Minor doc fixes

doc/check_docs_whitelist.txt

@@ -43,8 +43,6 @@ Mat cv::findFundamentalMat( InputArray points1, InputArray points2, OutputArray
 Mat findHomography( InputArray srcPoints, InputArray dstPoints, OutputArray mask, int method=0, double ransacReprojThreshold=3);
 
 ########################################## ml ######################################
-cvParamLattice
-cvDefaultParamLattice
 CvBoostTree
 CvForestTree
 CvSVMKernel
@@ -53,6 +51,8 @@ CvDTreeTrainData
 CvERTreeTrainData
 CvKNearest::CvKNearest
 CvKNearest::clear
+CvDTreeNode::get_num_valid
+CvDTreeNode::set_num_valid
 CvDTree::CvDTree
 CvDTree::clear
 CvDTree::read
@@ -87,3 +87,5 @@ CvANN_MLP::clear
 CvANN_MLP::read
 CvANN_MLP::write
 CvTrainTestSplit
+cvParamLattice
+cvDefaultParamLattice

modules/ml/doc/decision_trees.rst

@@ -247,7 +247,7 @@ CvDTree::train
 
 .. ocv:function:: bool CvDTree::train( CvMLData* trainData, CvDTreeParams params=CvDTreeParams() )
 
-.. ocv:function:: bool CvDTree::train( CvDTreeTrainData* train_data, const Mat& subsample_idx )
+.. ocv:function:: bool CvDTree::train( CvDTreeTrainData* trainData, const CvMat* subsampleIdx )
 
     Trains a decision tree.
 
@@ -257,7 +257,7 @@ There are four ``train`` methods in :ocv:class:`CvDTree`:
 
 * The **third** method uses :ocv:class:`CvMLData` to pass training data to a decision tree.
 
-* The **last** method ``train`` is mostly used for building tree ensembles. It takes the pre-constructed :ref:`CvDTreeTrainData` instance and an optional subset of the training set. The indices in ``subsample_idx`` are counted relatively to the ``_sample_idx`` , passed to the ``CvDTreeTrainData`` constructor. For example, if ``_sample_idx=[1, 5, 7, 100]`` , then ``subsample_idx=[0,3]`` means that the samples ``[1, 100]`` of the original training set are used.
+* The **last** method ``train`` is mostly used for building tree ensembles. It takes the pre-constructed :ref:`CvDTreeTrainData` instance and an optional subset of the training set. The indices in ``subsampleIdx`` are counted relatively to the ``_sample_idx`` , passed to the ``CvDTreeTrainData`` constructor. For example, if ``_sample_idx=[1, 5, 7, 100]`` , then ``subsampleIdx=[0,3]`` means that the samples ``[1, 100]`` of the original training set are used.
 
 
 .. index:: CvDTree::predict

modules/ml/doc/expectation_maximization.rst

@@ -102,9 +102,9 @@ All parameters are public. You can initialize them by a constructor and then ove
 
 CvEMParams::CvEMParams
 ----------------------
-.. ocv:function:: CvEMParams()
+.. ocv:function:: CvEMParams::CvEMParams()
 
-.. ocv:function:: CvEMParams( int nclusters, int cov_mat_type=1/*CvEM::COV_MAT_DIAGONAL*/, int start_step=0/*CvEM::START_AUTO_STEP*/, CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON), const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 ) 
+.. ocv:function:: CvEMParams::CvEMParams( int nclusters, int cov_mat_type=1/*CvEM::COV_MAT_DIAGONAL*/, int start_step=0/*CvEM::START_AUTO_STEP*/, CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON), const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 ) 
 
     :param nclusters: The number of mixtures in the gaussian mixture model.
 

modules/ml/doc/gradient_boosted_trees.rst

@@ -238,9 +238,9 @@ GBT model. ::
 
 CvGBTrees::train
 ----------------
-.. ocv:function:: bool train(const Mat & trainData, int tflag, const Mat & responses, const Mat & varIdx=Mat(), const Mat & sampleIdx=Mat(), const Mat & varType=Mat(), const Mat & missingDataMask=Mat(), CvGBTreesParams params=CvGBTreesParams(), bool update=false)
+.. ocv:function:: bool CvGBTrees::train(const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvGBTreesParams params=CvGBTreesParams(), bool update=false)
 
-.. ocv:function:: bool train(CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
+.. ocv:function:: bool CvGBTrees::train(CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
     
 	Trains a Gradient boosted tree model.
 	
@@ -265,7 +265,7 @@ as a :ocv:class:`CvGBTreesParams` structure.
 
 CvGBTrees::predict
 ------------------
-.. ocv:function:: float predict(const Mat & sample, const Mat & missing=Mat(), const Range & slice = Range::all(), int k=-1) const
+.. ocv:function:: float CvGBTrees::predict(const Mat& sample, const Mat& missing=Mat(), const Range& slice = Range::all(), int k=-1) const
 
     Predicts a response for an input sample.
  
@@ -308,7 +308,7 @@ of single trees are computed in a parallel fashion.
 
 CvGBTrees::clear
 ----------------
-.. ocv:function:: void clear()
+.. ocv:function:: void CvGBTrees::clear()
 
     Clears the model.
     
@@ -323,7 +323,7 @@ destructor.
 
 CvGBTrees::calc_error
 ---------------------
-.. ocv:function:: float calc_error( CvMLData* _data, int type, std::vector<float> *resp = 0 )
+.. ocv:function:: float CvGBTrees::calc_error( CvMLData* _data, int type, std::vector<float> *resp = 0 )
 
     Calculates a training or testing error.
     
@@ -340,4 +340,4 @@ used to get a training/testing error easily and (optionally) all predictions
 on the training/testing set. If the Intel* TBB* library is used, the error is computed in a
 parallel way, namely, predictions for different samples are computed at the same time.
 In case of a regression problem, a mean squared error is returned. For
-classifications, the result is a misclassification error in percent.
\ No newline at end of file
+classifications, the result is a misclassification error in percent.

modules/ml/doc/random_trees.rst

@@ -114,7 +114,7 @@ CvRTrees::train
 
 .. ocv:function:: bool CvRTrees::train( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvRTParams params=CvRTParams() )
 
-.. ocv:function:: bool CvRTress::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvRTParams params=CvRTParams() )
+.. ocv:function:: bool CvRTrees::train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvRTParams params=CvRTParams() )
 
     Trains the Random Tree model.