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Code Packages Releases Wiki Activity

corrected a few bugs in refman

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This commit is contained in:
Vadim Pisarevsky
2011-06-29 19:13:19 +00:00
parent 6d810b13be
commit 36af349ab4
11 changed files with 57 additions and 59 deletions
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modules/imgproc/doc/imgproc.rst
+1
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@@ -12,6 +12,7 @@ imgproc. Image Processing
miscellaneous_transformations
histograms
structural_analysis_and_shape_descriptors
planar_subdivisions
motion_analysis_and_object_tracking
feature_detection
object_detection
modules/imgproc/doc/planar_subdivisions.rst
+1 -5
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@@ -224,7 +224,7 @@ Returns one of the edges related to the given edge.
* **CV_PREV_AROUND_RIGHT** previous around the right facet (reversed ``eDnext`` )
.. image:: ../pics/quadedge.png
.. image:: pics/quadedge.png
The function returns one of the edges related to the input edge.
@@ -237,8 +237,6 @@ Returns next edge around the edge origin
:param edge: Subdivision edge (not a quad-edge)
.. image:: ../pics/quadedge.png
The function returns the next edge around the edge origin:
``eOnext``
on the picture above if
@@ -312,8 +310,6 @@ Returns another edge of the same quad-edge.
* **3** the reversed rotated edge (reversed ``eRot`` (in green))
.. image:: ../pics/quadedge.png
The function returns one of the edges of the same quad-edge as the input edge.
SubdivDelaunay2DInsert
modules/ml/doc/boosting.rst
+10 -10
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@@ -142,7 +142,7 @@ Default and training constructors.
.. ocv:function:: CvBoost::CvBoost( const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvBoostParams params=CvBoostParams() )
.. ocv:cfunction:: CvBoost::CvBoost( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams() )
.. ocv:function::CvBoost::CvBoost( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams() )
.. ocv:pyfunction:: cv2.Boost(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> <Boost object>
@@ -155,12 +155,12 @@ Trains a boosted tree classifier.
.. ocv:function:: bool CvBoost::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(), CvBoostParams params=CvBoostParams(), bool update=false )
.. ocv:function::bool CvBoost::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, CvBoostParams params=CvBoostParams(), bool update=false )
.. ocv:function::bool CvBoost::train( CvMLData* data, CvBoostParams params=CvBoostParams(), bool update=false )
.. ocv:pyfunction:: cv2.Boost.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params[, update]]]]]]) -> retval
.. ocv:cfunction:: bool CvBoost::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, CvBoostParams params=CvBoostParams(), bool update=false )
.. ocv:cfunction:: bool CvBoost::train( CvMLData* data, CvBoostParams params=CvBoostParams(), bool update=false )
:param update: Specifies whether the classifier needs to be updated (``true``, the new weak tree classifiers added to the existing ensemble) or the classifier needs to be rebuilt from scratch (``false``).
The train method follows the common template of :ocv:func:`CvStatModel::train`. The responses must be categorical, which means that boosted trees cannot be built for regression, and there should be two classes.
@@ -171,7 +171,7 @@ Predicts a response for an input sample.
.. ocv:function:: float CvBoost::predict( const Mat& sample, const Mat& missing=Mat(), const Range& slice=Range::all(), bool rawMode=false, bool returnSum=false ) const
.. ocv:cfunction:: float CvBoost::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ, bool raw_mode=false, bool return_sum=false ) const
.. ocv:function::float CvBoost::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ, bool raw_mode=false, bool return_sum=false ) const
.. ocv:pyfunction:: cv2.Boost.predict(sample[, missing[, slice[, rawMode[, returnSum]]]]) -> retval
@@ -193,7 +193,7 @@ CvBoost::prune
--------------
Removes the specified weak classifiers.
.. ocv:cfunction:: void CvBoost::prune( CvSlice slice )
.. ocv:function::void CvBoost::prune( CvSlice slice )
.. ocv:pyfunction:: cv2.Boost.prune(slice) -> None
@@ -208,7 +208,7 @@ CvBoost::calc_error
-------------------
Returns error of the boosted tree classifier.
