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ml: refactor non-virtual methods

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This commit is contained in:
berak 2018-04-24 12:11:59 +02:00
parent 4d7d630e92
commit fc5bba66af
4 changed files with 39 additions and 76 deletions
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14
modules/ml/include/opencv2/ml.hpp
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@ -198,7 +198,7 @@ public:
CV_WRAP virtual Mat getTestSampleWeights() const = 0;
CV_WRAP virtual Mat getVarIdx() const = 0;
CV_WRAP virtual Mat getVarType() const = 0;
CV_WRAP Mat getVarSymbolFlags() const;
CV_WRAP virtual Mat getVarSymbolFlags() const = 0;
CV_WRAP virtual int getResponseType() const = 0;
CV_WRAP virtual Mat getTrainSampleIdx() const = 0;
CV_WRAP virtual Mat getTestSampleIdx() const = 0;
@ -234,10 +234,10 @@ public:
CV_WRAP virtual void shuffleTrainTest() = 0;
/** @brief Returns matrix of test samples */
CV_WRAP Mat getTestSamples() const;
CV_WRAP virtual Mat getTestSamples() const = 0;
/** @brief Returns vector of symbolic names captured in loadFromCSV() */
CV_WRAP void getNames(std::vector<String>& names) const;
CV_WRAP virtual void getNames(std::vector<String>& names) const = 0;
CV_WRAP static Mat getSubVector(const Mat& vec, const Mat& idx);
@ -727,7 +727,7 @@ 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.
*/
CV_WRAP bool trainAuto(InputArray samples,
CV_WRAP virtual bool trainAuto(InputArray samples,
int layout,
InputArray responses,
int kFold = 10,
@ -737,7 +737,7 @@ public:
Ptr<ParamGrid> nuGrid = SVM::getDefaultGridPtr(SVM::NU),
Ptr<ParamGrid> coeffGrid = SVM::getDefaultGridPtr(SVM::COEF),
Ptr<ParamGrid> degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE),
bool balanced=false);
bool balanced=false) = 0;
/** @brief Retrieves all the support vectors
@ -752,7 +752,7 @@ public:
support vector, used for prediction, was derived from. They are returned in a floating-point
matrix, where the support vectors are stored as matrix rows.
*/
CV_WRAP Mat getUncompressedSupportVectors() const;
CV_WRAP virtual Mat getUncompressedSupportVectors() const = 0;
/** @brief Retrieves the decision function
@ -1273,7 +1273,7 @@ public:
@param results Array where the result of the calculation will be written.
@param flags Flags for defining the type of RTrees.
*/
CV_WRAP void getVotes(InputArray samples, OutputArray results, int flags) const;
CV_WRAP virtual void getVotes(InputArray samples, OutputArray results, int flags) const = 0;
/** Creates the empty model.
Use StatModel::train to train the model, StatModel::train to create and train the model,

59
modules/ml/src/data.cpp
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@ -50,13 +50,6 @@ static const int VAR_MISSED = VAR_ORDERED;
TrainData::~TrainData() {}
Mat TrainData::getTestSamples() const
{
Mat idx = getTestSampleIdx();
Mat samples = getSamples();
return idx.empty() ? Mat() : getSubVector(samples, idx);
}
Mat TrainData::getSubVector(const Mat& vec, const Mat& idx)
{
if( idx.empty() )
@ -119,6 +112,7 @@ Mat TrainData::getSubVector(const Mat& vec, const Mat& idx)
return subvec;
}
class TrainDataImpl CV_FINAL : public TrainData
{
public:
@ -155,6 +149,12 @@ public:
return layout == ROW_SAMPLE ? samples.cols : samples.rows;
}
Mat getTestSamples() const CV_OVERRIDE
{
Mat idx = getTestSampleIdx();
return idx.empty() ? Mat() : getSubVector(samples, idx);
}
Mat getSamples() const CV_OVERRIDE { return samples; }
Mat getResponses() const CV_OVERRIDE { return responses; }
Mat getMissing() const CV_OVERRIDE { return missing; }
@ -987,6 +987,27 @@ public:
}
}
void getNames(std::vector<String>& names) const CV_OVERRIDE
{
size_t n = nameMap.size();
TrainDataImpl::MapType::const_iterator it = nameMap.begin(),
it_end = nameMap.end();
names.resize(n+1);
names[0] = "?";
for( ; it != it_end; ++it )
{
String s = it->first;
int label = it->second;
CV_Assert( label > 0 && label <= (int)n );
names[label] = s;
}
}
Mat getVarSymbolFlags() const CV_OVERRIDE
{
return varSymbolFlags;
}
FILE* file;
int layout;
Mat samples, missing, varType, varIdx, varSymbolFlags, responses, missingSubst;
@ -996,30 +1017,6 @@ public:
MapType nameMap;
};
void TrainData::getNames(std::vector<String>& names) const
{
const TrainDataImpl* impl = dynamic_cast<const TrainDataImpl*>(this);
CV_Assert(impl != 0);
size_t n = impl->nameMap.size();
TrainDataImpl::MapType::const_iterator it = impl->nameMap.begin(),
it_end = impl->nameMap.end();
names.resize(n+1);
names[0] = "?";
for( ; it != it_end; ++it )
{
String s = it->first;
int label = it->second;
CV_Assert( label > 0 && label <= (int)n );
names[label] = s;
}
}
Mat TrainData::getVarSymbolFlags() const
{
const TrainDataImpl* impl = dynamic_cast<const TrainDataImpl*>(this);
CV_Assert(impl != 0);
return impl->varSymbolFlags;
}
Ptr<TrainData> TrainData::loadFromCSV(const String& filename,
int headerLines,

