mirror of
https://github.com/opencv/opencv.git
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the first round of cleaning up the RST docs
This commit is contained in:
Vadim Pisarevsky
@@ -83,7 +83,7 @@ training examples are recomputed at each training iteration. Examples deleted at
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CvBoostParams
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-------------
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.. ctype:: CvBoostParams
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.. c:type:: CvBoostParams
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Boosting training parameters. ::
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@@ -109,7 +109,7 @@ The structure is derived from
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CvBoostTree
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-----------
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.. ctype:: CvBoostTree
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.. c:type:: CvBoostTree
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Weak tree classifier. ::
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@@ -148,7 +148,7 @@ Note, that in the case of LogitBoost and Gentle AdaBoost each weak predictor is
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CvBoost
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-------
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.. ctype:: CvBoost
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.. c:type:: CvBoost
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Boosted tree classifier. ::
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@@ -212,7 +212,7 @@ Boosted tree classifier. ::
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CvBoost::train
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--------------
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.. cfunction:: bool CvBoost::train( const CvMat* _train_data, int _tflag, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, const CvMat* _var_type=0, const CvMat* _missing_mask=0, CvBoostParams params=CvBoostParams(), bool update=false )
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.. c:function:: bool CvBoost::train( const CvMat* _train_data, int _tflag, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, const CvMat* _var_type=0, const CvMat* _missing_mask=0, CvBoostParams params=CvBoostParams(), bool update=false )
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Trains a boosted tree classifier.
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@@ -224,7 +224,7 @@ The train method follows the common template; the last parameter ``update`` spec
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CvBoost::predict
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----------------
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.. cfunction:: float CvBoost::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ, bool raw_mode=false ) const
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.. c:function:: float CvBoost::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ, bool raw_mode=false ) const
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Predicts a response for the input sample.
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@@ -236,7 +236,7 @@ The method ``CvBoost::predict`` runs the sample through the trees in the ensembl
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CvBoost::prune
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--------------
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.. cfunction:: void CvBoost::prune( CvSlice slice )
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.. c:function:: void CvBoost::prune( CvSlice slice )
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Removes the specified weak classifiers.
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@@ -248,7 +248,7 @@ The method removes the specified weak classifiers from the sequence. Note that t
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CvBoost::get_weak_predictors
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----------------------------
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.. cfunction:: CvSeq* CvBoost::get_weak_predictors()
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.. c:function:: CvSeq* CvBoost::get_weak_predictors()
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Returns the sequence of weak tree classifiers.
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@@ -68,7 +68,7 @@ Importance of each variable is computed over all the splits on this variable in
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CvDTreeSplit
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------------
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.. ctype:: CvDTreeSplit
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.. c:type:: CvDTreeSplit
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Decision tree node split. ::
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@@ -97,7 +97,7 @@ Decision tree node split. ::
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CvDTreeNode
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-----------
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.. ctype:: CvDTreeNode
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.. c:type:: CvDTreeNode
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Decision tree node. ::
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@@ -127,7 +127,7 @@ Other numerous fields of ``CvDTreeNode`` are used internally at the training sta
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CvDTreeParams
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-------------
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.. ctype:: CvDTreeParams
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.. c:type:: CvDTreeParams
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Decision tree training parameters. ::
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@@ -164,7 +164,7 @@ The structure contains all the decision tree training parameters. There is a def
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CvDTreeTrainData
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----------------
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.. ctype:: CvDTreeTrainData
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.. c:type:: CvDTreeTrainData
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Decision tree training data and shared data for tree ensembles. ::
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@@ -289,7 +289,7 @@ There are 2 ways of using this structure. In simple cases (e.g. a standalone tre
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CvDTree
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-------
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.. ctype:: CvDTree
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.. c:type:: CvDTree
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Decision tree. ::
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@@ -376,9 +376,9 @@ Decision tree. ::
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CvDTree::train
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--------------
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.. cfunction:: bool CvDTree::train( const CvMat* _train_data, int _tflag, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, const CvMat* _var_type=0, const CvMat* _missing_mask=0, CvDTreeParams params=CvDTreeParams() )
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.. c:function:: bool CvDTree::train( const CvMat* _train_data, int _tflag, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, const CvMat* _var_type=0, const CvMat* _missing_mask=0, CvDTreeParams params=CvDTreeParams() )
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.. cfunction:: bool CvDTree::train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx )
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.. c:function:: bool CvDTree::train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx )
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Trains a decision tree.
