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Updated GBT docs
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Ilya Lysenkov
@@ -104,36 +104,30 @@ CvGBTreesParams
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---------------
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.. ocv:class:: CvGBTreesParams
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GBT training parameters. ::
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struct CvGBTreesParams : public CvDTreeParams
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{
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int weak_count;
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int loss_function_type;
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float subsample_portion;
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float shrinkage;
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CvGBTreesParams();
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CvGBTreesParams( int loss_function_type, int weak_count, float shrinkage,
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float subsample_portion, int max_depth, bool use_surrogates );
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};
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GBT training parameters.
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The structure contains parameters for each sigle decision tree in the ensemble,
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as well as the whole model characteristics. The structure is derived from
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:ocv:class:`CvDTreeParams` but not all of the decision tree parameters are supported:
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cross-validation, pruning, and class priorities are not used.
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:param weak_count: Count of boosting algorithm iterations. ``weak_count*K`` is the total
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count of trees in the GBT model, where ``K`` is the output classes count
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(equal to one in case of a regression).
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CvGBTreesParams::CvGBTreesParams
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--------------------------------
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.. ocv:function:: CvGBTreesParams::CvGBTreesParams()
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.. ocv:function:: CvGBTreesParams::CvGBTreesParams( int loss_function_type, int weak_count, float shrinkage, float subsample_portion, int max_depth, bool use_surrogates )
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:param loss_function_type: Type of the loss function used for training
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(see :ref:`Training the GBT model`). It must be one of the
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following types: ``CvGBTrees::SQUARED_LOSS``, ``CvGBTrees::ABSOLUTE_LOSS``,
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``CvGBTrees::HUBER_LOSS``, ``CvGBTrees::DEVIANCE_LOSS``. The first three
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types are used for regression problems, and the last one for
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classification.
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:param weak_count: Count of boosting algorithm iterations. ``weak_count*K`` is the total
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count of trees in the GBT model, where ``K`` is the output classes count
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(equal to one in case of a regression).
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:param shrinkage: Regularization parameter (see :ref:`Training the GBT model`).
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:param subsample_portion: Portion of the whole training set used for each algorithm iteration.
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@@ -155,81 +149,29 @@ CvGBTrees
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---------
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.. ocv:class:: CvGBTrees
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GBT model. ::
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The class implements the Gradient boosted tree model as described in the beginning of this section.
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class CvGBTrees : public CvStatModel
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{
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public:
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CvGBTrees::CvGBTrees
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--------------------
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Default and training constructors.
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enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
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.. ocv:function:: CvGBTrees::CvGBTrees()
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CvGBTrees();
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CvGBTrees( const cv::Mat& trainData, int tflag,
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const Mat& responses, const Mat& varIdx=Mat(),
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const Mat& sampleIdx=Mat(), const cv::Mat& varType=Mat(),
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const Mat& missingDataMask=Mat(),
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CvGBTreesParams params=CvGBTreesParams() );
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.. 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() )
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virtual ~CvGBTrees();
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virtual bool train( const Mat& trainData, int tflag,
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const Mat& responses, const Mat& varIdx=Mat(),
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const Mat& sampleIdx=Mat(), const Mat& varType=Mat(),
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const Mat& missingDataMask=Mat(),
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CvGBTreesParams params=CvGBTreesParams(),
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bool update=false );
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virtual bool train( CvMLData* data,
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CvGBTreesParams params=CvGBTreesParams(),
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bool update=false );
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virtual float predict( const Mat& sample, const Mat& missing=Mat(),
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const Range& slice = Range::all(),
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int k=-1 ) const;
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virtual void clear();
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virtual float calc_error( CvMLData* _data, int type,
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std::vector<float> *resp = 0 );
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virtual void write( CvFileStorage* fs, const char* name ) const;
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virtual void read( CvFileStorage* fs, CvFileNode* node );
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protected:
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CvDTreeTrainData* data;
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CvGBTreesParams params;
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CvSeq** weak;
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Mat& orig_response;
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Mat& sum_response;
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Mat& sum_response_tmp;
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Mat& weak_eval;
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Mat& sample_idx;
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Mat& subsample_train;
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Mat& subsample_test;
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Mat& missing;
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Mat& class_labels;
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RNG* rng;
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int class_count;
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float delta;
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float base_value;
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...
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};
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.. 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() )
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The constructors follow conventions of :ocv:func:`CvStatModel::CvStatModel`. See :ocv:func:`CvStatModel::train` for parameters descriptions.
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CvGBTrees::train
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----------------
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<<<<<<< .mine
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Trains a Gradient boosted tree model.
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.. 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)
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=======
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.. 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)
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>>>>>>> .r5669
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.. ocv:function:: bool CvGBTrees::train(CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
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.. 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 )
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.. ocv:cfunction:: bool CvGBTrees::train(CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
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The first train method follows the common template (see :ocv:func:`CvStatModel::train`).
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Both ``tflag`` values (``CV_ROW_SAMPLE``, ``CV_COL_SAMPLE``) are supported.
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@@ -248,14 +190,12 @@ as a :ocv:class:`CvGBTreesParams` structure.
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CvGBTrees::predict
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------------------
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<<<<<<< .mine
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Predicts a response for an input sample.
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=======
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.. ocv:function:: float CvGBTrees::predict(const Mat& sample, const Mat& missing=Mat(), const Range& slice = Range::all(), int k=-1) const
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>>>>>>> .r5669
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.. ocv:function:: float predict(const Mat & sample, const Mat & missing=Mat(), const Range & slice = Range::all(), int k=-1) const
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.. ocv:function:: float CvGBTrees::predict(const Mat& sample, const Mat& missing=Mat(), const Range& slice = Range::all(), int k=-1) const
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.. ocv:cfunction:: float CvGBTrees::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const
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:param sample: Input feature vector that has the same format as every training set
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element. If not all the variables were actualy used during training,
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``sample`` contains forged values at the appropriate places.
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@@ -305,7 +245,7 @@ CvGBTrees::calc_error
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Calculates a training or testing error.
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.. ocv:function:: float CvGBTrees::calc_error( CvMLData* _data, int type, std::vector<float> *resp = 0 )
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:param _data: Data set.
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:param type: Parameter defining the error that should be computed: train (``CV_TRAIN_ERROR``) or test
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