//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import org.opencv.core.Mat;
import org.opencv.core.TermCriteria;
// C++: class SVMSGD
/**
* *************************************************************************************\
* Stochastic Gradient Descent SVM Classifier *
* \***************************************************************************************
*/
public class SVMSGD extends StatModel {
protected SVMSGD(long addr) { super(addr); }
// internal usage only
public static SVMSGD __fromPtr__(long addr) { return new SVMSGD(addr); }
// C++: enum MarginType (cv.ml.SVMSGD.MarginType)
public static final int
SOFT_MARGIN = 0,
HARD_MARGIN = 1;
// C++: enum SvmsgdType (cv.ml.SVMSGD.SvmsgdType)
public static final int
SGD = 0,
ASGD = 1;
//
// C++: Mat cv::ml::SVMSGD::getWeights()
//
/**
* @return the weights of the trained model (decision function f(x) = weights * x + shift).
*/
public Mat getWeights() {
return new Mat(getWeights_0(nativeObj));
}
//
// C++: float cv::ml::SVMSGD::getShift()
//
/**
* @return the shift of the trained model (decision function f(x) = weights * x + shift).
*/
public float getShift() {
return getShift_0(nativeObj);
}
//
// C++: 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.
* @return automatically generated
*/
public static SVMSGD create() {
return SVMSGD.__fromPtr__(create_0());
}
//
// C++: 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
* @return automatically generated
*/
public static SVMSGD load(String filepath, String nodeName) {
return SVMSGD.__fromPtr__(load_0(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
* @return automatically generated
*/
public static SVMSGD load(String filepath) {
return SVMSGD.__fromPtr__(load_1(filepath));
}
//
// C++: 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.
*/
public void setOptimalParameters(int svmsgdType, int marginType) {
setOptimalParameters_0(nativeObj, svmsgdType, marginType);
}
/**
* Function sets optimal parameters values for chosen SVM SGD model.
* @param svmsgdType is the type of SVMSGD classifier.
*/
public void setOptimalParameters(int svmsgdType) {
setOptimalParameters_1(nativeObj, svmsgdType);
}
/**
* Function sets optimal parameters values for chosen SVM SGD model.
*/
public void setOptimalParameters() {
setOptimalParameters_2(nativeObj);
}
//
// C++: int cv::ml::SVMSGD::getSvmsgdType()
//
/**
* SEE: setSvmsgdType
* @return automatically generated
*/
public int getSvmsgdType() {
return getSvmsgdType_0(nativeObj);
}
//
// C++: void cv::ml::SVMSGD::setSvmsgdType(int svmsgdType)
//
/**
* getSvmsgdType SEE: getSvmsgdType
* @param svmsgdType automatically generated
*/
public void setSvmsgdType(int svmsgdType) {
setSvmsgdType_0(nativeObj, svmsgdType);
}
//
// C++: int cv::ml::SVMSGD::getMarginType()
//
/**
* SEE: setMarginType
* @return automatically generated
*/
public int getMarginType() {
return getMarginType_0(nativeObj);
}
//
// C++: void cv::ml::SVMSGD::setMarginType(int marginType)
//
/**
* getMarginType SEE: getMarginType
* @param marginType automatically generated
*/
public void setMarginType(int marginType) {
setMarginType_0(nativeObj, marginType);
}
//
// C++: float cv::ml::SVMSGD::getMarginRegularization()
//
/**
* SEE: setMarginRegularization
* @return automatically generated
*/
public float getMarginRegularization() {
return getMarginRegularization_0(nativeObj);
}
//
// C++: void cv::ml::SVMSGD::setMarginRegularization(float marginRegularization)
//
/**
* getMarginRegularization SEE: getMarginRegularization
* @param marginRegularization automatically generated
*/
public void setMarginRegularization(float marginRegularization) {
setMarginRegularization_0(nativeObj, marginRegularization);
}
//
// C++: float cv::ml::SVMSGD::getInitialStepSize()
//
/**
* SEE: setInitialStepSize
* @return automatically generated
*/
public float getInitialStepSize() {
