virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const;
CV_WRAP virtual void clear();
-#ifndef SWIG
CV_WRAP CvNormalBayesClassifier( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat() );
CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx = cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
bool update=false );
CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const;
-#endif
virtual void write( CvFileStorage* storage, const char* name ) const;
virtual void read( CvFileStorage* storage, CvFileNode* node );
virtual float find_nearest( const CvMat* samples, int k, CV_OUT CvMat* results=0,
const float** neighbors=0, CV_OUT CvMat* neighborResponses=0, CV_OUT CvMat* dist=0 ) const;
-#ifndef SWIG
CV_WRAP CvKNearest( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 );
cv::Mat* dist=0 ) const;
CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results,
CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const;
-#endif
virtual void clear();
int get_max_k() const;
virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
virtual float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
-#ifndef SWIG
CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
CvSVMParams params=CvSVMParams() );
bool balanced=false);
CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
CV_WRAP_AS(predict_all) virtual void predict( cv::InputArray samples, cv::OutputArray results ) const;
-#endif
CV_WRAP virtual int get_support_vector_count() const;
virtual const float* get_support_vector(int i) const;
virtual CvDTreeNode* predict( const CvMat* sample, const CvMat* missingDataMask=0,
bool preprocessedInput=false ) const;
-#ifndef SWIG
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(),
bool preprocessedInput=false ) const;
CV_WRAP virtual cv::Mat getVarImportance();
-#endif
virtual const CvMat* get_var_importance();
CV_WRAP virtual void clear();
virtual float predict( const CvMat* sample, const CvMat* missing = 0 ) const;
virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const;
-#ifndef SWIG
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
CV_WRAP virtual cv::Mat getVarImportance();
-#endif
CV_WRAP virtual void clear();
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvRTParams params=CvRTParams());
-#ifndef SWIG
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvRTParams params=CvRTParams());
-#endif
virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
protected:
virtual std::string getName() const;
CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ,
bool raw_mode=false, bool return_sum=false ) const;
-#ifndef SWIG
CV_WRAP CvBoost( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
const cv::Range& slice=cv::Range::all(), bool rawMode=false,
bool returnSum=false ) const;
-#endif
virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
int flags=0 );
virtual float predict( const CvMat* inputs, CV_OUT CvMat* outputs ) const;
-#ifndef SWIG
CV_WRAP CvANN_MLP( const cv::Mat& layerSizes,
int activateFunc=CvANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
int flags=0 );
CV_WRAP virtual float predict( const cv::Mat& inputs, CV_OUT cv::Mat& outputs ) const;
-#endif
CV_WRAP virtual void clear();