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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/svm.cpp
+39 -34
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@@ -149,7 +149,7 @@ struct SvmParams
};
/////////////////////////////////////// SVM kernel ///////////////////////////////////////
class SVMKernelImpl : public SVM::Kernel
class SVMKernelImpl CV_FINAL : public SVM::Kernel
{
public:
SVMKernelImpl( const SvmParams& _params = SvmParams() )
@@ -157,7 +157,7 @@ public:
params = _params;
}
int getType() const
int getType() const CV_OVERRIDE
{
return params.kernelType;
}
@@ -300,7 +300,7 @@ public:
}
void calc( int vcount, int var_count, const float* vecs,
const float* another, Qfloat* results )
const float* another, Qfloat* results ) CV_OVERRIDE
{
switch( params.kernelType )
{
@@ -414,7 +414,7 @@ ParamGrid SVM::getDefaultGrid( int param_id )
}
class SVMImpl : public SVM
class SVMImpl CV_FINAL : public SVM
{
public:
struct DecisionFunc
@@ -1241,7 +1241,7 @@ public:
clear();
}
void clear()
void clear() CV_OVERRIDE
{
decision_func.clear();
df_alpha.clear();
@@ -1255,34 +1255,39 @@ public:
return uncompressed_sv;
}
Mat getSupportVectors() const
Mat getSupportVectors() const CV_OVERRIDE
{
return sv;
}
CV_IMPL_PROPERTY(int, Type, params.svmType)
CV_IMPL_PROPERTY(double, Gamma, params.gamma)
CV_IMPL_PROPERTY(double, Coef0, params.coef0)
CV_IMPL_PROPERTY(double, Degree, params.degree)
CV_IMPL_PROPERTY(double, C, params.C)
CV_IMPL_PROPERTY(double, Nu, params.nu)
CV_IMPL_PROPERTY(double, P, params.p)
CV_IMPL_PROPERTY_S(cv::Mat, ClassWeights, params.classWeights)
CV_IMPL_PROPERTY_S(cv::TermCriteria, TermCriteria, params.termCrit)
inline int getType() const CV_OVERRIDE { return params.svmType; }
inline void setType(int val) CV_OVERRIDE { params.svmType = val; }
inline double getGamma() const CV_OVERRIDE { return params.gamma; }
inline void setGamma(double val) CV_OVERRIDE { params.gamma = val; }
inline double getCoef0() const CV_OVERRIDE { return params.coef0; }
inline void setCoef0(double val) CV_OVERRIDE { params.coef0 = val; }
inline double getDegree() const CV_OVERRIDE { return params.degree; }
inline void setDegree(double val) CV_OVERRIDE { params.degree = val; }
inline double getC() const CV_OVERRIDE { return params.C; }
inline void setC(double val) CV_OVERRIDE { params.C = val; }
inline double getNu() const CV_OVERRIDE { return params.nu; }
inline void setNu(double val) CV_OVERRIDE { params.nu = val; }
inline double getP() const CV_OVERRIDE { return params.p; }
inline void setP(double val) CV_OVERRIDE { params.p = val; }
inline cv::Mat getClassWeights() const CV_OVERRIDE { return params.classWeights; }
inline void setClassWeights(const cv::Mat& val) CV_OVERRIDE { params.classWeights = val; }
inline cv::TermCriteria getTermCriteria() const CV_OVERRIDE { return params.termCrit; }
inline void setTermCriteria(const cv::TermCriteria& val) CV_OVERRIDE { params.termCrit = val; }
int getKernelType() const
{
return params.kernelType;
}
void setKernel(int kernelType)
int getKernelType() const CV_OVERRIDE { return params.kernelType; }
void setKernel(int kernelType) CV_OVERRIDE
{
params.kernelType = kernelType;
if (kernelType != CUSTOM)
kernel = makePtr<SVMKernelImpl>(params);
}
void setCustomKernel(const Ptr<Kernel> &_kernel)
void setCustomKernel(const Ptr<Kernel> &_kernel) CV_OVERRIDE
{
params.kernelType = CUSTOM;
kernel = _kernel;
@@ -1606,7 +1611,7 @@ public:
std::swap(decision_func, new_df);
}
bool train( const Ptr<TrainData>& data, int )
bool train( const Ptr<TrainData>& data, int ) CV_OVERRIDE
{
clear();
@@ -1651,7 +1656,7 @@ public:
sidx(_sidx), is_classification(_is_classification), k_fold(_k_fold), result(_result)
{}
void operator()( const cv::Range& range ) const
void operator()( const cv::Range& range ) const CV_OVERRIDE
{
int sample_count = samples.rows;
int var_count_ = samples.cols;
@@ -1732,7 +1737,7 @@ public:
bool trainAuto( const Ptr<TrainData>& data, int k_fold,
ParamGrid C_grid, ParamGrid gamma_grid, ParamGrid p_grid,
ParamGrid nu_grid, ParamGrid coef_grid, ParamGrid degree_grid,
bool balanced )
bool balanced ) CV_OVERRIDE
{
checkParams();
@@ -1902,7 +1907,7 @@ public:
returnDFVal = _returnDFVal;
}
void operator()( const Range& range ) const
void operator()(const Range& range) const CV_OVERRIDE
{
int svmType = svm->params.svmType;
int sv_total = svm->sv.rows;
@@ -1995,7 +2000,7 @@ public:
}
float predict( InputArray _samples, OutputArray _results, int flags ) const
float predict( InputArray _samples, OutputArray _results, int flags ) const CV_OVERRIDE
{
float result = 0;
Mat samples = _samples.getMat(), results;
@@ -2023,7 +2028,7 @@ public:
return result;
}
double getDecisionFunction(int i, OutputArray _alpha, OutputArray _svidx ) const
double getDecisionFunction(int i, OutputArray _alpha, OutputArray _svidx ) const CV_OVERRIDE
{
CV_Assert( 0 <= i && i < (int)decision_func.size());
const DecisionFunc& df = decision_func[i];
@@ -2085,27 +2090,27 @@ public:
fs << "}";
}
bool isTrained() const
bool isTrained() const CV_OVERRIDE
{
return !sv.empty();
}
bool isClassifier() const
bool isClassifier() const CV_OVERRIDE
{
return params.svmType == C_SVC || params.svmType == NU_SVC || params.svmType == ONE_CLASS;
}
int getVarCount() const
int getVarCount() const CV_OVERRIDE
{
return var_count;
}
String getDefaultName() const
String getDefaultName() const CV_OVERRIDE
{
return "opencv_ml_svm";
}
void write( FileStorage& fs ) const
void write( FileStorage& fs ) const CV_OVERRIDE
{
int class_count = !class_labels.empty() ? (int)class_labels.total() :
params.svmType == ONE_CLASS ? 1 : 0;
@@ -2238,7 +2243,7 @@ public:
setParams( _params );
}
void read( const FileNode& fn )
void read( const FileNode& fn ) CV_OVERRIDE
{
clear();