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## 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.

Author:
  jheaton

Method Summary
`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.

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