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Documentation: fixed class/struc members documentation; added warning on incorrectly documented member
This commit is contained in:
Andrey Kamaev
+11
-11
@@ -69,6 +69,17 @@ CvBoostParams
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Boosting training parameters.
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There is one structure member that you can set directly:
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.. ocv:member:: int split_criteria
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Splitting criteria used to choose optimal splits during a weak tree construction. Possible values are:
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* **CvBoost::DEFAULT** Use the default for the particular boosting method, see below.
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* **CvBoost::GINI** Use Gini index. This is default option for Real AdaBoost; may be also used for Discrete AdaBoost.
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* **CvBoost::MISCLASS** Use misclassification rate. This is default option for Discrete AdaBoost; may be also used for Real AdaBoost.
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* **CvBoost::SQERR** Use least squares criteria. This is default and the only option for LogitBoost and Gentle AdaBoost.
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The structure is derived from :ocv:class:`CvDTreeParams` but not all of the decision tree parameters are supported. In particular, cross-validation is not supported.
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All parameters are public. You can initialize them by a constructor and then override some of them directly if you want.
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@@ -96,17 +107,6 @@ The constructors.
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See :ocv:func:`CvDTreeParams::CvDTreeParams` for description of other parameters.
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Also there is one structure member that you can set directly:
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.. ocv:member:: int split_criteria
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Splitting criteria used to choose optimal splits during a weak tree construction. Possible values are:
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* **CvBoost::DEFAULT** Use the default for the particular boosting method, see below.
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* **CvBoost::GINI** Use Gini index. This is default option for Real AdaBoost; may be also used for Discrete AdaBoost.
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* **CvBoost::MISCLASS** Use misclassification rate. This is default option for Discrete AdaBoost; may be also used for Real AdaBoost.
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* **CvBoost::SQERR** Use least squares criteria. This is default and the only option for LogitBoost and Gentle AdaBoost.
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Default parameters are:
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::
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@@ -60,84 +60,84 @@ CvDTreeSplit
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.. ocv:struct:: CvDTreeSplit
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The structure represents a possible decision tree node split. It has public members:
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The structure represents a possible decision tree node split. It has public members:
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.. ocv:member:: int var_idx
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.. ocv:member:: int var_idx
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Index of variable on which the split is created.
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Index of variable on which the split is created.
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.. ocv:member:: int inversed
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.. ocv:member:: int inversed
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If it is not null then inverse split rule is used that is left and right branches are exchanged in the rule expressions below.
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If it is not null then inverse split rule is used that is left and right branches are exchanged in the rule expressions below.
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.. ocv:member:: float quality
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.. ocv:member:: float quality
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The split quality, a positive number. It is used to choose the best primary split, then to choose and sort the surrogate splits. After the tree is constructed, it is also used to compute variable importance.
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The split quality, a positive number. It is used to choose the best primary split, then to choose and sort the surrogate splits. After the tree is constructed, it is also used to compute variable importance.
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.. ocv:member:: CvDTreeSplit* next
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.. ocv:member:: CvDTreeSplit* next
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Pointer to the next split in the node list of splits.
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Pointer to the next split in the node list of splits.
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.. ocv:member:: int[] subset
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.. ocv:member:: int[] subset
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Bit array indicating the value subset in case of split on a categorical variable. The rule is: ::
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Bit array indicating the value subset in case of split on a categorical variable. The rule is: ::
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if var_value in subset
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then next_node <- left
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else next_node <- right
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.. ocv:member:: float ord::c
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.. ocv:member:: float ord::c
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The threshold value in case of split on an ordered variable. The rule is: ::
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The threshold value in case of split on an ordered variable. The rule is: ::
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if var_value < ord.c
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then next_node<-left
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else next_node<-right
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.. ocv:member:: int ord::split_point
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.. ocv:member:: int ord::split_point
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Used internally by the training algorithm.
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Used internally by the training algorithm.
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CvDTreeNode
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-----------
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.. ocv:struct:: CvDTreeNode
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The structure represents a node in a decision tree. It has public members:
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The structure represents a node in a decision tree. It has public members:
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.. ocv:member:: int class_idx
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.. ocv:member:: int class_idx
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Class index normalized to 0..class_count-1 range and assigned to the node. It is used internally in classification trees and tree ensembles.
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.. ocv:member:: int Tn
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.. ocv:member:: int Tn
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Tree index in a ordered sequence of pruned trees. The indices are used during and after the pruning procedure. The root node has the maximum value ``Tn`` of the whole tree, child nodes have ``Tn`` less than or equal to the parent's ``Tn``, and nodes with :math:`Tn \leq CvDTree::pruned\_tree\_idx` are not used at prediction stage (the corresponding branches are considered as cut-off), even if they have not been physically deleted from the tree at the pruning stage.
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.. ocv:member:: double value
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.. ocv:member:: double value
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Value at the node: a class label in case of classification or estimated function value in case of regression.
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.. ocv:member:: CvDTreeNode* parent
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.. ocv:member:: CvDTreeNode* parent
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Pointer to the parent node.