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@@ -396,7 +396,7 @@ The second method ``train`` is mostly used for building tree ensembles. It takes
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CvDTree::predict
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----------------
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.. cfunction:: CvDTreeNode* CvDTree::predict( const CvMat* _sample, const CvMat* _missing_data_mask=0, bool raw_mode=false ) const
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.. c:function:: CvDTreeNode* CvDTree::predict( const CvMat* _sample, const CvMat* _missing_data_mask=0, bool raw_mode=false ) const
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Returns the leaf node of the decision tree corresponding to the input vector.
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@@ -88,7 +88,7 @@ already a good enough approximation).
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CvEMParams
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----------
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.. ctype:: CvEMParams
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.. c:type:: CvEMParams
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Parameters of the EM algorithm. ::
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@@ -134,7 +134,7 @@ The structure has 2 constructors, the default one represents a rough rule-of-thu
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CvEM
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----
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.. ctype:: CvEM
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.. c:type:: CvEM
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EM model. ::
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@@ -194,7 +194,7 @@ EM model. ::
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CvEM::train
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-----------
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.. cfunction:: void CvEM::train( const CvMat* samples, const CvMat* sample_idx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )
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.. c:function:: void CvEM::train( const CvMat* samples, const CvMat* sample_idx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )
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Estimates the Gaussian mixture parameters from the sample set.
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@@ -13,7 +13,7 @@ The algorithm caches all of the training samples, and predicts the response for
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CvKNearest
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----------
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.. ctype:: CvKNearest
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.. c:type:: CvKNearest
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K Nearest Neighbors model. ::
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@@ -51,7 +51,7 @@ K Nearest Neighbors model. ::
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CvKNearest::train
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-----------------
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.. cfunction:: bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _sample_idx=0, bool is_regression=false, int _max_k=32, bool _update_base=false )
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.. c:function:: bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _sample_idx=0, bool is_regression=false, int _max_k=32, bool _update_base=false )
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Trains the model.
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@@ -68,7 +68,7 @@ The parameter ``_update_base`` specifies whether the model is trained from scrat
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CvKNearest::find_nearest
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------------------------
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.. cfunction:: float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* results=0, const float** neighbors=0, CvMat* neighbor_responses=0, CvMat* dist=0 ) const
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.. c:function:: float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* results=0, const float** neighbors=0, CvMat* neighbor_responses=0, CvMat* dist=0 ) const
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Finds the neighbors for the input vectors.
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@@ -16,6 +16,6 @@ Most of the classification and regression algorithms are implemented as C++ clas
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decision_trees
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boosting
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random_trees
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expectation-maximization
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expectation_maximization
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neural_networks
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@@ -98,7 +98,7 @@ References:
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CvANN_MLP_TrainParams
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---------------------
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.. ctype:: CvANN_MLP_TrainParams
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.. c:type:: CvANN_MLP_TrainParams
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Parameters of the MLP training algorithm. ::
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@@ -120,6 +120,7 @@ Parameters of the MLP training algorithm. ::
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// rprop parameters
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double rp_dw0, rp_dw_plus, rp_dw_minus, rp_dw_min, rp_dw_max;
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};
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..
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The structure has default constructor that initializes parameters for ``RPROP`` algorithm. There is also more advanced constructor to customize the parameters and/or choose backpropagation algorithm. Finally, the individual parameters can be adjusted after the structure is created.
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@@ -130,7 +131,7 @@ The structure has default constructor that initializes parameters for ``RPROP``
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CvANN_MLP
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---------
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.. ctype:: CvANN_MLP
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.. c:type:: CvANN_MLP
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MLP model. ::
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@@ -210,6 +211,7 @@ MLP model. ::
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CvANN_MLP_TrainParams params;
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CvRNG rng;
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};
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..
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Unlike many other models in ML that are constructed and trained at once, in the MLP model these steps are separated. First, a network with the specified topology is created using the non-default constructor or the method ``create`` . All the weights are set to zeros. Then the network is trained using the set of input and output vectors. The training procedure can be repeated more than once, i.e. the weights can be adjusted based on the new training data.
