2010-05-12 01:44:00 +08:00
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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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//
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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 "precomp.hpp"
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CvNormalBayesClassifier::CvNormalBayesClassifier()
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{
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var_count = var_all = 0;
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var_idx = 0;
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cls_labels = 0;
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count = 0;
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sum = 0;
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productsum = 0;
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avg = 0;
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inv_eigen_values = 0;
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cov_rotate_mats = 0;
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c = 0;
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default_model_name = "my_nb";
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}
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void CvNormalBayesClassifier::clear()
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{
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if( cls_labels )
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{
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for( int cls = 0; cls < cls_labels->cols; cls++ )
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{
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cvReleaseMat( &count[cls] );
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cvReleaseMat( &sum[cls] );
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cvReleaseMat( &productsum[cls] );
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cvReleaseMat( &avg[cls] );
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cvReleaseMat( &inv_eigen_values[cls] );
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cvReleaseMat( &cov_rotate_mats[cls] );
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}
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}
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cvReleaseMat( &cls_labels );
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cvReleaseMat( &var_idx );
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cvReleaseMat( &c );
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cvFree( &count );
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}
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CvNormalBayesClassifier::~CvNormalBayesClassifier()
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{
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clear();
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}
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CvNormalBayesClassifier::CvNormalBayesClassifier(
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const CvMat* _train_data, const CvMat* _responses,
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const CvMat* _var_idx, const CvMat* _sample_idx )
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{
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var_count = var_all = 0;
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var_idx = 0;
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cls_labels = 0;
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count = 0;
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sum = 0;
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productsum = 0;
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avg = 0;
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inv_eigen_values = 0;
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cov_rotate_mats = 0;
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c = 0;
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default_model_name = "my_nb";
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train( _train_data, _responses, _var_idx, _sample_idx );
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}
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bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _responses,
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const CvMat* _var_idx, const CvMat* _sample_idx, bool update )
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{
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const float min_variation = FLT_EPSILON;
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bool result = false;
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CvMat* responses = 0;
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const float** train_data = 0;
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CvMat* __cls_labels = 0;
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CvMat* __var_idx = 0;
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CvMat* cov = 0;
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CV_FUNCNAME( "CvNormalBayesClassifier::train" );
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__BEGIN__;
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int cls, nsamples = 0, _var_count = 0, _var_all = 0, nclasses = 0;
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int s, c1, c2;
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const int* responses_data;
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CV_CALL( cvPrepareTrainData( 0,
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_train_data, CV_ROW_SAMPLE, _responses, CV_VAR_CATEGORICAL,
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_var_idx, _sample_idx, false, &train_data,
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&nsamples, &_var_count, &_var_all, &responses,
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&__cls_labels, &__var_idx ));
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if( !update )
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{
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const size_t mat_size = sizeof(CvMat*);
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size_t data_size;
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clear();
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var_idx = __var_idx;
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cls_labels = __cls_labels;
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__var_idx = __cls_labels = 0;
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var_count = _var_count;
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var_all = _var_all;
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nclasses = cls_labels->cols;
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data_size = nclasses*6*mat_size;
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CV_CALL( count = (CvMat**)cvAlloc( data_size ));
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memset( count, 0, data_size );
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sum = count + nclasses;
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productsum = sum + nclasses;
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avg = productsum + nclasses;
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inv_eigen_values= avg + nclasses;
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cov_rotate_mats = inv_eigen_values + nclasses;
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CV_CALL( c = cvCreateMat( 1, nclasses, CV_64FC1 ));
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for( cls = 0; cls < nclasses; cls++ )
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{
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CV_CALL(count[cls] = cvCreateMat( 1, var_count, CV_32SC1 ));
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CV_CALL(sum[cls] = cvCreateMat( 1, var_count, CV_64FC1 ));
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CV_CALL(productsum[cls] = cvCreateMat( var_count, var_count, CV_64FC1 ));
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CV_CALL(avg[cls] = cvCreateMat( 1, var_count, CV_64FC1 ));
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CV_CALL(inv_eigen_values[cls] = cvCreateMat( 1, var_count, CV_64FC1 ));
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CV_CALL(cov_rotate_mats[cls] = cvCreateMat( var_count, var_count, CV_64FC1 ));
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CV_CALL(cvZero( count[cls] ));
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CV_CALL(cvZero( sum[cls] ));
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CV_CALL(cvZero( productsum[cls] ));
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CV_CALL(cvZero( avg[cls] ));
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CV_CALL(cvZero( inv_eigen_values[cls] ));
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CV_CALL(cvZero( cov_rotate_mats[cls] ));
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}
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}
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else
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{
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// check that the new training data has the same dimensionality etc.
