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#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 )
{
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();
}
/* End of file. */