refactored likelihood computing

modules/ml/src/em.cpp

@@ -58,7 +58,7 @@ EM::EM(int _nclusters, int _covMatType, const TermCriteria& _criteria)
 
 EM::~EM()
 {
-    clear();
+    //clear();
 }
 
 void EM::clear()
@@ -322,6 +322,8 @@ void EM::clusterTrainSamples()
     int nsamples = trainSamples.rows;
 
     // Cluster samples, compute/update means
+
+    // Convert samples and means to 32F, because kmeans requires this type.
     Mat trainSamplesFlt, meansFlt;
     if(trainSamples.type() != CV_32FC1)
         trainSamples.convertTo(trainSamplesFlt, CV_32FC1);
@@ -338,6 +340,7 @@ void EM::clusterTrainSamples()
     Mat labels;
     kmeans(trainSamplesFlt, nclusters, labels,  TermCriteria(TermCriteria::COUNT, means.empty() ? 10 : 1, 0.5), 10, KMEANS_PP_CENTERS, meansFlt);
 
+    // Convert samples and means back to 64F.
     CV_Assert(meansFlt.type() == CV_32FC1);
     if(trainSamples.type() != CV_64FC1)
     {
@@ -476,6 +479,8 @@ void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double*
     // L_ik = log(weight_k) - 0.5 * log(|det(cov_k)|) - 0.5 *(x_i - mean_k)' cov_k^(-1) (x_i - mean_k)]
     // q = arg(max_k(L_ik))
     // probs_ik = exp(L_ik - L_iq) / (1 + sum_j!=q (exp(L_ij - L_iq))
+    // see Alex Smola's blog http://blog.smola.org/page/2 for
+    // details on the log-sum-exp trick
 
     CV_Assert(!means.empty());
     CV_Assert(sample.type() == CV_64FC1);
@@ -511,29 +516,22 @@ void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double*
     if(!probs && !logLikelihood)
         return;
 
-    Mat expL_Lmax(L.size(), CV_64FC1);
     double maxLVal = L.at<double>(label);
+    Mat expL_Lmax = L; // exp(L_ij - L_iq)
     for(int i = 0; i < L.cols; i++)
         expL_Lmax.at<double>(i) = std::exp(L.at<double>(i) - maxLVal);
-
-    double partSum = 0; // sum_j!=q (exp(L_ij - L_iq))
-    for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
-        if(clusterIndex != label)
-            partSum += expL_Lmax.at<double>(clusterIndex);
+    double expDiffSum = sum(expL_Lmax)[0]; // sum_j(exp(L_ij - L_iq))
 
     if(probs)
     {
         probs->create(1, nclusters, CV_64FC1);
-        double factor = 1./(1 + partSum);
+        double factor = 1./expDiffSum;
         expL_Lmax *= factor;
         expL_Lmax.copyTo(*probs);
     }
 
     if(logLikelihood)
-    {
-        double logWeightProbs = std::log((1 + partSum) * std::exp(maxLVal)) - 0.5 * dim * CV_LOG2PI;
-        *logLikelihood = logWeightProbs;
-    }
+        *logLikelihood = std::log(expDiffSum)  + maxLVal - 0.5 * dim * CV_LOG2PI;
 }
 
 void EM::eStep()