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if( _var_count != var_count || _var_all != var_all || !((!_var_idx && !var_idx) ||
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(_var_idx && var_idx && cvNorm(_var_idx,var_idx,CV_C) < DBL_EPSILON)) )
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CV_ERROR( CV_StsBadArg,
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"The new training data is inconsistent with the original training data" );
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if( cls_labels->cols != __cls_labels->cols ||
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cvNorm(cls_labels, __cls_labels, CV_C) > DBL_EPSILON )
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CV_ERROR( CV_StsNotImplemented,
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"In the current implementation the new training data must have absolutely "
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"the same set of class labels as used in the original training data" );
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nclasses = cls_labels->cols;
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}
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responses_data = responses->data.i;
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CV_CALL( cov = cvCreateMat( _var_count, _var_count, CV_64FC1 ));
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/* process train data (count, sum , productsum) */
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for( s = 0; s < nsamples; s++ )
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{
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cls = responses_data[s];
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int* count_data = count[cls]->data.i;
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double* sum_data = sum[cls]->data.db;
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double* prod_data = productsum[cls]->data.db;
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const float* train_vec = train_data[s];
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for( c1 = 0; c1 < _var_count; c1++, prod_data += _var_count )
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{
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double val1 = train_vec[c1];
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sum_data[c1] += val1;
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count_data[c1]++;
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for( c2 = c1; c2 < _var_count; c2++ )
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prod_data[c2] += train_vec[c2]*val1;
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}
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}
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2013-09-19 03:49:32 +08:00
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cvReleaseMat( &responses );
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responses = 0;
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2010-05-12 01:44:00 +08:00
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/* calculate avg, covariance matrix, c */
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for( cls = 0; cls < nclasses; cls++ )
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{
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double det = 1;
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int i, j;
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CvMat* w = inv_eigen_values[cls];
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int* count_data = count[cls]->data.i;
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double* avg_data = avg[cls]->data.db;
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double* sum1 = sum[cls]->data.db;
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cvCompleteSymm( productsum[cls], 0 );
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for( j = 0; j < _var_count; j++ )
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{
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int n = count_data[j];
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avg_data[j] = n ? sum1[j] / n : 0.;
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}
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count_data = count[cls]->data.i;
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avg_data = avg[cls]->data.db;
