org.bytedeco.opencv.opencv_ml.LogisticRegression Maven / Gradle / Ivy
// Targeted by JavaCPP version 1.5.3: DO NOT EDIT THIS FILE
package org.bytedeco.opencv.opencv_ml;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;
import static org.bytedeco.javacpp.presets.javacpp.*;
import static org.bytedeco.openblas.global.openblas_nolapack.*;
import static org.bytedeco.openblas.global.openblas.*;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_ml.*;
// #endif
/****************************************************************************************\
* Logistic Regression *
\****************************************************************************************/
/** \brief Implements Logistic Regression classifier.
@see \ref ml_intro_lr
*/
@Namespace("cv::ml") @Properties(inherit = org.bytedeco.opencv.presets.opencv_ml.class)
public class LogisticRegression extends StatModel {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
public LogisticRegression(Pointer p) { super(p); }
/** Learning rate. */
/** @see setLearningRate */
public native double getLearningRate();
/** \copybrief getLearningRate @see getLearningRate */
public native void setLearningRate(double val);
/** Number of iterations. */
/** @see setIterations */
public native int getIterations();
/** \copybrief getIterations @see getIterations */
public native void setIterations(int val);
/** Kind of regularization to be applied. See LogisticRegression::RegKinds. */
/** @see setRegularization */
public native int getRegularization();
/** \copybrief getRegularization @see getRegularization */
public native void setRegularization(int val);
/** Kind of training method used. See LogisticRegression::Methods. */
/** @see setTrainMethod */
public native int getTrainMethod();
/** \copybrief getTrainMethod @see getTrainMethod */
public native void setTrainMethod(int val);
/** Specifies the number of training samples taken in each step of Mini-Batch Gradient
Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It
has to take values less than the total number of training samples. */
/** @see setMiniBatchSize */
public native int getMiniBatchSize();
/** \copybrief getMiniBatchSize @see getMiniBatchSize */
public native void setMiniBatchSize(int val);
/** Termination criteria of the algorithm. */
/** @see setTermCriteria */
public native @ByVal TermCriteria getTermCriteria();
/** \copybrief getTermCriteria @see getTermCriteria */
public native void setTermCriteria(@ByVal TermCriteria val);
/** Regularization kinds */
/** enum cv::ml::LogisticRegression::RegKinds */
public static final int
/** Regularization disabled */
REG_DISABLE = -1,
/** %L1 norm */
REG_L1 = 0,
/** %L2 norm */
REG_L2 = 1;
/** Training methods */
/** enum cv::ml::LogisticRegression::Methods */
public static final int
BATCH = 0,
/** Set MiniBatchSize to a positive integer when using this method. */
MINI_BATCH = 1;
/** \brief Predicts responses for input samples and returns a float type.
@param 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.
@param results Predicted labels as a column matrix of type CV_32S.
@param flags Not used.
*/
public native @Override float predict( @ByVal Mat samples, @ByVal(nullValue = "cv::OutputArray(cv::noArray())") Mat results, int flags/*=0*/ );
public native float predict( @ByVal Mat samples );
public native @Override float predict( @ByVal UMat samples, @ByVal(nullValue = "cv::OutputArray(cv::noArray())") UMat results, int flags/*=0*/ );
public native float predict( @ByVal UMat samples );
public native @Override float predict( @ByVal GpuMat samples, @ByVal(nullValue = "cv::OutputArray(cv::noArray())") GpuMat results, int flags/*=0*/ );
public native float predict( @ByVal GpuMat samples );
/** \brief 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.
*/
public native @ByVal Mat get_learnt_thetas();
/** \brief Creates empty model.
Creates Logistic Regression model with parameters given.
*/
public static native @Ptr LogisticRegression create();
/** \brief 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
*
* @param filepath path to serialized LogisticRegression
* @param nodeName name of node containing the classifier
*/
public static native @Ptr LogisticRegression load(@Str BytePointer filepath, @Str BytePointer nodeName/*=cv::String()*/);
public static native @Ptr LogisticRegression load(@Str BytePointer filepath);
public static native @Ptr LogisticRegression load(@Str String filepath, @Str String nodeName/*=cv::String()*/);
public static native @Ptr LogisticRegression load(@Str String filepath);
}