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ml: apply CV_OVERRIDE/CV_FINAL

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This commit is contained in:
Alexander Alekhin
2018-03-15 16:16:58 +03:00
parent 3314966acb
commit 4d0dd3e509
13 changed files with 370 additions and 301 deletions
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modules/ml/src/boost.cpp
+48 -36
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@@ -69,7 +69,7 @@ BoostTreeParams::BoostTreeParams( int _boostType, int _weak_count,
weightTrimRate = _weightTrimRate;
}
class DTreesImplForBoost : public DTreesImpl
class DTreesImplForBoost CV_FINAL : public DTreesImpl
{
public:
DTreesImplForBoost()
@@ -79,14 +79,14 @@ public:
}
virtual ~DTreesImplForBoost() {}
bool isClassifier() const { return true; }
bool isClassifier() const CV_OVERRIDE { return true; }
void clear()
void clear() CV_OVERRIDE
{
DTreesImpl::clear();
}
void startTraining( const Ptr<TrainData>& trainData, int flags )
void startTraining( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
{
DTreesImpl::startTraining(trainData, flags);
sumResult.assign(w->sidx.size(), 0.);
@@ -132,7 +132,7 @@ public:
}
}
void endTraining()
void endTraining() CV_OVERRIDE
{
DTreesImpl::endTraining();
vector<double> e;
@@ -167,7 +167,7 @@ public:
}
}
void calcValue( int nidx, const vector<int>& _sidx )
void calcValue( int nidx, const vector<int>& _sidx ) CV_OVERRIDE
{
DTreesImpl::calcValue(nidx, _sidx);
WNode* node = &w->wnodes[nidx];
@@ -182,7 +182,7 @@ public:
}
}
bool train( const Ptr<TrainData>& trainData, int flags )
bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
{
startTraining(trainData, flags);
int treeidx, ntrees = bparams.weakCount >= 0 ? bparams.weakCount : 10000;
@@ -356,7 +356,7 @@ public:
}
}
float predictTrees( const Range& range, const Mat& sample, int flags0 ) const
float predictTrees( const Range& range, const Mat& sample, int flags0 ) const CV_OVERRIDE
{
int flags = (flags0 & ~PREDICT_MASK) | PREDICT_SUM;
float val = DTreesImpl::predictTrees(range, sample, flags);
@@ -370,7 +370,7 @@ public:
return val;
}
void writeTrainingParams( FileStorage& fs ) const
void writeTrainingParams( FileStorage& fs ) const CV_OVERRIDE
{
fs << "boosting_type" <<
(bparams.boostType == Boost::DISCRETE ? "DiscreteAdaboost" :
@@ -382,7 +382,7 @@ public:
fs << "weight_trimming_rate" << bparams.weightTrimRate;
}
void write( FileStorage& fs ) const
void write( FileStorage& fs ) const CV_OVERRIDE
{
if( roots.empty() )
CV_Error( CV_StsBadArg, "RTrees have not been trained" );
@@ -405,7 +405,7 @@ public:
fs << "]";
}
void readParams( const FileNode& fn )
void readParams( const FileNode& fn ) CV_OVERRIDE
{
DTreesImpl::readParams(fn);
@@ -423,7 +423,7 @@ public:
tparams_node["weight_trimming_rate"] : fn["weight_trimming_rate"]);
}
void read( const FileNode& fn )
void read( const FileNode& fn ) CV_OVERRIDE
{
clear();
@@ -452,51 +452,63 @@ public:
BoostImpl() {}
virtual ~BoostImpl() {}
CV_IMPL_PROPERTY(int, BoostType, impl.bparams.boostType)
CV_IMPL_PROPERTY(int, WeakCount, impl.bparams.weakCount)
CV_IMPL_PROPERTY(double, WeightTrimRate, impl.bparams.weightTrimRate)
inline int getBoostType() const CV_OVERRIDE { return impl.bparams.boostType; }
inline void setBoostType(int val) CV_OVERRIDE { impl.bparams.boostType = val; }
inline int getWeakCount() const CV_OVERRIDE { return impl.bparams.weakCount; }
inline void setWeakCount(int val) CV_OVERRIDE { impl.bparams.weakCount = val; }
inline double getWeightTrimRate() const CV_OVERRIDE { return impl.bparams.weightTrimRate; }
inline void setWeightTrimRate(double val) CV_OVERRIDE { impl.bparams.weightTrimRate = val; }
