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initial commit; ml has been refactored; it compiles and the tests run well; some other modules, apps and samples do not compile; to be fixed

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This commit is contained in:
Vadim Pisarevsky
2014-07-29 23:54:23 +04:00
parent dce1824a91
commit ba3783d205
25 changed files with 8320 additions and 21792 deletions
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155
modules/ml/src/testset.cpp
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@@ -40,131 +40,74 @@
#include "precomp.hpp"
typedef struct CvDI
namespace cv { namespace ml {
struct PairDI
{
double d;
int i;
} CvDI;
};
static int CV_CDECL
icvCmpDI( const void* a, const void* b, void* )
struct CmpPairDI
{
const CvDI* e1 = (const CvDI*) a;
const CvDI* e2 = (const CvDI*) b;
bool operator ()(const PairDI& e1, const PairDI& e2) const
{
return (e1.d < e2.d) || (e1.d == e2.d && e1.i < e2.i);
}
};
return (e1->d < e2->d) ? -1 : (e1->d > e2->d);
}
CV_IMPL void
cvCreateTestSet( int type, CvMat** samples,
int num_samples,
int num_features,
CvMat** responses,
int num_classes, ... )
void createConcentricSpheresTestSet( int num_samples, int num_features, int num_classes,
OutputArray _samples, OutputArray _responses)
{
CvMat* mean = NULL;
CvMat* cov = NULL;
CvMemStorage* storage = NULL;
CV_FUNCNAME( "cvCreateTestSet" );
__BEGIN__;
if( samples )
*samples = NULL;
if( responses )
*responses = NULL;
if( type != CV_TS_CONCENTRIC_SPHERES )
CV_ERROR( CV_StsBadArg, "Invalid type parameter" );
if( !samples )
CV_ERROR( CV_StsNullPtr, "samples parameter must be not NULL" );
if( !responses )
CV_ERROR( CV_StsNullPtr, "responses parameter must be not NULL" );
if( num_samples < 1 )
CV_ERROR( CV_StsBadArg, "num_samples parameter must be positive" );
CV_Error( CV_StsBadArg, "num_samples parameter must be positive" );
if( num_features < 1 )
CV_ERROR( CV_StsBadArg, "num_features parameter must be positive" );
CV_Error( CV_StsBadArg, "num_features parameter must be positive" );
if( num_classes < 1 )
CV_ERROR( CV_StsBadArg, "num_classes parameter must be positive" );
CV_Error( CV_StsBadArg, "num_classes parameter must be positive" );
if( type == CV_TS_CONCENTRIC_SPHERES )
int i, cur_class;
_samples.create( num_samples, num_features, CV_32F );
_responses.create( 1, num_samples, CV_32S );
Mat responses = _responses.getMat();
Mat mean = Mat::zeros(1, num_features, CV_32F);
Mat cov = Mat::eye(num_features, num_features, CV_32F);
// fill the feature values matrix with random numbers drawn from standard normal distribution
randMVNormal( mean, cov, num_samples, _samples );
Mat samples = _samples.getMat();
// calculate distances from the origin to the samples and put them
// into the sequence along with indices
std::vector<PairDI> dis(samples.rows);
for( i = 0; i < samples.rows; i++ )
{
CvSeqWriter writer;
CvSeqReader reader;
CvMat sample;
CvDI elem;
CvSeq* seq = NULL;
int i, cur_class;
CV_CALL( *samples = cvCreateMat( num_samples, num_features, CV_32FC1 ) );
CV_CALL( *responses = cvCreateMat( 1, num_samples, CV_32SC1 ) );
CV_CALL( mean = cvCreateMat( 1, num_features, CV_32FC1 ) );
CV_CALL( cvSetZero( mean ) );
CV_CALL( cov = cvCreateMat( num_features, num_features, CV_32FC1 ) );
CV_CALL( cvSetIdentity( cov ) );
/* fill the feature values matrix with random numbers drawn from standard
normal distribution */
CV_CALL( cvRandMVNormal( mean, cov, *samples ) );
/* calculate distances from the origin to the samples and put them
into the sequence along with indices */
CV_CALL( storage = cvCreateMemStorage() );
CV_CALL( cvStartWriteSeq( 0, sizeof( CvSeq ), sizeof( CvDI ), storage, &writer ));
for( i = 0; i < (*samples)->rows; ++i )
{
CV_CALL( cvGetRow( *samples, &sample, i ));
elem.i = i;
CV_CALL( elem.d = cvNorm( &sample, NULL, CV_L2 ));
CV_WRITE_SEQ_ELEM( elem, writer );
}
CV_CALL( seq = cvEndWriteSeq( &writer ) );
/* sort the sequence in a distance ascending order */
CV_CALL( cvSeqSort( seq, icvCmpDI, NULL ) );
/* assign class labels */
num_classes = MIN( num_samples, num_classes );
CV_CALL( cvStartReadSeq( seq, &reader ) );
CV_READ_SEQ_ELEM( elem, reader );
for( i = 0, cur_class = 0; i < num_samples; ++cur_class )
{
int last_idx;
double max_dst;
last_idx = num_samples * (cur_class + 1) / num_classes - 1;
CV_CALL( max_dst = (*((CvDI*) cvGetSeqElem( seq, last_idx ))).d );
max_dst = MAX( max_dst, elem.d );
for( ; elem.d <= max_dst && i < num_samples; ++i )
{
CV_MAT_ELEM( **responses, int, 0, elem.i ) = cur_class;
if( i < num_samples - 1 )
{
CV_READ_SEQ_ELEM( elem, reader );
}
}
}
PairDI& elem = dis[i];
elem.i = i;
elem.d = norm(samples.row(i), NORM_L2);
}
__END__;
std::sort(dis.begin(), dis.end(), CmpPairDI());
if( cvGetErrStatus() < 0 )
// assign class labels
num_classes = std::min( num_samples, num_classes );
for( i = 0, cur_class = 0; i < num_samples; ++cur_class )
{
if( samples )
cvReleaseMat( samples );
if( responses )
cvReleaseMat( responses );
int last_idx = num_samples * (cur_class + 1) / num_classes - 1;
double max_dst = dis[last_idx].d;
max_dst = std::max( max_dst, dis[i].d );
for( ; i < num_samples && dis[i].d <= max_dst; ++i )
responses.at<int>(i) = cur_class;
}
cvReleaseMat( &mean );
cvReleaseMat( &cov );
cvReleaseMemStorage( &storage );
}
}}
/* End of file. */