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Commit f7495e58 authored by Ilya Lysenkov's avatar Ilya Lysenkov
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Updated GBT docs

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modules/ml/doc/gradient_boosted_trees.rst
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...@@ -104,28 +104,18 @@ CvGBTreesParams ...@@ -104,28 +104,18 @@ CvGBTreesParams
--------------- ---------------
.. ocv:class:: CvGBTreesParams .. ocv:class:: CvGBTreesParams
GBT training parameters. :: GBT training parameters.
struct CvGBTreesParams : public CvDTreeParams
{
int weak_count;
int loss_function_type;
float subsample_portion;
float shrinkage;
CvGBTreesParams();
CvGBTreesParams( int loss_function_type, int weak_count, float shrinkage,
float subsample_portion, int max_depth, bool use_surrogates );
};
The structure contains parameters for each sigle decision tree in the ensemble, The structure contains parameters for each sigle decision tree in the ensemble,
as well as the whole model characteristics. The structure is derived from as well as the whole model characteristics. The structure is derived from
:ocv:class:`CvDTreeParams` but not all of the decision tree parameters are supported: :ocv:class:`CvDTreeParams` but not all of the decision tree parameters are supported:
cross-validation, pruning, and class priorities are not used. cross-validation, pruning, and class priorities are not used.
:param weak_count: Count of boosting algorithm iterations. ``weak_count*K`` is the total CvGBTreesParams::CvGBTreesParams
count of trees in the GBT model, where ``K`` is the output classes count --------------------------------
(equal to one in case of a regression). .. ocv:function:: CvGBTreesParams::CvGBTreesParams()
.. ocv:function:: CvGBTreesParams::CvGBTreesParams( int loss_function_type, int weak_count, float shrinkage, float subsample_portion, int max_depth, bool use_surrogates )
:param loss_function_type: Type of the loss function used for training :param loss_function_type: Type of the loss function used for training
(see :ref:`Training the GBT model`). It must be one of the (see :ref:`Training the GBT model`). It must be one of the
...@@ -134,6 +124,10 @@ cross-validation, pruning, and class priorities are not used. ...@@ -134,6 +124,10 @@ cross-validation, pruning, and class priorities are not used.
types are used for regression problems, and the last one for types are used for regression problems, and the last one for
classification. classification.
:param weak_count: Count of boosting algorithm iterations. ``weak_count*K`` is the total
count of trees in the GBT model, where ``K`` is the output classes count
(equal to one in case of a regression).
:param shrinkage: Regularization parameter (see :ref:`Training the GBT model`). :param shrinkage: Regularization parameter (see :ref:`Training the GBT model`).
:param subsample_portion: Portion of the whole training set used for each algorithm iteration. :param subsample_portion: Portion of the whole training set used for each algorithm iteration.
...@@ -155,81 +149,29 @@ CvGBTrees ...@@ -155,81 +149,29 @@ CvGBTrees
--------- ---------
.. ocv:class:: CvGBTrees .. ocv:class:: CvGBTrees
GBT model. :: The class implements the Gradient boosted tree model as described in the beginning of this section.
class CvGBTrees : public CvStatModel
{
public:
enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS}; CvGBTrees::CvGBTrees
--------------------
Default and training constructors.
CvGBTrees(); .. ocv:function:: CvGBTrees::CvGBTrees()
CvGBTrees( const cv::Mat& trainData, int tflag,
const Mat& responses, const Mat& varIdx=Mat(),
const Mat& sampleIdx=Mat(), const cv::Mat& varType=Mat(),
const Mat& missingDataMask=Mat(),
CvGBTreesParams params=CvGBTreesParams() );
virtual ~CvGBTrees(); .. ocv:function:: CvGBTrees::CvGBTrees( 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() )
virtual 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 );
virtual bool train( CvMLData* data, .. ocv:cfunction:: CvGBTrees::CvGBTrees( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams() )
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
virtual float predict( const Mat& sample, const Mat& missing=Mat(),
const Range& slice = Range::all(),
int k=-1 ) const;
virtual void clear();
virtual float calc_error( CvMLData* _data, int type,
std::vector<float> *resp = 0 );
virtual void write( CvFileStorage* fs, const char* name ) const;
virtual void read( CvFileStorage* fs, CvFileNode* node );
protected:
CvDTreeTrainData* data;
CvGBTreesParams params;
CvSeq** weak;
Mat& orig_response;
Mat& sum_response;
Mat& sum_response_tmp;
Mat& weak_eval;
Mat& sample_idx;
Mat& subsample_train;
Mat& subsample_test;
Mat& missing;
Mat& class_labels;
RNG* rng;
int class_count;
float delta;
float base_value;
...
};
The constructors follow conventions of :ocv:func:`CvStatModel::CvStatModel`. See :ocv:func:`CvStatModel::train` for parameters descriptions.
CvGBTrees::train CvGBTrees::train
---------------- ----------------
<<<<<<< .mine
Trains a Gradient boosted tree model. Trains a Gradient boosted tree model.
.. 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 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)
>>>>>>> .r5669
.. ocv:function:: bool CvGBTrees::train(CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false) .. ocv:cfunction:: bool CvGBTrees::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, CvGBTreesParams params=CvGBTreesParams(), bool update=false )
.. ocv:cfunction:: bool CvGBTrees::train(CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
The first train method follows the common template (see :ocv:func:`CvStatModel::train`). The first train method follows the common template (see :ocv:func:`CvStatModel::train`).
Both ``tflag`` values (``CV_ROW_SAMPLE``, ``CV_COL_SAMPLE``) are supported. Both ``tflag`` values (``CV_ROW_SAMPLE``, ``CV_COL_SAMPLE``) are supported.
...@@ -248,13 +190,11 @@ as a :ocv:class:`CvGBTreesParams` structure. ...@@ -248,13 +190,11 @@ as a :ocv:class:`CvGBTreesParams` structure.
CvGBTrees::predict CvGBTrees::predict
------------------ ------------------
<<<<<<< .mine
Predicts a response for an input sample. Predicts a response for an input sample.
=======
.. ocv:function:: float CvGBTrees::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
>>>>>>> .r5669
.. ocv:function:: float predict(const Mat & sample, const Mat & missing=Mat(), const Range & slice = Range::all(), int k=-1) const .. ocv:cfunction:: float CvGBTrees::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const
:param sample: Input feature vector that has the same format as every training set :param sample: Input feature vector that has the same format as every training set
element. If not all the variables were actualy used during training, element. If not all the variables were actualy used during training,
......
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