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## org.encog.ml.data.basic
Class BasicMLDataSet

    java.lang.Object
      org.encog.ml.data.basic.BasicMLDataSet

All Implemented Interfaces:
  Serializable, Iterable<MLDataPair>, MLDataSet

Direct Known Subclasses:
  BasicNeuralDataSet, CSVNeuralDataSet, ImageMLDataSet, SQLNeuralDataSet

    public class BasicMLDataSet

extends Object
implements Serializable, MLDataSet

Stores data in an ArrayList. This class is memory based, so large enough datasets could cause memory issues. Many other dataset types extend this class.

Author:
  jheaton
See Also:
  Serialized Form

Nested Class Summary
`class` | `BasicMLDataSet.BasicMLIterator` An iterator to be used with the BasicMLDataSet.

Constructor Summary
`BasicMLDataSet()` Default constructor.
`BasicMLDataSet(double[][] input, double[][] ideal)` Construct a data set from an input and idea array.
`BasicMLDataSet(List<MLDataPair> theData)` Construct a data set from an already created list.
`BasicMLDataSet(MLDataSet set)` Copy whatever dataset type is specified into a memory dataset.

Method Summary
`void` | `add(MLData theData)` Add a object to the dataset.
`void` | `add(MLData inputData, MLData idealData)` Add a set of input and ideal data to the dataset.
`void` | `add(MLDataPair inputData)` Add a an object to the dataset.
`Object` | `clone()`
`void` | `close()` Close this datasource and release any resources obtained by it, including any iterators created.
`List<MLDataPair>` | `getData()` Get the data held by this container.
`int` | `getIdealSize()`
`int` | `getInputSize()`
`void` | `getRecord(long index, MLDataPair pair)` Read an individual record, specified by index, in random order.
`long` | `getRecordCount()` Determine the total number of records in the set.
`boolean` | `isSupervised()`
`Iterator<MLDataPair>` | `iterator()`
`MLDataSet` | `openAdditional()` Opens an additional instance of this dataset.
`void` | `setData(List<MLDataPair> theData)`

Methods inherited from class java.lang.Object
`equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### BasicMLDataSet

    public BasicMLDataSet()

  Default constructor.

### BasicMLDataSet

    public BasicMLDataSet(double[][] input,
                          double[][] ideal)

  Construct a data set from an input and idea array.

  Parameters:
    `input` - The input into the machine learning method for training.
    `ideal` - The ideal output for training.

### BasicMLDataSet

    public BasicMLDataSet(List<MLDataPair> theData)

  Construct a data set from an already created list. Mostly used to duplicate this class.

  Parameters:
    `theData` - The data to use.

### BasicMLDataSet

    public BasicMLDataSet(MLDataSet set)

  Copy whatever dataset type is specified into a memory dataset.

  Parameters:
    `set` - The dataset to copy.

Method Detail

### add

    public void add(MLData theData)

  Add a object to the dataset. This is used with unsupervised training, as no ideal output is provided. Note: not all implemenations support the add methods.

  Specified by:
    `add` in interface `MLDataSet`
  Parameters:
    `theData` - The data item to be added.

### add

    public void add(MLData inputData,
                    MLData idealData)

  Add a set of input and ideal data to the dataset. This is used with supervised training, as ideal output is provided. Note: not all implementations support the add methods.

  Specified by:
    `add` in interface `MLDataSet`
  Parameters:
    `inputData` - Input data.
    `idealData` - Ideal data.

### add

    public void add(MLDataPair inputData)

  Add a an object to the dataset. This is used with unsupervised training, as no ideal output is provided. Note: not all implementations support the add methods.

  Specified by:
    `add` in interface `MLDataSet`
  Parameters:
    `inputData` - A MLDataPair object that contains both input and ideal data.

### clone

    public final Object clone()

  Overrides:
    `clone` in class `Object`

### close

    public final void close()

  Close this datasource and release any resources obtained by it, including any iterators created.

  Specified by:
    `close` in interface `MLDataSet`

### getData

    public final List<MLDataPair> getData()

  Get the data held by this container.

  Returns:
    the data

### getIdealSize

    public final int getIdealSize()

  Specified by:
    `getIdealSize` in interface `MLDataSet`
  Returns:
    The size of the input data.

### getInputSize

    public final int getInputSize()

  Specified by:
    `getInputSize` in interface `MLDataSet`
  Returns:
    The size of the input data.

### getRecord

    public final void getRecord(long index,
                                MLDataPair pair)

  Read an individual record, specified by index, in random order.

  Specified by:
    `getRecord` in interface `MLDataSet`
  Parameters:
    `index` - The index to read.
    `pair` - The pair that the record will be copied into.

### getRecordCount

    public final long getRecordCount()

  Determine the total number of records in the set.

  Specified by:
    `getRecordCount` in interface `MLDataSet`
  Returns:
    The total number of records in the set.

### isSupervised

    public final boolean isSupervised()

  Specified by:
    `isSupervised` in interface `MLDataSet`
  Returns:
    True if this is a supervised training set.

### iterator

    public final Iterator<MLDataPair> iterator()

  Specified by:
    `iterator` in interface `Iterable<MLDataPair>`

### openAdditional

    public final MLDataSet openAdditional()

  Opens an additional instance of this dataset.

  Specified by:
    `openAdditional` in interface `MLDataSet`
  Returns:
    The new instance.

### setData

    public final void setData(List<MLDataPair> theData)

  Parameters:
    `theData` - the data to set

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