org.encog.neural.pnn

Class BasicPNN

java.lang.Object

org.encog.ml.BasicML

org.encog.neural.pnn.AbstractPNN

org.encog.neural.pnn.BasicPNN

All Implemented Interfaces:

Serializable, MLClassification, MLError, MLInput, MLInputOutput, MLMethod, MLOutput, MLProperties, MLRegression

public class BasicPNN
extends AbstractPNN
implements MLRegression, MLError, MLClassification

This class implements either a: Probabilistic Neural Network (PNN) General Regression Neural Network (GRNN) To use a PNN specify an output mode of classification, to make use of a GRNN specify either an output mode of regression or un-supervised autoassociation. The PNN/GRNN networks are potentially very useful. They share some similarities with RBF-neural networks and also the Support Vector Machine (SVM). These network types directly support the use of classification. The following book was very helpful in implementing PNN/GRNN's in Encog. Advanced Algorithms for Neural Networks: A C++ Sourcebook by Timothy Masters, PhD (http://www.timothymasters.info/) John Wiley & Sons Inc (Computers); April 3, 1995, ISBN: 0471105880

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
BasicPNN(PNNKernelType kernel, PNNOutputMode outmodel, int inputCount, int outputCount) Construct a BasicPNN network.

Method Summary

Methods

Modifier and Type	Method and Description
double	calculateError(MLDataSet data) Calculate the error of the ML method, given a dataset.
int	classify(MLData input) Classify the input into a group.
MLData	compute(MLData input) Compute the output from this network.
int[]	getCountPer()
double[]	getPriors()
BasicMLDataSet	getSamples()
double[]	getSigma()
void	setSamples(BasicMLDataSet samples)
void	updateProperties() Update any objeccts when a property changes.

Methods inherited from class org.encog.neural.pnn.AbstractPNN

getDeriv, getDeriv2, getError, getExclude, getInputCount, getKernel, getOutputCount, getOutputMode, isSeparateClass, isTrained, resetConfusion, setError, setExclude, setSeparateClass, setTrained

Methods inherited from class org.encog.ml.BasicML

getProperties, getPropertyDouble, getPropertyLong, getPropertyString, setProperty, setProperty, setProperty

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.encog.ml.MLInput

getInputCount

Methods inherited from interface org.encog.ml.MLOutput

getOutputCount

Constructor Detail

BasicPNN

public BasicPNN(PNNKernelType kernel,
        PNNOutputMode outmodel,
        int inputCount,
        int outputCount)

Construct a BasicPNN network.

Parameters:

kernel - The kernel to use.

outmodel - The output model for this network.

inputCount - The number of inputs in this network.

outputCount - The number of outputs in this network.

Method Detail

compute

public MLData compute(MLData input)

Compute the output from this network.

Specified by:

compute in interface MLRegression

Specified by:

compute in class AbstractPNN

Parameters:

input - The input to the network.

Returns:

The output from the network.

getCountPer

public int[] getCountPer()

Returns:

the countPer

getPriors

public double[] getPriors()

Returns:

the priors

getSamples

public BasicMLDataSet getSamples()

Returns:

the samples

getSigma

public double[] getSigma()

Returns:

the sigma

setSamples

public void setSamples(BasicMLDataSet samples)

Parameters:

samples - the samples to set

updateProperties

public void updateProperties()

Update any objeccts when a property changes.

Specified by:

updateProperties in interface MLProperties

Specified by:

updateProperties in class BasicML

calculateError

public double calculateError(MLDataSet data)

Calculate the error of the ML method, given a dataset.

Specified by:

calculateError in interface MLError

Parameters:

data - The dataset.

Returns:

The error.

classify

public int classify(MLData input)

Classify the input into a group.

Specified by:

classify in interface MLClassification

Parameters:

input - The input data to classify.

Returns:

The group that the data was classified into.