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Open Source Computer Vision Library
https://opencv.org/
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797 lines
29 KiB
797 lines
29 KiB
14 years ago
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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using namespace cv;
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using namespace std;
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// auxiliary functions
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// 1. nbayes
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void nbayes_check_data( CvMLData* _data )
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{
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if( _data->get_missing() )
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CV_Error( CV_StsBadArg, "missing values are not supported" );
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const CvMat* var_types = _data->get_var_types();
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bool is_classifier = var_types->data.ptr[var_types->cols-1] == CV_VAR_CATEGORICAL;
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if( ( fabs( cvNorm( var_types, 0, CV_L1 ) -
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(var_types->rows + var_types->cols - 2)*CV_VAR_ORDERED - CV_VAR_CATEGORICAL ) > FLT_EPSILON ) ||
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!is_classifier )
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CV_Error( CV_StsBadArg, "incorrect types of predictors or responses" );
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}
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bool nbayes_train( CvNormalBayesClassifier* nbayes, CvMLData* _data )
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{
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nbayes_check_data( _data );
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const CvMat* values = _data->get_values();
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const CvMat* responses = _data->get_responses();
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const CvMat* train_sidx = _data->get_train_sample_idx();
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const CvMat* var_idx = _data->get_var_idx();
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return nbayes->train( values, responses, var_idx, train_sidx );
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}
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float nbayes_calc_error( CvNormalBayesClassifier* nbayes, CvMLData* _data, int type, vector<float> *resp )
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{
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float err = 0;
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nbayes_check_data( _data );
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const CvMat* values = _data->get_values();
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const CvMat* response = _data->get_responses();
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const CvMat* sample_idx = (type == CV_TEST_ERROR) ? _data->get_test_sample_idx() : _data->get_train_sample_idx();
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int* sidx = sample_idx ? sample_idx->data.i : 0;
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int r_step = CV_IS_MAT_CONT(response->type) ?
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1 : response->step / CV_ELEM_SIZE(response->type);
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int sample_count = sample_idx ? sample_idx->cols : 0;
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sample_count = (type == CV_TRAIN_ERROR && sample_count == 0) ? values->rows : sample_count;
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float* pred_resp = 0;
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if( resp && (sample_count > 0) )
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{
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resp->resize( sample_count );
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pred_resp = &((*resp)[0]);
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}
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for( int i = 0; i < sample_count; i++ )
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{
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CvMat sample;
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int si = sidx ? sidx[i] : i;
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cvGetRow( values, &sample, si );
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float r = (float)nbayes->predict( &sample, 0 );
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if( pred_resp )
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pred_resp[i] = r;
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int d = fabs((double)r - response->data.fl[si*r_step]) <= FLT_EPSILON ? 0 : 1;
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err += d;
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}
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err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
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return err;
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}
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// 2. knearest
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void knearest_check_data_and_get_predictors( CvMLData* _data, CvMat* _predictors )
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{
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const CvMat* values = _data->get_values();
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const CvMat* var_idx = _data->get_var_idx();
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if( var_idx->cols + var_idx->rows != values->cols )
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CV_Error( CV_StsBadArg, "var_idx is not supported" );
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if( _data->get_missing() )
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CV_Error( CV_StsBadArg, "missing values are not supported" );
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int resp_idx = _data->get_response_idx();
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if( resp_idx == 0)
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cvGetCols( values, _predictors, 1, values->cols );
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else if( resp_idx == values->cols - 1 )
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cvGetCols( values, _predictors, 0, values->cols - 1 );
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else
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CV_Error( CV_StsBadArg, "responses must be in the first or last column; other cases are not supported" );
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}
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bool knearest_train( CvKNearest* knearest, CvMLData* _data )
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{
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const CvMat* responses = _data->get_responses();
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const CvMat* train_sidx = _data->get_train_sample_idx();
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bool is_regression = _data->get_var_type( _data->get_response_idx() ) == CV_VAR_ORDERED;
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CvMat predictors;
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knearest_check_data_and_get_predictors( _data, &predictors );
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return knearest->train( &predictors, responses, train_sidx, is_regression );
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}
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float knearest_calc_error( CvKNearest* knearest, CvMLData* _data, int k, int type, vector<float> *resp )
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{
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float err = 0;
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const CvMat* response = _data->get_responses();
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const CvMat* sample_idx = (type == CV_TEST_ERROR) ? _data->get_test_sample_idx() : _data->get_train_sample_idx();
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int* sidx = sample_idx ? sample_idx->data.i : 0;
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int r_step = CV_IS_MAT_CONT(response->type) ?