.. ocv:cfunction:: float CvBoost::calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 )
.. ocv:function::float CvBoost::calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 )
The method is identical to :ocv:func:`CvDTree::calc_error` but uses the boosted tree classifier as predictor.
@@ -217,7 +217,7 @@ CvBoost::get_weak_predictors
----------------------------
Returns the sequence of weak tree classifiers.
.. ocv:cfunction:: CvSeq* CvBoost::get_weak_predictors()
.. ocv:function::CvSeq* CvBoost::get_weak_predictors()
The method returns the sequence of weak classifiers. Each element of the sequence is a pointer to the :ocv:class:`CvBoostTree` class or to some of its derivatives.
@@ -232,5 +232,5 @@ CvBoost::get_data
-----------------
Returns used train data of the boosted tree classifier.
.. ocv:cfunction:: const CvDTreeTrainData* CvBoost::get_data() const
.. ocv:function::const CvDTreeTrainData* CvBoost::get_data() const
modules/ml/doc/decision_trees.rst
+7 -7
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@@ -227,11 +227,11 @@ Trains a decision tree.
.. ocv:function:: bool CvDTree::train( const Mat& train_data, int tflag, const Mat& responses, const Mat& var_idx=Mat(), const Mat& sample_idx=Mat(), const Mat& var_type=Mat(), const Mat& missing_mask=Mat(), CvDTreeParams params=CvDTreeParams() )
.. ocv:cfunction:: bool CvDTree::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, CvDTreeParams params=CvDTreeParams() )
.. ocv:function::bool CvDTree::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, CvDTreeParams params=CvDTreeParams() )
.. ocv:cfunction:: bool CvDTree::train( CvMLData* trainData, CvDTreeParams params=CvDTreeParams() )
.. ocv:function::bool CvDTree::train( CvMLData* trainData, CvDTreeParams params=CvDTreeParams() )
.. ocv:cfunction:: bool CvDTree::train( CvDTreeTrainData* trainData, const CvMat* subsampleIdx )
.. ocv:function::bool CvDTree::train( CvDTreeTrainData* trainData, const CvMat* subsampleIdx )
.. ocv:pyfunction:: cv2.DTree.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> retval
@@ -251,7 +251,7 @@ Returns the leaf node of a decision tree corresponding to the input vector.
.. ocv:function:: CvDTreeNode* CvDTree::predict( const Mat& sample, const Mat& missingDataMask=Mat(), bool preprocessedInput=false ) const
.. ocv:cfunction:: CvDTreeNode* CvDTree::predict( const CvMat* sample, const CvMat* missingDataMask=0, bool preprocessedInput=false ) const
.. ocv:function::CvDTreeNode* CvDTree::predict( const CvMat* sample, const CvMat* missingDataMask=0, bool preprocessedInput=false ) const
.. ocv:pyfunction:: cv2.DTree.predict(sample[, missingDataMask[, preprocessedInput]]) -> retval
@@ -269,7 +269,7 @@ CvDTree::calc_error
-------------------
Returns error of the decision tree.
.. ocv:cfunction:: float CvDTree::calc_error( CvMLData* trainData, int type, std::vector<float> *resp = 0 )
.. ocv:function::float CvDTree::calc_error( CvMLData* trainData, int type, std::vector<float> *resp = 0 )
:param data: Data for the decision tree.
@@ -290,7 +290,7 @@ Returns the variable importance array.
.. ocv:function:: Mat CvDTree::getVarImportance()
.. ocv:cfunction:: const CvMat* CvDTree::get_var_importance()
.. ocv:function::const CvMat* CvDTree::get_var_importance()
.. ocv:pyfunction:: cv2.DTree.getVarImportance() -> importanceVector
@@ -313,7 +313,7 @@ CvDTree::get_data
-----------------
Returns used train data of the decision tree.
