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fixed test on em (old interface)

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pull/13383/head
Maria Dimashova 13 years ago
parent 965dbf3620
commit cdb3f11d5e
1 changed files with 31 additions and 27 deletions
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  1. 58
      modules/legacy/test/test_em.cpp

58
modules/legacy/test/test_em.cpp
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@ -45,45 +45,45 @@ using namespace std;
using namespace cv;
static
void defaultDistribs( Mat& means, vector<Mat>& covs )
void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 )
{
float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
means.create(3, 2, CV_32FC1);
means.create(3, 2, type);
Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
means.resize(3), covs.resize(3);
Mat mr0 = means.row(0);
m0.copyTo(mr0);
c0.copyTo(covs[0]);
m0.convertTo(mr0, type);
c0.convertTo(covs[0], type);
Mat mr1 = means.row(1);
m1.copyTo(mr1);
c1.copyTo(covs[1]);
m1.convertTo(mr1, type);
c1.convertTo(covs[1], type);
Mat mr2 = means.row(2);
m2.copyTo(mr2);
c2.copyTo(covs[2]);
m2.convertTo(mr2, type);
c2.convertTo(covs[2], type);
}
// generate points sets by normal distributions
static
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int labelType )
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType )
{
vector<int>::const_iterator sit = sizes.begin();
int total = 0;
for( ; sit != sizes.end(); ++sit )
total += *sit;
assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
assert( !data.empty() && data.rows == total );
assert( data.type() == CV_32FC1 );
CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
CV_Assert( !data.empty() && data.rows == total );
CV_Assert( data.type() == dataType );
labels.create( data.rows, 1, labelType );
randn( data, Scalar::all(0.0), Scalar::all(1.0) );
randn( data, Scalar::all(-1.0), Scalar::all(1.0) );
vector<Mat> means(sizes.size());
for(int i = 0; i < _means.rows; i++)
means[i] = _means.row(i);
@ -98,8 +98,8 @@ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat&
assert( cit->rows == data.cols && cit->cols == data.cols );
for( int i = bi; i < ei; i++, p++ )
{
Mat r(1, data.cols, CV_32FC1, data.ptr<float>(i));
r = r * (*cit) + *mit;
Mat r = data.row(i);
r = r * (*cit) + *mit;
if( labelType == CV_32FC1 )
labels.at<float>(p, 0) = (float)l;
else if( labelType == CV_32SC1 )
@ -129,7 +129,7 @@ int maxIdx( const vector<int>& count )
}
static
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap )
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq=true )
{
size_t total = 0, nclusters = sizes.size();
for(size_t i = 0; i < sizes.size(); i++)
@ -158,21 +158,25 @@ bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& lab
startIndex += sizes[clusterIndex];
int cls = maxIdx( count );
CV_Assert( !buzy[cls] );
CV_Assert( !checkClusterUniq || !buzy[cls] );
labelsMap[clusterIndex] = cls;
buzy[cls] = true;
}
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i])
return false;
if(checkClusterUniq)
{
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i])
return false;
}
return true;
}
static
bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true )
bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent, bool checkClusterUniq )
{
err = 0;
CV_Assert( !labels.empty() && !origLabels.empty() );
@ -186,7 +190,7 @@ bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes
bool isFlt = labels.type() == CV_32FC1;
if( !labelsEquivalent )
{
if( !getLabelsMap( labels, sizes, labelsMap ) )
if( !getLabelsMap( labels, sizes, labelsMap, checkClusterUniq ) )
return false;
for( int i = 0; i < labels.rows; i++ )
@ -234,7 +238,7 @@ int CV_CvEMTest::runCase( int caseIndex, const CvEMParams& params,
em.train( trainData, Mat(), params, &labels );
// check train error
if( !calcErr( labels, trainLabels, sizes, err , false ) )
if( !calcErr( labels, trainLabels, sizes, err , false, false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
@ -252,7 +256,7 @@ int CV_CvEMTest::runCase( int caseIndex, const CvEMParams& params,
Mat sample = testData.row(i);
labels.at<int>(i,0) = (int)em.predict( sample, 0 );
}
if( !calcErr( labels, testLabels, sizes, err, false ) )
if( !calcErr( labels, testLabels, sizes, err, false, false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
@ -279,11 +283,11 @@ void CV_CvEMTest::run( int /*start_from*/ )
// train data
Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
generateData( trainData, trainLabels, sizes, means, covs, CV_32SC1 );
generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32SC1 );
// test data
Mat testData( pointsCount, 2, CV_32FC1 ), testLabels;
generateData( testData, testLabels, sizes, means, covs, CV_32SC1 );
generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32SC1 );
CvEMParams params;
params.nclusters = 3;

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