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1 : response->step / CV_ELEM_SIZE(response->type);
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bool is_regression = _data->get_var_type( _data->get_response_idx() ) == CV_VAR_ORDERED;
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CvMat predictors;
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knearest_check_data_and_get_predictors( _data, &predictors );
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int sample_count = sample_idx ? sample_idx->cols : 0;
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sample_count = (type == CV_TRAIN_ERROR && sample_count == 0) ? predictors.rows : sample_count;
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float* pred_resp = 0;
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if( resp && (sample_count > 0) )
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{
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resp->resize( sample_count );
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pred_resp = &((*resp)[0]);
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}
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if ( !is_regression )
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{
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for( int i = 0; i < sample_count; i++ )
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{
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CvMat sample;
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int si = sidx ? sidx[i] : i;
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cvGetRow( &predictors, &sample, si );
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float r = knearest->find_nearest( &sample, k );
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if( pred_resp )
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pred_resp[i] = r;
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int d = fabs((double)r - response->data.fl[si*r_step]) <= FLT_EPSILON ? 0 : 1;
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err += d;
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}
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err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
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}
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else
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{
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for( int i = 0; i < sample_count; i++ )
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{
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CvMat sample;
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int si = sidx ? sidx[i] : i;
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cvGetRow( &predictors, &sample, si );
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float r = knearest->find_nearest( &sample, k );
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if( pred_resp )
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pred_resp[i] = r;
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float d = r - response->data.fl[si*r_step];
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err += d*d;
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}
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err = sample_count ? err / (float)sample_count : -FLT_MAX;
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}
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return err;
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}
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// 3. svm
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int str_to_svm_type(string& str)
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{
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if( !str.compare("C_SVC") )
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return CvSVM::C_SVC;
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if( !str.compare("NU_SVC") )
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return CvSVM::NU_SVC;
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if( !str.compare("ONE_CLASS") )
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return CvSVM::ONE_CLASS;
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if( !str.compare("EPS_SVR") )
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return CvSVM::EPS_SVR;
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if( !str.compare("NU_SVR") )
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return CvSVM::NU_SVR;
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CV_Error( CV_StsBadArg, "incorrect svm type string" );
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return -1;
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}
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int str_to_svm_kernel_type( string& str )
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{
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if( !str.compare("LINEAR") )
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return CvSVM::LINEAR;
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if( !str.compare("POLY") )
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return CvSVM::POLY;
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if( !str.compare("RBF") )
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return CvSVM::RBF;
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if( !str.compare("SIGMOID") )
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return CvSVM::SIGMOID;
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CV_Error( CV_StsBadArg, "incorrect svm type string" );
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return -1;
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}
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void svm_check_data( CvMLData* _data )
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{
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if( _data->get_missing() )
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CV_Error( CV_StsBadArg, "missing values are not supported" );
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const CvMat* var_types = _data->get_var_types();
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for( int i = 0; i < var_types->cols-1; i++ )
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if (var_types->data.ptr[i] == CV_VAR_CATEGORICAL)
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{
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char msg[50];
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sprintf( msg, "incorrect type of %d-predictor", i );
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CV_Error( CV_StsBadArg, msg );
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}
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}
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bool svm_train( CvSVM* svm, CvMLData* _data, CvSVMParams _params )
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{
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svm_check_data(_data);
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const CvMat* _train_data = _data->get_values();
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const CvMat* _responses = _data->get_responses();
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const CvMat* _var_idx = _data->get_var_idx();
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const CvMat* _sample_idx = _data->get_train_sample_idx();
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return svm->train( _train_data, _responses, _var_idx, _sample_idx, _params );
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}
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bool svm_train_auto( CvSVM* svm, CvMLData* _data, CvSVMParams _params,
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int k_fold, CvParamGrid C_grid, CvParamGrid gamma_grid,
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CvParamGrid p_grid, CvParamGrid nu_grid, CvParamGrid coef_grid,
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CvParamGrid degree_grid )
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{
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svm_check_data(_data);
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const CvMat* _train_data = _data->get_values();
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const CvMat* _responses = _data->get_responses();
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const CvMat* _var_idx = _data->get_var_idx();
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const CvMat* _sample_idx = _data->get_train_sample_idx();
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return svm->train_auto( _train_data, _responses, _var_idx,
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_sample_idx, _params, k_fold, C_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid );
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}
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float svm_calc_error( CvSVM* svm, CvMLData* _data, int type, vector<float> *resp )
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{
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svm_check_data(_data);
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float err = 0;
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const CvMat* values = _data->get_values();
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const CvMat* response = _data->get_responses();
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const CvMat* sample_idx = (type == CV_TEST_ERROR) ? _data->get_test_sample_idx() : _data->get_train_sample_idx();
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const CvMat* var_types = _data->get_var_types();
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int* sidx = sample_idx ? sample_idx->data.i : 0;
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int r_step = CV_IS_MAT_CONT(response->type) ?