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sum1 = sum[cls]->data.db;
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for( i = 0; i < _var_count; i++ )
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{
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double* avg2_data = avg[cls]->data.db;
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double* sum2 = sum[cls]->data.db;
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double* prod_data = productsum[cls]->data.db + i*_var_count;
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double* cov_data = cov->data.db + i*_var_count;
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double s1val = sum1[i];
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double avg1 = avg_data[i];
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2012-06-15 21:04:17 +08:00
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int _count = count_data[i];
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2010-05-12 01:44:00 +08:00
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for( j = 0; j <= i; j++ )
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{
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double avg2 = avg2_data[j];
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2012-06-15 21:04:17 +08:00
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double cov_val = prod_data[j] - avg1 * sum2[j] - avg2 * s1val + avg1 * avg2 * _count;
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cov_val = (_count > 1) ? cov_val / (_count - 1) : cov_val;
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2010-05-12 01:44:00 +08:00
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cov_data[j] = cov_val;
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}
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}
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CV_CALL( cvCompleteSymm( cov, 1 ));
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CV_CALL( cvSVD( cov, w, cov_rotate_mats[cls], 0, CV_SVD_U_T ));
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CV_CALL( cvMaxS( w, min_variation, w ));
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for( j = 0; j < _var_count; j++ )
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det *= w->data.db[j];
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CV_CALL( cvDiv( NULL, w, w ));
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2010-12-13 06:45:31 +08:00
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c->data.db[cls] = det > 0 ? log(det) : -700;
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2010-05-12 01:44:00 +08:00
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}
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result = true;
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__END__;
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if( !result || cvGetErrStatus() < 0 )
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clear();
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cvReleaseMat( &cov );
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cvReleaseMat( &__cls_labels );
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cvReleaseMat( &__var_idx );
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cvFree( &train_data );
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return result;
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}
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2013-05-30 22:44:33 +08:00
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struct predict_body : cv::ParallelLoopBody {
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2011-04-15 01:04:39 +08:00
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predict_body(CvMat* _c, CvMat** _cov_rotate_mats, CvMat** _inv_eigen_values, CvMat** _avg,
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const CvMat* _samples, const int* _vidx, CvMat* _cls_labels,
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2014-01-29 19:57:51 +08:00
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CvMat* _results, float* _value, int _var_count1, CvMat* _results_prob
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2011-04-15 01:04:39 +08:00
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)
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{
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c = _c;