.. ocv:cfunction:: const CvDTreeTrainData* CvDTree::get_data() const
.. ocv:function::const CvDTreeTrainData* CvDTree::get_data() const
Example: building a tree for classifying mushrooms. See the ``mushroom.cpp`` sample that demonstrates how to build and use the
decision tree.
modules/ml/doc/gradient_boosted_trees.rst
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@@ -159,7 +159,7 @@ Default and training constructors.
.. 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() )
.. 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() )
.. ocv:function::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() )
.. ocv:pyfunction:: cv2.GBTrees([trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]]) -> <GBTrees object>
@@ -171,11 +171,11 @@ Trains a Gradient boosted tree model.
.. 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 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:function::bool CvGBTrees::train(CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
.. ocv:pyfunction:: cv2.GBTrees.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params[, update]]]]]]) -> retval
.. 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`).
Both ``tflag`` values (``CV_ROW_SAMPLE``, ``CV_COL_SAMPLE``) are supported.
@@ -198,9 +198,9 @@ 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:pyfunction:: cv2.GBTrees.predict(sample[, missing[, slice[, k]]]) -> retval
.. ocv:function::float CvGBTrees::predict( const CvMat* sample, const CvMat* missing=0, CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ, 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
.. ocv:pyfunction:: cv2.GBTrees.predict(sample[, missing[, slice[, k]]]) -> retval
: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,
modules/ml/doc/k_nearest_neighbors.rst
+5 -4
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@@ -19,7 +19,7 @@ Default and training constructors.
.. ocv:function:: CvKNearest::CvKNearest( const Mat& trainData, const Mat& responses, const Mat& sampleIdx=Mat(), bool isRegression=false, int max_k=32 )
.. ocv:cfunction:: CvKNearest::CvKNearest( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool isRegression=false, int max_k=32 )
.. ocv:function::CvKNearest::CvKNearest( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool isRegression=false, int max_k=32 )
See :ocv:func:`CvKNearest::train` for additional parameters descriptions.
@@ -29,9 +29,9 @@ Trains the model.
.. ocv:function:: bool CvKNearest::train( const Mat& trainData, const Mat& responses, const Mat& sampleIdx=Mat(), bool isRegression=false, int maxK=32, bool updateBase=false )
.. ocv:pyfunction:: cv2.KNearest.train(trainData, responses[, sampleIdx[, isRegression[, maxK[, updateBase]]]]) -> retval
.. ocv:function::bool CvKNearest::train( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool is_regression=false, int maxK=32, bool updateBase=false )
.. ocv:cfunction:: bool CvKNearest::train( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool is_regression=false, int maxK=32, bool updateBase=false )
.. ocv:pyfunction:: cv2.KNearest.train(trainData, responses[, sampleIdx[, isRegression[, maxK[, updateBase]]]]) -> retval
:param isRegression: Type of the problem: ``true`` for regression and ``false`` for classification.
@@ -54,9 +54,10 @@ Finds the neighbors and predicts responses for input vectors.
.. ocv:function:: float CvKNearest::find_nearest( const Mat& samples, int k, Mat& results, Mat& neighborResponses, Mat& dists) const
.. ocv:function::float CvKNearest::find_nearest( const CvMat* samples, int k, CvMat* results=0, const float** neighbors=0, CvMat* neighborResponses=0, CvMat* dist=0 ) const
.. ocv:pyfunction:: cv2.KNearest.find_nearest(samples, k[, results[, neighborResponses[, dists]]]) -> retval, results, neighborResponses, dists
.. ocv:cfunction:: float CvKNearest::find_nearest( const CvMat* samples, int k, CvMat* results=0, const float** neighbors=0, CvMat* neighborResponses=0, CvMat* dist=0 ) const
:param samples: Input samples stored by rows. It is a single-precision floating-point matrix of :math:`number\_of\_samples \times number\_of\_features` size.
modules/ml/doc/neural_networks.rst
+5 -5
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@@ -182,7 +182,7 @@ The constructors.