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1 : response->step / CV_ELEM_SIZE(response->type);
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bool is_classifier = var_types->data.ptr[var_types->cols-1] == CV_VAR_CATEGORICAL;
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int sample_count = sample_idx ? sample_idx->cols : 0;
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sample_count = (type == CV_TRAIN_ERROR && sample_count == 0) ? values->rows : sample_count;
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float* pred_resp = 0;
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if( resp && (sample_count > 0) )
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{
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resp->resize( sample_count );
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pred_resp = &((*resp)[0]);
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}
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if ( is_classifier )
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{
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for( int i = 0; i < sample_count; i++ )
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{
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CvMat sample;
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int si = sidx ? sidx[i] : i;
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cvGetRow( values, &sample, si );
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float r = svm->predict( &sample );
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if( pred_resp )
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pred_resp[i] = r;
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int d = fabs((double)r - response->data.fl[si*r_step]) <= FLT_EPSILON ? 0 : 1;
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err += d;
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}
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err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
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}
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else
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{
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for( int i = 0; i < sample_count; i++ )
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{
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CvMat sample;
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int si = sidx ? sidx[i] : i;
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cvGetRow( values, &sample, si );
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float r = svm->predict( &sample );
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if( pred_resp )
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pred_resp[i] = r;
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float d = r - response->data.fl[si*r_step];
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err += d*d;
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}
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err = sample_count ? err / (float)sample_count : -FLT_MAX;
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}
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return err;
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}
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// 4. em
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// 5. ann
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int str_to_ann_train_method( string& str )
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{
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if( !str.compare("BACKPROP") )
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return CvANN_MLP_TrainParams::BACKPROP;
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if( !str.compare("RPROP") )
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return CvANN_MLP_TrainParams::RPROP;
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CV_Error( CV_StsBadArg, "incorrect ann train method string" );
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return -1;
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}
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void ann_check_data_and_get_predictors( CvMLData* _data, CvMat* _inputs )
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{
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const CvMat* values = _data->get_values();
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const CvMat* var_idx = _data->get_var_idx();
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if( var_idx->cols + var_idx->rows != values->cols )
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CV_Error( CV_StsBadArg, "var_idx is not supported" );
|
||
|
|
if( _data->get_missing() )
|
||
|
|
CV_Error( CV_StsBadArg, "missing values are not supported" );
|
||
|
|
int resp_idx = _data->get_response_idx();
|
||
|
|
if( resp_idx == 0)
|
||
|
|
cvGetCols( values, _inputs, 1, values->cols );
|
||
|
|
else if( resp_idx == values->cols - 1 )
|
||
|
|
cvGetCols( values, _inputs, 0, values->cols - 1 );
|
||
|
|
else
|
||
|
|
CV_Error( CV_StsBadArg, "outputs must be in the first or last column; other cases are not supported" );
|
||
|
|
}
|
||
|
|
void ann_get_new_responses( CvMLData* _data, Mat& new_responses, map<int, int>& cls_map )
|
||
|
|
{
|
||
|
|
const CvMat* train_sidx = _data->get_train_sample_idx();
|
||
|
|
int* train_sidx_ptr = train_sidx->data.i;
|
||
|
|
const CvMat* responses = _data->get_responses();
|
||
|
|
float* responses_ptr = responses->data.fl;
|
||
|
|
int r_step = CV_IS_MAT_CONT(responses->type) ?
|
||
|
|
1 : responses->step / CV_ELEM_SIZE(responses->type);
|
||
|
|
int cls_count = 0;
|
||
|
|
// construct cls_map
|
||
|
|
cls_map.clear();
|
||
|
|
for( int si = 0; si < train_sidx->cols; si++ )
|
||
|
|
{
|
||
|
|
int sidx = train_sidx_ptr[si];
|
||
|
|
int r = cvRound(responses_ptr[sidx*r_step]);
|
||
|
|
CV_DbgAssert( fabs(responses_ptr[sidx*r_step]-r) < FLT_EPSILON );
|
||
|
|
int cls_map_size = (int)cls_map.size();
|
||
|
|
cls_map[r];
|
||
|
|
if ( (int)cls_map.size() > cls_map_size )
|
||
|
|
cls_map[r] = cls_count++;
|
||
|
|
}
|
||
|
|
new_responses.create( responses->rows, cls_count, CV_32F );
|
||
|
|
new_responses.setTo( 0 );
|
||
|
|
for( int si = 0; si < train_sidx->cols; si++ )
|
||
|
|
{
|
||
|
|
int sidx = train_sidx_ptr[si];
|
||
|
|
int r = cvRound(responses_ptr[sidx*r_step]);
|
||
|
|
int cidx = cls_map[r];
|
||
|
|
new_responses.ptr<float>(sidx)[cidx] = 1;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
int ann_train( CvANN_MLP* ann, CvMLData* _data, Mat& new_responses, CvANN_MLP_TrainParams _params, int flags = 0 )
|
||
|
|
{
|
||
|
|
const CvMat* train_sidx = _data->get_train_sample_idx();
|
||
|
|
CvMat predictors;
|
||
|
|
ann_check_data_and_get_predictors( _data, &predictors );
|
||
|
|
CvMat _new_responses = CvMat( new_responses );
|
||
|
|
return ann->train( &predictors, &_new_responses, 0, train_sidx, _params, flags );
|
||
|
|
}
|
||
|
|
float ann_calc_error( CvANN_MLP* ann, CvMLData* _data, map<int, int>& cls_map, int type , vector<float> *resp_labels )
|
||
|
|
{
|
||
|
|
float err = 0;
|
||
|
|
const CvMat* responses = _data->get_responses();
|
||
|
|
const CvMat* sample_idx = (type == CV_TEST_ERROR) ? _data->get_test_sample_idx() : _data->get_train_sample_idx();
|
||
|
|
int* sidx = sample_idx ? sample_idx->data.i : 0;
|
||
|
|
int r_step = CV_IS_MAT_CONT(responses->type) ?
