Overview | Package | Class | Tree | Deprecated | Index | Help | Weka's home
PREV CLASS NEXT CLASS | FRAMES NO FRAMES
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD

## weka.datagenerators.classifiers.classification
Class RandomRBF

    java.lang.Object
      weka.datagenerators.DataGenerator
          weka.datagenerators.ClassificationGenerator
              weka.datagenerators.classifiers.classification.RandomRBF

All Implemented Interfaces:
  java.io.Serializable, OptionHandler, Randomizable, RevisionHandler

    public class RandomRBF

extends ClassificationGenerator

RandomRBF data is generated by first creating a random set of centers for each class. Each center is randomly assigned a weight, a central point per attribute, and a standard deviation. To generate new instances, a center is chosen at random taking the weights of each center into consideration. Attribute values are randomly generated and offset from the center, where the overall vector has been scaled so that its length equals a value sampled randomly from the Gaussian distribution of the center. The particular center chosen determines the class of the instance.
RandomRBF data contains only numeric attributes as it is non-trivial to include nominal values.

Valid options are:

     -h
      Prints this help.

     -o <file>
      The name of the output file, otherwise the generated data is
      printed to stdout.

     -r <name>
      The name of the relation.

     -d
      Whether to print debug informations.

     -S
      The seed for random function (default 1)

     -n <num>
      The number of examples to generate (default 100)

     -a <num>
      The number of attributes (default 10).

     -c <num>
      The number of classes (default 2)

     -C <num>
      The number of centroids to use. (default 50)

Version:
  $Revision: 5987 $
Author:
  Richard Kirkby (rkirkby at cs dot waikato dot ac dot nz), FracPete (fracpete at waikato dot ac dot nz)
See Also:
  Serialized Form

Constructor Summary
`RandomRBF()` initializes the generator with default values

Method Summary
`Instances` | `defineDataFormat()` Initializes the format for the dataset produced.
`Instance` | `generateExample()` Generates one example of the dataset.
`Instances` | `generateExamples()` Generates all examples of the dataset.
`java.lang.String` | `generateFinished()` Generates a comment string that documentats the data generator.
`java.lang.String` | `generateStart()` Generates a comment string that documentates the data generator.
`int` | `getNumAttributes()` Gets the number of attributes that should be produced.
`int` | `getNumCentroids()` Gets the number of centroids.
`int` | `getNumClasses()` Gets the number of classes the dataset should have.
`java.lang.String[]` | `getOptions()` Gets the current settings of the datagenerator.
`java.lang.String` | `getRevision()` Returns the revision string.
`boolean` | `getSingleModeFlag()` Return if single mode is set for the given data generator mode depends on option setting and or generator type.
`java.lang.String` | `globalInfo()` Returns a string describing this data generator.
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] args)` Main method for executing this class.
`java.lang.String` | `numAttributesTipText()` Returns the tip text for this property
`java.lang.String` | `numCentroidsTipText()` Returns the tip text for this property
`java.lang.String` | `numClassesTipText()` Returns the tip text for this property
`void` | `setNumAttributes(int numAttributes)` Sets the number of attributes the dataset should have.
`void` | `setNumCentroids(int value)` Sets the number of centroids to use.
`void` | `setNumClasses(int numClasses)` Sets the number of classes the dataset should have.
`void` | `setOptions(java.lang.String[] options)` Parses a list of options for this object.

Methods inherited from class weka.datagenerators.ClassificationGenerator
`getNumExamples, numExamplesTipText, setNumExamples`

Methods inherited from class weka.datagenerators.DataGenerator
`debugTipText, defaultOutput, formatTipText, getDatasetFormat, getDebug, getNumExamplesAct, getOutput, getRandom, getRelationName, getSeed, makeData, outputTipText, randomTipText, relationNameTipText, seedTipText, setDatasetFormat, setDebug, setOutput, setRandom, setRelationName, setSeed`

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

Constructor Detail

### RandomRBF

    public RandomRBF()

  initializes the generator with default values

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this data generator.

  Returns:
    a description of the data generator suitable for displaying in the explorer/experimenter gui

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `ClassificationGenerator`
  Returns:
    an enumeration of all the available options

### setOptions

    public void setOptions(java.lang.String[] options)
                    throws java.lang.Exception

  Parses a list of options for this object.

  Valid options are:

       -h
        Prints this help.