.. ocv:function:: CvANN_MLP::CvANN_MLP()
.. ocv:cfunction:: CvANN_MLP::CvANN_MLP( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 )
.. ocv:function::CvANN_MLP::CvANN_MLP( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 )
.. ocv:pyfunction:: cv2.ANN_MLP(layerSizes[, activateFunc[, fparam1[, fparam2]]]) -> <ANN_MLP object>
@@ -194,7 +194,7 @@ Constructs MLP with the specified topology.
.. ocv:function:: void CvANN_MLP::create( const Mat& layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 )
.. ocv:cfunction:: void CvANN_MLP::create( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 )
.. ocv:function::void CvANN_MLP::create( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 )
.. ocv:pyfunction:: cv2.ANN_MLP.create(layerSizes[, activateFunc[, fparam1[, fparam2]]]) -> None
@@ -212,7 +212,7 @@ Trains/updates MLP.
.. ocv:function:: int CvANN_MLP::train( const Mat& inputs, const Mat& outputs, const Mat& sampleWeights, const Mat& sampleIdx=Mat(), CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 )
.. ocv:cfunction:: int CvANN_MLP::train( const CvMat* inputs, const CvMat* outputs, const CvMat* sampleWeights, const CvMat* sampleIdx=0, CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 )
.. ocv:function::int CvANN_MLP::train( const CvMat* inputs, const CvMat* outputs, const CvMat* sampleWeights, const CvMat* sampleIdx=0, CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 )
.. ocv:pyfunction:: cv2.ANN_MLP.train(inputs, outputs, sampleWeights[, sampleIdx[, params[, flags]]]) -> niterations
@@ -242,7 +242,7 @@ Predicts responses for input samples.
.. ocv:function:: float CvANN_MLP::predict( const Mat& inputs, Mat& outputs ) const
.. ocv:cfunction:: float CvANN_MLP::predict( const CvMat* inputs, CvMat* outputs ) const
.. ocv:function::float CvANN_MLP::predict( const CvMat* inputs, CvMat* outputs ) const
.. ocv:pyfunction:: cv2.ANN_MLP.predict(inputs, outputs) -> retval
@@ -262,7 +262,7 @@ CvANN_MLP::get_layer_sizes
--------------------------
Returns numbers of neurons in each layer of the MLP.
.. ocv:cfunction:: const CvMat* CvANN_MLP::get_layer_sizes()
.. ocv:function::const CvMat* CvANN_MLP::get_layer_sizes()
The method returns the integer vector specifying the number of neurons in each layer including the input and output layers of the MLP.
modules/ml/doc/normal_bayes_classifier.rst
+5 -5
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@@ -23,7 +23,7 @@ Default and training constructors.
.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat() )
.. ocv:cfunction:: CvNormalBayesClassifier::CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 )
.. ocv:function::CvNormalBayesClassifier::CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 )
.. ocv:pyfunction:: cv2.NormalBayesClassifier(trainData, responses[, varIdx[, sampleIdx]]) -> <NormalBayesClassifier object>
@@ -35,9 +35,9 @@ Trains the model.
.. ocv:function:: bool CvNormalBayesClassifier::train( const Mat& trainData, const Mat& responses, const Mat& varIdx = Mat(), const Mat& sampleIdx=Mat(), bool update=false )
.. ocv:pyfunction:: cv2.NormalBayesClassifier.train(trainData, responses[, varIdx[, sampleIdx[, update]]]) -> retval
.. ocv:function::bool CvNormalBayesClassifier::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false )
.. ocv:cfunction:: bool CvNormalBayesClassifier::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false )
.. ocv:pyfunction:: cv2.NormalBayesClassifier.train(trainData, responses[, varIdx[, sampleIdx[, update]]]) -> retval
:param update: Identifies whether the model should be trained from scratch (``update=false``) or should be updated using the new training data (``update=true``).
@@ -54,9 +54,9 @@ Predicts the response for sample(s).