|
||
|
|
1 : responses->step / CV_ELEM_SIZE(responses->type);
|
||
|
|
CvMat predictors;
|
||
|
|
ann_check_data_and_get_predictors( _data, &predictors );
|
||
|
|
int sample_count = sample_idx ? sample_idx->cols : 0;
|
||
|
|
sample_count = (type == CV_TRAIN_ERROR && sample_count == 0) ? predictors.rows : sample_count;
|
||
|
|
float* pred_resp = 0;
|
||
|
|
vector<float> innresp;
|
||
|
|
if( sample_count > 0 )
|
||
|
|
{
|
||
|
|
if( resp_labels )
|
||
|
|
{
|
||
|
|
resp_labels->resize( sample_count );
|
||
|
|
pred_resp = &((*resp_labels)[0]);
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
innresp.resize( sample_count );
|
||
|
|
pred_resp = &(innresp[0]);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
int cls_count = (int)cls_map.size();
|
||
|
|
Mat output( 1, cls_count, CV_32FC1 );
|
||
|
|
CvMat _output = CvMat(output);
|
||
|
|
map<int, int>::iterator b_it = cls_map.begin();
|
||
|
|
for( int i = 0; i < sample_count; i++ )
|
||
|
|
{
|
||
|
|
CvMat sample;
|
||
|
|
int si = sidx ? sidx[i] : i;
|
||
|
|
cvGetRow( &predictors, &sample, si );
|
||
|
|
ann->predict( &sample, &_output );
|
||
|
|
CvPoint best_cls = {0,0};
|
||
|
|
cvMinMaxLoc( &_output, 0, 0, 0, &best_cls, 0 );
|
||
|
|
int r = cvRound(responses->data.fl[si*r_step]);
|
||
|
|
CV_DbgAssert( fabs(responses->data.fl[si*r_step]-r) < FLT_EPSILON );
|
||
|
|
r = cls_map[r];
|
||
|
|
int d = best_cls.x == r ? 0 : 1;
|
||
|
|
err += d;
|
||
|
|
pred_resp[i] = (float)best_cls.x;
|
||
|
|
}
|
||
|
|
err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
|
||
|
|
return err;
|
||
|
|
}
|
||
|
|
|
||
|
|
// 6. dtree
|
||
|
|
// 7. boost
|
||
|
|
int str_to_boost_type( string& str )
|
||
|
|
{
|
||
|
|
if ( !str.compare("DISCRETE") )
|
||
|
|
return CvBoost::DISCRETE;
|
||
|
|
if ( !str.compare("REAL") )
|
||
|
|
return CvBoost::REAL;
|
||
|
|
if ( !str.compare("LOGIT") )
|
||
|
|
return CvBoost::LOGIT;
|
||
|
|
if ( !str.compare("GENTLE") )
|
||
|
|
return CvBoost::GENTLE;
|
||
|
|
CV_Error( CV_StsBadArg, "incorrect boost type string" );
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
// 8. rtrees
|
||
|
|
// 9. ertrees
|
||
|
|
|
||
|
|
// ---------------------------------- MLBaseTest ---------------------------------------------------
|
||
|
|
|
||
|
|
CV_MLBaseTest::CV_MLBaseTest(const char* _modelName)
|
||
|
|
{
|
||
|
|
int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52),
|
||
|
|
CV_BIG_INT(0x0000a17166072c7c),
|
||
|
|
CV_BIG_INT(0x0201b32115cd1f9a),
|
||
|
|
CV_BIG_INT(0x0513cb37abcd1234),
|
||
|
|
CV_BIG_INT(0x0001a2b3c4d5f678)
|
||
|
|
};
|
||
|
|
|
||
|
|
int seedCount = sizeof(seeds)/sizeof(seeds[0]);
|
||
|
|
RNG& rng = theRNG();
|
||
|
|
|
||
|
|
initSeed = rng.state;
|
||
|
|
|
||
|
|
rng.state = seeds[rng(seedCount)];
|
||
|
|
|
||
|
|
modelName = _modelName;
|
||
|
|
nbayes = 0;
|
||
|
|
knearest = 0;
|
||
|
|
svm = 0;
|
||
|
|
em = 0;
|
||
|
|
ann = 0;
|
||
|
|
dtree = 0;
|
||
|
|
boost = 0;
|
||
|
|
rtrees = 0;
|
||
|
|
ertrees = 0;
|
||
|
|
if( !modelName.compare(CV_NBAYES) )
|
||
|
|
nbayes = new CvNormalBayesClassifier;
|
||
|
|
else if( !modelName.compare(CV_KNEAREST) )
|
||
|
|
knearest = new CvKNearest;
|
||
|
|
else if( !modelName.compare(CV_SVM) )
|
||
|
|
svm = new CvSVM;
|
||
|
|
else if( !modelName.compare(CV_EM) )
|
||
|
|
em = new CvEM;
|
||