CV_WRAP_SAME_PROPERTY(int, MaxCategories, impl.params)
CV_WRAP_SAME_PROPERTY(int, MaxDepth, impl.params)
CV_WRAP_SAME_PROPERTY(int, MinSampleCount, impl.params)
CV_WRAP_SAME_PROPERTY(int, CVFolds, impl.params)
CV_WRAP_SAME_PROPERTY(bool, UseSurrogates, impl.params)
CV_WRAP_SAME_PROPERTY(bool, Use1SERule, impl.params)
CV_WRAP_SAME_PROPERTY(bool, TruncatePrunedTree, impl.params)
CV_WRAP_SAME_PROPERTY(float, RegressionAccuracy, impl.params)
CV_WRAP_SAME_PROPERTY_S(cv::Mat, Priors, impl.params)
inline int getMaxCategories() const CV_OVERRIDE { return impl.params.getMaxCategories(); }
inline void setMaxCategories(int val) CV_OVERRIDE { impl.params.setMaxCategories(val); }
inline int getMaxDepth() const CV_OVERRIDE { return impl.params.getMaxDepth(); }
inline void setMaxDepth(int val) CV_OVERRIDE { impl.params.setMaxDepth(val); }
inline int getMinSampleCount() const CV_OVERRIDE { return impl.params.getMinSampleCount(); }
inline void setMinSampleCount(int val) CV_OVERRIDE { impl.params.setMinSampleCount(val); }
inline int getCVFolds() const CV_OVERRIDE { return impl.params.getCVFolds(); }
inline void setCVFolds(int val) CV_OVERRIDE { impl.params.setCVFolds(val); }
inline bool getUseSurrogates() const CV_OVERRIDE { return impl.params.getUseSurrogates(); }
inline void setUseSurrogates(bool val) CV_OVERRIDE { impl.params.setUseSurrogates(val); }
inline bool getUse1SERule() const CV_OVERRIDE { return impl.params.getUse1SERule(); }
inline void setUse1SERule(bool val) CV_OVERRIDE { impl.params.setUse1SERule(val); }
inline bool getTruncatePrunedTree() const CV_OVERRIDE { return impl.params.getTruncatePrunedTree(); }
inline void setTruncatePrunedTree(bool val) CV_OVERRIDE { impl.params.setTruncatePrunedTree(val); }
inline float getRegressionAccuracy() const CV_OVERRIDE { return impl.params.getRegressionAccuracy(); }
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); }
String getDefaultName() const { return "opencv_ml_boost"; }
String getDefaultName() const CV_OVERRIDE { return "opencv_ml_boost"; }
bool train( const Ptr<TrainData>& trainData, int flags )
bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
{
return impl.train(trainData, flags);
}
float predict( InputArray samples, OutputArray results, int flags ) const
float predict( InputArray samples, OutputArray results, int flags ) const CV_OVERRIDE
{
return impl.predict(samples, results, flags);
}
void write( FileStorage& fs ) const
void write( FileStorage& fs ) const CV_OVERRIDE
{
impl.write(fs);
}
void read( const FileNode& fn )
void read( const FileNode& fn ) CV_OVERRIDE
{
impl.read(fn);
}
int getVarCount() const { return impl.getVarCount(); }
int getVarCount() const CV_OVERRIDE { return impl.getVarCount(); }
bool isTrained() const { return impl.isTrained(); }
bool isClassifier() const { return impl.isClassifier(); }
bool isTrained() const CV_OVERRIDE { return impl.isTrained(); }
bool isClassifier() const CV_OVERRIDE { return impl.isClassifier(); }
const vector<int>& getRoots() const { return impl.getRoots(); }
const vector<Node>& getNodes() const { return impl.getNodes(); }
const vector<Split>& getSplits() const { return impl.getSplits(); }
const vector<int>& getSubsets() const { return impl.getSubsets(); }
const vector<int>& getRoots() const CV_OVERRIDE { return impl.getRoots(); }
const vector<Node>& getNodes() const CV_OVERRIDE { return impl.getNodes(); }
const vector<Split>& getSplits() const CV_OVERRIDE { return impl.getSplits(); }
const vector<int>& getSubsets() const CV_OVERRIDE { return impl.getSubsets(); }
DTreesImplForBoost impl;
};