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cov_rotate_mats = _cov_rotate_mats;
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inv_eigen_values = _inv_eigen_values;
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avg = _avg;
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samples = _samples;
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vidx = _vidx;
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cls_labels = _cls_labels;
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results = _results;
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value = _value;
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var_count1 = _var_count1;
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2014-01-29 19:57:51 +08:00
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results_prob = _results_prob;
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2011-04-15 01:04:39 +08:00
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}
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2012-06-15 21:04:17 +08:00
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2011-04-15 01:04:39 +08:00
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CvMat* c;
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CvMat** cov_rotate_mats;
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CvMat** inv_eigen_values;
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CvMat** avg;
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const CvMat* samples;
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const int* vidx;
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CvMat* cls_labels;
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2014-01-29 19:57:51 +08:00
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CvMat* results_prob;
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2011-04-15 01:04:39 +08:00
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CvMat* results;
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float* value;
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int var_count1;
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2012-06-15 21:04:17 +08:00
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2013-05-30 22:44:33 +08:00
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void operator()( const cv::Range& range ) const
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2011-04-15 01:04:39 +08:00
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{
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int cls = -1;
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2014-01-29 19:57:51 +08:00
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int rtype = 0, rstep = 0, rptype = 0, rpstep = 0;
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2011-02-18 18:29:57 +08:00
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int nclasses = cls_labels->cols;
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int _var_count = avg[0]->cols;
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2014-01-29 19:57:51 +08:00
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double probability = 0;
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2012-06-15 21:04:17 +08:00
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2011-04-15 01:04:39 +08:00
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if (results)
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2010-05-12 01:44:00 +08:00
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{
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rtype = CV_MAT_TYPE(results->type);
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rstep = CV_IS_MAT_CONT(results->type) ? 1 : results->step/CV_ELEM_SIZE(rtype);
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}
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2014-01-29 19:57:51 +08:00
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if (results_prob)
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{
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rptype = CV_MAT_TYPE(results_prob->type);
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rpstep = CV_IS_MAT_CONT(results_prob->type) ? 1 : results_prob->step/CV_ELEM_SIZE(rptype);
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}
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2011-04-15 01:04:39 +08:00
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// allocate memory and initializing headers for calculating
