183 lines
4.6 KiB
Objective-C
183 lines
4.6 KiB
Objective-C
//
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// This file is auto-generated. Please don't modify it!
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//
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#pragma once
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#ifdef __cplusplus
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//#import "opencv.hpp"
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#import "opencv2/ml.hpp"
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#else
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#define CV_EXPORTS
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#endif
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#import <Foundation/Foundation.h>
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#import "DTrees.h"
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@class Mat;
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@class TermCriteria;
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NS_ASSUME_NONNULL_BEGIN
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// C++: class RTrees
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/**
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* The class implements the random forest predictor.
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*
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* @see REF: ml_intro_rtrees
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*
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* Member of `Ml`
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*/
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CV_EXPORTS @interface RTrees : DTrees
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#ifdef __cplusplus
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@property(readonly)cv::Ptr<cv::ml::RTrees> nativePtrRTrees;
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#endif
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#ifdef __cplusplus
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- (instancetype)initWithNativePtr:(cv::Ptr<cv::ml::RTrees>)nativePtr;
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+ (instancetype)fromNative:(cv::Ptr<cv::ml::RTrees>)nativePtr;
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#endif
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#pragma mark - Methods
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//
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// bool cv::ml::RTrees::getCalculateVarImportance()
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//
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/**
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* @see `-setCalculateVarImportance:`
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*/
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- (BOOL)getCalculateVarImportance NS_SWIFT_NAME(getCalculateVarImportance());
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//
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// void cv::ml::RTrees::setCalculateVarImportance(bool val)
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//
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/**
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* getCalculateVarImportance @see `-getCalculateVarImportance:`
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*/
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- (void)setCalculateVarImportance:(BOOL)val NS_SWIFT_NAME(setCalculateVarImportance(val:));
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//
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// int cv::ml::RTrees::getActiveVarCount()
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//
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/**
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* @see `-setActiveVarCount:`
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*/
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- (int)getActiveVarCount NS_SWIFT_NAME(getActiveVarCount());
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//
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// void cv::ml::RTrees::setActiveVarCount(int val)
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//
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/**
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* getActiveVarCount @see `-getActiveVarCount:`
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*/
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- (void)setActiveVarCount:(int)val NS_SWIFT_NAME(setActiveVarCount(val:));
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//
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// TermCriteria cv::ml::RTrees::getTermCriteria()
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//
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/**
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* @see `-setTermCriteria:`
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*/
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- (TermCriteria*)getTermCriteria NS_SWIFT_NAME(getTermCriteria());
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//
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// void cv::ml::RTrees::setTermCriteria(TermCriteria val)
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//
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/**
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* getTermCriteria @see `-getTermCriteria:`
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*/
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- (void)setTermCriteria:(TermCriteria*)val NS_SWIFT_NAME(setTermCriteria(val:));
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//
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// Mat cv::ml::RTrees::getVarImportance()
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//
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/**
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* Returns the variable importance array.
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* The method returns the variable importance vector, computed at the training stage when
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* CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is
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* returned.
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*/
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- (Mat*)getVarImportance NS_SWIFT_NAME(getVarImportance());
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//
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// void cv::ml::RTrees::getVotes(Mat samples, Mat& results, int flags)
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//
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/**
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* Returns the result of each individual tree in the forest.
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* In case the model is a regression problem, the method will return each of the trees'
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* results for each of the sample cases. If the model is a classifier, it will return
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* a Mat with samples + 1 rows, where the first row gives the class number and the
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* following rows return the votes each class had for each sample.
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* @param samples Array containing the samples for which votes will be calculated.
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* @param results Array where the result of the calculation will be written.
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* @param flags Flags for defining the type of RTrees.
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*/
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- (void)getVotes:(Mat*)samples results:(Mat*)results flags:(int)flags NS_SWIFT_NAME(getVotes(samples:results:flags:));
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//
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// double cv::ml::RTrees::getOOBError()
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//
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/**
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* Returns the OOB error value, computed at the training stage when calcOOBError is set to true.
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* If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.
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*/
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- (double)getOOBError NS_SWIFT_NAME(getOOBError());
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//
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// static Ptr_RTrees cv::ml::RTrees::create()
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//
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/**
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* Creates the empty model.
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* Use StatModel::train to train the model, StatModel::train to create and train the model,
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* Algorithm::load to load the pre-trained model.
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*/
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+ (RTrees*)create NS_SWIFT_NAME(create());
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//
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// static Ptr_RTrees cv::ml::RTrees::load(String filepath, String nodeName = String())
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//
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/**
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* Loads and creates a serialized RTree from a file
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*
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* Use RTree::save to serialize and store an RTree to disk.
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* Load the RTree from this file again, by calling this function with the path to the file.
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* Optionally specify the node for the file containing the classifier
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*
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* @param filepath path to serialized RTree
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* @param nodeName name of node containing the classifier
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*/
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+ (RTrees*)load:(NSString*)filepath nodeName:(NSString*)nodeName NS_SWIFT_NAME(load(filepath:nodeName:));
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/**
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* Loads and creates a serialized RTree from a file
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*
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* Use RTree::save to serialize and store an RTree to disk.
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* Load the RTree from this file again, by calling this function with the path to the file.
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* Optionally specify the node for the file containing the classifier
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*
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* @param filepath path to serialized RTree
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*/
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+ (RTrees*)load:(NSString*)filepath NS_SWIFT_NAME(load(filepath:));
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@end
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NS_ASSUME_NONNULL_END
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