--- /dev/null
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// Intel License Agreement
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of Intel Corporation may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "_ml.h"
+
+CvNormalBayesClassifier::CvNormalBayesClassifier()
+{
+ 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";
+}
+
+
+void CvNormalBayesClassifier::clear()
+{
+ if( cls_labels )
+ {
+ for( int cls = 0; cls < cls_labels->cols; cls++ )
+ {
+ cvReleaseMat( &count[cls] );
+ cvReleaseMat( &sum[cls] );
+ cvReleaseMat( &productsum[cls] );
+ cvReleaseMat( &avg[cls] );
+ cvReleaseMat( &inv_eigen_values[cls] );
+ cvReleaseMat( &cov_rotate_mats[cls] );
+ }
+ }
+
+ cvReleaseMat( &cls_labels );
+ cvReleaseMat( &var_idx );
+ cvReleaseMat( &c );
+ cvFree( &count );
+}
+
+
+CvNormalBayesClassifier::~CvNormalBayesClassifier()
+{
+ clear();
+}
+
+
+CvNormalBayesClassifier::CvNormalBayesClassifier(
+ const CvMat* _train_data, const CvMat* _responses,
+ const CvMat* _var_idx, const CvMat* _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";
+
+ train( _train_data, _responses, _var_idx, _sample_idx );
+}
+
+
+bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _responses,
+ const CvMat* _var_idx, const CvMat* _sample_idx, bool update )
+{
+ const float min_variation = FLT_EPSILON;
+ bool result = false;
+ CvMat* responses = 0;
+ const float** train_data = 0;
+ CvMat* __cls_labels = 0;
+ CvMat* __var_idx = 0;
+ CvMat* cov = 0;
+
+ CV_FUNCNAME( "CvNormalBayesClassifier::train" );
+
+ __BEGIN__;
+
+ int cls, nsamples = 0, _var_count = 0, _var_all = 0, nclasses = 0;
+ int s, c1, c2;
+ const int* responses_data;
+
+ CV_CALL( cvPrepareTrainData( 0,
+ _train_data, CV_ROW_SAMPLE, _responses, CV_VAR_CATEGORICAL,
+ _var_idx, _sample_idx, false, &train_data,
+ &nsamples, &_var_count, &_var_all, &responses,
+ &__cls_labels, &__var_idx ));
+
+ if( !update )
+ {
+ const size_t mat_size = sizeof(CvMat*);
+ size_t data_size;
+
+ clear();
+
+ var_idx = __var_idx;
+ cls_labels = __cls_labels;
+ __var_idx = __cls_labels = 0;
+ var_count = _var_count;
+ var_all = _var_all;
+
+ nclasses = cls_labels->cols;
+ data_size = nclasses*6*mat_size;
+
+ 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( c = cvCreateMat( 1, nclasses, CV_64FC1 ));
+
+ for( cls = 0; cls < nclasses; cls++ )
+ {
+ CV_CALL(count[cls] = cvCreateMat( 1, var_count, CV_32SC1 ));
+ CV_CALL(sum[cls] = cvCreateMat( 1, var_count, CV_64FC1 ));
+ CV_CALL(productsum[cls] = cvCreateMat( var_count, var_count, CV_64FC1 ));
+ CV_CALL(avg[cls] = cvCreateMat( 1, var_count, CV_64FC1 ));
+ CV_CALL(inv_eigen_values[cls] = cvCreateMat( 1, var_count, CV_64FC1 ));
+ CV_CALL(cov_rotate_mats[cls] = cvCreateMat( var_count, var_count, CV_64FC1 ));
+ CV_CALL(cvZero( count[cls] ));
+ CV_CALL(cvZero( sum[cls] ));
+ CV_CALL(cvZero( productsum[cls] ));
+ CV_CALL(cvZero( avg[cls] ));
+ CV_CALL(cvZero( inv_eigen_values[cls] ));
+ CV_CALL(cvZero( cov_rotate_mats[cls] ));
+ }
+ }
+ else
+ {
+ // check that the new training data has the same dimensionality etc.
