//
// 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 ANN_MLP
/**
* Artificial Neural Networks - Multi-Layer Perceptrons.
*
* Unlike many other models in ML that are constructed and trained at once, in the MLP model these
* steps are separated. First, a network with the specified topology is created using the non-default
* constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is
* trained using a set of input and output vectors. The training procedure can be repeated more than
* once, that is, the weights can be adjusted based on the new training data.
*
* Additional flags for StatModel::train are available: ANN_MLP::TrainFlags.
*
* SEE: REF: ml_intro_ann
*/
public class ANN_MLP extends StatModel {
protected ANN_MLP(long addr) { super(addr); }
// internal usage only
public static ANN_MLP __fromPtr__(long addr) { return new ANN_MLP(addr); }
// C++: enum ActivationFunctions (cv.ml.ANN_MLP.ActivationFunctions)
public static final int
IDENTITY = 0,
SIGMOID_SYM = 1,
GAUSSIAN = 2,
RELU = 3,
LEAKYRELU = 4;
// C++: enum TrainFlags (cv.ml.ANN_MLP.TrainFlags)
public static final int
UPDATE_WEIGHTS = 1,
NO_INPUT_SCALE = 2,
NO_OUTPUT_SCALE = 4;
// C++: enum TrainingMethods (cv.ml.ANN_MLP.TrainingMethods)
public static final int
BACKPROP = 0,
RPROP = 1,
ANNEAL = 2;
//
// C++: void cv::ml::ANN_MLP::setTrainMethod(int method, double param1 = 0, double param2 = 0)
//
/**
* Sets training method and common parameters.
* @param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
* @param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
* @param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.
*/
public void setTrainMethod(int method, double param1, double param2) {
setTrainMethod_0(nativeObj, method, param1, param2);
}
/**
* Sets training method and common parameters.
* @param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
* @param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
*/
public void setTrainMethod(int method, double param1) {
setTrainMethod_1(nativeObj, method, param1);
}
/**
* Sets training method and common parameters.
* @param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
*/
public void setTrainMethod(int method) {
setTrainMethod_2(nativeObj, method);
}
//
// C++: int cv::ml::ANN_MLP::getTrainMethod()
//
/**
* Returns current training method
* @return automatically generated
*/
public int getTrainMethod() {
return getTrainMethod_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setActivationFunction(int type, double param1 = 0, double param2 = 0)
//
/**
* Initialize the activation function for each neuron.
* Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
* @param type The type of activation function. See ANN_MLP::ActivationFunctions.
* @param param1 The first parameter of the activation function, \(\alpha\). Default value is 0.
* @param param2 The second parameter of the activation function, \(\beta\). Default value is 0.
*/
public void setActivationFunction(int type, double param1, double param2) {
setActivationFunction_0(nativeObj, type, param1, param2);
}
/**
* Initialize the activation function for each neuron.
* Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
* @param type The type of activation function. See ANN_MLP::ActivationFunctions.
* @param param1 The first parameter of the activation function, \(\alpha\). Default value is 0.
*/
public void setActivationFunction(int type, double param1) {
setActivationFunction_1(nativeObj, type, param1);
}
/**
* Initialize the activation function for each neuron.
* Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
* @param type The type of activation function. See ANN_MLP::ActivationFunctions.
*/
public void setActivationFunction(int type) {
setActivationFunction_2(nativeObj, type);
}
//
// C++: void cv::ml::ANN_MLP::setLayerSizes(Mat _layer_sizes)
//
/**
* Integer vector specifying the number of neurons in each layer including the input and output layers.
* The very first element specifies the number of elements in the input layer.
* The last element - number of elements in the output layer. Default value is empty Mat.
* SEE: getLayerSizes
* @param _layer_sizes automatically generated
*/
public void setLayerSizes(Mat _layer_sizes) {
setLayerSizes_0(nativeObj, _layer_sizes.nativeObj);
}
//
// C++: Mat cv::ml::ANN_MLP::getLayerSizes()
//
/**
* Integer vector specifying the number of neurons in each layer including the input and output layers.
* The very first element specifies the number of elements in the input layer.
* The last element - number of elements in the output layer.