.. ocv:function:: float CvNormalBayesClassifier::predict( const Mat& samples, Mat* results=0 ) const
.. ocv:pyfunction:: cv2.NormalBayesClassifier.predict(samples) -> retval, results
.. ocv:function::float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
.. ocv:cfunction:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
.. ocv:pyfunction:: cv2.NormalBayesClassifier.predict(samples) -> retval, results
The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix ``samples``. In case of multiple input vectors, there should be one output vector ``results``. The predicted class for a single input vector is returned by the method.
modules/ml/doc/random_trees.rst
+9 -9
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@@ -110,9 +110,9 @@ Trains the Random Trees model.
.. ocv:function:: bool CvRTrees::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(), CvRTParams params=CvRTParams() )
.. ocv:cfunction:: bool CvRTrees::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, CvRTParams params=CvRTParams() )
.. ocv:function::bool CvRTrees::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, CvRTParams params=CvRTParams() )
.. ocv:cfunction:: bool CvRTrees::train( CvMLData* data, CvRTParams params=CvRTParams() )
.. ocv:function::bool CvRTrees::train( CvMLData* data, CvRTParams params=CvRTParams() )
.. ocv:pyfunction:: cv2.RTrees.train(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> retval
@@ -124,7 +124,7 @@ Predicts the output for an input sample.
.. ocv:function:: double CvRTrees::predict( const Mat& sample, const Mat& missing=Mat() ) const
.. ocv:cfunction:: float CvRTrees::predict( const CvMat* sample, const CvMat* missing = 0 ) const
.. ocv:function::float CvRTrees::predict( const CvMat* sample, const CvMat* missing = 0 ) const
.. ocv:pyfunction:: cv2.RTrees.predict(sample[, missing]) -> retval
@@ -141,7 +141,7 @@ Returns a fuzzy-predicted class label.
.. ocv:function:: float CvRTrees::predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const
.. ocv:cfunction:: float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const
.. ocv:function::float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const
.. ocv:pyfunction:: cv2.RTrees.predict_prob(sample[, missing]) -> retval
@@ -158,9 +158,9 @@ Returns the variable importance array.
.. ocv:function:: Mat CvRTrees::getVarImportance()
.. ocv:pyfunction:: cv2.RTrees.getVarImportance() -> importanceVector
.. ocv:function::const CvMat* CvRTrees::get_var_importance()
.. ocv:cfunction:: const CvMat* CvRTrees::get_var_importance()
.. ocv:pyfunction:: cv2.RTrees.getVarImportance() -> importanceVector
The method returns the variable importance vector, computed at the training stage when ``CvRTParams::calc_var_importance`` is set to true. If this flag was set to false, the ``NULL`` pointer is returned. This differs from the decision trees where variable importance can be computed anytime after the training.
@@ -169,7 +169,7 @@ CvRTrees::get_proximity
-----------------------
Retrieves the proximity measure between two training samples.
.. ocv:cfunction:: float CvRTrees::get_proximity( const CvMat* sample1, const CvMat* sample2, const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const
.. ocv:function::float CvRTrees::get_proximity( const CvMat* sample1, const CvMat* sample2, const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const
:param sample_1: The first sample.
@@ -185,7 +185,7 @@ CvRTrees::calc_error
--------------------
Returns error of the random forest.
.. ocv:cfunction:: float CvRTrees::calc_error( CvMLData* data, int type, std::vector<float> *resp = 0 )
.. ocv:function::float CvRTrees::calc_error( CvMLData* data, int type, std::vector<float> *resp = 0 )
The method is identical to :ocv:func:`CvDTree::calc_error` but uses the random forest as predictor.
@@ -203,7 +203,7 @@ CvRTrees::get_rng
-----------------
Returns the state of the used random number generator.
.. ocv:cfunction:: CvRNG* CvRTrees::get_rng()
.. ocv:function::CvRNG* CvRTrees::get_rng()
CvRTrees::get_tree_count
modules/ml/doc/support_vector_machines.rst
+5 -5
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@@ -158,7 +158,7 @@ Default and training constructors.