+ if( _var_count != var_count || _var_all != var_all || !((!_var_idx && !var_idx) ||
+ (_var_idx && var_idx && cvNorm(_var_idx,var_idx,CV_C) < DBL_EPSILON)) )
+ CV_ERROR( CV_StsBadArg,
+ "The new training data is inconsistent with the original training data" );
+
+ if( cls_labels->cols != __cls_labels->cols ||
+ cvNorm(cls_labels, __cls_labels, CV_C) > DBL_EPSILON )
+ CV_ERROR( CV_StsNotImplemented,
+ "In the current implementation the new training data must have absolutely "
+ "the same set of class labels as used in the original training data" );
+
+ nclasses = cls_labels->cols;
+ }
+
+ responses_data = responses->data.i;
+ CV_CALL( cov = cvCreateMat( _var_count, _var_count, CV_64FC1 ));
+
+ /* process train data (count, sum , productsum) */
+ for( s = 0; s < nsamples; s++ )
+ {
+ cls = responses_data[s];
+ int* count_data = count[cls]->data.i;
+ double* sum_data = sum[cls]->data.db;
+ double* prod_data = productsum[cls]->data.db;
+ const float* train_vec = train_data[s];
+
+ for( c1 = 0; c1 < _var_count; c1++, prod_data += _var_count )
+ {
+ double val1 = train_vec[c1];
+ sum_data[c1] += val1;
+ count_data[c1]++;
+ for( c2 = c1; c2 < _var_count; c2++ )
+ prod_data[c2] += train_vec[c2]*val1;
+ }
+ }
+
+ /* calculate avg, covariance matrix, c */
+ for( cls = 0; cls < nclasses; cls++ )
+ {
+ double det = 1;
+ int i, j;
+ CvMat* w = inv_eigen_values[cls];
+ int* count_data = count[cls]->data.i;
+ double* avg_data = avg[cls]->data.db;
+ double* sum1 = sum[cls]->data.db;
+
+ cvCompleteSymm( productsum[cls], 0 );
+
+ for( j = 0; j < _var_count; j++ )
+ {
+ int n = count_data[j];
+ avg_data[j] = n ? sum1[j] / n : 0.;
+ }
+
+ count_data = count[cls]->data.i;
+ avg_data = avg[cls]->data.db;
+ sum1 = sum[cls]->data.db;
+
+ for( i = 0; i < _var_count; i++ )
+ {
+ double* avg2_data = avg[cls]->data.db;
+ double* sum2 = sum[cls]->data.db;
+ double* prod_data = productsum[cls]->data.db + i*_var_count;
+ double* cov_data = cov->data.db + i*_var_count;
+ double s1val = sum1[j];
+ double avg1 = avg_data[i];
+ int count = count_data[i];
+
+ for( j = 0; j <= i; j++ )
+ {
+ double avg2 = avg2_data[j];
+ double cov_val = prod_data[j] - avg1 * sum2[j] - avg2 * s1val + avg1 * avg2 * count;
+ cov_val = (count > 1) ? cov_val / (count - 1) : cov_val;
+ cov_data[j] = cov_val;
+ }
+ }
+
+ CV_CALL( cvCompleteSymm( cov, 1 ));
+ CV_CALL( cvSVD( cov, w, cov_rotate_mats[cls], 0, CV_SVD_U_T ));
+ CV_CALL( cvMaxS( w, min_variation, w ));
+ for( j = 0; j < _var_count; j++ )
+ det *= w->data.db[j];
+
+ CV_CALL( cvDiv( NULL, w, w ));
+ c->data.db[cls] = log( det );
+ }
+
+ result = true;
+
+ __END__;
+
+ if( !result || cvGetErrStatus() < 0 )
+ clear();
+
+ cvReleaseMat( &cov );
+ cvReleaseMat( &__cls_labels );
+ cvReleaseMat( &__var_idx );
+ cvFree( &train_data );
+
+ return result;
+}
+
+
+float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results ) const
+{
+ float value = 0;
+ void* buffer = 0;
+ int allocated_buffer = 0;
+
+ CV_FUNCNAME( "CvNormalBayesClassifier::predict" );
+
+ __BEGIN__;
+
+ int i, j, k, cls = -1, _var_count, nclasses;
+ double opt = FLT_MAX;
+ CvMat diff;
+ int rtype = 0, rstep = 0, size;
+ const int* vidx = 0;
+
+ nclasses = cls_labels->cols;
+ _var_count = avg[0]->cols;
+
+ 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" );
+
+ rtype = CV_MAT_TYPE(results->type);
+ rstep = CV_IS_MAT_CONT(results->type) ? 1 : results->step/CV_ELEM_SIZE(rtype);
+ }
+
+ if( var_idx )
+ vidx = var_idx->data.i;
+
+// allocate memory and initializing headers for calculating
+ size = sizeof(double) * (nclasses + var_count);
+ if( size <= CV_MAX_LOCAL_SIZE )
+ buffer = cvStackAlloc( size );
+ else
+ {
+ CV_CALL( buffer = cvAlloc( size ));
+ allocated_buffer = 1;
+ }
+
+ diff = cvMat( 1, var_count, CV_64FC1, buffer );
+
+ for( k = 0; k < samples->rows; k++ )
+ {
+ int ival;
+
+ for( i = 0; i < nclasses; i++ )
+ {
+ double cur = c->data.db[i];
+ CvMat* u = cov_rotate_mats[i];
+ CvMat* w = inv_eigen_values[i];
+ 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'
+ for( j = 0; j < _var_count; j++ )
+ diff.data.db[j] = avg_data[j] - x[vidx ? vidx[j] : j];
+
+ CV_CALL(cvGEMM( &diff, u, 1, 0, 0, &diff, CV_GEMM_B_T ));
+ for( j = 0; j < _var_count; j++ )
+ {
+ 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 ) */
+ }
+
+ 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;
+ }
+ if( k == 0 )
+ value = (float)ival;
+
+ /*if( _probs )
+ {
+ CV_CALL( cvConvertScale( &expo, &expo, -0.5 ));
+ CV_CALL( cvExp( &expo, &expo ));
+ if( _probs->cols == 1 )
+ CV_CALL( cvReshape( &expo, &expo, 1, nclasses ));
+ CV_CALL( cvConvertScale( &expo, _probs, 1./cvSum( &expo ).val[0] ));
+ }*/
+ }
+
+ __END__;
+
+ if( allocated_buffer )
+ cvFree( &buffer );
+
+ 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;
+
+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. */
+
projects / opencv / blobdiff