* SEE: setLayerSizes
* @return automatically generated
*/
public Mat getLayerSizes() {
return new Mat(getLayerSizes_0(nativeObj));
}
//
// C++: TermCriteria cv::ml::ANN_MLP::getTermCriteria()
//
/**
* SEE: setTermCriteria
* @return automatically generated
*/
public TermCriteria getTermCriteria() {
return new TermCriteria(getTermCriteria_0(nativeObj));
}
//
// C++: void cv::ml::ANN_MLP::setTermCriteria(TermCriteria val)
//
/**
* getTermCriteria SEE: getTermCriteria
* @param val automatically generated
*/
public void setTermCriteria(TermCriteria val) {
setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
}
//
// C++: double cv::ml::ANN_MLP::getBackpropWeightScale()
//
/**
* SEE: setBackpropWeightScale
* @return automatically generated
*/
public double getBackpropWeightScale() {
return getBackpropWeightScale_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setBackpropWeightScale(double val)
//
/**
* getBackpropWeightScale SEE: getBackpropWeightScale
* @param val automatically generated
*/
public void setBackpropWeightScale(double val) {
setBackpropWeightScale_0(nativeObj, val);
}
//
// C++: double cv::ml::ANN_MLP::getBackpropMomentumScale()
//
/**
* SEE: setBackpropMomentumScale
* @return automatically generated
*/
public double getBackpropMomentumScale() {
return getBackpropMomentumScale_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setBackpropMomentumScale(double val)
//
/**
* getBackpropMomentumScale SEE: getBackpropMomentumScale
* @param val automatically generated
*/
public void setBackpropMomentumScale(double val) {
setBackpropMomentumScale_0(nativeObj, val);
}
//
// C++: double cv::ml::ANN_MLP::getRpropDW0()
//
/**
* SEE: setRpropDW0
* @return automatically generated
*/
public double getRpropDW0() {
return getRpropDW0_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setRpropDW0(double val)
//
/**
* getRpropDW0 SEE: getRpropDW0
* @param val automatically generated
*/
public void setRpropDW0(double val) {
setRpropDW0_0(nativeObj, val);
}
//
// C++: double cv::ml::ANN_MLP::getRpropDWPlus()
//
/**
* SEE: setRpropDWPlus
* @return automatically generated
*/
public double getRpropDWPlus() {
return getRpropDWPlus_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setRpropDWPlus(double val)
//
/**
* getRpropDWPlus SEE: getRpropDWPlus
* @param val automatically generated
*/
public void setRpropDWPlus(double val) {
setRpropDWPlus_0(nativeObj, val);
}
//
// C++: double cv::ml::ANN_MLP::getRpropDWMinus()
//
/**
* SEE: setRpropDWMinus
* @return automatically generated
*/
public double getRpropDWMinus() {
return getRpropDWMinus_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setRpropDWMinus(double val)
//
/**
* getRpropDWMinus SEE: getRpropDWMinus
* @param val automatically generated
*/
public void setRpropDWMinus(double val) {
setRpropDWMinus_0(nativeObj, val);
}
//
// C++: double cv::ml::ANN_MLP::getRpropDWMin()
//
/**
* SEE: setRpropDWMin
* @return automatically generated
*/
public double getRpropDWMin() {
return getRpropDWMin_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setRpropDWMin(double val)
//
/**
* getRpropDWMin SEE: getRpropDWMin
* @param val automatically generated
*/
public void setRpropDWMin(double val) {
setRpropDWMin_0(nativeObj, val);
}
//
// C++: double cv::ml::ANN_MLP::getRpropDWMax()
//
/**
* SEE: setRpropDWMax
* @return automatically generated
*/
public double getRpropDWMax() {
return getRpropDWMax_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setRpropDWMax(double val)
//
/**
* getRpropDWMax SEE: getRpropDWMax
* @param val automatically generated
*/
public void setRpropDWMax(double val) {
setRpropDWMax_0(nativeObj, val);
}
//
// C++: double cv::ml::ANN_MLP::getAnnealInitialT()
//
/**
* SEE: setAnnealInitialT
* @return automatically generated
*/
public double getAnnealInitialT() {
return getAnnealInitialT_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setAnnealInitialT(double val)
//
/**
* getAnnealInitialT SEE: getAnnealInitialT
* @param val automatically generated
*/
public void setAnnealInitialT(double val) {
setAnnealInitialT_0(nativeObj, val);
}
//
// C++: double cv::ml::ANN_MLP::getAnnealFinalT()
//
/**
* SEE: setAnnealFinalT
* @return automatically generated
*/
public double getAnnealFinalT() {
return getAnnealFinalT_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setAnnealFinalT(double val)
//
/**
* getAnnealFinalT SEE: getAnnealFinalT
* @param val automatically generated
*/
public void setAnnealFinalT(double val) {
setAnnealFinalT_0(nativeObj, val);
}
//
// C++: double cv::ml::ANN_MLP::getAnnealCoolingRatio()
//
/**
* SEE: setAnnealCoolingRatio
* @return automatically generated
*/
public double getAnnealCoolingRatio() {
return getAnnealCoolingRatio_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setAnnealCoolingRatio(double val)
//
/**
* getAnnealCoolingRatio SEE: getAnnealCoolingRatio
* @param val automatically generated
*/
public void setAnnealCoolingRatio(double val) {
setAnnealCoolingRatio_0(nativeObj, val);
}
//
// C++: int cv::ml::ANN_MLP::getAnnealItePerStep()
//
/**
* SEE: setAnnealItePerStep
* @return automatically generated
*/
public int getAnnealItePerStep() {
return getAnnealItePerStep_0(nativeObj);
}
//
// C++: void cv::ml::ANN_MLP::setAnnealItePerStep(int val)
//
/**
* getAnnealItePerStep SEE: getAnnealItePerStep
* @param val automatically generated
*/
public void setAnnealItePerStep(int val) {
setAnnealItePerStep_0(nativeObj, val);
}
//
// C++: Mat cv::ml::ANN_MLP::getWeights(int layerIdx)
//
public Mat getWeights(int layerIdx) {
return new Mat(getWeights_0(nativeObj, layerIdx));
}
//
// C++: static Ptr_ANN_MLP cv::ml::ANN_MLP::create()
//
/**
* Creates empty model
*
* Use StatModel::train to train the model, Algorithm::load<ANN_MLP>(filename) to load the pre-trained model.
