moved to double in EM; fixed bug

modules/legacy/src/em.cpp

@@ -205,6 +205,7 @@ CvEM::CvEM( const Mat& samples, const Mat& sample_idx, CvEMParams params )
 bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
                  CvEMParams _params, Mat* _labels )
 {
+    CV_Assert(_sample_idx.empty());
     Mat prbs, weights, means, likelihoods;
     std::vector<Mat> covsHdrs;
     init_params(_params, prbs, weights, means, covsHdrs);

modules/ml/include/opencv2/ml/ml.hpp

@@ -578,7 +578,7 @@ public:
     CV_WRAP virtual bool train(InputArray samples,
                        OutputArray labels=noArray(),
                        OutputArray probs=noArray(),
-                       OutputArray likelihoods=noArray());
+                       OutputArray logLikelihoods=noArray());
     
     CV_WRAP virtual bool trainE(InputArray samples,
                         InputArray means0,
@@ -586,17 +586,17 @@ public:
                         InputArray weights0=noArray(),
                         OutputArray labels=noArray(),
                         OutputArray probs=noArray(),
-                        OutputArray likelihoods=noArray());
+                        OutputArray logLikelihoods=noArray());
     
     CV_WRAP virtual bool trainM(InputArray samples,
                         InputArray probs0,
                         OutputArray labels=noArray(),
                         OutputArray probs=noArray(),
-                        OutputArray likelihoods=noArray());
+                        OutputArray logLikelihoods=noArray());
     
     CV_WRAP int predict(InputArray sample,
                 OutputArray probs=noArray(),
-                CV_OUT double* likelihood=0) const;
+                CV_OUT double* logLikelihood=0) const;
 
     CV_WRAP bool isTrained() const;
 
@@ -614,7 +614,7 @@ protected:
     bool doTrain(int startStep,
                  OutputArray labels,
                  OutputArray probs,
-                 OutputArray likelihoods);
+                 OutputArray logLikelihoods);
     virtual void eStep();
     virtual void mStep();
 
@@ -622,9 +622,9 @@ protected:
     void decomposeCovs();
     void computeLogWeightDivDet();
 
-    void computeProbabilities(const Mat& sample, int& label, Mat* probs, float* likelihood) const;
+    void computeProbabilities(const Mat& sample, int& label, Mat* probs, double* logLikelihood) const;
 
-    // all inner matrices have type CV_32FC1
+    // all inner matrices have type CV_64FC1
     CV_PROP_RW int nclusters;
     CV_PROP_RW int covMatType;
     CV_PROP_RW int maxIters;
@@ -632,7 +632,7 @@ protected:
 
     Mat trainSamples;
     Mat trainProbs;
-    Mat trainLikelihoods;
+    Mat trainLogLikelihoods;
     Mat trainLabels;
     Mat trainCounts;
 

modules/ml/test/test_emknearestkmeans.cpp

@@ -45,33 +45,33 @@ 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;
@@ -79,7 +79,7 @@ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat&
         total += *sit;
     assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
     assert( !data.empty() && data.rows == total );
-    assert( data.type() == CV_32FC1 );
+    assert( data.type() == dataType );
     
     labels.create( data.rows, 1, labelType );
 
@@ -98,7 +98,7 @@ 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));
+            Mat r = data.row(i);
             r =  r * (*cit) + *mit; 
             if( labelType == CV_32FC1 )
                 labels.at<float>(p, 0) = (float)l;
@@ -226,7 +226,7 @@ void CV_KMeansTest::run( int /*start_from*/ )
     Mat means;
     vector<Mat> covs;
     defaultDistribs( means, covs );
-    generateData( data, labels, sizes, means, covs, CV_32SC1 );
+    generateData( data, labels, sizes, means, covs, CV_32FC1, CV_32SC1 );
     
     int code = cvtest::TS::OK;
     float err;
@@ -296,11 +296,11 @@ void CV_KNearestTest::run( int /*start_from*/ )
     Mat means;
     vector<Mat> covs;
     defaultDistribs( means, covs );
-    generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1 );
+    generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
 
     // test data
     Mat testData( pointsCount, 2, CV_32FC1 ), testLabels, bestLabels;
-    generateData( testData, testLabels, sizes, means, covs, CV_32FC1 );
+    generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
 
     int code = cvtest::TS::OK;
     KNearest knearest;
@@ -392,7 +392,9 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
     for( int i = 0; i < testData.rows; i++ )
     {
         Mat sample = testData.row(i);
-        labels.at<int>(i,0) = (int)em.predict( sample, noArray() );
+        double likelihood = 0;
+        Mat probs;
+        labels.at<int>(i,0) = (int)em.predict( sample, probs, &likelihood );
     }
     if( !calcErr( labels, testLabels, sizes, err, false ) )
     {
@@ -416,22 +418,22 @@ void CV_EMTest::run( int /*start_from*/ )
     // Points distribution
     Mat means;
     vector<Mat> covs;
-    defaultDistribs( means, covs );
+    defaultDistribs( means, covs, CV_64FC1 );
 
     // train data
-    Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
+    Mat trainData( pointsCount, 2, CV_64FC1 ), 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_64FC1, CV_32SC1 );
 
     // test data
-    Mat testData( pointsCount, 2, CV_32FC1 ), testLabels;
-    generateData( testData, testLabels, sizes, means, covs, CV_32SC1 );
+    Mat testData( pointsCount, 2, CV_64FC1 ), testLabels;
+    generateData( testData, testLabels, sizes, means, covs, CV_64FC1, CV_32SC1 );
 
     EM_Params params;
     params.nclusters = 3;
-    Mat probs(trainData.rows, params.nclusters, CV_32FC1, cv::Scalar(1));
+    Mat probs(trainData.rows, params.nclusters, CV_64FC1, cv::Scalar(1));
     params.probs = &probs;
-    Mat weights(1, params.nclusters, CV_32FC1, cv::Scalar(1));
+    Mat weights(1, params.nclusters, CV_64FC1, cv::Scalar(1));
     params.weights = &weights;
     params.means = &means;
     params.covs = &covs;
@@ -505,18 +507,18 @@ protected:
         int code = cvtest::TS::OK;
         cv::EM em(2);
 
-        Mat samples = Mat(3,1,CV_32F);
-        samples.at<float>(0,0) = 1;
-        samples.at<float>(1,0) = 2;
-        samples.at<float>(2,0) = 3;
+        Mat samples = Mat(3,1,CV_64FC1);
+        samples.at<double>(0,0) = 1;
+        samples.at<double>(1,0) = 2;
+        samples.at<double>(2,0) = 3;
 
         Mat labels;
 
         em.train(samples, labels);
 
-        Mat firstResult(samples.rows, 1, CV_32FC1);
+        Mat firstResult(samples.rows, 1, CV_32SC1);
         for( int i = 0; i < samples.rows; i++)
-            firstResult.at<float>(i) = (float)em.predict( samples.row(i) );
+            firstResult.at<int>(i) = em.predict(samples.row(i));
 
         // Write out
         string filename = tempfile() + ".xml";
@@ -557,7 +559,7 @@ protected:
 
         int errCaseCount = 0;
         for( int i = 0; i < samples.rows; i++)
-            errCaseCount = std::abs(em.predict(samples.row(i)) - firstResult.at<float>(i)) < FLT_EPSILON ? 0 : 1;
+            errCaseCount = std::abs(em.predict(samples.row(i)) - firstResult.at<int>(i)) < FLT_EPSILON ? 0 : 1;
 
         if( errCaseCount > 0 )
         {