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cv::AutoBuffer<double> buffer(nclasses + var_count1);
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CvMat diff = cvMat( 1, var_count1, CV_64FC1, &buffer[0] );
|
2012-06-15 21:04:17 +08:00
|
|
|
|
2013-05-30 22:44:33 +08:00
|
|
|
for(int k = range.start; k < range.end; k += 1 )
|
2010-05-12 01:44:00 +08:00
|
|
|
{
|
|
|
|
int ival;
|
2011-04-15 01:04:39 +08:00
|
|
|
double opt = FLT_MAX;
|
2010-05-12 01:44:00 +08:00
|
|
|
|
2011-04-15 01:04:39 +08:00
|
|
|
for(int i = 0; i < nclasses; i++ )
|
2010-05-12 01:44:00 +08:00
|
|
|
{
|
|
|
|
double cur = c->data.db[i];
|
|
|
|
CvMat* u = cov_rotate_mats[i];
|
|
|
|
CvMat* w = inv_eigen_values[i];
|
2011-04-15 01:04:39 +08:00
|
|
|
|
2010-05-12 01:44:00 +08:00
|
|
|
const double* avg_data = avg[i]->data.db;
|
|
|
|
const float* x = (const float*)(samples->data.ptr + samples->step*k);
|
|
|
|
|
|
|
|
// cov = u w u' --> cov^(-1) = u w^(-1) u'
|
2011-04-15 01:04:39 +08:00
|
|
|
for(int j = 0; j < _var_count; j++ )
|
2010-05-12 01:44:00 +08:00
|
|
|
diff.data.db[j] = avg_data[j] - x[vidx ? vidx[j] : j];
|
|
|
|
|
2011-02-18 18:29:57 +08:00
|
|
|
cvGEMM( &diff, u, 1, 0, 0, &diff, CV_GEMM_B_T );
|
2011-04-15 01:04:39 +08:00
|
|
|
for(int j = 0; j < _var_count; j++ )
|
2010-05-12 01:44:00 +08:00
|
|
|
{
|
|
|
|
double d = diff.data.db[j];
|
|
|
|
cur += d*d*w->data.db[j];
|
|
|
|
}
|
|
|
|
|
|
|
|
if( cur < opt )
|
|
|
|
{
|
|
|
|
cls = i;
|
|
|
|
opt = cur;
|
|
|
|
}
|
|
|
|
/* probability = exp( -0.5 * cur ) */
|
2014-01-29 19:57:51 +08:00
|
|
|
probability = exp( -0.5 * cur );
|
2010-05-12 01:44:00 +08:00
|
|
|
}
|
|
|
|
|
|
|
|
ival = cls_labels->data.i[cls];
|
|
|
|
if( results )
|
|
|
|
{
|
|
|
|
if( rtype == CV_32SC1 )
|
|
|
|
results->data.i[k*rstep] = ival;
|
|
|
|
else
|
|
|
|
results->data.fl[k*rstep] = (float)ival;
|
|
|
|
}
|
2014-01-29 19:57:51 +08:00
|
|
|
if ( results_prob )
|
|
|
|
{
|
|
|
|
if ( rptype == CV_32FC1 )
|
|
|
|
results_prob->data.fl[k*rpstep] = (float)probability;
|
|
|
|
else
|
|
|
|
results_prob->data.db[k*rpstep] = probability;
|
|
|
|
}
|
2010-05-12 01:44:00 +08:00
|
|
|
if( k == 0 )
|
2011-04-15 01:04:39 +08:00
|
|
|
*value = (float)ival;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
};
|
2010-05-12 01:44:00 +08:00
|
|
|
|
2011-04-15 01:04:39 +08:00
|
|
|
|
2014-01-29 19:57:51 +08:00
|
|
|
float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results, CvMat* results_prob ) const
|
2011-04-15 01:04:39 +08:00
|
|
|
{
|
|
|
|
float value = 0;
|
|
|
|
|
|
|
|
if( !CV_IS_MAT(samples) || CV_MAT_TYPE(samples->type) != CV_32FC1 || samples->cols != var_all )
|
|
|
|
CV_Error( CV_StsBadArg,
|
|
|
|
"The input samples must be 32f matrix with the number of columns = var_all" );
|
|
|
|
|
|
|
|
if( samples->rows > 1 && !results )
|
|
|
|
CV_Error( CV_StsNullPtr,
|
|
|
|
"When the number of input samples is >1, the output vector of results must be passed" );
|
|
|
|
|
|
|
|
if( results )
|
|
|
|
{
|
|
|
|
if( !CV_IS_MAT(results) || (CV_MAT_TYPE(results->type) != CV_32FC1 &&
|
|
|
|
CV_MAT_TYPE(results->type) != CV_32SC1) ||
|
|
|
|
(results->cols != 1 && results->rows != 1) ||
|
|
|
|
results->cols + results->rows - 1 != samples->rows )
|
|
|
|
CV_Error( CV_StsBadArg, "The output array must be integer or floating-point vector "
|
|
|
|
"with the number of elements = number of rows in the input matrix" );
|
2010-05-12 01:44:00 +08:00
|
|
|
}
|
|
|
|
|
2014-01-29 19:57:51 +08:00
|
|
|
if( results_prob )
|
|
|
|
{
|
|
|
|
if( !CV_IS_MAT(results_prob) || (CV_MAT_TYPE(results_prob->type) != CV_32FC1 &&
|
|
|
|
CV_MAT_TYPE(results_prob->type) != CV_64FC1) ||
|
|
|
|
(results_prob->cols != 1 && results_prob->rows != 1) ||
|
|
|
|
results_prob->cols + results_prob->rows - 1 != samples->rows )
|
|
|
|
CV_Error( CV_StsBadArg, "The output array must be double or float vector "
|
|
|
|
"with the number of elements = number of rows in the input matrix" );
|
|
|
|
}
|
|
|
|
|
2011-04-15 01:04:39 +08:00
|
|
|
const int* vidx = var_idx ? var_idx->data.i : 0;
|
|
|
|
|
2013-05-30 22:44:33 +08:00
|
|
|
cv::parallel_for_(cv::Range(0, samples->rows),
|
|
|
|
predict_body(c, cov_rotate_mats, inv_eigen_values, avg, samples,
|
2014-01-29 19:57:51 +08:00
|
|
|
vidx, cls_labels, results, &value, var_count, results_prob));
|
2011-04-15 01:04:39 +08:00
|
|
|
|
2010-05-12 01:44:00 +08:00
|
|
|