return getInitialStepSize_0(nativeObj);
}
//
// C++: void cv::ml::SVMSGD::setInitialStepSize(float InitialStepSize)
//
/**
* getInitialStepSize SEE: getInitialStepSize
* @param InitialStepSize automatically generated
*/
public void setInitialStepSize(float InitialStepSize) {
setInitialStepSize_0(nativeObj, InitialStepSize);
}
//
// C++: float cv::ml::SVMSGD::getStepDecreasingPower()
//
/**
* SEE: setStepDecreasingPower
* @return automatically generated
*/
public float getStepDecreasingPower() {
return getStepDecreasingPower_0(nativeObj);
}
//
// C++: void cv::ml::SVMSGD::setStepDecreasingPower(float stepDecreasingPower)
//
/**
* getStepDecreasingPower SEE: getStepDecreasingPower
* @param stepDecreasingPower automatically generated
*/
public void setStepDecreasingPower(float stepDecreasingPower) {
setStepDecreasingPower_0(nativeObj, stepDecreasingPower);
}
//
// C++: TermCriteria cv::ml::SVMSGD::getTermCriteria()
//
/**
* SEE: setTermCriteria
* @return automatically generated
*/
public TermCriteria getTermCriteria() {
return new TermCriteria(getTermCriteria_0(nativeObj));
}
//
// C++: void cv::ml::SVMSGD::setTermCriteria(TermCriteria val)
//
/**
* getTermCriteria SEE: getTermCriteria
* @param val automatically generated
*/
public void setTermCriteria(TermCriteria val) {
setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: Mat cv::ml::SVMSGD::getWeights()
private static native long getWeights_0(long nativeObj);
// C++: float cv::ml::SVMSGD::getShift()
private static native float getShift_0(long nativeObj);
// C++: static Ptr_SVMSGD cv::ml::SVMSGD::create()
private static native long create_0();
// C++: static Ptr_SVMSGD cv::ml::SVMSGD::load(String filepath, String nodeName = String())
private static native long load_0(String filepath, String nodeName);
private static native long load_1(String filepath);
// C++: void cv::ml::SVMSGD::setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN)
private static native void setOptimalParameters_0(long nativeObj, int svmsgdType, int marginType);
private static native void setOptimalParameters_1(long nativeObj, int svmsgdType);
private static native void setOptimalParameters_2(long nativeObj);
// C++: int cv::ml::SVMSGD::getSvmsgdType()
private static native int getSvmsgdType_0(long nativeObj);
// C++: void cv::ml::SVMSGD::setSvmsgdType(int svmsgdType)
private static native void setSvmsgdType_0(long nativeObj, int svmsgdType);
// C++: int cv::ml::SVMSGD::getMarginType()
private static native int getMarginType_0(long nativeObj);
// C++: void cv::ml::SVMSGD::setMarginType(int marginType)
private static native void setMarginType_0(long nativeObj, int marginType);
// C++: float cv::ml::SVMSGD::getMarginRegularization()
private static native float getMarginRegularization_0(long nativeObj);
// C++: void cv::ml::SVMSGD::setMarginRegularization(float marginRegularization)
private static native void setMarginRegularization_0(long nativeObj, float marginRegularization);
// C++: float cv::ml::SVMSGD::getInitialStepSize()
private static native float getInitialStepSize_0(long nativeObj);
// C++: void cv::ml::SVMSGD::setInitialStepSize(float InitialStepSize)
private static native void setInitialStepSize_0(long nativeObj, float InitialStepSize);
// C++: float cv::ml::SVMSGD::getStepDecreasingPower()
private static native float getStepDecreasingPower_0(long nativeObj);
// C++: void cv::ml::SVMSGD::setStepDecreasingPower(float stepDecreasingPower)
private static native void setStepDecreasingPower_0(long nativeObj, float stepDecreasingPower);
// C++: TermCriteria cv::ml::SVMSGD::getTermCriteria()
private static native double[] getTermCriteria_0(long nativeObj);
// C++: void cv::ml::SVMSGD::setTermCriteria(TermCriteria val)
private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
// native support for java finalize()
private static native void delete(long nativeObj);
}
