diff --git a/modules/ml/doc/decision_trees.rst b/modules/ml/doc/decision_trees.rst index ec9dc1d743..7212eb37b5 100644 --- a/modules/ml/doc/decision_trees.rst +++ b/modules/ml/doc/decision_trees.rst @@ -63,18 +63,35 @@ Importance of each variable is computed over all the splits on this variable in CvDTreeSplit ------------ -.. c:type:: struct CvDTreeSplit +.. c:type:: CvDTreeSplit Decision tree node split. The structure represents a possible decision tree node split. It has public members: - * ``int var_idx`` Index of variable on which the split is created. - * ``int inversed`` If it is not null then inverse split rule is used that is a left branch and a right branch are switched. - * ``float quality`` Quality of the split. - * ``CvDTreeSplit* next`` Pointer to the next split in the node list of splits. - * ``int subset[2]`` Parameters of the split on a categorical variable. - * ``struct {float c; int split_point;} ord`` Parameters of the split on ordered variable. +.. ocv:member:: int var_idx + + Index of variable on which the split is created. + +.. ocv:member:: int inversed + + If it is not null then inverse split rule is used that is a left branch and a right branch are switched. + +.. ocv:member:: float quality + + Quality of the split. + +.. ocv:member:: CvDTreeSplit* next + + Pointer to the next split in the node list of splits. + +.. ocv:member:: int subset[2] + + Parameters of the split on a categorical variable. + +.. ocv:member:: struct {float c; int split_point;} ord + + Parameters of the split on ordered variable. .. index:: CvDTreeNode @@ -83,20 +100,43 @@ The structure represents a possible decision tree node split. It has public memb CvDTreeNode ----------- -.. c:type:: struct CvDTreeNode +.. c:type:: CvDTreeNode Decision tree node. The structure represents a node in a decision tree. It has public members: - * ``int Tn`` Tree index in a sequence of pruned trees. Nodes with :math:`Tn \leq CvDTree::pruned\_tree\_idx` are not used at prediction stage (they are pruned). - * ``double value`` Value at the node: a class label in case of classification or estimated function value in case of regression. - * ``CvDTreeNode* parent`` Pointer to the parent node. - * ``CvDTreeNode* left`` Pointer to the left child node. - * ``CvDTreeNode* right`` Pointer to the right child node. - * ``CvDTreeSplit* split`` Pointer to the first (primary) split in the node list of splits. - * ``int sample_count`` Number of samples in the node. - * ``int depth`` Depth of the node. +.. ocv:member:: int Tn + + Tree index in a sequence of pruned trees. Nodes with :math:`Tn \leq CvDTree::pruned\_tree\_idx` are not used at prediction stage (they are pruned). + +.. ocv:member:: double value + + Value at the node: a class label in case of classification or estimated function value in case of regression. + +.. ocv:member:: CvDTreeNode* parent + + Pointer to the parent node. + +.. ocv:mebmer:: CvDTreeNode* left + + Pointer to the left child node. + +.. ocv:member:: CvDTreeNode* right + + Pointer to the right child node. + +.. ocv:member:: CvDTreeSplit* split + + Pointer to the first (primary) split in the node list of splits. + +.. ocv:mebmer:: int sample_count + + Number of samples in the node. + +.. ocv:member:: int depth + + Depth of the node. Other numerous fields of ``CvDTreeNode`` are used internally at the training stage. @@ -107,7 +147,7 @@ Other numerous fields of ``CvDTreeNode`` are used internally at the training sta CvDTreeParams ------------- -.. c:type:: struct CvDTreeParams +.. c:type:: CvDTreeParams Decision tree training parameters. @@ -157,7 +197,7 @@ The default constructor initializes all the parameters with the default values t CvDTreeTrainData ---------------- -.. c:type:: struct CvDTreeTrainData +.. c:type:: CvDTreeTrainData Decision tree training data and shared data for tree ensembles. @@ -188,7 +228,7 @@ There are two ways of using this structure. In simple cases (for example, a stan CvDTree ------- -.. ocv:class:: class CvDTree : public CvStatModel +.. ocv:class:: CvDTree Decision tree. diff --git a/modules/ml/doc/random_trees.rst b/modules/ml/doc/random_trees.rst index d8ee76b27e..37e17d334b 100644 --- a/modules/ml/doc/random_trees.rst +++ b/modules/ml/doc/random_trees.rst @@ -53,7 +53,7 @@ In random trees there is no need for any accuracy estimation procedures, such as CvRTParams ---------- -.. ocv:class:: struct CvRTParams : public CvDTreeParams +.. ocv:class:: CvRTParams Training parameters of random trees. @@ -97,7 +97,7 @@ The default constructor sets all parameters to some default values and they are CvRTrees -------- -.. ocv:class:: class CvRTrees : public CvStatModel +.. ocv:class:: CvRTrees Random trees. diff --git a/modules/ml/doc/statistical_models.rst b/modules/ml/doc/statistical_models.rst index 4c3cd450a2..66b2256c9c 100644 --- a/modules/ml/doc/statistical_models.rst +++ b/modules/ml/doc/statistical_models.rst @@ -97,7 +97,7 @@ CvStatModel::clear Deallocates memory and resets the model state. -The method ``clear`` does the same job as the destructor: it deallocates all the memory occupied by the class members. But the object itself is not destructed and can be reused further. This method is called from the destructor, from the ``train`` methods of the derived classes, from the methods ``load()``,``read()`` , or even explicitly by the user. +The method ``clear`` does the same job as the destructor: it deallocates all the memory occupied by the class members. But the object itself is not destructed and can be reused further. This method is called from the destructor, from the ``train`` methods of the derived classes, from the methods ``load()``, ``read()``, or even explicitly by the user. .. index:: CvStatModel::save @@ -189,7 +189,7 @@ Usually, the previous model state is cleared by ``clear()`` before running the t CvStatModel::predict -------------------- -.. ocv:function:: float CvStatMode::predict( const Mat& sample[, ] ) const +.. ocv:function:: float CvStatModel::predict( const Mat& sample[, ] ) const Predicts the response for a sample.