|
|
else if( !modelName.compare(CV_ANN) )
|
||
|
|
ann = new CvANN_MLP;
|
||
|
|
else if( !modelName.compare(CV_DTREE) )
|
||
|
|
dtree = new CvDTree;
|
||
|
|
else if( !modelName.compare(CV_BOOST) )
|
||
|
|
boost = new CvBoost;
|
||
|
|
else if( !modelName.compare(CV_RTREES) )
|
||
|
|
rtrees = new CvRTrees;
|
||
|
|
else if( !modelName.compare(CV_ERTREES) )
|
||
|
|
ertrees = new CvERTrees;
|
||
|
|
}
|
||
|
|
|
||
|
|
CV_MLBaseTest::~CV_MLBaseTest()
|
||
|
|
{
|
||
|
|
if( validationFS.isOpened() )
|
||
|
|
validationFS.release();
|
||
|
|
if( nbayes )
|
||
|
|
delete nbayes;
|
||
|
|
if( knearest )
|
||
|
|
delete knearest;
|
||
|
|
if( svm )
|
||
|
|
delete svm;
|
||
|
|
if( em )
|
||
|
|
delete em;
|
||
|
|
if( ann )
|
||
|
|
delete ann;
|
||
|
|
if( dtree )
|
||
|
|
delete dtree;
|
||
|
|
if( boost )
|
||
|
|
delete boost;
|
||
|
|
if( rtrees )
|
||
|
|
delete rtrees;
|
||
|
|
if( ertrees )
|
||
|
|
delete ertrees;
|
||
|
|
theRNG().state = initSeed;
|
||
|
|
}
|
||
|
|
|
||
|
|
int CV_MLBaseTest::read_params( CvFileStorage* _fs )
|
||
|
|
{
|
||
|
|
if( !_fs )
|
||
|
|
test_case_count = -1;
|
||
|
|
else
|
||
|
|
{
|
||
|
|
CvFileNode* fn = cvGetRootFileNode( _fs, 0 );
|
||
|
|
fn = (CvFileNode*)cvGetSeqElem( fn->data.seq, 0 );
|
||
|
|
fn = cvGetFileNodeByName( _fs, fn, "run_params" );
|
||
|
|
CvSeq* dataSetNamesSeq = cvGetFileNodeByName( _fs, fn, modelName.c_str() )->data.seq;
|
||
|
|
test_case_count = dataSetNamesSeq ? dataSetNamesSeq->total : -1;
|
||
|
|
if( test_case_count > 0 )
|
||
|
|
{
|
||
|
|
dataSetNames.resize( test_case_count );
|
||
|
|
vector<string>::iterator it = dataSetNames.begin();
|
||
|
|
for( int i = 0; i < test_case_count; i++, it++ )
|
||
|
|
*it = ((CvFileNode*)cvGetSeqElem( dataSetNamesSeq, i ))->data.str.ptr;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return cvtest::TS::OK;;
|
||
|
|
}
|
||
|
|
|
||
|
|
void CV_MLBaseTest::run( int start_from )
|
||
|
|
{
|
||
|
|
string filename = ts->get_data_path();
|
||
|
|
filename += get_validation_filename();
|
||
|
|
validationFS.open( filename, FileStorage::READ );
|
||
|
|
read_params( *validationFS );
|
||
|
|
|
||
|
|
int code = cvtest::TS::OK;
|
||
|
|
start_from = 0;
|
||
|
|
for (int i = 0; i < test_case_count; i++)
|
||
|
|
{
|
||
|
|
int temp_code = run_test_case( i );
|
||
|
|
if (temp_code == cvtest::TS::OK)
|
||
|
|
temp_code = validate_test_results( i );
|
||
|
|
if (temp_code != cvtest::TS::OK)
|
||
|
|
code = temp_code;
|
||
|
|
}
|
||
|
|
if ( test_case_count <= 0)
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "validation file is not determined or not correct" );
|
||
|
|
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
|
||
|
|
}
|
||
|
|
ts->set_failed_test_info( code );
|
||
|
|
}
|
||
|
|
|
||
|
|
int CV_MLBaseTest::prepare_test_case( int test_case_idx )
|
||
|
|
{
|
||
|
|
int trainSampleCount, respIdx;
|
||
|
|
string varTypes;
|
||
|
|
clear();
|
||
|
|
|
||
|
|
string dataPath = ts->get_data_path();
|
||
|
|
if ( dataPath.empty() )
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "data path is empty" );
|
||
|
|
return cvtest::TS::FAIL_INVALID_TEST_DATA;
|
||
|
|
}
|
||
|
|
|
||
|
|
string dataName = dataSetNames[test_case_idx],
|
||
|
|
filename = dataPath + dataName + ".data";
|
||
|
|
if ( data.read_csv( filename.c_str() ) != 0)
