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Updated ml module interfaces and documentation

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This commit is contained in:
Maksim Shabunin
2015-02-11 13:24:14 +03:00
parent da383e65e2
commit 79e8f0680c
32 changed files with 1403 additions and 1528 deletions
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32
modules/ml/test/test_emknearestkmeans.cpp
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@@ -330,7 +330,8 @@ void CV_KNearestTest::run( int /*start_from*/ )
}
// KNearest KDTree implementation
Ptr<KNearest> knearestKdt = KNearest::create(ml::KNearest::Params(10, true, INT_MAX, ml::KNearest::KDTREE));
Ptr<KNearest> knearestKdt = KNearest::create();
knearestKdt->setAlgorithmType(KNearest::KDTREE);
knearestKdt->train(trainData, ml::ROW_SAMPLE, trainLabels);
knearestKdt->findNearest(testData, 4, bestLabels);
if( !calcErr( bestLabels, testLabels, sizes, err, true ) )
@@ -394,16 +395,18 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
cv::Mat labels;
float err;
Ptr<EM> em;
EM::Params emp(params.nclusters, params.covMatType, params.termCrit);
Ptr<EM> em = EM::create();
em->setClustersNumber(params.nclusters);
em->setCovarianceMatrixType(params.covMatType);
em->setTermCriteria(params.termCrit);
if( params.startStep == EM::START_AUTO_STEP )
em = EM::train( trainData, noArray(), labels, noArray(), emp );
em->trainEM( trainData, noArray(), labels, noArray() );
else if( params.startStep == EM::START_E_STEP )
em = EM::train_startWithE( trainData, *params.means, *params.covs,
*params.weights, noArray(), labels, noArray(), emp );
em->trainE( trainData, *params.means, *params.covs,
*params.weights, noArray(), labels, noArray() );
else if( params.startStep == EM::START_M_STEP )
em = EM::train_startWithM( trainData, *params.probs,
noArray(), labels, noArray(), emp );
em->trainM( trainData, *params.probs,
noArray(), labels, noArray() );
// check train error
if( !calcErr( labels, trainLabels, sizes, err , false, false ) )
@@ -543,7 +546,9 @@ protected:
Mat labels;
Ptr<EM> em = EM::train(samples, noArray(), labels, noArray(), EM::Params(nclusters));
Ptr<EM> em = EM::create();
em->setClustersNumber(nclusters);
em->trainEM(samples, noArray(), labels, noArray());
Mat firstResult(samples.rows, 1, CV_32SC1);
for( int i = 0; i < samples.rows; i++)
@@ -644,8 +649,13 @@ protected:
samples1.push_back(sample);
}
}
Ptr<EM> model0 = EM::train(samples0, noArray(), noArray(), noArray(), EM::Params(3));
Ptr<EM> model1 = EM::train(samples1, noArray(), noArray(), noArray(), EM::Params(3));
Ptr<EM> model0 = EM::create();
model0->setClustersNumber(3);
model0->trainEM(samples0, noArray(), noArray(), noArray());
Ptr<EM> model1 = EM::create();
model1->setClustersNumber(3);
model1->trainEM(samples1, noArray(), noArray(), noArray());
Mat trainConfusionMat(2, 2, CV_32SC1, Scalar(0)),
testConfusionMat(2, 2, CV_32SC1, Scalar(0));