org.opencv.ml

Class SVMSGD

java.lang.Object

org.opencv.core.Algorithm

org.opencv.ml.StatModel

org.opencv.ml.SVMSGD

public class SVMSGD
extends StatModel

*************************************************************************************\ Stochastic Gradient Descent SVM Classifier * \***************************************************************************************

Field Summary

Fields

Modifier and Type	Field and Description
static int	ASGD
static int	HARD_MARGIN
static int	SGD
static int	SOFT_MARGIN

Fields inherited from class org.opencv.ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Method Summary

Modifier and Type	Method and Description
static SVMSGD	__fromPtr__(long addr)
static SVMSGD	create() Creates empty model.
float	getInitialStepSize() SEE: setInitialStepSize
float	getMarginRegularization() SEE: setMarginRegularization
int	getMarginType() SEE: setMarginType
float	getShift()
float	getStepDecreasingPower() SEE: setStepDecreasingPower
int	getSvmsgdType() SEE: setSvmsgdType
TermCriteria	getTermCriteria() SEE: setTermCriteria
Mat	getWeights()
static SVMSGD	load(java.lang.String filepath) Loads and creates a serialized SVMSGD from a file Use SVMSGD::save to serialize and store an SVMSGD to disk.
static SVMSGD	load(java.lang.String filepath, java.lang.String nodeName) Loads and creates a serialized SVMSGD from a file Use SVMSGD::save to serialize and store an SVMSGD to disk.
void	setInitialStepSize(float InitialStepSize) getInitialStepSize SEE: getInitialStepSize
void	setMarginRegularization(float marginRegularization) getMarginRegularization SEE: getMarginRegularization
void	setMarginType(int marginType) getMarginType SEE: getMarginType
void	setOptimalParameters() Function sets optimal parameters values for chosen SVM SGD model.
void	setOptimalParameters(int svmsgdType) Function sets optimal parameters values for chosen SVM SGD model.
void	setOptimalParameters(int svmsgdType, int marginType) Function sets optimal parameters values for chosen SVM SGD model.
void	setStepDecreasingPower(float stepDecreasingPower) getStepDecreasingPower SEE: getStepDecreasingPower
void	setSvmsgdType(int svmsgdType) getSvmsgdType SEE: getSvmsgdType
void	setTermCriteria(TermCriteria val) getTermCriteria SEE: getTermCriteria

Methods inherited from class org.opencv.ml.StatModel

calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, train, train, train

Methods inherited from class org.opencv.core.Algorithm

clear, getDefaultName, getNativeObjAddr, save

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

ASGD

public static final int ASGD

See Also:

Constant Field Values

HARD_MARGIN

public static final int HARD_MARGIN

See Also:

Constant Field Values

SGD

public static final int SGD

See Also:

Constant Field Values

SOFT_MARGIN

public static final int SOFT_MARGIN

See Also:

Constant Field Values

Method Detail

__fromPtr__

public static SVMSGD __fromPtr__(long addr)

create

public static 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.

Returns:

automatically generated

getInitialStepSize

public float getInitialStepSize()

SEE: setInitialStepSize

Returns:

automatically generated

getMarginRegularization

public float getMarginRegularization()

SEE: setMarginRegularization

Returns:

automatically generated

getMarginType

public int getMarginType()

SEE: setMarginType

Returns:

automatically generated

getShift

public float getShift()

Returns:

the shift of the trained model (decision function f(x) = weights * x + shift).

getStepDecreasingPower

public float getStepDecreasingPower()

SEE: setStepDecreasingPower

Returns:

automatically generated

getSvmsgdType

public int getSvmsgdType()

SEE: setSvmsgdType

Returns:

automatically generated

getTermCriteria

public TermCriteria getTermCriteria()

SEE: setTermCriteria

Returns:

automatically generated

getWeights

public Mat getWeights()

Returns:

the weights of the trained model (decision function f(x) = weights * x + shift).

load

public static SVMSGD load(java.lang.String filepath)

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

Parameters:

filepath - path to serialized SVMSGD

Returns:

automatically generated

load

public static SVMSGD load(java.lang.String filepath,
                          java.lang.String 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

Parameters:

filepath - path to serialized SVMSGD

nodeName - name of node containing the classifier

Returns:

automatically generated

setInitialStepSize

public void setInitialStepSize(float InitialStepSize)

getInitialStepSize SEE: getInitialStepSize

Parameters:

InitialStepSize - automatically generated

setMarginRegularization

public void setMarginRegularization(float marginRegularization)

getMarginRegularization SEE: getMarginRegularization

Parameters:

marginRegularization - automatically generated

setMarginType

public void setMarginType(int marginType)

getMarginType SEE: getMarginType

Parameters:

marginType - automatically generated

setOptimalParameters

public void setOptimalParameters()

Function sets optimal parameters values for chosen SVM SGD model.

setOptimalParameters

public void setOptimalParameters(int svmsgdType)

Function sets optimal parameters values for chosen SVM SGD model.

Parameters:

svmsgdType - is the type of SVMSGD classifier.

setOptimalParameters

public void setOptimalParameters(int svmsgdType,
                                 int marginType)

Function sets optimal parameters values for chosen SVM SGD model.

Parameters:

svmsgdType - is the type of SVMSGD classifier.

marginType - is the type of margin constraint.

setStepDecreasingPower

public void setStepDecreasingPower(float stepDecreasingPower)

getStepDecreasingPower SEE: getStepDecreasingPower

Parameters:

stepDecreasingPower - automatically generated

setSvmsgdType

public void setSvmsgdType(int svmsgdType)

getSvmsgdType SEE: getSvmsgdType

Parameters:

svmsgdType - automatically generated

setTermCriteria

public void setTermCriteria(TermCriteria val)

getTermCriteria SEE: getTermCriteria

Parameters:

val - automatically generated