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@@ -220,7 +222,7 @@ Unlike many other models in ML that are constructed and trained at once, in the
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CvANN_MLP::create
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-----------------
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.. cfunction:: void CvANN_MLP::create( const CvMat* _layer_sizes, int _activ_func=SIGMOID_SYM, double _f_param1=0, double _f_param2=0 )
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.. c:function:: void CvANN_MLP::create( const CvMat* _layer_sizes, int _activ_func=SIGMOID_SYM, double _f_param1=0, double _f_param2=0 )
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Constructs the MLP with the specified topology
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@@ -238,7 +240,7 @@ The method creates a MLP network with the specified topology and assigns the sam
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CvANN_MLP::train
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----------------
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.. cfunction:: int CvANN_MLP::train( const CvMat* _inputs, const CvMat* _outputs, const CvMat* _sample_weights, const CvMat* _sample_idx=0, CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(), int flags=0 )
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.. c:function:: int CvANN_MLP::train( const CvMat* _inputs, const CvMat* _outputs, const CvMat* _sample_weights, const CvMat* _sample_idx=0, CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(), int flags=0 )
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Trains/updates MLP.
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@@ -13,7 +13,7 @@ This is a simple classification model assuming that feature vectors from each cl
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CvNormalBayesClassifier
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-----------------------
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.. ctype:: CvNormalBayesClassifier
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.. c:type:: CvNormalBayesClassifier
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Bayes classifier for normally distributed data. ::
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@@ -48,7 +48,7 @@ Bayes classifier for normally distributed data. ::
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CvNormalBayesClassifier::train
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------------------------------
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.. cfunction:: bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx =0, const CvMat* _sample_idx=0, bool update=false )
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.. c:function:: bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx =0, const CvMat* _sample_idx=0, bool update=false )
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Trains the model.
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@@ -62,7 +62,7 @@ In addition, there is an ``update`` flag that identifies whether the model shoul
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CvNormalBayesClassifier::predict
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--------------------------------
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.. cfunction:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
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.. c:function:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
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Predicts the response for sample(s)
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@@ -53,7 +53,7 @@ In random trees there is no need for any accuracy estimation procedures, such as
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CvRTParams
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----------
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.. ctype:: CvRTParams
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.. c:type:: CvRTParams
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Training Parameters of Random Trees. ::
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@@ -86,7 +86,7 @@ The set of training parameters for the forest is the superset of the training pa
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CvRTrees
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--------
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.. ctype:: CvRTrees
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.. c:type:: CvRTrees
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Random Trees. ::
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@@ -136,7 +136,7 @@ Random Trees. ::
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CvRTrees::train
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---------------
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.. cfunction:: bool CvRTrees::train( const CvMat* train_data, int tflag, const CvMat* responses, const CvMat* comp_idx=0, const CvMat* sample_idx=0, const CvMat* var_type=0, const CvMat* missing_mask=0, CvRTParams params=CvRTParams() )
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.. c:function:: bool CvRTrees::train( const CvMat* train_data, int tflag, const CvMat* responses, const CvMat* comp_idx=0, const CvMat* sample_idx=0, const CvMat* var_type=0, const CvMat* missing_mask=0, CvRTParams params=CvRTParams() )
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Trains the Random Trees model.
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@@ -149,7 +149,7 @@ The method ``CvRTrees::train`` is very similar to the first form of ``CvDTree::t
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CvRTrees::predict
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-----------------
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.. cfunction:: double CvRTrees::predict( const CvMat* sample, const CvMat* missing=0 ) const
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.. c:function:: double CvRTrees::predict( const CvMat* sample, const CvMat* missing=0 ) const
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Predicts the output for the input sample.
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@@ -161,7 +161,7 @@ The input parameters of the prediction method are the same as in ``CvDTree::pred
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CvRTrees::get_var_importance
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----------------------------
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.. cfunction:: const CvMat* CvRTrees::get_var_importance() const
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.. c:function:: const CvMat* CvRTrees::get_var_importance() const
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Retrieves the variable importance array.