return value;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void CvNormalBayesClassifier::write( CvFileStorage* fs, const char* name ) const
|
|
|
|
{
|
|
|
|
CV_FUNCNAME( "CvNormalBayesClassifier::write" );
|
|
|
|
|
|
|
|
__BEGIN__;
|
|
|
|
|
|
|
|
int nclasses, i;
|
|
|
|
|
|
|
|
nclasses = cls_labels->cols;
|
|
|
|
|
|
|
|
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_NBAYES );
|
|
|
|
|
|
|
|
CV_CALL( cvWriteInt( fs, "var_count", var_count ));
|
|
|
|
CV_CALL( cvWriteInt( fs, "var_all", var_all ));
|
|
|
|
|
|
|
|
if( var_idx )
|
|
|
|
CV_CALL( cvWrite( fs, "var_idx", var_idx ));
|
|
|
|
CV_CALL( cvWrite( fs, "cls_labels", cls_labels ));
|
|
|
|
|
|
|
|
CV_CALL( cvStartWriteStruct( fs, "count", CV_NODE_SEQ ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
CV_CALL( cvWrite( fs, NULL, count[i] ));
|
|
|
|
CV_CALL( cvEndWriteStruct( fs ));
|
|
|
|
|
|
|
|
CV_CALL( cvStartWriteStruct( fs, "sum", CV_NODE_SEQ ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
CV_CALL( cvWrite( fs, NULL, sum[i] ));
|
|
|
|
CV_CALL( cvEndWriteStruct( fs ));
|
|
|
|
|
|
|
|
CV_CALL( cvStartWriteStruct( fs, "productsum", CV_NODE_SEQ ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
CV_CALL( cvWrite( fs, NULL, productsum[i] ));
|
|
|
|
CV_CALL( cvEndWriteStruct( fs ));
|
|
|
|
|
|
|
|
CV_CALL( cvStartWriteStruct( fs, "avg", CV_NODE_SEQ ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
CV_CALL( cvWrite( fs, NULL, avg[i] ));
|
|
|
|
CV_CALL( cvEndWriteStruct( fs ));
|
|
|
|
|
|
|
|
CV_CALL( cvStartWriteStruct( fs, "inv_eigen_values", CV_NODE_SEQ ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
CV_CALL( cvWrite( fs, NULL, inv_eigen_values[i] ));
|
|
|
|
CV_CALL( cvEndWriteStruct( fs ));
|
|
|
|
|
|
|
|
CV_CALL( cvStartWriteStruct( fs, "cov_rotate_mats", CV_NODE_SEQ ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
CV_CALL( cvWrite( fs, NULL, cov_rotate_mats[i] ));
|
|
|
|
CV_CALL( cvEndWriteStruct( fs ));
|
|
|
|
|
|
|
|
CV_CALL( cvWrite( fs, "c", c ));
|
|
|
|
|
|
|
|
cvEndWriteStruct( fs );
|
|
|
|
|
|
|
|
__END__;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void CvNormalBayesClassifier::read( CvFileStorage* fs, CvFileNode* root_node )
|
|
|
|
{
|
|
|
|
bool ok = false;
|
|
|
|
CV_FUNCNAME( "CvNormalBayesClassifier::read" );
|
|
|
|
|
|
|
|
__BEGIN__;
|
|
|
|
|
|
|
|
int nclasses, i;
|
|
|
|
size_t data_size;
|
|
|
|
CvFileNode* node;
|
|
|
|
CvSeq* seq;
|
|
|
|
CvSeqReader reader;
|
|
|
|
|
|
|
|
clear();
|
|
|
|
|
|
|
|
CV_CALL( var_count = cvReadIntByName( fs, root_node, "var_count", -1 ));
|
|
|
|
CV_CALL( var_all = cvReadIntByName( fs, root_node, "var_all", -1 ));
|
|
|
|
CV_CALL( var_idx = (CvMat*)cvReadByName( fs, root_node, "var_idx" ));
|
|
|
|
CV_CALL( cls_labels = (CvMat*)cvReadByName( fs, root_node, "cls_labels" ));
|
|
|
|
if( !cls_labels )
|
|
|
|
CV_ERROR( CV_StsParseError, "No \"cls_labels\" in NBayes classifier" );
|
|
|
|
if( cls_labels->cols < 1 )
|
|
|
|
CV_ERROR( CV_StsBadArg, "Number of classes is less 1" );
|
|
|
|
if( var_count <= 0 )
|
|
|
|
CV_ERROR( CV_StsParseError,
|
|
|
|
"The field \"var_count\" of NBayes classifier is missing" );
|
|
|
|
nclasses = cls_labels->cols;
|
|
|
|
|
|
|
|
data_size = nclasses*6*sizeof(CvMat*);
|
|
|
|
CV_CALL( count = (CvMat**)cvAlloc( data_size ));
|
|
|
|
memset( count, 0, data_size );
|
|
|
|
|
|
|
|
sum = count + nclasses;
|
|
|
|
productsum = sum + nclasses;
|
|
|
|
avg = productsum + nclasses;
|
|
|
|
inv_eigen_values = avg + nclasses;
|
|
|
|
cov_rotate_mats = inv_eigen_values + nclasses;
|
|
|
|
|
|
|
|
CV_CALL( node = cvGetFileNodeByName( fs, root_node, "count" ));
|
|
|
|
seq = node->data.seq;
|
|
|
|
if( !CV_NODE_IS_SEQ(node->tag) || seq->total != nclasses)
|
|
|
|
CV_ERROR( CV_StsBadArg, "" );
|
|
|
|
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
{
|
|
|
|
CV_CALL( count[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
|
|
|
|
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
|
|
|
|
}
|
|
|
|
|
|
|
|
CV_CALL( node = cvGetFileNodeByName( fs, root_node, "sum" ));
|
|
|
|
seq = node->data.seq;
|
|
|
|
if( !CV_NODE_IS_SEQ(node->tag) || seq->total != nclasses)
|
|
|
|
CV_ERROR( CV_StsBadArg, "" );
|
|
|
|
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
{
|
|
|
|
CV_CALL( sum[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
|
|
|
|
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
|
|
|
|
}
|
|
|
|
|
|
|
|
