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Merge pull request #7371 from mshabunin:aruco-java-wrap

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This commit is contained in:
Vadim Pisarevsky
2016-10-01 15:38:59 +00:00
parent b741ec2dc1 7a7a2749e0
commit 991c41c849
2 changed files with 11 additions and 8 deletions
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modules/ml/include/opencv2/ml.hpp
+5 -5
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@@ -289,7 +289,7 @@ public:
<number_of_variables_in_responses>`, containing types of each input and output variable. See
ml::VariableTypes.
*/
CV_WRAP static Ptr<cv::ml::TrainData> create(InputArray samples, int layout, InputArray responses,
CV_WRAP static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
InputArray sampleWeights=noArray(), InputArray varType=noArray());
};
@@ -324,7 +324,7 @@ public:
@param flags optional flags, depending on the model. Some of the models can be updated with the
new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
*/
CV_WRAP virtual bool train( const Ptr<cv::ml::TrainData>& trainData, int flags=0 );
CV_WRAP virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
/** @brief Trains the statistical model
@@ -347,7 +347,7 @@ public:
The method uses StatModel::predict to compute the error. For regression models the error is
computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
*/
CV_WRAP virtual float calcError( const Ptr<cv::ml::TrainData>& data, bool test, OutputArray resp ) const;
CV_WRAP virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
/** @brief Predicts response(s) for the provided sample(s)
@@ -361,7 +361,7 @@ public:
The class must implement static `create()` method with no parameters or with all default parameter values
*/
template<typename _Tp> static Ptr<_Tp> train(const Ptr<cv::ml::TrainData>& data, int flags=0)
template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, int flags=0)
{
Ptr<_Tp> model = _Tp::create();
return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
@@ -671,7 +671,7 @@ public:
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.
*/
virtual bool trainAuto( const Ptr<cv::ml::TrainData>& data, int kFold = 10,
virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C),
ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA),
ParamGrid pGrid = SVM::getDefaultGrid(SVM::P),