Commit 570c8254 authored by Maria Dimashova's avatar Maria Dimashova

fixed test on em

parent 30f8d5a7
...@@ -129,7 +129,7 @@ int maxIdx( const vector<int>& count ) ...@@ -129,7 +129,7 @@ int maxIdx( const vector<int>& count )
} }
static 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(); size_t total = 0, nclusters = sizes.size();
for(size_t i = 0; i < sizes.size(); i++) for(size_t i = 0; i < sizes.size(); i++)
...@@ -158,21 +158,26 @@ bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& lab ...@@ -158,21 +158,26 @@ bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& lab
startIndex += sizes[clusterIndex]; startIndex += sizes[clusterIndex];
int cls = maxIdx( count ); int cls = maxIdx( count );
CV_Assert( !buzy[cls] ); if(checkClusterUniq)
CV_Assert( !buzy[cls] );
labelsMap[clusterIndex] = cls; labelsMap[clusterIndex] = cls;
buzy[cls] = true; buzy[cls] = true;
} }
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i]) if(checkClusterUniq)
return false; {
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i])
return false;
}
return true; return true;
} }
static 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 = true, bool checkClusterUniq=true )
{ {
err = 0; err = 0;
CV_Assert( !labels.empty() && !origLabels.empty() ); CV_Assert( !labels.empty() && !origLabels.empty() );
...@@ -186,7 +191,7 @@ bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes ...@@ -186,7 +191,7 @@ bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes
bool isFlt = labels.type() == CV_32FC1; bool isFlt = labels.type() == CV_32FC1;
if( !labelsEquivalent ) if( !labelsEquivalent )
{ {
if( !getLabelsMap( labels, sizes, labelsMap ) ) if( !getLabelsMap( labels, sizes, labelsMap, checkClusterUniq ) )
return false; return false;
for( int i = 0; i < labels.rows; i++ ) for( int i = 0; i < labels.rows; i++ )
...@@ -376,7 +381,7 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params, ...@@ -376,7 +381,7 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
em.trainM( trainData, *params.probs, labels ); em.trainM( trainData, *params.probs, labels );
// check train error // 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 ); ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT; code = cvtest::TS::FAIL_INVALID_OUTPUT;
...@@ -396,7 +401,7 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params, ...@@ -396,7 +401,7 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
Mat probs; Mat probs;
labels.at<int>(i,0) = (int)em.predict( sample, probs, &likelihood ); labels.at<int>(i,0) = (int)em.predict( sample, probs, &likelihood );
} }
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 ); ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT; code = cvtest::TS::FAIL_INVALID_OUTPUT;
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment