"atomic bomb" commit. Reorganized OpenCV directory structure
This commit is contained in:
Vadim Pisarevsky
595
modules/ml/src/nbayes.cpp
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595
modules/ml/src/nbayes.cpp
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@@ -0,0 +1,595 @@
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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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/* 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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int count = count_data[i];
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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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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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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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c->data.db[cls] = log( det );
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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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float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) const
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{
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float value = 0;
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void* buffer = 0;
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int allocated_buffer = 0;
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CV_FUNCNAME( "CvNormalBayesClassifier::predict" );
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__BEGIN__;
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int i, j, k, cls = -1, _var_count, nclasses;
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double opt = FLT_MAX;
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CvMat diff;
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int rtype = 0, rstep = 0, size;
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const int* vidx = 0;
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nclasses = cls_labels->cols;
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_var_count = avg[0]->cols;
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if( !CV_IS_MAT(samples) || CV_MAT_TYPE(samples->type) != CV_32FC1 || samples->cols != var_all )
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CV_ERROR( CV_StsBadArg,
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"The input samples must be 32f matrix with the number of columns = var_all" );
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if( samples->rows > 1 && !results )
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CV_ERROR( CV_StsNullPtr,
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"When the number of input samples is >1, the output vector of results must be passed" );
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if( results )
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{
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if( !CV_IS_MAT(results) || (CV_MAT_TYPE(results->type) != CV_32FC1 &&
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CV_MAT_TYPE(results->type) != CV_32SC1) ||
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(results->cols != 1 && results->rows != 1) ||
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results->cols + results->rows - 1 != samples->rows )
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CV_ERROR( CV_StsBadArg, "The output array must be integer or floating-point vector "
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"with the number of elements = number of rows in the input matrix" );
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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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if( var_idx )
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vidx = var_idx->data.i;
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// allocate memory and initializing headers for calculating
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size = sizeof(double) * (nclasses + var_count);
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if( size <= CV_MAX_LOCAL_SIZE )
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buffer = cvStackAlloc( size );
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else
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{
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CV_CALL( buffer = cvAlloc( size ));
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allocated_buffer = 1;
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}
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diff = cvMat( 1, var_count, CV_64FC1, buffer );
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for( k = 0; k < samples->rows; k++ )
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{
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int ival;
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for( i = 0; i < nclasses; i++ )
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{
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double cur = c->data.db[i];
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CvMat* u = cov_rotate_mats[i];
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CvMat* w = inv_eigen_values[i];
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const double* avg_data = avg[i]->data.db;
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const float* x = (const float*)(samples->data.ptr + samples->step*k);
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// cov = u w u' --> cov^(-1) = u w^(-1) u'
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for( j = 0; j < _var_count; j++ )
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diff.data.db[j] = avg_data[j] - x[vidx ? vidx[j] : j];
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CV_CALL(cvGEMM( &diff, u, 1, 0, 0, &diff, CV_GEMM_B_T ));
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for( j = 0; j < _var_count; j++ )
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{
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double d = diff.data.db[j];
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cur += d*d*w->data.db[j];
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}
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if( cur < opt )
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{
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cls = i;
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opt = cur;
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}
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/* probability = exp( -0.5 * cur ) */
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}
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ival = cls_labels->data.i[cls];
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if( results )
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{
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if( rtype == CV_32SC1 )
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results->data.i[k*rstep] = ival;
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else
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results->data.fl[k*rstep] = (float)ival;
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}
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if( k == 0 )
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value = (float)ival;
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/*if( _probs )
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{
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CV_CALL( cvConvertScale( &expo, &expo, -0.5 ));
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CV_CALL( cvExp( &expo, &expo ));
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if( _probs->cols == 1 )
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CV_CALL( cvReshape( &expo, &expo, 1, nclasses ));
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CV_CALL( cvConvertScale( &expo, _probs, 1./cvSum( &expo ).val[0] ));
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}*/
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}
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__END__;
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if( allocated_buffer )
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cvFree( &buffer );
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return value;
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}
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void CvNormalBayesClassifier::write( CvFileStorage* fs, const char* name ) const
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{
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CV_FUNCNAME( "CvNormalBayesClassifier::write" );
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__BEGIN__;
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int nclasses, i;
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nclasses = cls_labels->cols;
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cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_NBAYES );
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CV_CALL( cvWriteInt( fs, "var_count", var_count ));
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CV_CALL( cvWriteInt( fs, "var_all", var_all ));
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if( var_idx )
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CV_CALL( cvWrite( fs, "var_idx", var_idx ));
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CV_CALL( cvWrite( fs, "cls_labels", cls_labels ));
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CV_CALL( cvStartWriteStruct( fs, "count", CV_NODE_SEQ ));
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for( i = 0; i < nclasses; i++ )
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CV_CALL( cvWrite( fs, NULL, count[i] ));
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CV_CALL( cvEndWriteStruct( fs ));
|
||||
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||||
CV_CALL( cvStartWriteStruct( fs, "sum", CV_NODE_SEQ ));
|
||||
for( i = 0; i < nclasses; i++ )
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||||
CV_CALL( cvWrite( fs, NULL, sum[i] ));
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||||
CV_CALL( cvEndWriteStruct( fs ));
|
||||
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||||
CV_CALL( cvStartWriteStruct( fs, "productsum", CV_NODE_SEQ ));
|
||||
for( i = 0; i < nclasses; i++ )
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CV_CALL( cvWrite( fs, NULL, productsum[i] ));
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||||
CV_CALL( cvEndWriteStruct( fs ));
|
||||
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||||
CV_CALL( cvStartWriteStruct( fs, "avg", CV_NODE_SEQ ));
|
||||
for( i = 0; i < nclasses; i++ )
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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++ )
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CV_CALL( cvWrite( fs, NULL, inv_eigen_values[i] ));
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||||
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 );
|
||||
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||||
__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;
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results ) const
|
||||
{
|
||||
CvMat samples = _samples, results, *presults = 0;
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
return predict(&samples, presults);
|
||||
}
|
||||
|
||||
/* End of file. */
|
||||
|
||||
Reference in New Issue
Block a user