CV_CALL( node = cvGetFileNodeByName( fs, root_node, "productsum" ));
|
|
|
|
seq = node->data.seq;
|
|
|
|
if( !CV_NODE_IS_SEQ(node->tag) || seq->total != nclasses)
|
|
|
|
CV_ERROR( CV_StsBadArg, "" );
|
|
|
|
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
{
|
|
|
|
CV_CALL( productsum[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
|
|
|
|
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
|
|
|
|
}
|
|
|
|
|
|
|
|
CV_CALL( node = cvGetFileNodeByName( fs, root_node, "avg" ));
|
|
|
|
seq = node->data.seq;
|
|
|
|
if( !CV_NODE_IS_SEQ(node->tag) || seq->total != nclasses)
|
|
|
|
CV_ERROR( CV_StsBadArg, "" );
|
|
|
|
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
{
|
|
|
|
CV_CALL( avg[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
|
|
|
|
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
|
|
|
|
}
|
|
|
|
|
|
|
|
CV_CALL( node = cvGetFileNodeByName( fs, root_node, "inv_eigen_values" ));
|
|
|
|
seq = node->data.seq;
|
|
|
|
if( !CV_NODE_IS_SEQ(node->tag) || seq->total != nclasses)
|
|
|
|
CV_ERROR( CV_StsBadArg, "" );
|
|
|
|
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
{
|
|
|
|
CV_CALL( inv_eigen_values[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
|
|
|
|
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
|
|
|
|
}
|
|
|
|
|
|
|
|
CV_CALL( node = cvGetFileNodeByName( fs, root_node, "cov_rotate_mats" ));
|
|
|
|
seq = node->data.seq;
|
|
|
|
if( !CV_NODE_IS_SEQ(node->tag) || seq->total != nclasses)
|
|
|
|
CV_ERROR( CV_StsBadArg, "" );
|
|
|
|
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
|
|
|
|
for( i = 0; i < nclasses; i++ )
|
|
|
|
{
|
|
|
|
CV_CALL( cov_rotate_mats[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
|
|
|
|
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
|
|
|
|
}
|
|
|
|
|
|
|
|
CV_CALL( c = (CvMat*)cvReadByName( fs, root_node, "c" ));
|
|
|
|
|
|
|
|
ok = true;
|
|
|
|
|
|
|
|
__END__;
|
|
|
|
|
|
|
|
if( !ok )
|
|
|
|
clear();
|
|
|
|
}
|
|
|
|
|
|
|
|
using namespace cv;
|
|
|
|
|
2010-11-03 01:58:22 +08:00
|
|
|
CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& _train_data, const Mat& _responses,
|
|
|
|
const Mat& _var_idx, const Mat& _sample_idx )
|
|
|
|
{
|
|
|
|
var_count = var_all = 0;
|
|
|
|
var_idx = 0;
|
|
|
|
cls_labels = 0;
|
|
|
|
count = 0;
|
|
|
|
sum = 0;
|
|
|
|
productsum = 0;
|
|
|
|
avg = 0;
|
|
|
|
inv_eigen_values = 0;
|
|
|
|
cov_rotate_mats = 0;
|
|
|
|
c = 0;
|
|
|
|
default_model_name = "my_nb";
|
2012-06-15 21:04:17 +08:00
|
|
|
|
2010-11-03 01:58:22 +08:00
|
|
|
CvMat tdata = _train_data, responses = _responses, vidx = _var_idx, sidx = _sample_idx;
|
|
|
|
train(&tdata, &responses, vidx.data.ptr ? &vidx : 0,
|
|
|
|
sidx.data.ptr ? &sidx : 0);
|
|
|
|
}
|
|
|
|
|
2010-05-12 01:44:00 +08:00
|
|
|
bool CvNormalBayesClassifier::train( const Mat& _train_data, const Mat& _responses,
|
|
|
|
const Mat& _var_idx, const Mat& _sample_idx, bool update )
|
|
|
|
{
|
|
|
|
CvMat tdata = _train_data, responses = _responses, vidx = _var_idx, sidx = _sample_idx;
|
|
|
|
return train(&tdata, &responses, vidx.data.ptr ? &vidx : 0,
|
|
|
|
sidx.data.ptr ? &sidx : 0, update);
|
|
|
|
}
|
|
|
|
|
2014-01-29 19:57:51 +08:00
|
|
|
float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results, Mat* _results_prob ) const
|
2010-05-12 01:44:00 +08:00
|
|
|
{
|
2014-01-29 19:57:51 +08:00
|
|
|
CvMat samples = _samples, results, *presults = 0, results_prob, *presults_prob = 0;
|
2012-06-15 21:04:17 +08:00
|
|
|
|
2010-05-12 01:44:00 +08:00
|
|
|
if( _results )
|
|
|
|
{
|
|
|
|
if( !(_results->data && _results->type() == CV_32F &&
|
|
|
|
(_results->cols == 1 || _results->rows == 1) &&
|
|
|
|
_results->cols + _results->rows - 1 == _samples.rows) )
|
|
|
|
_results->create(_samples.rows, 1, CV_32F);
|
|
|
|
presults = &(results = *_results);
|
|
|
|
}
|
2012-06-15 21:04:17 +08:00
|
|
|
|
2014-01-29 19:57:51 +08:00
|
|
|
if( _results_prob )
|
|
|
|
{
|
|
|
|
if( !(_results_prob->data && _results_prob->type() == CV_64F &&
|
|
|
|
(_results_prob->cols == 1 || _results_prob->rows == 1) &&
|
|
|
|
_results_prob->cols + _results_prob->rows - 1 == _samples.rows) )
|
|
|
|
_results_prob->create(_samples.rows, 1, CV_64F);
|
|
|
|
presults_prob = &(results_prob = *_results_prob);
|
|
|
|
}
|
|
|
|
|
|
|
|
return predict(&samples, presults, presults_prob);
|
2010-05-12 01:44:00 +08:00
|
|
|
}
|
|
|
|
|
|
|
|
/* End of file. */
|