.. ocv:function:: CvSVM::CvSVM( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), CvSVMParams params=CvSVMParams() )
.. ocv:cfunction:: CvSVM::CvSVM( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() )
.. ocv:function::CvSVM::CvSVM( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() )
.. ocv:pyfunction:: cv2.SVM(trainData, responses[, varIdx[, sampleIdx[, params]]]) -> <SVM object>
@@ -170,7 +170,7 @@ Trains an SVM.
.. ocv:function:: bool CvSVM::train( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), CvSVMParams params=CvSVMParams() )
.. ocv:cfunction:: bool CvSVM::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() )
.. ocv:function::bool CvSVM::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() )
.. ocv:pyfunction:: cv2.SVM.train(trainData, responses[, varIdx[, sampleIdx[, params]]]) -> retval
@@ -194,7 +194,7 @@ Trains an SVM with optimal parameters.
.. ocv:function:: bool CvSVM::train_auto( const Mat& trainData, const Mat& responses, const Mat& varIdx, const Mat& sampleIdx, CvSVMParams params, int k_fold = 10, CvParamGrid Cgrid = CvSVM::get_default_grid(CvSVM::C), CvParamGrid gammaGrid = CvSVM::get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid = CvSVM::get_default_grid(CvSVM::P), CvParamGrid nuGrid = CvSVM::get_default_grid(CvSVM::NU), CvParamGrid coeffGrid = CvSVM::get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE), bool balanced=false)
.. ocv:cfunction:: bool CvSVM::train_auto( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params, int kfold = 10, CvParamGrid Cgrid = get_default_grid(CvSVM::C), CvParamGrid gammaGrid = get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid = get_default_grid(CvSVM::P), CvParamGrid nuGrid = get_default_grid(CvSVM::NU), CvParamGrid coeffGrid = get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid = get_default_grid(CvSVM::DEGREE), bool balanced=false )
.. ocv:function::bool CvSVM::train_auto( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params, int kfold = 10, CvParamGrid Cgrid = get_default_grid(CvSVM::C), CvParamGrid gammaGrid = get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid = get_default_grid(CvSVM::P), CvParamGrid nuGrid = get_default_grid(CvSVM::NU), CvParamGrid coeffGrid = get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid = get_default_grid(CvSVM::DEGREE), bool balanced=false )
.. ocv:pyfunction:: cv2.SVM.train_auto(trainData, responses, varIdx, sampleIdx, params[, k_fold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]]) -> retval
@@ -226,9 +226,9 @@ Predicts the response for input sample(s).
.. ocv:function:: float CvSVM::predict( const Mat& sample, bool returnDFVal=false ) const
.. ocv:cfunction:: float CvSVM::predict( const CvMat* sample, bool returnDFVal=false ) const
.. ocv:function::float CvSVM::predict( const CvMat* sample, bool returnDFVal=false ) const
.. ocv:cfunction:: float CvSVM::predict( const CvMat* samples, CvMat* results ) const
.. ocv:function::float CvSVM::predict( const CvMat* samples, CvMat* results ) const
.. ocv:pyfunction:: cv2.SVM.predict(sample[, returnDFVal]) -> retval
modules/video/doc/motion_analysis_and_object_tracking.rst
+2 -2
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@@ -81,7 +81,7 @@ The function finds an optical flow for each ``prevImg`` pixel using the [Farneba
.. math::
\texttt{prevImg} (x,y) \sim \texttt{nextImg} ( \texttt{flow} (x,y)[0], \texttt{flow} (x,y)[1])
\texttt{prevImg} (y,x) \sim \texttt{nextImg} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])
@@ -441,7 +441,7 @@ Re-initializes Kalman filter. The previous content is destroyed.
.. ocv:function:: void KalmanFilter::init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
:param dynamParams: Dimensionality of the state.
:param dynamParams: Dimensionalityensionality of the state.
:param measureParams: Dimensionality of the measurement.