#include <ml.hpp>
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| CvERTrees () |
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virtual | ~CvERTrees () |
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virtual bool | train (const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvRTParams params=CvRTParams()) |
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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()) |
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virtual bool | train (CvMLData *data, CvRTParams params=CvRTParams()) |
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| CvRTrees () |
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virtual | ~CvRTrees () |
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virtual float | predict (const CvMat *sample, const CvMat *missing=0) const |
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virtual float | predict_prob (const CvMat *sample, const CvMat *missing=0) const |
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virtual float | predict (const cv::Mat &sample, const cv::Mat &missing=cv::Mat()) const |
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virtual float | predict_prob (const cv::Mat &sample, const cv::Mat &missing=cv::Mat()) const |
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virtual cv::Mat | getVarImportance () |
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virtual void | clear () |
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virtual const CvMat * | get_var_importance () |
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virtual float | get_proximity (const CvMat *sample1, const CvMat *sample2, const CvMat *missing1=0, const CvMat *missing2=0) const |
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virtual float | calc_error (CvMLData *data, int type, std::vector< float > *resp=0) |
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virtual float | get_train_error () |
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virtual void | read (CvFileStorage *fs, CvFileNode *node) |
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virtual void | write (CvFileStorage *fs, const char *name) const |
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CvMat * | get_active_var_mask () |
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CvRNG * | get_rng () |
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int | get_tree_count () const |
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CvForestTree * | get_tree (int i) const |
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| CvStatModel () |
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virtual | ~CvStatModel () |
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virtual void | save (const char *filename, const char *name=0) const |
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virtual void | load (const char *filename, const char *name=0) |
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virtual CvERTrees::~CvERTrees |
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virtual std::string CvERTrees::getName |
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The documentation for this class was generated from the following file: