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## weka.gui.boundaryvisualizer
Class KDDataGenerator

    java.lang.Object
      weka.gui.boundaryvisualizer.KDDataGenerator

All Implemented Interfaces:
  java.io.Serializable, DataGenerator

    public class KDDataGenerator

extends java.lang.Object
implements DataGenerator, java.io.Serializable

KDDataGenerator. Class that uses kernels to generate new random instances based on a supplied set of instances.

Since:
  1.0
Version:
  $Revision: 7059 $
Author:
  Mark Hall
See Also:
  `DataGenerator`, `Serializable`, Serialized Form

Constructor Summary
`KDDataGenerator()`

Method Summary
`void` | `buildGenerator(Instances inputInstances)` Initialize the generator using the supplied instances
`double[][]` | `generateInstances(int[] indices)` Generates a new instance using one kernel estimator.
`int` | `getKernelBandwidth()` Get the kernel bandwidth
`int` | `getNumGeneratingModels()` Return the number of kernels (there is one per training instance)
`double[]` | `getWeights()` Get weights
`void` | `setKernelBandwidth(int kb)` Set the kernel bandwidth (number of nearest neighbours to cover)
`void` | `setSeed(int seed)` Initializes a new random number generator using the supplied seed.
`void` | `setWeightingDimensions(boolean[] dims)` Set which dimensions to use when computing a weight for the next instance to generate
`void` | `setWeightingValues(double[] vals)` Set the values for the weighting dimensions to be used when computing the weight for the next instance to be generated

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

Constructor Detail

### KDDataGenerator

    public KDDataGenerator()

Method Detail

### buildGenerator

    public void buildGenerator(Instances inputInstances)
                        throws java.lang.Exception

  Initialize the generator using the supplied instances

  Specified by:
    `buildGenerator` in interface `DataGenerator`
  Parameters:
    `inputInstances` - the instances to use as the basis of the kernels
  Throws:
    `java.lang.Exception` - if an error occurs

### getWeights

    public double[] getWeights()

  Description copied from interface: `DataGenerator`
  Get weights

  Specified by:
    `getWeights` in interface `DataGenerator`

### generateInstances

    public double[][] generateInstances(int[] indices)
                                 throws java.lang.Exception

  Generates a new instance using one kernel estimator. Each successive call to this method incremets the index of the kernel to use.

  Specified by:
    `generateInstances` in interface `DataGenerator`
  Returns:
    the new random instance
  Throws:
    `java.lang.Exception` - if an error occurs

### setWeightingDimensions

    public void setWeightingDimensions(boolean[] dims)

  Set which dimensions to use when computing a weight for the next instance to generate

  Specified by:
    `setWeightingDimensions` in interface `DataGenerator`
  Parameters:
    `dims` - an array of booleans indicating which dimensions to use

### setWeightingValues

    public void setWeightingValues(double[] vals)

  Set the values for the weighting dimensions to be used when computing the weight for the next instance to be generated

  Specified by:
    `setWeightingValues` in interface `DataGenerator`
  Parameters:
    `vals` - an array of doubles containing the values of the weighting dimensions (corresponding to the entries that are set to true throw setWeightingDimensions)

### getNumGeneratingModels

    public int getNumGeneratingModels()

  Return the number of kernels (there is one per training instance)

  Specified by:
    `getNumGeneratingModels` in interface `DataGenerator`
  Returns:
    the number of kernels

### setKernelBandwidth

    public void setKernelBandwidth(int kb)

  Set the kernel bandwidth (number of nearest neighbours to cover)

  Parameters:
    `kb` - an `int` value

### getKernelBandwidth

    public int getKernelBandwidth()

  Get the kernel bandwidth

  Returns:
    an `int` value

### setSeed

    public void setSeed(int seed)

  Initializes a new random number generator using the supplied seed.

  Specified by:
    `setSeed` in interface `DataGenerator`
  Parameters:
    `seed` - an `int` value

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