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@@ -173,7 +173,7 @@ The method returns the variable importance vector, computed at the training stag
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CvRTrees::get_proximity
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-----------------------
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.. cfunction:: float CvRTrees::get_proximity( const CvMat* sample_1, const CvMat* sample_2 ) const
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.. c:function:: float CvRTrees::get_proximity( const CvMat* sample_1, const CvMat* sample_2 ) const
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Retrieves the proximity measure between two training samples.
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@@ -9,7 +9,7 @@ Statistical Models
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CvStatModel
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-----------
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.. ctype:: CvStatModel
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.. c:type:: CvStatModel
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Base class for the statistical models in ML. ::
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@@ -48,7 +48,7 @@ In this declaration some methods are commented off. Actually, these are methods
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CvStatModel::CvStatModel
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------------------------
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.. cfunction:: CvStatModel::CvStatModel()
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.. c:function:: CvStatModel::CvStatModel()
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Default constructor.
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@@ -60,7 +60,7 @@ Each statistical model class in ML has a default constructor without parameters.
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CvStatModel::CvStatModel(...)
|
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-----------------------------
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.. cfunction:: CvStatModel::CvStatModel( const CvMat* train_data ... )
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.. c:function:: CvStatModel::CvStatModel( const CvMat* train_data ... )
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Training constructor.
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@@ -72,7 +72,7 @@ Most ML classes provide single-step construct and train constructors. This const
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CvStatModel::~CvStatModel
|
||||
-------------------------
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.. cfunction:: CvStatModel::~CvStatModel()
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.. c:function:: CvStatModel::~CvStatModel()
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Virtual destructor.
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@@ -95,7 +95,7 @@ Normally, the destructor of each derived class does nothing, but in this instanc
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CvStatModel::clear
|
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------------------
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.. cfunction:: void CvStatModel::clear()
|
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.. c:function:: void CvStatModel::clear()
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Deallocates memory and resets the model state.
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@@ -107,7 +107,7 @@ The method ``clear`` does the same job as the destructor; it deallocates all the
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CvStatModel::save
|
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-----------------
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.. cfunction:: void CvStatModel::save( const char* filename, const char* name=0 )
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.. c:function:: void CvStatModel::save( const char* filename, const char* name=0 )
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Saves the model to a file.
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@@ -119,7 +119,7 @@ The method ``save`` stores the complete model state to the specified XML or YAML
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CvStatModel::load
|
||||
-----------------
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||||
.. cfunction:: void CvStatModel::load( const char* filename, const char* name=0 )
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.. c:function:: void CvStatModel::load( const char* filename, const char* name=0 )
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Loads the model from a file.
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@@ -135,7 +135,7 @@ cross{cvLoad}, here the model type must be known, because an empty model must be
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CvStatModel::write
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------------------
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.. cfunction:: void CvStatModel::write( CvFileStorage* storage, const char* name )
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.. c:function:: void CvStatModel::write( CvFileStorage* storage, const char* name )
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Writes the model to file storage.
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@@ -147,7 +147,7 @@ The method ``write`` stores the complete model state to the file storage with th
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CvStatModel::read
|
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-----------------
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.. cfunction:: void CvStatMode::read( CvFileStorage* storage, CvFileNode* node )
|
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.. c:function:: void CvStatMode::read( CvFileStorage* storage, CvFileNode* node )
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||||
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Reads the model from file storage.
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@@ -162,7 +162,7 @@ The previous model state is cleared by ``clear()`` .
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CvStatModel::train
|
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------------------
|
||||
.. cfunction:: bool CvStatMode::train( const CvMat* train_data, [int tflag,] ..., const CvMat* responses, ..., [const CvMat* var_idx,] ..., [const CvMat* sample_idx,] ... [const CvMat* var_type,] ..., [const CvMat* missing_mask,] <misc_training_alg_params> ... )
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.. c:function:: bool CvStatMode::train( const CvMat* train_data, [int tflag,] ..., const CvMat* responses, ..., [const CvMat* var_idx,] ..., [const CvMat* sample_idx,] ... [const CvMat* var_type,] ..., [const CvMat* missing_mask,] <misc_training_alg_params> ... )
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Trains the model.