|
||
|
|
{
|
||
|
|
char msg[100];
|
||
|
|
sprintf( msg, "file %s can not be read", filename.c_str() );
|
||
|
|
ts->printf( cvtest::TS::LOG, msg );
|
||
|
|
return cvtest::TS::FAIL_INVALID_TEST_DATA;
|
||
|
|
}
|
||
|
|
|
||
|
|
FileNode dataParamsNode = validationFS.getFirstTopLevelNode()["validation"][modelName][dataName]["data_params"];
|
||
|
|
CV_DbgAssert( !dataParamsNode.empty() );
|
||
|
|
|
||
|
|
CV_DbgAssert( !dataParamsNode["LS"].empty() );
|
||
|
|
dataParamsNode["LS"] >> trainSampleCount;
|
||
|
|
CvTrainTestSplit spl( trainSampleCount );
|
||
|
|
data.set_train_test_split( &spl );
|
||
|
|
|
||
|
|
CV_DbgAssert( !dataParamsNode["resp_idx"].empty() );
|
||
|
|
dataParamsNode["resp_idx"] >> respIdx;
|
||
|
|
data.set_response_idx( respIdx );
|
||
|
|
|
||
|
|
CV_DbgAssert( !dataParamsNode["types"].empty() );
|
||
|
|
dataParamsNode["types"] >> varTypes;
|
||
|
|
data.set_var_types( varTypes.c_str() );
|
||
|
|
|
||
|
|
return cvtest::TS::OK;
|
||
|
|
}
|
||
|
|
|
||
|
|
string& CV_MLBaseTest::get_validation_filename()
|
||
|
|
{
|
||
|
|
return validationFN;
|
||
|
|
}
|
||
|
|
|
||
|
|
int CV_MLBaseTest::train( int testCaseIdx )
|
||
|
|
{
|
||
|
|
bool is_trained = false;
|
||
|
|
FileNode modelParamsNode =
|
||
|
|
validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["model_params"];
|
||
|
|
|
||
|
|
if( !modelName.compare(CV_NBAYES) )
|
||
|
|
is_trained = nbayes_train( nbayes, &data );
|
||
|
|
else if( !modelName.compare(CV_KNEAREST) )
|
||
|
|
{
|
||
|
|
assert( 0 );
|
||
|
|
//is_trained = knearest->train( &data );
|
||
|
|
}
|
||
|
|
else if( !modelName.compare(CV_SVM) )
|
||
|
|
{
|
||
|
|
string svm_type_str, kernel_type_str;
|
||
|
|
modelParamsNode["svm_type"] >> svm_type_str;
|
||
|
|
modelParamsNode["kernel_type"] >> kernel_type_str;
|
||
|
|
CvSVMParams params;
|
||
|
|
params.svm_type = str_to_svm_type( svm_type_str );
|
||
|
|
params.kernel_type = str_to_svm_kernel_type( kernel_type_str );
|
||
|
|
modelParamsNode["degree"] >> params.degree;
|
||
|
|
modelParamsNode["gamma"] >> params.gamma;
|
||
|
|
modelParamsNode["coef0"] >> params.coef0;
|
||
|
|
modelParamsNode["C"] >> params.C;
|
||
|
|
modelParamsNode["nu"] >> params.nu;
|
||
|
|
modelParamsNode["p"] >> params.p;
|
||
|
|
is_trained = svm_train( svm, &data, params );
|
||
|
|
}
|
||
|
|
else if( !modelName.compare(CV_EM) )
|
||
|
|
{
|
||
|
|
assert( 0 );
|
||
|
|
}
|
||
|
|
else if( !modelName.compare(CV_ANN) )
|
||
|
|
{
|
||
|
|
string train_method_str;
|
||
|
|
double param1, param2;
|
||
|
|
modelParamsNode["train_method"] >> train_method_str;
|
||
|
|
modelParamsNode["param1"] >> param1;
|
||
|
|
modelParamsNode["param2"] >> param2;
|
||
|
|
Mat new_responses;
|
||
|
|
ann_get_new_responses( &data, new_responses, cls_map );
|
||
|
|
int layer_sz[] = { data.get_values()->cols - 1, 100, 100, (int)cls_map.size() };
|
||
|
|
CvMat layer_sizes =
|
||
|
|
cvMat( 1, (int)(sizeof(layer_sz)/sizeof(layer_sz[0])), CV_32S, layer_sz );
|
||
|
|
ann->create( &layer_sizes );
|
||
|
|
is_trained = ann_train( ann, &data, new_responses, CvANN_MLP_TrainParams(cvTermCriteria(CV_TERMCRIT_ITER,300,0.01),
|
||
|
|
str_to_ann_train_method(train_method_str), param1, param2) ) >= 0;
|
||
|
|
}
|
||
|
|
else if( !modelName.compare(CV_DTREE) )
|
||
|
|
{
|
||
|
|
