RandomProjection

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## weka.filters.unsupervised.attribute
Class RandomProjection

    java.lang.Object
      weka.filters.Filter
          weka.filters.unsupervised.attribute.RandomProjection

All Implemented Interfaces:
  java.io.Serializable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, UnsupervisedFilter

    public class RandomProjection

extends Filter
implements UnsupervisedFilter, OptionHandler, TechnicalInformationHandler

Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (i.e. It will reduce the number of attributes in the data while preserving much of its variation like PCA, but at a much less computational cost).
It first applies the NominalToBinary filter to convert all attributes to numeric before reducing the dimension. It preserves the class attribute.
For more information, see:
Dmitriy Fradkin, David Madigan: Experiments with random projections for machine learning. In: KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 517-522, 003.

BibTeX:

     @inproceedings{Fradkin003,
        address = {New York, NY, USA},
        author = {Dmitriy Fradkin and David Madigan},
        booktitle = {KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining},
        pages = {517-522},
        publisher = {ACM Press},
        title = {Experiments with random projections for machine learning},
        year = {003}
     }

Valid options are:

     -N <number>
      The number of dimensions (attributes) the data should be reduced to
      (default 10; exclusive of the class attribute, if it is set).

     -D [SPARSE1|SPARSE2|GAUSSIAN]
      The distribution to use for calculating the random matrix.
      Sparse1 is:
        sqrt(3)*{-1 with prob(1/6), 0 with prob(2/3), +1 with prob(1/6)}
      Sparse2 is:
        {-1 with prob(1/2), +1 with prob(1/2)}

     -P <percent>
      The percentage of dimensions (attributes) the data should
      be reduced to (exclusive of the class attribute, if it is set). This -N
      option is ignored if this option is present or is greater
      than zero.

     -M
      Replace missing values using the ReplaceMissingValues filter

     -R <num>
      The random seed for the random number generator used for
      calculating the random matrix (default 42).

Version:
  $Revision: 1.11 $ [1.0 - 22 July 2003 - Initial version (Ashraf M. Kibriya)]
Author:
  Ashraf M. Kibriya (amk14@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary
`static int` | `GAUSSIAN` distribution type: gaussian
`static int` | `SPARSE1` distribution type: sparse 1
`static int` | `SPARSE2` distribution type: sparse 2
`static Tag[]` | `TAGS_DSTRS_TYPE` The types of distributions that can be used for calculating the random matrix

Constructor Summary
`RandomProjection()`

Method Summary
`boolean` | `batchFinished()` Signify that this batch of input to the filter is finished.
`java.lang.String` | `distributionTipText()` Returns the tip text for this property
`Capabilities` | `getCapabilities()` Returns the Capabilities of this filter.
`SelectedTag` | `getDistribution()` Returns the current distribution that'll be used for calculating the random matrix
`int` | `getNumberOfAttributes()` Gets the current number of attributes (dimensionality) to which the data will be reduced to.
`java.lang.String[]` | `getOptions()` Gets the current settings of the filter.
`double` | `getPercent()` Gets the percent the attributes (dimensions) of the data will be reduced to
`long` | `getRandomSeed()` Gets the random seed of the random number generator
`boolean` | `getReplaceMissingValues()` Gets the current setting for using ReplaceMissingValues filter
`java.lang.String` | `getRevision()` Returns the revision string.
`TechnicalInformation` | `getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`java.lang.String` | `globalInfo()` Returns a string describing this filter
`boolean` | `input(Instance instance)` Input an instance for filtering.
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String` | `numberOfAttributesTipText()` Returns the tip text for this property
`java.lang.String` | `percentTipText()` Returns the tip text for this property
`java.lang.String` | `randomSeedTipText()` Returns the tip text for this property
`java.lang.String` | `replaceMissingValuesTipText()` Returns the tip text for this property
`void` | `setDistribution(SelectedTag newDstr)` Sets the distribution to use for calculating the random matrix
`boolean` | `setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`void` | `setNumberOfAttributes(int newAttNum)` Sets the number of attributes (dimensions) the data should be reduced to
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setPercent(double newPercent)` Sets the percent the attributes (dimensions) of the data should be reduced to
`void` | `setRandomSeed(long seed)` Sets the random seed of the random number generator
`void` | `setReplaceMissingValues(boolean t)` Sets either to use replace missing values filter or not

Methods inherited from class weka.filters.Filter
`batchFilterFile, filterFile, getCapabilities, getOutputFormat, isFirstBatchDone, isNewBatch, isOutputFormatDefined, makeCopies, makeCopy, numPendingOutput, output, outputPeek, toString, useFilter, wekaStaticWrapper`

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

Field Detail

### SPARSE1

    public static final int SPARSE1

  distribution type: sparse 1

  See Also:
    Constant Field Values

### SPARSE2

    public static final int SPARSE2

  distribution type: sparse 2

  See Also:
    Constant Field Values

### GAUSSIAN

    public static final int GAUSSIAN

  distribution type: gaussian

  See Also:
    Constant Field Values

### TAGS_DSTRS_TYPE

    public static final Tag[] TAGS_DSTRS_TYPE

  The types of distributions that can be used for calculating the random matrix

Constructor Detail

### RandomProjection

    public RandomProjection()

Method Detail

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

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

### setOptions

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

  Parses a given list of options.

