org.opencv.ml

Class LogisticRegression

java.lang.Object

org.opencv.core.Algorithm

org.opencv.ml.StatModel

org.opencv.ml.LogisticRegression

public class LogisticRegression
extends StatModel

Implements Logistic Regression classifier. SEE: REF: ml_intro_lr

Field Summary

Fields

Modifier and Type	Field and Description
static int	BATCH
static int	MINI_BATCH
static int	REG_DISABLE
static int	REG_L1
static int	REG_L2

Fields inherited from class org.opencv.ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Fields inherited from class org.opencv.core.Algorithm

nativeObj

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	LogisticRegression(long addr)

Method Summary

Modifier and Type	Method and Description
static LogisticRegression	__fromPtr__(long addr)
static LogisticRegression	create() Creates empty model.
protected void	finalize()
Mat	get_learnt_thetas() This function returns the trained parameters arranged across rows.
int	getIterations() SEE: setIterations
double	getLearningRate() SEE: setLearningRate
int	getMiniBatchSize() SEE: setMiniBatchSize
int	getRegularization() SEE: setRegularization
TermCriteria	getTermCriteria() SEE: setTermCriteria
int	getTrainMethod() SEE: setTrainMethod
static LogisticRegression	load(String filepath) Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk.
static LogisticRegression	load(String filepath, String nodeName) Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk.
float	predict(Mat samples) Predicts responses for input samples and returns a float type.
float	predict(Mat samples, Mat results) Predicts responses for input samples and returns a float type.
float	predict(Mat samples, Mat results, int flags) Predicts responses for input samples and returns a float type.
void	setIterations(int val) getIterations SEE: getIterations
void	setLearningRate(double val) getLearningRate SEE: getLearningRate
void	setMiniBatchSize(int val) getMiniBatchSize SEE: getMiniBatchSize
void	setRegularization(int val) getRegularization SEE: getRegularization
void	setTermCriteria(TermCriteria val) getTermCriteria SEE: getTermCriteria
void	setTrainMethod(int val) getTrainMethod SEE: getTrainMethod

Methods inherited from class org.opencv.ml.StatModel

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

Methods inherited from class org.opencv.core.Algorithm

clear, getDefaultName, getNativeObjAddr, save

Methods inherited from class java.lang.Object

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

Field Detail

REG_DISABLE

public static final int REG_DISABLE

See Also:

Constant Field Values

REG_L1

public static final int REG_L1

See Also:

Constant Field Values

REG_L2

public static final int REG_L2

See Also:

Constant Field Values

BATCH

public static final int BATCH

See Also:

Constant Field Values

MINI_BATCH

public static final int MINI_BATCH

See Also:

Constant Field Values

Constructor Detail

LogisticRegression

protected LogisticRegression(long addr)

Method Detail

__fromPtr__

public static LogisticRegression __fromPtr__(long addr)

get_learnt_thetas

public Mat get_learnt_thetas()

This function returns the trained parameters arranged across rows. For a two class classification problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.

Returns:

automatically generated

create

public static LogisticRegression create()

Creates empty model. Creates Logistic Regression model with parameters given.

Returns:

automatically generated

load

public static LogisticRegression load(String filepath,
                                      String nodeName)

Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression 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 LogisticRegression

nodeName - name of node containing the classifier

Returns:

automatically generated

load

public static LogisticRegression load(String filepath)

Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression 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 LogisticRegression

Returns:

automatically generated

getTermCriteria

public TermCriteria getTermCriteria()

SEE: setTermCriteria

Returns:

automatically generated

getLearningRate

public double getLearningRate()

SEE: setLearningRate

Returns:

automatically generated

predict

public float predict(Mat samples,
                     Mat results,
                     int flags)

Predicts responses for input samples and returns a float type.

Overrides:

predict in class StatModel

Parameters:

samples - The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.

results - Predicted labels as a column matrix of type CV_32S.

flags - Not used.

Returns:

automatically generated

predict

public float predict(Mat samples,
                     Mat results)

Predicts responses for input samples and returns a float type.

Overrides:

predict in class StatModel

Parameters:

samples - The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.

results - Predicted labels as a column matrix of type CV_32S.

Returns:

automatically generated

predict

public float predict(Mat samples)

Predicts responses for input samples and returns a float type.

Overrides:

predict in class StatModel

Parameters:

samples - The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.

Returns:

automatically generated

getIterations

public int getIterations()

SEE: setIterations

Returns:

automatically generated

getMiniBatchSize

public int getMiniBatchSize()

SEE: setMiniBatchSize

Returns:

automatically generated

getRegularization

public int getRegularization()

SEE: setRegularization

Returns:

automatically generated

getTrainMethod

public int getTrainMethod()

SEE: setTrainMethod

Returns:

automatically generated

setIterations

public void setIterations(int val)

getIterations SEE: getIterations

Parameters:

val - automatically generated

setLearningRate

public void setLearningRate(double val)

getLearningRate SEE: getLearningRate

Parameters:

val - automatically generated

setMiniBatchSize

public void setMiniBatchSize(int val)

getMiniBatchSize SEE: getMiniBatchSize

Parameters:

val - automatically generated

setRegularization

public void setRegularization(int val)

getRegularization SEE: getRegularization

Parameters:

val - automatically generated

setTermCriteria

public void setTermCriteria(TermCriteria val)

getTermCriteria SEE: getTermCriteria

Parameters:

val - automatically generated

setTrainMethod

public void setTrainMethod(int val)

getTrainMethod SEE: getTrainMethod

Parameters:

val - automatically generated

finalize

protected void finalize()
                 throws Throwable

Overrides:

finalize in class StatModel

Throws:

Throwable