finished for one sample

Finished with several samples support, need regression testing

Gave a more relevant name to function (getVotes)

Finished implicit implementation

Removed printf, finished regresion testing

Fixed conversion warning

Finished test for Rtrees

Fixed documentation

Initialized variable

Added doxygen documentation

Added parameter name

modules/ml/src/rtrees.cpp

@@ -349,6 +349,60 @@ public:
         }
     }
 
+    void getVotes( InputArray input, OutputArray output, int flags ) const
+    {
+        CV_Assert( !roots.empty() );
+        int nclasses = (int)classLabels.size(), ntrees = (int)roots.size();
+        Mat samples = input.getMat(), results;
+        int i, j, nsamples = samples.rows;
+
+        int predictType = flags & PREDICT_MASK;
+        if( predictType == PREDICT_AUTO )
+        {
+            predictType = !_isClassifier || (classLabels.size() == 2 && (flags & RAW_OUTPUT) != 0) ?
+                PREDICT_SUM : PREDICT_MAX_VOTE;
+        }
+
+        if( predictType == PREDICT_SUM )
+        {
+            output.create(nsamples, ntrees, CV_32F);
+            results = output.getMat();
+            for( i = 0; i < nsamples; i++ )
+            {
+                for( j = 0; j < ntrees; j++ )
+                {
+                    float val = predictTrees( Range(j, j+1), samples.row(i), flags);
+                    results.at<float> (i, j) = val;
+                }
+            }
+        } else
+        {
+            vector<int> votes;
+            output.create(nsamples+1, nclasses, CV_32S);
+            results = output.getMat();
+
+            for ( j = 0; j < nclasses; j++)
+            {
+                results.at<int> (0, j) = classLabels[j];
+            }
+
+            for( i = 0; i < nsamples; i++ )
+            {
+                votes.clear();
+                for( j = 0; j < ntrees; j++ )
+                {
+                    int val = (int)predictTrees( Range(j, j+1), samples.row(i), flags);
+                    votes.push_back(val);
+                }
+
+                for ( j = 0; j < nclasses; j++)
+                {
+                    results.at<int> (i+1, j) = (int)std::count(votes.begin(), votes.end(), classLabels[j]);
+                }
+            }
+        }
+    }
+
     RTreeParams rparams;
     double oobError;
     vector<float> varImportance;
@@ -401,6 +455,11 @@ public:
         impl.read(fn);
     }
 
+    void getVotes_( InputArray samples, OutputArray results, int flags ) const
+    {
+      impl.getVotes(samples, results, flags);
+    }
+
     Mat getVarImportance() const { return Mat_<float>(impl.varImportance, true); }
     int getVarCount() const { return impl.getVarCount(); }
 
@@ -427,6 +486,14 @@ Ptr<RTrees> RTrees::load(const String& filepath, const String& nodeName)
     return Algorithm::load<RTrees>(filepath, nodeName);
 }
 
+void RTrees::getVotes(InputArray input, OutputArray output, int flags) const
+{
+    const RTreesImpl* this_ = dynamic_cast<const RTreesImpl*>(this);
+    if(!this_)
+        CV_Error(Error::StsNotImplemented, "the class is not RTreesImpl");
+    return this_->getVotes_(input, output, flags);
+}
+
 }}
 
 // End of file.