Health/Assets/OpenCVForUnity/Plugins/iOS/opencv2.framework/Headers/SVMSGD.h

253 lines
6.0 KiB
Objective-C

//
// This file is auto-generated. Please don't modify it!
//
#pragma once
#ifdef __cplusplus
//#import "opencv.hpp"
#import "opencv2/ml.hpp"
#else
#define CV_EXPORTS
#endif
#import <Foundation/Foundation.h>
#import "StatModel.h"
@class Mat;
@class TermCriteria;
// C++: enum MarginType (cv.ml.SVMSGD.MarginType)
typedef NS_ENUM(int, MarginType) {
SVMSGD_SOFT_MARGIN NS_SWIFT_NAME(SOFT_MARGIN) = 0,
SVMSGD_HARD_MARGIN NS_SWIFT_NAME(HARD_MARGIN) = 1
};
// C++: enum SvmsgdType (cv.ml.SVMSGD.SvmsgdType)
typedef NS_ENUM(int, SvmsgdType) {
SVMSGD_SGD NS_SWIFT_NAME(SGD) = 0,
SVMSGD_ASGD NS_SWIFT_NAME(ASGD) = 1
};
NS_ASSUME_NONNULL_BEGIN
// C++: class SVMSGD
/**
* *************************************************************************************\
* Stochastic Gradient Descent SVM Classifier *
* \***************************************************************************************
*
* Member of `Ml`
*/
CV_EXPORTS @interface SVMSGD : StatModel
#ifdef __cplusplus
@property(readonly)cv::Ptr<cv::ml::SVMSGD> nativePtrSVMSGD;
#endif
#ifdef __cplusplus
- (instancetype)initWithNativePtr:(cv::Ptr<cv::ml::SVMSGD>)nativePtr;
+ (instancetype)fromNative:(cv::Ptr<cv::ml::SVMSGD>)nativePtr;
#endif
#pragma mark - Methods
//
// Mat cv::ml::SVMSGD::getWeights()
//
/**
* @return the weights of the trained model (decision function f(x) = weights * x + shift).
*/
- (Mat*)getWeights NS_SWIFT_NAME(getWeights());
//
// float cv::ml::SVMSGD::getShift()
//
/**
* @return the shift of the trained model (decision function f(x) = weights * x + shift).
*/
- (float)getShift NS_SWIFT_NAME(getShift());
//
// static Ptr_SVMSGD cv::ml::SVMSGD::create()
//
/**
* Creates empty model.
* Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to
* find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
*/
+ (SVMSGD*)create NS_SWIFT_NAME(create());
//
// static Ptr_SVMSGD cv::ml::SVMSGD::load(String filepath, String nodeName = String())
//
/**
* Loads and creates a serialized SVMSGD from a file
*
* Use SVMSGD::save to serialize and store an SVMSGD to disk.
* Load the SVMSGD from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized SVMSGD
* @param nodeName name of node containing the classifier
*/
+ (SVMSGD*)load:(NSString*)filepath nodeName:(NSString*)nodeName NS_SWIFT_NAME(load(filepath:nodeName:));
/**
* Loads and creates a serialized SVMSGD from a file
*
* Use SVMSGD::save to serialize and store an SVMSGD to disk.
* Load the SVMSGD from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized SVMSGD
*/
+ (SVMSGD*)load:(NSString*)filepath NS_SWIFT_NAME(load(filepath:));
//
// void cv::ml::SVMSGD::setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN)
//
/**
* Function sets optimal parameters values for chosen SVM SGD model.
* @param svmsgdType is the type of SVMSGD classifier.
* @param marginType is the type of margin constraint.
*/
- (void)setOptimalParameters:(int)svmsgdType marginType:(int)marginType NS_SWIFT_NAME(setOptimalParameters(svmsgdType:marginType:));
/**
* Function sets optimal parameters values for chosen SVM SGD model.
* @param svmsgdType is the type of SVMSGD classifier.
*/
- (void)setOptimalParameters:(int)svmsgdType NS_SWIFT_NAME(setOptimalParameters(svmsgdType:));
/**
* Function sets optimal parameters values for chosen SVM SGD model.
*/
- (void)setOptimalParameters NS_SWIFT_NAME(setOptimalParameters());
//
// int cv::ml::SVMSGD::getSvmsgdType()
//
/**
* @see `-setSvmsgdType:`
*/
- (int)getSvmsgdType NS_SWIFT_NAME(getSvmsgdType());
//
// void cv::ml::SVMSGD::setSvmsgdType(int svmsgdType)
//
/**
* getSvmsgdType @see `-getSvmsgdType:`
*/
- (void)setSvmsgdType:(int)svmsgdType NS_SWIFT_NAME(setSvmsgdType(svmsgdType:));
//
// int cv::ml::SVMSGD::getMarginType()
//
/**
* @see `-setMarginType:`
*/
- (int)getMarginType NS_SWIFT_NAME(getMarginType());
//
// void cv::ml::SVMSGD::setMarginType(int marginType)
//
/**
* getMarginType @see `-getMarginType:`
*/
- (void)setMarginType:(int)marginType NS_SWIFT_NAME(setMarginType(marginType:));
//
// float cv::ml::SVMSGD::getMarginRegularization()
//
/**
* @see `-setMarginRegularization:`
*/
- (float)getMarginRegularization NS_SWIFT_NAME(getMarginRegularization());
//
// void cv::ml::SVMSGD::setMarginRegularization(float marginRegularization)
//
/**
* getMarginRegularization @see `-getMarginRegularization:`
*/
- (void)setMarginRegularization:(float)marginRegularization NS_SWIFT_NAME(setMarginRegularization(marginRegularization:));
//
// float cv::ml::SVMSGD::getInitialStepSize()
//
/**
* @see `-setInitialStepSize:`
*/
- (float)getInitialStepSize NS_SWIFT_NAME(getInitialStepSize());
//
// void cv::ml::SVMSGD::setInitialStepSize(float InitialStepSize)
//
/**
* getInitialStepSize @see `-getInitialStepSize:`
*/
- (void)setInitialStepSize:(float)InitialStepSize NS_SWIFT_NAME(setInitialStepSize(InitialStepSize:));
//
// float cv::ml::SVMSGD::getStepDecreasingPower()
//
/**
* @see `-setStepDecreasingPower:`
*/
- (float)getStepDecreasingPower NS_SWIFT_NAME(getStepDecreasingPower());
//
// void cv::ml::SVMSGD::setStepDecreasingPower(float stepDecreasingPower)
//
/**
* getStepDecreasingPower @see `-getStepDecreasingPower:`
*/
- (void)setStepDecreasingPower:(float)stepDecreasingPower NS_SWIFT_NAME(setStepDecreasingPower(stepDecreasingPower:));
//
// TermCriteria cv::ml::SVMSGD::getTermCriteria()
//
/**
* @see `-setTermCriteria:`
*/
- (TermCriteria*)getTermCriteria NS_SWIFT_NAME(getTermCriteria());
//
// void cv::ml::SVMSGD::setTermCriteria(TermCriteria val)
//
/**
* getTermCriteria @see `-getTermCriteria:`
*/
- (void)setTermCriteria:(TermCriteria*)val NS_SWIFT_NAME(setTermCriteria(val:));
@end
NS_ASSUME_NONNULL_END