int MAX_DEPTH, MIN_SAMPLE_COUNT, MAX_CATEGORIES, CV_FOLDS;
|
||
|
|
float REG_ACCURACY = 0;
|
||
|
|
bool USE_SURROGATE, IS_PRUNED;
|
||
|
|
modelParamsNode["max_depth"] >> MAX_DEPTH;
|
||
|
|
modelParamsNode["min_sample_count"] >> MIN_SAMPLE_COUNT;
|
||
|
|
modelParamsNode["use_surrogate"] >> USE_SURROGATE;
|
||
|
|
modelParamsNode["max_categories"] >> MAX_CATEGORIES;
|
||
|
|
modelParamsNode["cv_folds"] >> CV_FOLDS;
|
||
|
|
modelParamsNode["is_pruned"] >> IS_PRUNED;
|
||
|
|
is_trained = dtree->train( &data,
|
||
|
|
CvDTreeParams(MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY, USE_SURROGATE,
|
||
|
|
MAX_CATEGORIES, CV_FOLDS, false, IS_PRUNED, 0 )) != 0;
|
||
|
|
}
|
||
|
|
else if( !modelName.compare(CV_BOOST) )
|
||
|
|
{
|
||
|
|
int BOOST_TYPE, WEAK_COUNT, MAX_DEPTH;
|
||
|
|
float WEIGHT_TRIM_RATE;
|
||
|
|
bool USE_SURROGATE;
|
||
|
|
string typeStr;
|
||
|
|
modelParamsNode["type"] >> typeStr;
|
||
|
|
BOOST_TYPE = str_to_boost_type( typeStr );
|
||
|
|
modelParamsNode["weak_count"] >> WEAK_COUNT;
|
||
|
|
modelParamsNode["weight_trim_rate"] >> WEIGHT_TRIM_RATE;
|
||
|
|
modelParamsNode["max_depth"] >> MAX_DEPTH;
|
||
|
|
modelParamsNode["use_surrogate"] >> USE_SURROGATE;
|
||
|
|
is_trained = boost->train( &data,
|
||
|
|
CvBoostParams(BOOST_TYPE, WEAK_COUNT, WEIGHT_TRIM_RATE, MAX_DEPTH, USE_SURROGATE, 0) ) != 0;
|
||
|
|
}
|
||
|
|
else if( !modelName.compare(CV_RTREES) )
|
||
|
|
{
|
||
|
|
int MAX_DEPTH, MIN_SAMPLE_COUNT, MAX_CATEGORIES, CV_FOLDS, NACTIVE_VARS, MAX_TREES_NUM;
|
||
|
|
float REG_ACCURACY = 0, OOB_EPS = 0.0;
|
||
|
|
bool USE_SURROGATE, IS_PRUNED;
|
||
|
|
modelParamsNode["max_depth"] >> MAX_DEPTH;
|
||
|
|
modelParamsNode["min_sample_count"] >> MIN_SAMPLE_COUNT;
|
||
|
|
modelParamsNode["use_surrogate"] >> USE_SURROGATE;
|
||
|
|
modelParamsNode["max_categories"] >> MAX_CATEGORIES;
|
||
|
|
modelParamsNode["cv_folds"] >> CV_FOLDS;
|
||
|
|
modelParamsNode["is_pruned"] >> IS_PRUNED;
|
||
|
|
modelParamsNode["nactive_vars"] >> NACTIVE_VARS;
|
||
|
|
modelParamsNode["max_trees_num"] >> MAX_TREES_NUM;
|
||
|
|
is_trained = rtrees->train( &data, CvRTParams( MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY,
|
||
|
|
USE_SURROGATE, MAX_CATEGORIES, 0, true, // (calc_var_importance == true) <=> RF processes variable importance
|
||
|
|
NACTIVE_VARS, MAX_TREES_NUM, OOB_EPS, CV_TERMCRIT_ITER)) != 0;
|
||
|
|
}
|
||
|
|
else if( !modelName.compare(CV_ERTREES) )
|
||
|
|
{
|
||
|
|
int MAX_DEPTH, MIN_SAMPLE_COUNT, MAX_CATEGORIES, CV_FOLDS, NACTIVE_VARS, MAX_TREES_NUM;
|
||
|
|
float REG_ACCURACY = 0, OOB_EPS = 0.0;
|
||
|
|
bool USE_SURROGATE, IS_PRUNED;
|
||
|
|
modelParamsNode["max_depth"] >> MAX_DEPTH;
|
||
|
|
modelParamsNode["min_sample_count"] >> MIN_SAMPLE_COUNT;
|
||
|
|
modelParamsNode["use_surrogate"] >> USE_SURROGATE;
|
||
|
|
modelParamsNode["max_categories"] >> MAX_CATEGORIES;
|
||
|
|
modelParamsNode["cv_folds"] >> CV_FOLDS;
|
||
|
|
modelParamsNode["is_pruned"] >> IS_PRUNED;
|
||
|
|
modelParamsNode["nactive_vars"] >> NACTIVE_VARS;
|
||
|
|
modelParamsNode["max_trees_num"] >> MAX_TREES_NUM;
|
||
|
|
is_trained = ertrees->train( &data, CvRTParams( MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY,
|
||
|
|
USE_SURROGATE, MAX_CATEGORIES, 0, false, // (calc_var_importance == true) <=> RF processes variable importance
|
||
|
|
