org.gcube.dataanalysis.ecoengine.models.cores.neuralnetworks.neurosolutions

Class NeuralNet

java.lang.Object

org.gcube.dataanalysis.ecoengine.models.cores.neuralnetworks.neurosolutions.NeuralNet

All Implemented Interfaces:

Serializable

public class NeuralNet
extends Object
implements Serializable

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
NeuralNet(int[] noofneurons, double[] learningratecoefficient, char[] axonfamily, double[] momentumrate, double[] axonfuncflatness, Randomizer randomizer)
NeuralNet(int N, int M, int[] t)
NeuralNet(String path, Randomizer randomizer)

Method Summary

Modifier and Type	Method and Description
void	BatchTrain(double rate, Pattern pattern)
void	BatchTrainPatterns(Pattern[] patterns, double rate)
double	CrossValErrorRatio(org.gcube.dataanalysis.ecoengine.models.cores.neuralnetworks.neurosolutions.PatternSet patternset)
void	FeedForward(double[] inputs)
double[]	getNegativeCase()
double[]	getPositiveCase()
void	IncrementalTrain(double rate, Pattern pattern)
void	IncrementalTrainPatterns(Pattern[] patterns, double rate)
void	init(int[] noofneurons, double[] learningratecoefficient, char[] axonfamily, double[] momentumrate, double[] axonfuncflatness, Randomizer randomizer)
void	LoadWeights(String path)
void	MinibatchTrainPatterns(Pattern[] patterns, double rate, int nopatterns)
double[]	Output(double[] inputs)
static double[]	preprocessObjects(Object[] vector)
void	SaveConfig(String path)
void	SaveWeights(String path)
static int[]	setupInnerLayers(int... numberOfNeurons)
double	TestErrorRatio(org.gcube.dataanalysis.ecoengine.models.cores.neuralnetworks.neurosolutions.PatternSet patternset)

Methods inherited from class java.lang.Object

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

Constructor Detail

NeuralNet

public NeuralNet(String path,
                 Randomizer randomizer)

NeuralNet

public NeuralNet(int N,
                 int M,
                 int[] t)

NeuralNet

public NeuralNet(int[] noofneurons,
                 double[] learningratecoefficient,
                 char[] axonfamily,
                 double[] momentumrate,
                 double[] axonfuncflatness,
                 Randomizer randomizer)

Method Detail

preprocessObjects

public static double[] preprocessObjects(Object[] vector)

getNegativeCase

public double[] getNegativeCase()

getPositiveCase

public double[] getPositiveCase()

setupInnerLayers

public static int[] setupInnerLayers(int... numberOfNeurons)

init

public void init(int[] noofneurons,
                 double[] learningratecoefficient,
                 char[] axonfamily,
                 double[] momentumrate,
                 double[] axonfuncflatness,
                 Randomizer randomizer)

SaveConfig

public void SaveConfig(String path)
                throws IOException

Throws:

IOException

LoadWeights

public void LoadWeights(String path)

SaveWeights

public void SaveWeights(String path)
                 throws IOException

Throws:

IOException

FeedForward

public void FeedForward(double[] inputs)

Output

public double[] Output(double[] inputs)

CrossValErrorRatio

public double CrossValErrorRatio(org.gcube.dataanalysis.ecoengine.models.cores.neuralnetworks.neurosolutions.PatternSet patternset)

TestErrorRatio

public double TestErrorRatio(org.gcube.dataanalysis.ecoengine.models.cores.neuralnetworks.neurosolutions.PatternSet patternset)

IncrementalTrainPatterns

public void IncrementalTrainPatterns(Pattern[] patterns,
                                     double rate)

IncrementalTrain

public void IncrementalTrain(double rate,
                             Pattern pattern)

MinibatchTrainPatterns

public void MinibatchTrainPatterns(Pattern[] patterns,
                                   double rate,
                                   int nopatterns)

BatchTrainPatterns

public void BatchTrainPatterns(Pattern[] patterns,
                               double rate)

BatchTrain

public void BatchTrain(double rate,
                       Pattern pattern)