16
modules/ml/src/rtrees.cpp
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@ -453,6 +453,7 @@ public:
inline void setRegressionAccuracy(float val) CV_OVERRIDE { impl.params.setRegressionAccuracy(val); }
inline cv::Mat getPriors() const CV_OVERRIDE { return impl.params.getPriors(); }
inline void setPriors(const cv::Mat& val) CV_OVERRIDE { impl.params.setPriors(val); }
inline void getVotes(InputArray input, OutputArray output, int flags) const CV_OVERRIDE {return impl.getVotes(input,output,flags);}
RTreesImpl() {}
virtual ~RTreesImpl() CV_OVERRIDE {}
@ -485,12 +486,6 @@ public:
impl.read(fn);
}
void getVotes_( InputArray samples, OutputArray results, int flags ) const
{
CV_TRACE_FUNCTION();
impl.getVotes(samples, results, flags);
}
Mat getVarImportance() const CV_OVERRIDE { return Mat_<float>(impl.varImportance, true); }
int getVarCount() const CV_OVERRIDE { return impl.getVarCount(); }
@ -519,15 +514,6 @@ Ptr<RTrees> RTrees::load(const String& filepath, const String& nodeName)
return Algorithm::load<RTrees>(filepath, nodeName);
}
void RTrees::getVotes(InputArray input, OutputArray output, int flags) const
{
CV_TRACE_FUNCTION();
const RTreesImpl* this_ = dynamic_cast<const RTreesImpl*>(this);
if(!this_)
CV_Error(Error::StsNotImplemented, "the class is not RTreesImpl");
return this_->getVotes_(input, output, flags);
}
}}
// End of file.

26
modules/ml/src/svm.cpp
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@ -1250,7 +1250,7 @@ public:
uncompressed_sv.release();
}
Mat getUncompressedSupportVectors_() const
Mat getUncompressedSupportVectors() const CV_OVERRIDE
{
return uncompressed_sv;
}
@ -1982,10 +1982,10 @@ public:
bool returnDFVal;
};
bool trainAuto_(InputArray samples, int layout,
bool trainAuto(InputArray samples, int layout,
InputArray responses, int kfold, Ptr<ParamGrid> Cgrid,
Ptr<ParamGrid> gammaGrid, Ptr<ParamGrid> pGrid, Ptr<ParamGrid> nuGrid,
Ptr<ParamGrid> coeffGrid, Ptr<ParamGrid> degreeGrid, bool balanced)
Ptr<ParamGrid> coeffGrid, Ptr<ParamGrid> degreeGrid, bool balanced) CV_OVERRIDE
{
Ptr<TrainData> data = TrainData::create(samples, layout, responses);
return this->trainAuto(
@ -2353,26 +2353,6 @@ Ptr<SVM> SVM::load(const String& filepath)
return svm;
}
Mat SVM::getUncompressedSupportVectors() const
{
const SVMImpl* this_ = dynamic_cast<const SVMImpl*>(this);
if(!this_)
CV_Error(Error::StsNotImplemented, "the class is not SVMImpl");
return this_->getUncompressedSupportVectors_();
}
bool SVM::trainAuto(InputArray samples, int layout,
InputArray responses, int kfold, Ptr<ParamGrid> Cgrid,
Ptr<ParamGrid> gammaGrid, Ptr<ParamGrid> pGrid, Ptr<ParamGrid> nuGrid,
Ptr<ParamGrid> coeffGrid, Ptr<ParamGrid> degreeGrid, bool balanced)
{
SVMImpl* this_ = dynamic_cast<SVMImpl*>(this);
if (!this_) {
CV_Error(Error::StsNotImplemented, "the class is not SVMImpl");
}
return this_->trainAuto_(samples, layout, responses,
kfold, Cgrid, gammaGrid, pGrid, nuGrid, coeffGrid, degreeGrid, balanced);
}
}
}