NACTIVE_VARS, MAX_TREES_NUM, OOB_EPS, CV_TERMCRIT_ITER)) != 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
if( !is_trained )
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "in test case %d model training was failed", testCaseIdx );
|
||
|
|
return cvtest::TS::FAIL_INVALID_OUTPUT;
|
||
|
|
}
|
||
|
|
return cvtest::TS::OK;
|
||
|
|
}
|
||
|
|
|
||
|
|
float CV_MLBaseTest::get_error( int testCaseIdx, int type, vector<float> *resp )
|
||
|
|
{
|
||
|
|
float err = 0;
|
||
|
|
if( !modelName.compare(CV_NBAYES) )
|
||
|
|
err = nbayes_calc_error( nbayes, &data, type, resp );
|
||
|
|
else if( !modelName.compare(CV_KNEAREST) )
|
||
|
|
{
|
||
|
|
assert( 0 );
|
||
|
|
testCaseIdx = 0;
|
||
|
|
/*int k = 2;
|
||
|
|
validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["model_params"]["k"] >> k;
|
||
|
|
err = knearest->calc_error( &data, k, type, resp );*/
|
||
|
|
}
|
||
|
|
else if( !modelName.compare(CV_SVM) )
|
||
|
|
err = svm_calc_error( svm, &data, type, resp );
|
||
|
|
else if( !modelName.compare(CV_EM) )
|
||
|
|
assert( 0 );
|
||
|
|
else if( !modelName.compare(CV_ANN) )
|
||
|
|
err = ann_calc_error( ann, &data, cls_map, type, resp );
|
||
|
|
else if( !modelName.compare(CV_DTREE) )
|
||
|
|
err = dtree->calc_error( &data, type, resp );
|
||
|
|
else if( !modelName.compare(CV_BOOST) )
|
||
|
|
err = boost->calc_error( &data, type, resp );
|
||
|
|
else if( !modelName.compare(CV_RTREES) )
|
||
|
|
err = rtrees->calc_error( &data, type, resp );
|
||
|
|
else if( !modelName.compare(CV_ERTREES) )
|
||
|
|
err = ertrees->calc_error( &data, type, resp );
|
||
|
|
return err;
|
||
|
|
}
|
||
|
|
|
||
|
|
void CV_MLBaseTest::save( const char* filename )
|
||
|
|
{
|
||
|
|
if( !modelName.compare(CV_NBAYES) )
|
||
|
|
nbayes->save( filename );
|
||
|
|
else if( !modelName.compare(CV_KNEAREST) )
|
||
|
|
knearest->save( filename );
|
||
|
|
else if( !modelName.compare(CV_SVM) )
|
||
|
|
svm->save( filename );
|
||
|
|
else if( !modelName.compare(CV_EM) )
|
||
|
|
em->save( filename );
|
||
|
|
else if( !modelName.compare(CV_ANN) )
|
||
|
|
ann->save( filename );
|
||
|
|
else if( !modelName.compare(CV_DTREE) )
|
||
|
|
dtree->save( filename );
|
||
|
|
else if( !modelName.compare(CV_BOOST) )
|
||
|
|
boost->save( filename );
|
||
|
|
else if( !modelName.compare(CV_RTREES) )
|
||
|
|
rtrees->save( filename );
|
||
|
|
else if( !modelName.compare(CV_ERTREES) )
|
||
|
|
ertrees->save( filename );
|
||
|
|
}
|
||
|
|
|
||
|
|
void CV_MLBaseTest::load( const char* filename )
|
||
|
|
{
|
||
|
|
if( !modelName.compare(CV_NBAYES) )
|
||
|
|
nbayes->load( filename );
|
||
|
|
else if( !modelName.compare(CV_KNEAREST) )
|
||
|
|
knearest->load( filename );
|
||
|
|
else if( !modelName.compare(CV_SVM) )
|
||
|
|
svm->load( filename );
|
||
|
|
else if( !modelName.compare(CV_EM) )
|
||
|
|
em->load( filename );
|
||
|
|
else if( !modelName.compare(CV_ANN) )
|
||
|
|
ann->load( filename );
|
||
|
|
else if( !modelName.compare(CV_DTREE) )
|
||
|
|
dtree->load( filename );
|
||
|
|
else if( !modelName.compare(CV_BOOST) )
|
||
|
|
boost->load( filename );
|
||
|
|
else if( !modelName.compare(CV_RTREES) )
|
||
|
|
rtrees->load( filename );
|
||
|
|
else if( !modelName.compare(CV_ERTREES) )
|
||
|
|
ertrees->load( filename );
|
||
|
|
}
|
||
|
|
|
||
|
|
/* End of file. */
|