  Valid options are:

       -N <number>
        The number of dimensions (attributes) the data should be reduced to
        (default 10; exclusive of the class attribute, if it is set).

       -D [SPARSE1|SPARSE2|GAUSSIAN]
        The distribution to use for calculating the random matrix.
        Sparse1 is:
          sqrt(3)*{-1 with prob(1/6), 0 with prob(2/3), +1 with prob(1/6)}
        Sparse2 is:
          {-1 with prob(1/2), +1 with prob(1/2)}

       -P <percent>
        The percentage of dimensions (attributes) the data should
        be reduced to (exclusive of the class attribute, if it is set). This -N
        option is ignored if this option is present or is greater
        than zero.

       -M
        Replace missing values using the ReplaceMissingValues filter

       -R <num>
        The random seed for the random number generator used for
        calculating the random matrix (default 42).

  Specified by:
    `setOptions` in interface `OptionHandler`
  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 filter.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Returns:
    an array of strings suitable for passing to setOptions

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this filter

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

### getTechnicalInformation

    public TechnicalInformation getTechnicalInformation()

  Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

  Specified by:
    `getTechnicalInformation` in interface `TechnicalInformationHandler`
  Returns:
    the technical information about this class

### numberOfAttributesTipText

    public java.lang.String numberOfAttributesTipText()

  Returns the tip text for this property

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

### setNumberOfAttributes

    public void setNumberOfAttributes(int newAttNum)

  Sets the number of attributes (dimensions) the data should be reduced to

  Parameters:
    `newAttNum` - the goal for the dimensions

### getNumberOfAttributes

    public int getNumberOfAttributes()

  Gets the current number of attributes (dimensionality) to which the data will be reduced to.

  Returns:
    the number of dimensions

### percentTipText

    public java.lang.String percentTipText()

  Returns the tip text for this property

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

### setPercent

    public void setPercent(double newPercent)

  Sets the percent the attributes (dimensions) of the data should be reduced to

  Parameters:
    `newPercent` - the percentage of attributes

### getPercent

    public double getPercent()

  Gets the percent the attributes (dimensions) of the data will be reduced to

  Returns:
    the percentage of attributes

### randomSeedTipText

    public java.lang.String randomSeedTipText()

  Returns the tip text for this property

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

### setRandomSeed

    public void setRandomSeed(long seed)

  Sets the random seed of the random number generator

  Parameters:
    `seed` - the random seed value

### getRandomSeed

    public long getRandomSeed()

  Gets the random seed of the random number generator

  Returns:
    the random seed value

### distributionTipText

    public java.lang.String distributionTipText()

  Returns the tip text for this property

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

### setDistribution

    public void setDistribution(SelectedTag newDstr)

  Sets the distribution to use for calculating the random matrix

  Parameters:
    `newDstr` - the distribution to use

### getDistribution

    public SelectedTag getDistribution()

  Returns the current distribution that'll be used for calculating the random matrix

  Returns:
    the current distribution

### replaceMissingValuesTipText

    public java.lang.String replaceMissingValuesTipText()

  Returns the tip text for this property

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

### setReplaceMissingValues

    public void setReplaceMissingValues(boolean t)

  Sets either to use replace missing values filter or not

  Parameters:
    `t` - if true then the replace missing values is used

### getReplaceMissingValues

    public boolean getReplaceMissingValues()

  Gets the current setting for using ReplaceMissingValues filter

  Returns:
    true if the replace missing values filter is used

### getCapabilities

    public Capabilities getCapabilities()

  Returns the Capabilities of this filter.

  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `Filter`
  Returns:
    the capabilities of this object
  See Also:
    `Capabilities`

### setInputFormat

    public boolean setInputFormat(Instances instanceInfo)
                           throws java.lang.Exception

  Sets the format of the input instances.

  Overrides:
    `setInputFormat` in class `Filter`
  Parameters:
    `instanceInfo` - an Instances object containing the input instance structure (any instances contained in the object are ignored - only the structure is required).
  Returns:
    true if the outputFormat may be collected immediately
  Throws:
    `java.lang.Exception` - if the input format can't be set successfully

### input

    public boolean input(Instance instance)
                  throws java.lang.Exception

  Input an instance for filtering.

  Overrides:
    `input` in class `Filter`
  Parameters:
    `instance` - the input instance
  Returns:
    true if the filtered instance may now be collected with output().
  Throws:
    `java.lang.IllegalStateException` - if no input format has been set
    `java.lang.NullPointerException` - if the input format has not been defined.
    `java.lang.Exception` - if the input instance was not of the correct format or if there was a problem with the filtering.

### batchFinished

    public boolean batchFinished()
                          throws java.lang.Exception

  Signify that this batch of input to the filter is finished.

  Overrides:
    `batchFinished` in class `Filter`
  Returns:
    true if there are instances pending output
  Throws:
    `java.lang.NullPointerException` - if no input structure has been defined,
    `java.lang.Exception` - if there was a problem finishing the batch.

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Returns:
    the revision

### main

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

  Main method for testing this class.

  Parameters:
    `argv` - should contain arguments to the filter: use -h for help

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