|
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@@ -194,7 +194,7 @@ Usually, the previous model state is cleared by ``clear()`` before running the t
|
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|
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CvStatModel::predict
|
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--------------------
|
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.. cfunction:: float CvStatMode::predict( const CvMat* sample[, <prediction_params>] ) const
|
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.. c:function:: float CvStatMode::predict( const CvMat* sample[, <prediction_params>] ) const
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Predicts the response for the sample.
|
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|
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|
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@@ -27,7 +27,7 @@ There are a lot of good references on SVM. Here are only a few ones to start wit
|
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|
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CvSVM
|
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-----
|
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.. ctype:: CvSVM
|
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.. c:type:: CvSVM
|
||||
|
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Support Vector Machines. ::
|
||||
|
||||
@@ -90,7 +90,7 @@ Support Vector Machines. ::
|
||||
|
||||
CvSVMParams
|
||||
-----------
|
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.. ctype:: CvSVMParams
|
||||
.. c:type:: CvSVMParams
|
||||
|
||||
SVM training parameters. ::
|
||||
|
||||
@@ -125,7 +125,7 @@ The structure must be initialized and passed to the training method of
|
||||
|
||||
CvSVM::train
|
||||
------------
|
||||
.. cfunction:: bool CvSVM::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, CvSVMParams _params=CvSVMParams() )
|
||||
.. c:function:: bool CvSVM::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx=0, const CvMat* _sample_idx=0, CvSVMParams _params=CvSVMParams() )
|
||||
|
||||
Trains SVM.
|
||||
|
||||
@@ -140,7 +140,7 @@ All the other parameters are gathered in
|
||||
|
||||
CvSVM::train_auto
|
||||
-----------------
|
||||
.. cfunction:: train_auto( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx, const CvMat* _sample_idx, CvSVMParams params, int k_fold = 10, CvParamGrid C_grid = get_default_grid(CvSVM::C), CvParamGrid gamma_grid = get_default_grid(CvSVM::GAMMA), CvParamGrid p_grid = get_default_grid(CvSVM::P), CvParamGrid nu_grid = get_default_grid(CvSVM::NU), CvParamGrid coef_grid = get_default_grid(CvSVM::COEF), CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) )
|
||||
.. c:function:: train_auto( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx, const CvMat* _sample_idx, CvSVMParams params, int k_fold = 10, CvParamGrid C_grid = get_default_grid(CvSVM::C), CvParamGrid gamma_grid = get_default_grid(CvSVM::GAMMA), CvParamGrid p_grid = get_default_grid(CvSVM::P), CvParamGrid nu_grid = get_default_grid(CvSVM::NU), CvParamGrid coef_grid = get_default_grid(CvSVM::COEF), CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) )
|
||||
|
||||
Trains SVM with optimal parameters.
|
||||
|
||||
@@ -182,7 +182,7 @@ as well as for the regression
|
||||
|
||||
CvSVM::get_default_grid
|
||||
-----------------------
|
||||
.. cfunction:: CvParamGrid CvSVM::get_default_grid( int param_id )
|
||||
.. c:function:: CvParamGrid CvSVM::get_default_grid( int param_id )
|
||||
|
||||
Generates a grid for the SVM parameters.
|
||||
|
||||
@@ -211,7 +211,7 @@ The function generates a grid for the specified parameter of the SVM algorithm.
|
||||
|
||||
CvSVM::get_params
|
||||
-----------------
|
||||
.. cfunction:: CvSVMParams CvSVM::get_params() const
|
||||
.. c:function:: CvSVMParams CvSVM::get_params() const
|
||||
|
||||
Returns the current SVM parameters.
|
||||
|
||||
@@ -223,9 +223,9 @@ This function may be used to get the optimal parameters that were obtained while
|
||||
|
||||
CvSVM::get_support_vector*
|
||||
--------------------------
|
||||
.. cfunction:: int CvSVM::get_support_vector_count() const
|
||||
.. c:function:: int CvSVM::get_support_vector_count() const
|
||||
|
||||
.. cfunction:: const float* CvSVM::get_support_vector(int i) const
|
||||
.. c:function:: const float* CvSVM::get_support_vector(int i) const
|
||||
|
||||
Retrieves the number of support vectors and the particular vector.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user