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.. ocv:member:: CvDTreeNode* left
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.. ocv:member:: CvDTreeNode* left
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Pointer to the left child node.
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.. ocv:member:: CvDTreeNode* right
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.. ocv:member:: CvDTreeNode* right
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Pointer to the right child node.
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.. ocv:member:: CvDTreeSplit* split
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.. ocv:member:: CvDTreeSplit* split
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Pointer to the first (primary) split in the node list of splits.
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.. ocv:member:: int sample_count
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.. ocv:member:: int sample_count
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The number of samples that fall into the node at the training stage. It is used to resolve the difficult cases - when the variable for the primary split is missing and all the variables for other surrogate splits are missing too. In this case the sample is directed to the left if ``left->sample_count > right->sample_count`` and to the right otherwise.
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.. ocv:member:: int depth
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.. ocv:member:: int depth
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Depth of the node. The root node depth is 0, the child nodes depth is the parent's depth + 1.
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@@ -12,4 +12,4 @@ CvERTrees
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----------
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.. ocv:class:: CvERTrees : public CvRTrees
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The class implements the Extremely randomized trees algorithm. ``CvERTrees`` is inherited from :ocv:class:`CvRTrees` and has the same interface, so see description of :ocv:class:`CvRTrees` class to get details. To set the training parameters of Extremely randomized trees the same class :ocv:class:`CvRTParams` is used.
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The class implements the Extremely randomized trees algorithm. ``CvERTrees`` is inherited from :ocv:class:`CvRTrees` and has the same interface, so see description of :ocv:class:`CvRTrees` class to get details. To set the training parameters of Extremely randomized trees the same class :ocv:struct:`CvRTParams` is used.
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@@ -99,39 +99,39 @@ CvANN_MLP_TrainParams
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---------------------
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.. ocv:struct:: CvANN_MLP_TrainParams
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Parameters of the MLP training algorithm. You can initialize the structure by a constructor or the individual parameters can be adjusted after the structure is created.
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Parameters of the MLP training algorithm. You can initialize the structure by a constructor or the individual parameters can be adjusted after the structure is created.
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The back-propagation algorithm parameters:
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The back-propagation algorithm parameters:
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.. ocv:member:: double bp_dw_scale
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.. ocv:member:: double bp_dw_scale
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Strength of the weight gradient term. The recommended value is about 0.1.
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Strength of the weight gradient term. The recommended value is about 0.1.
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.. ocv:member:: double bp_moment_scale
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.. ocv:member:: double bp_moment_scale
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Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough
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Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough
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The RPROP algorithm parameters (see [RPROP93]_ for details):
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The RPROP algorithm parameters (see [RPROP93]_ for details):
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.. ocv:member:: double rp_dw0
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.. ocv:member:: double rp_dw0
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Initial value :math:`\Delta_0` of update-values :math:`\Delta_{ij}`.
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Initial value :math:`\Delta_0` of update-values :math:`\Delta_{ij}`.
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.. ocv:member:: double rp_dw_plus
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.. ocv:member:: double rp_dw_plus
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Increase factor :math:`\eta^+`. It must be >1.
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Increase factor :math:`\eta^+`. It must be >1.
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.. ocv:member:: double rp_dw_minus
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.. ocv:member:: double rp_dw_minus
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Decrease factor :math:`\eta^-`. It must be <1.
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Decrease factor :math:`\eta^-`. It must be <1.
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.. ocv:member:: double rp_dw_min
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.. ocv:member:: double rp_dw_min
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Update-values lower limit :math:`\Delta_{min}`. It must be positive.
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Update-values lower limit :math:`\Delta_{min}`. It must be positive.
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.. ocv:member:: double rp_dw_max
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.. ocv:member:: double rp_dw_max
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Update-values upper limit :math:`\Delta_{max}`. It must be >1.
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Update-values upper limit :math:`\Delta_{max}`. It must be >1.
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CvANN_MLP_TrainParams::CvANN_MLP_TrainParams
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@@ -18,19 +18,19 @@ CvParamGrid
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-----------
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.. ocv:struct:: CvParamGrid
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The structure represents the logarithmic grid range of statmodel parameters. It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate being computed by cross-validation.
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The structure represents the logarithmic grid range of statmodel parameters. It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate being computed by cross-validation.
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.. ocv:member:: double CvParamGrid::min_val
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.. ocv:member:: double CvParamGrid::min_val
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Minimum value of the statmodel parameter.
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Minimum value of the statmodel parameter.
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.. ocv:member:: double CvParamGrid::max_val
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.. ocv:member:: double CvParamGrid::max_val
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Maximum value of the statmodel parameter.
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Maximum value of the statmodel parameter.
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.. ocv:member:: double CvParamGrid::step
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.. ocv:member:: double CvParamGrid::step
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Logarithmic step for iterating the statmodel parameter.
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Logarithmic step for iterating the statmodel parameter.
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The grid determines the following iteration sequence of the statmodel parameter values:
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