253 lines
6.0 KiB
Objective-C
253 lines
6.0 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 "StatModel.h"
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@class Mat;
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@class TermCriteria;
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// C++: enum MarginType (cv.ml.SVMSGD.MarginType)
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typedef NS_ENUM(int, MarginType) {
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SVMSGD_SOFT_MARGIN NS_SWIFT_NAME(SOFT_MARGIN) = 0,
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SVMSGD_HARD_MARGIN NS_SWIFT_NAME(HARD_MARGIN) = 1
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};
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// C++: enum SvmsgdType (cv.ml.SVMSGD.SvmsgdType)
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typedef NS_ENUM(int, SvmsgdType) {
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SVMSGD_SGD NS_SWIFT_NAME(SGD) = 0,
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SVMSGD_ASGD NS_SWIFT_NAME(ASGD) = 1
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};
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NS_ASSUME_NONNULL_BEGIN
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// C++: class SVMSGD
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/**
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* *************************************************************************************\
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* Stochastic Gradient Descent SVM Classifier *
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* \***************************************************************************************
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*
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* Member of `Ml`
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*/
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CV_EXPORTS @interface SVMSGD : StatModel
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#ifdef __cplusplus
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@property(readonly)cv::Ptr<cv::ml::SVMSGD> nativePtrSVMSGD;
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#endif
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#ifdef __cplusplus
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- (instancetype)initWithNativePtr:(cv::Ptr<cv::ml::SVMSGD>)nativePtr;
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+ (instancetype)fromNative:(cv::Ptr<cv::ml::SVMSGD>)nativePtr;
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#endif
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#pragma mark - Methods
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//
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// Mat cv::ml::SVMSGD::getWeights()
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//
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/**
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* @return the weights of the trained model (decision function f(x) = weights * x + shift).
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*/
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- (Mat*)getWeights NS_SWIFT_NAME(getWeights());
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//
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// float cv::ml::SVMSGD::getShift()
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//
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/**
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* @return the shift of the trained model (decision function f(x) = weights * x + shift).
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*/
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- (float)getShift NS_SWIFT_NAME(getShift());
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//
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// static Ptr_SVMSGD cv::ml::SVMSGD::create()
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//
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/**
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* Creates empty model.
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* Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to
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* find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
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*/
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+ (SVMSGD*)create NS_SWIFT_NAME(create());
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//
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// static Ptr_SVMSGD cv::ml::SVMSGD::load(String filepath, String nodeName = String())
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//
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/**
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* Loads and creates a serialized SVMSGD from a file
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*
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* Use SVMSGD::save to serialize and store an SVMSGD to disk.
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* Load the SVMSGD 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 SVMSGD
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* @param nodeName name of node containing the classifier
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*/
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+ (SVMSGD*)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 SVMSGD from a file
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*
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* Use SVMSGD::save to serialize and store an SVMSGD to disk.
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* Load the SVMSGD 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 SVMSGD
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*/
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+ (SVMSGD*)load:(NSString*)filepath NS_SWIFT_NAME(load(filepath:));
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//
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// void cv::ml::SVMSGD::setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN)
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//
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/**
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* Function sets optimal parameters values for chosen SVM SGD model.
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* @param svmsgdType is the type of SVMSGD classifier.
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* @param marginType is the type of margin constraint.
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*/
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- (void)setOptimalParameters:(int)svmsgdType marginType:(int)marginType NS_SWIFT_NAME(setOptimalParameters(svmsgdType:marginType:));
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/**
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* Function sets optimal parameters values for chosen SVM SGD model.
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* @param svmsgdType is the type of SVMSGD classifier.
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*/
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- (void)setOptimalParameters:(int)svmsgdType NS_SWIFT_NAME(setOptimalParameters(svmsgdType:));
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/**
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* Function sets optimal parameters values for chosen SVM SGD model.
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*/
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- (void)setOptimalParameters NS_SWIFT_NAME(setOptimalParameters());
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//
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// int cv::ml::SVMSGD::getSvmsgdType()
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//
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/**
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* @see `-setSvmsgdType:`
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*/
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- (int)getSvmsgdType NS_SWIFT_NAME(getSvmsgdType());
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//
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// void cv::ml::SVMSGD::setSvmsgdType(int svmsgdType)
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//
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/**
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* getSvmsgdType @see `-getSvmsgdType:`
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*/
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- (void)setSvmsgdType:(int)svmsgdType NS_SWIFT_NAME(setSvmsgdType(svmsgdType:));
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//
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// int cv::ml::SVMSGD::getMarginType()
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//
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/**
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* @see `-setMarginType:`
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*/
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- (int)getMarginType NS_SWIFT_NAME(getMarginType());
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//
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// void cv::ml::SVMSGD::setMarginType(int marginType)
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//
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/**
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* getMarginType @see `-getMarginType:`
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*/
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- (void)setMarginType:(int)marginType NS_SWIFT_NAME(setMarginType(marginType:));
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//
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// float cv::ml::SVMSGD::getMarginRegularization()
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//
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/**
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* @see `-setMarginRegularization:`
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*/
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- (float)getMarginRegularization NS_SWIFT_NAME(getMarginRegularization());
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//
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// void cv::ml::SVMSGD::setMarginRegularization(float marginRegularization)
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//
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/**
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* getMarginRegularization @see `-getMarginRegularization:`
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*/
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- (void)setMarginRegularization:(float)marginRegularization NS_SWIFT_NAME(setMarginRegularization(marginRegularization:));
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//
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// float cv::ml::SVMSGD::getInitialStepSize()
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//
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/**
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* @see `-setInitialStepSize:`
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*/
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- (float)getInitialStepSize NS_SWIFT_NAME(getInitialStepSize());
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//
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// void cv::ml::SVMSGD::setInitialStepSize(float InitialStepSize)
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//
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/**
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* getInitialStepSize @see `-getInitialStepSize:`
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*/
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- (void)setInitialStepSize:(float)InitialStepSize NS_SWIFT_NAME(setInitialStepSize(InitialStepSize:));
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//
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// float cv::ml::SVMSGD::getStepDecreasingPower()
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//
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/**
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* @see `-setStepDecreasingPower:`
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*/
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- (float)getStepDecreasingPower NS_SWIFT_NAME(getStepDecreasingPower());
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//
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// void cv::ml::SVMSGD::setStepDecreasingPower(float stepDecreasingPower)
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//
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/**
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* getStepDecreasingPower @see `-getStepDecreasingPower:`
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*/
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- (void)setStepDecreasingPower:(float)stepDecreasingPower NS_SWIFT_NAME(setStepDecreasingPower(stepDecreasingPower:));
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//
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// TermCriteria cv::ml::SVMSGD::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::SVMSGD::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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@end
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NS_ASSUME_NONNULL_END
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