org.encog.ml.data.basic

## Class BasicMLDataPair

- java.lang.Object
- - org.encog.ml.data.basic.BasicMLDataPair

- All Implemented Interfaces:
    Serializable, MLDataPair, CentroidFactory<MLDataPair>
  Direct Known Subclasses:
    BasicNeuralDataPair

      public class BasicMLDataPair
      extends Object
      implements MLDataPair, Serializable

  A basic implementation of the MLDataPair interface. This implementation simply holds and input and ideal MLData object. For supervised training both input and ideal should be specified. For unsupervised training the input property should be valid, however the ideal property should contain null.
  Author:
    jheaton
  See Also:
    Serialized Form

- - ### Constructor Summary

    Constructors
    Constructor and Description
    `BasicMLDataPair(MLData theInput)` Construct the object with only input.
    `BasicMLDataPair(MLData theInput, MLData theIdeal)` Construct a BasicMLDataPair class with the specified input and ideal values.

  - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `Centroid<MLDataPair>` | `createCentroid()`
    `static MLDataPair` | `createPair(int inputSize, int idealSize)` Create a new data pair object of the correct size for the machine learning method that is being trained.
    `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 significance)` Set the significance, 1.0 is neutral.
    `String` | `toString()`

    - ### Methods inherited from class java.lang.Object

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

- - ### Constructor Detail

    - #### BasicMLDataPair

          public BasicMLDataPair(MLData theInput)

      Construct the object with only input. If this constructor is used, then unsupervised training is being used.
      Parameters:
        `theInput` - The input to the machine learning method.

    - #### BasicMLDataPair

          public BasicMLDataPair(MLData theInput,
                         MLData theIdeal)

      Construct a BasicMLDataPair class with the specified input and ideal values.
      Parameters:
        `theInput` - The input to the machine learning method.
        `theIdeal` - The expected results from the machine learning method.

  - ### Method Detail

    - #### createPair

          public static MLDataPair createPair(int inputSize,
                              int idealSize)

      Create a new data pair object of the correct size for the machine learning method that is being trained. This object will be passed to the getPair method to allow the data pair objects to be copied to it.
      Parameters:
        `inputSize` - The size of the input data.
        `idealSize` - The size of the ideal data.
      Returns:
        A new data pair object.

    - #### getIdeal

          public MLData getIdeal()

      Specified by:
        `getIdeal` in interface `MLDataPair`
      Returns:
        The ideal data that the neural network should produce for the specified input.

    - #### getIdealArray

          public double[] getIdealArray()

      Specified by:
        `getIdealArray` in interface `MLDataPair`
      Returns:
        The ideal data that the machine learning method should produce for the specified input.

    - #### getInput

          public MLData getInput()

      Specified by:
        `getInput` in interface `MLDataPair`
      Returns:
        The input that the neural network

    - #### getInputArray

          public double[] getInputArray()

      Specified by:
        `getInputArray` in interface `MLDataPair`
      Returns:
        The input that the neural network

    - #### isSupervised

          public boolean isSupervised()

      Specified by:
        `isSupervised` in interface `MLDataPair`
      Returns:
        True if this training pair is supervised. That is, it has both input and ideal data.

    - #### setIdealArray

          public void setIdealArray(double[] data)

      Set the ideal data, the desired output.
      Specified by:
        `setIdealArray` in interface `MLDataPair`
      Parameters:
        `data` - The ideal data.

    - #### setInputArray

          public void setInputArray(double[] data)

      Set the input.
      Specified by:
        `setInputArray` in interface `MLDataPair`
      Parameters:
        `data` - The input.

    - #### toString

          public String toString()

      Overrides:
        `toString` in class `Object`

    - #### getSignificance

          public double getSignificance()

      Get the significance, 1.0 is neutral.
      Specified by:
        `getSignificance` in interface `MLDataPair`
      Returns:
        The significance.

    - #### setSignificance

          public void setSignificance(double significance)

      Set the significance, 1.0 is neutral.
      Specified by:
        `setSignificance` in interface `MLDataPair`
      Parameters:
        `significance` - The significance.

    - #### createCentroid

          public Centroid<MLDataPair> createCentroid()

      Specified by:
        `createCentroid` in interface `CentroidFactory<MLDataPair>`
      Returns:
        The centroid.

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