* Note that the train method has optional flags: ANN_MLP::TrainFlags.
* @return automatically generated
*/
public static ANN_MLP create() {
return ANN_MLP.__fromPtr__(create_0());
}
//
// C++: static Ptr_ANN_MLP cv::ml::ANN_MLP::load(String filepath)
//
/**
* Loads and creates a serialized ANN from a file
*
* Use ANN::save to serialize and store an ANN to disk.
* Load the ANN from this file again, by calling this function with the path to the file.
*
* @param filepath path to serialized ANN
* @return automatically generated
*/
public static ANN_MLP load(String filepath) {
return ANN_MLP.__fromPtr__(load_0(filepath));
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: void cv::ml::ANN_MLP::setTrainMethod(int method, double param1 = 0, double param2 = 0)
private static native void setTrainMethod_0(long nativeObj, int method, double param1, double param2);
private static native void setTrainMethod_1(long nativeObj, int method, double param1);
private static native void setTrainMethod_2(long nativeObj, int method);
// C++: int cv::ml::ANN_MLP::getTrainMethod()
private static native int getTrainMethod_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setActivationFunction(int type, double param1 = 0, double param2 = 0)
private static native void setActivationFunction_0(long nativeObj, int type, double param1, double param2);
private static native void setActivationFunction_1(long nativeObj, int type, double param1);
private static native void setActivationFunction_2(long nativeObj, int type);
// C++: void cv::ml::ANN_MLP::setLayerSizes(Mat _layer_sizes)
private static native void setLayerSizes_0(long nativeObj, long _layer_sizes_nativeObj);
// C++: Mat cv::ml::ANN_MLP::getLayerSizes()
private static native long getLayerSizes_0(long nativeObj);
// C++: TermCriteria cv::ml::ANN_MLP::getTermCriteria()
private static native double[] getTermCriteria_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setTermCriteria(TermCriteria val)
private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
// C++: double cv::ml::ANN_MLP::getBackpropWeightScale()
private static native double getBackpropWeightScale_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setBackpropWeightScale(double val)
private static native void setBackpropWeightScale_0(long nativeObj, double val);
// C++: double cv::ml::ANN_MLP::getBackpropMomentumScale()
private static native double getBackpropMomentumScale_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setBackpropMomentumScale(double val)
private static native void setBackpropMomentumScale_0(long nativeObj, double val);
// C++: double cv::ml::ANN_MLP::getRpropDW0()
private static native double getRpropDW0_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setRpropDW0(double val)
private static native void setRpropDW0_0(long nativeObj, double val);
// C++: double cv::ml::ANN_MLP::getRpropDWPlus()
private static native double getRpropDWPlus_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setRpropDWPlus(double val)
private static native void setRpropDWPlus_0(long nativeObj, double val);
// C++: double cv::ml::ANN_MLP::getRpropDWMinus()
private static native double getRpropDWMinus_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setRpropDWMinus(double val)
private static native void setRpropDWMinus_0(long nativeObj, double val);
// C++: double cv::ml::ANN_MLP::getRpropDWMin()
private static native double getRpropDWMin_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setRpropDWMin(double val)
private static native void setRpropDWMin_0(long nativeObj, double val);
// C++: double cv::ml::ANN_MLP::getRpropDWMax()
private static native double getRpropDWMax_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setRpropDWMax(double val)
private static native void setRpropDWMax_0(long nativeObj, double val);
// C++: double cv::ml::ANN_MLP::getAnnealInitialT()
private static native double getAnnealInitialT_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setAnnealInitialT(double val)
private static native void setAnnealInitialT_0(long nativeObj, double val);
// C++: double cv::ml::ANN_MLP::getAnnealFinalT()
private static native double getAnnealFinalT_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setAnnealFinalT(double val)
private static native void setAnnealFinalT_0(long nativeObj, double val);
// C++: double cv::ml::ANN_MLP::getAnnealCoolingRatio()
private static native double getAnnealCoolingRatio_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setAnnealCoolingRatio(double val)
private static native void setAnnealCoolingRatio_0(long nativeObj, double val);
// C++: int cv::ml::ANN_MLP::getAnnealItePerStep()
private static native int getAnnealItePerStep_0(long nativeObj);
// C++: void cv::ml::ANN_MLP::setAnnealItePerStep(int val)
private static native void setAnnealItePerStep_0(long nativeObj, int val);
// C++: Mat cv::ml::ANN_MLP::getWeights(int layerIdx)
private static native long getWeights_0(long nativeObj, int layerIdx);
// C++: static Ptr_ANN_MLP cv::ml::ANN_MLP::create()
private static native long create_0();
// C++: static Ptr_ANN_MLP cv::ml::ANN_MLP::load(String filepath)
private static native long load_0(String filepath);
// native support for java finalize()
private static native void delete(long nativeObj);
}