       -o <file>
        The name of the output file, otherwise the generated data is
        printed to stdout.

       -r <name>
        The name of the relation.

       -d
        Whether to print debug informations.

       -S
        The seed for random function (default 1)

       -n <num>
        The number of examples to generate (default 100)

       -a <num>
        The number of attributes (default 10).

       -c <num>
        The number of classes (default 2)

       -C <num>
        The number of centroids to use. (default 50)

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `ClassificationGenerator`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an option is not supported

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the datagenerator.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `ClassificationGenerator`
  Returns:
    an array of strings suitable for passing to setOptions
  See Also:
    `DataGenerator.removeBlacklist(String[])`

### setNumAttributes

    public void setNumAttributes(int numAttributes)

  Sets the number of attributes the dataset should have.

  Parameters:
    `numAttributes` - the new number of attributes

### getNumAttributes

    public int getNumAttributes()

  Gets the number of attributes that should be produced.

  Returns:
    the number of attributes that should be produced

### numAttributesTipText

    public java.lang.String numAttributesTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setNumClasses

    public void setNumClasses(int numClasses)

  Sets the number of classes the dataset should have.

  Parameters:
    `numClasses` - the new number of classes

### getNumClasses

    public int getNumClasses()

  Gets the number of classes the dataset should have.

  Returns:
    the number of classes the dataset should have

### numClassesTipText

    public java.lang.String numClassesTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getNumCentroids

    public int getNumCentroids()

  Gets the number of centroids.

  Returns:
    the number of centroids.

### setNumCentroids

    public void setNumCentroids(int value)

  Sets the number of centroids to use.

  Parameters:
    `value` - the number of centroids to use.

### numCentroidsTipText

    public java.lang.String numCentroidsTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getSingleModeFlag

    public boolean getSingleModeFlag()
                              throws java.lang.Exception

  Return if single mode is set for the given data generator mode depends on option setting and or generator type.

  Specified by:
    `getSingleModeFlag` in class `DataGenerator`
  Returns:
    single mode flag
  Throws:
    `java.lang.Exception` - if mode is not set yet

### defineDataFormat

    public Instances defineDataFormat()
                               throws java.lang.Exception

  Initializes the format for the dataset produced. Must be called before the generateExample or generateExamples methods are used. Re-initializes the random number generator with the given seed.

  Overrides:
    `defineDataFormat` in class `DataGenerator`
  Returns:
    the format for the dataset
  Throws:
    `java.lang.Exception` - if the generating of the format failed
  See Also:
    `DataGenerator.getSeed()`

### generateExample

    public Instance generateExample()
                             throws java.lang.Exception

  Generates one example of the dataset.

  Specified by:
    `generateExample` in class `DataGenerator`
  Returns:
    the generated example
  Throws:
    `java.lang.Exception` - if the format of the dataset is not yet defined
    `java.lang.Exception` - if the generator only works with generateExamples which means in non single mode

### generateExamples

    public Instances generateExamples()
                               throws java.lang.Exception

  Generates all examples of the dataset. Re-initializes the random number generator with the given seed, before generating instances.

  Specified by:
    `generateExamples` in class `DataGenerator`
  Returns:
    the generated dataset
  Throws:
    `java.lang.Exception` - if the format of the dataset is not yet defined
    `java.lang.Exception` - if the generator only works with generateExample, which means in single mode
  See Also:
    `DataGenerator.getSeed()`

### generateStart

    public java.lang.String generateStart()

  Generates a comment string that documentates the data generator. By default this string is added at the beginning of the produced output as ARFF file type, next after the options.

  Specified by:
    `generateStart` in class `DataGenerator`
  Returns:
    string contains info about the generated rules

### generateFinished

    public java.lang.String generateFinished()
                                      throws java.lang.Exception

  Generates a comment string that documentats the data generator. By default this string is added at the end of theproduces output as ARFF file type.

  Specified by:
    `generateFinished` in class `DataGenerator`
  Returns:
    string contains info about the generated rules
  Throws:
    `java.lang.Exception` - if the generating of the documentaion fails

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Returns:
    the revision

### main

    public static void main(java.lang.String[] args)

  Main method for executing this class.

  Parameters:
    `args` - should contain arguments for the data producer:

Overview | Package | Class | Tree | Deprecated | Index | Help | Weka's home
PREV CLASS NEXT CLASS | FRAMES NO FRAMES
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD