generic-library/opencv
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Documentation: fixed about 100 cross-referencing errors.

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This commit is contained in:
Andrey Kamaev
2011-08-13 16:49:40 +00:00
parent 6395a738cb
commit d6e3ccc1b2
34 changed files with 128 additions and 125 deletions
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16
modules/ml/doc/random_trees.rst
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@@ -98,9 +98,9 @@ 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: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: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:function::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
@@ -112,7 +112,7 @@ Predicts the output for an input sample.
.. ocv:function:: double CvRTrees::predict( const Mat& sample, const Mat& missing=Mat() ) const
.. ocv:function::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
@@ -129,7 +129,7 @@ Returns a fuzzy-predicted class label.
.. ocv:function:: float CvRTrees::predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const
.. ocv:function::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
@@ -146,7 +146,7 @@ Returns the variable importance array.
.. ocv:function:: Mat CvRTrees::getVarImportance()
.. ocv:function::const CvMat* CvRTrees::get_var_importance()
.. ocv:function:: const CvMat* CvRTrees::get_var_importance()
.. ocv:pyfunction:: cv2.RTrees.getVarImportance() -> importanceVector
@@ -157,7 +157,7 @@ CvRTrees::get_proximity
-----------------------
Retrieves the proximity measure between two training samples.
.. ocv:function::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.
@@ -173,7 +173,7 @@ CvRTrees::calc_error
--------------------
Returns error of the random forest.
.. ocv:function::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.
@@ -191,7 +191,7 @@ CvRTrees::get_rng
-----------------
Returns the state of the used random number generator.
.. ocv:function::CvRNG* CvRTrees::get_rng()
.. ocv:function:: CvRNG* CvRTrees::get_rng()
CvRTrees::get_tree_count