Example #1
0
AdaBoost::AdaBoost(const unsigned int no_of_classes, const unsigned int no_of_features)
{
	this->is_modelfile_loaded_ = false;
	this->number_of_features_ = no_of_features;
	this->number_of_classes_ = no_of_classes;

	this->boost_parameters_ = CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 );
}
Example #2
0
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;
}
Example #3
0
	void Test::trainedClassifier(){
		
		/* 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(40, 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
		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);
        
		/* STEP 6. Save your classifier */
		// Save the trained classifier
		boost.save("./trained_boost.xml", "boost");
        
		//return EXIT_SUCCESS;
    }
Example #4
0
/** 
 * @author     	JIA Pei
 * @version    	2009-10-04
 * @brief      	Training
 * @param      	data     		Input - input data
 * @param		categories		Input - column vector
 * @return		classification time cost
*/
void CClassificationAlgs::Training(const Mat_<float>& data, const Mat_<int>& categories)
{
	unsigned int NbOfSamples = data.rows;
	set<int> ClassSet;
	for(int i = 0; i < categories.rows; i++)
	{
		ClassSet.insert(categories(i, 0));
	}
	this->m_iNbOfCategories = ClassSet.size();
	
	switch(this->m_iClassificationMethod)
	{
		case CClassificationAlgs::DecisionTree:
			this->m_CVDtree.train( 	data,
									CV_ROW_SAMPLE,
									categories,
									Mat(),
									Mat(),
									Mat(),
									Mat(),
									CvDTreeParams( INT_MAX, 2, 0, false, this->m_iNbOfCategories, 0, false, false, 0 ) );
		break;
		case CClassificationAlgs::Boost:
		    this->m_CVBoost.train( 	data,
									CV_ROW_SAMPLE,
									categories,
									Mat(),
									Mat(),
									Mat(),
									Mat(),
									CvBoostParams(CvBoost::DISCRETE, 50, 0.95, INT_MAX, false, 0),
									false );
		break;
		case CClassificationAlgs::RandomForest:
			this->m_CVRTrees.train( data, 
									CV_ROW_SAMPLE,
									categories,
									Mat(),
									Mat(),
									Mat(),
									Mat(),
									CvRTParams( INT_MAX, 2, 0, false, this->m_iNbOfCategories, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ) );
		break;
		case CClassificationAlgs::ExtremeRandomForest:
			this->m_CVERTrees.train(data,
									CV_ROW_SAMPLE,
									categories,
									Mat(),
									Mat(),
									Mat(),
									Mat(),
									CvRTParams( INT_MAX, 2, 0, false, this->m_iNbOfCategories, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ) );
		break;
		case CClassificationAlgs::SVM:
			this->m_CVSVM.train(	data,
									categories,
									Mat(),
									Mat(),
									CvSVMParams(CvSVM::C_SVC, CvSVM::RBF,
									0, 1, 0,
									1, 0, 0,
									NULL, cvTermCriteria(CV_TERMCRIT_ITER, 1000, 1E-6) ) );
		break;
	}
}
static
int build_boost_classifier( char* data_filename,
    char* filename_to_save, char* filename_to_load )
{
    const int class_count = 26;
    CvMat* data = 0;
    CvMat* responses = 0;
    CvMat* var_type = 0;
    CvMat* temp_sample = 0;
    CvMat* weak_responses = 0;

    int ok = read_num_class_data( data_filename, 16, &data, &responses );
    int nsamples_all = 0, ntrain_samples = 0;
    int var_count;
    int i, j, k;
    double train_hr = 0, test_hr = 0;
    CvBoost boost;

    if( !ok )
    {
        printf( "Could not read the database %s\n", data_filename );
        return -1;
    }

    printf( "The database %s is loaded.\n", data_filename );
    nsamples_all = data->rows;
    ntrain_samples = (int)(nsamples_all*0.5);
    var_count = data->cols;

    // Create or load Boosted Tree classifier
    if( filename_to_load )
    {
        // load classifier from the specified file
        boost.load( filename_to_load );
        ntrain_samples = 0;
        if( !boost.get_weak_predictors() )
        {
            printf( "Could not read the classifier %s\n", filename_to_load );
            return -1;
        }
        printf( "The classifier %s is loaded.\n", data_filename );
    }
    else
    {
        // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
        //
        // As currently boosted tree classifier in MLL can only be trained
        // for 2-class problems, we transform the training database by
        // "unrolling" each training sample as many times as the number of
        // classes (26) that we have.
        //
        // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

        CvMat* new_data = cvCreateMat( ntrain_samples*class_count, var_count + 1, CV_32F );
        CvMat* new_responses = cvCreateMat( ntrain_samples*class_count, 1, CV_32S );

        // 1. unroll the database type mask
        printf( "Unrolling the database...\n");
        for( i = 0; i < ntrain_samples; i++ )
        {
            float* data_row = (float*)(data->data.ptr + data->step*i);
            for( j = 0; j < class_count; j++ )
            {
                float* new_data_row = (float*)(new_data->data.ptr +
                                new_data->step*(i*class_count+j));
                for( k = 0; k < var_count; k++ )
                    new_data_row[k] = data_row[k];
                new_data_row[var_count] = (float)j;
                new_responses->data.i[i*class_count + j] = responses->data.fl[i] == j+'A';
            }
        }

        // 2. create type mask
        var_type = cvCreateMat( var_count + 2, 1, CV_8U );
        cvSet( var_type, cvScalarAll(CV_VAR_ORDERED) );
        // the last indicator variable, as well
        // as the new (binary) response are categorical
        cvSetReal1D( var_type, var_count, CV_VAR_CATEGORICAL );
        cvSetReal1D( var_type, var_count+1, CV_VAR_CATEGORICAL );

        // 3. train classifier
        printf( "Training the classifier (may take a few minutes)...\n");
        boost.train( new_data, CV_ROW_SAMPLE, new_responses, 0, 0, var_type, 0,
            CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 ));
        cvReleaseMat( &new_data );
        cvReleaseMat( &new_responses );
        printf("\n");
    }

    temp_sample = cvCreateMat( 1, var_count + 1, CV_32F );
    weak_responses = cvCreateMat( 1, boost.get_weak_predictors()->total, CV_32F ); 

    // compute prediction error on train and test data
    for( i = 0; i < nsamples_all; i++ )
    {
        int best_class = 0;
        double max_sum = -DBL_MAX;
        double r;
        CvMat sample;
        cvGetRow( data, &sample, i );
        for( k = 0; k < var_count; k++ )
            temp_sample->data.fl[k] = sample.data.fl[k];

        for( j = 0; j < class_count; j++ )
        {
            temp_sample->data.fl[var_count] = (float)j;
            boost.predict( temp_sample, 0, weak_responses );
            double sum = cvSum( weak_responses ).val[0];
            if( max_sum < sum )
            {
                max_sum = sum;
                best_class = j + 'A';
            }
        }

        r = fabs(best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;

        if( i < ntrain_samples )
            train_hr += r;
        else
            test_hr += r;
    }

    test_hr /= (double)(nsamples_all-ntrain_samples);
    train_hr /= (double)ntrain_samples;
    printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
            train_hr*100., test_hr*100. );

    printf( "Number of trees: %d\n", boost.get_weak_predictors()->total );

    // Save classifier to file if needed
    if( filename_to_save )
        boost.save( filename_to_save );

    cvReleaseMat( &temp_sample );
    cvReleaseMat( &weak_responses );
    cvReleaseMat( &var_type );
    cvReleaseMat( &data );
    cvReleaseMat( &responses );

    return 0;
}
Example #6
0
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;
}