public class Tool4NeuralNetwork
extends java.lang.Object
Tool4NeuralNetwork
class provides the functionality
of a neural network.
title: SystemCoreExpLib
description: SystemCoreExpLib
copyright: (c) 2017 thomas kaffka, born at 11/08/1959 in düsseldorf - germany , all rights reserved.
Modifier and Type | Field and Description |
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int |
errCode |
Constructor and Description |
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Tool4NeuralNetwork(java.util.ArrayList<java.util.ArrayList<java.lang.Number>> examples,
long x_layer,
long y_layer,
long hidden,
double epsilon,
boolean std_deviation)
The constructor
Tool4NeuralNetwork creates a new instance(object) of the class. |
Modifier and Type | Method and Description |
---|---|
double[] |
executeFunction(double[] x)
The method
executeFunction executes the function f(x). |
double |
getAverageError() |
double[] |
getFactor() |
long |
getLearnStep() |
double |
getMaxError() |
double |
getMinError() |
double |
getStdDeviation() |
double[][] |
getW1() |
double[][] |
getW2() |
void |
learning()
The method
learning the learning procedure. |
void |
setExamples(java.util.ArrayList<java.util.ArrayList<java.lang.Number>> examples) |
void |
setLearnStep(long learn_step) |
void |
setValues(int nH,
int count_x,
int count_y,
double[] factor,
double epsilon)
The method
setValues set the values for executing the neural network. |
void |
setW1(double[][] input) |
void |
setW2(double[][] input) |
public Tool4NeuralNetwork(java.util.ArrayList<java.util.ArrayList<java.lang.Number>> examples, long x_layer, long y_layer, long hidden, double epsilon, boolean std_deviation)
Tool4NeuralNetwork
creates a new instance(object) of the class.examples
- the example data.x_layer
- the number of the elements in the x layer.y_layer
- the number of the elements in the y layer.hidden
- size of the hidden layer.epsilon
- the learn parameter epsilon.std_deviation
- thue = calculate the standard deviation.public long getLearnStep()
public void setLearnStep(long learn_step)
public double getMaxError()
public double getMinError()
public double getAverageError()
public double getStdDeviation()
public double[][] getW1()
public void setW1(double[][] input)
public double[][] getW2()
public void setW2(double[][] input)
public double[] getFactor()
public void setExamples(java.util.ArrayList<java.util.ArrayList<java.lang.Number>> examples)
public void setValues(int nH, int count_x, int count_y, double[] factor, double epsilon)
setValues
set the values for executing the neural network.nH
- the hidden layer.count_x
- the number of the x coordinates.count_y
- the number of the y coordinates.factor
- the data factor.epsilon
- the learn parameter epsilon.public double[] executeFunction(double[] x)
executeFunction
executes the function f(x).x
- the arguments of the function.public void learning()
learning
the learning procedure.