org.encog.ml.data

Interface MLDataPair

All Superinterfaces:

CentroidFactory<MLDataPair>

All Known Subinterfaces:

NeuralDataPair

All Known Implementing Classes:

BasicMLDataPair, BasicNeuralDataPair

public interface MLDataPair
extends CentroidFactory<MLDataPair>

Training data is stored in two ways, depending on if the data is for supervised, or unsupervised training. For unsupervised training just an input value is provided, and the ideal output values are null. For supervised training both input and the expected ideal outputs are provided. This interface abstracts classes that provide a holder for both of these two data items.

Method Summary

Modifier and Type	Method and Description
MLData	getIdeal()
double[]	getIdealArray()
MLData	getInput()
double[]	getInputArray()
double	getSignificance() Get the significance, 1.0 is neutral.
boolean	isSupervised()
void	setIdealArray(double[] data) Set the ideal data, the desired output.
void	setInputArray(double[] data) Set the input.
void	setSignificance(double s) Set the significance, 1.0 is neutral.

Methods inherited from interface org.encog.util.kmeans.CentroidFactory

createCentroid

Method Detail

getIdealArray

double[] getIdealArray()

Returns:

The ideal data that the machine learning method should produce for the specified input.

getInputArray

double[] getInputArray()

Returns:

The input that the neural network

setIdealArray

void setIdealArray(double[] data)

Set the ideal data, the desired output.

Parameters:

data - The ideal data.

setInputArray

void setInputArray(double[] data)

Set the input.

Parameters:

data - The input.

isSupervised

boolean isSupervised()

Returns:

True if this training pair is supervised. That is, it has both input and ideal data.

getIdeal

MLData getIdeal()

Returns:

The ideal data that the neural network should produce for the specified input.

getInput

MLData getInput()

Returns:

The input that the neural network

getSignificance

double getSignificance()

Get the significance, 1.0 is neutral.

Returns:

The significance.

setSignificance

void setSignificance(double s)

Set the significance, 1.0 is neutral.

Parameters:

s - The significance.