char
static const float arr[] = {0,1.659899,0.684169,0.412175,150.000000,81.000000,0.540000,0.358025,0.151203,0.000000,0.000000};
vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
float prediction = boost.predict( Mat(sample), Mat(), Range::all(), false, false );
float votes = boost.predict( Mat(sample), Mat(), Range::all(), false, true );
printf("\n The sample (360) is predicted as: %f (with number of votes = %f)\n", prediction,votes);
//Try a NONchar
static const float arr2[] = {0,1.250000,0.433013,0.346410,9.000000,8.000000,0.888889,0.833333,0.375000,0.000000,0.000000};
vector<float> sample2 (arr2, arr2 + sizeof(arr2) / sizeof(arr2[0]) );
prediction = boost.predict( Mat(sample2), Mat(), Range::all(), false, false );
votes = boost.predict( Mat(sample2), Mat(), Range::all(), false, true );
printf("\n The sample (367) is predicted as: %f (with number of votes = %f)\n", prediction,votes);
/* STEP 6. Save your classifier */
// Save the trained classifier
boost.save("./trained_boost_char.xml", "boost");
return EXIT_SUCCESS;
}