generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Adding support for pointer generation. Fixes #6605

Browse Source
This commit is contained in:
Marek Smigielski
2016-05-31 08:35:50 +02:00
parent eca752368b
commit ef45005056
2 changed files with 12 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
modules/ml/include/opencv2/ml.hpp
View File

@@ -285,7 +285,7 @@ public:
<number_of_variables_in_responses>`, containing types of each input and output variable. See
ml::VariableTypes.
*/
CV_WRAP static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
CV_WRAP static Ptr<cv::ml::TrainData> create(InputArray samples, int layout, InputArray responses,
InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
InputArray sampleWeights=noArray(), InputArray varType=noArray());
};
@@ -320,7 +320,7 @@ 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<TrainData>& trainData, int flags=0 );
CV_WRAP virtual bool train( const Ptr<cv::ml::TrainData>& trainData, int flags=0 );
/** @brief Trains the statistical model
@@ -343,7 +343,7 @@ 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<TrainData>& data, bool test, OutputArray resp ) const;
CV_WRAP virtual float calcError( const Ptr<cv::ml::TrainData>& data, bool test, OutputArray resp ) const;
/** @brief Predicts response(s) for the provided sample(s)
@@ -357,7 +357,7 @@ 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<TrainData>& data, int flags=0)
template<typename _Tp> static Ptr<_Tp> train(const Ptr<cv::ml::TrainData>& data, int flags=0)
{
Ptr<_Tp> model = _Tp::create();
return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
@@ -667,7 +667,7 @@ 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<TrainData>& data, int kFold = 10,
virtual bool trainAuto( const Ptr<cv::ml::TrainData>& data, int kFold = 10,
ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C),
ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA),
ParamGrid pGrid = SVM::getDefaultGrid(SVM::P),