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## weka.classifiers.misc.monotone
Class DistributionUtils

    java.lang.Object
      weka.classifiers.misc.monotone.DistributionUtils

All Implemented Interfaces:
  RevisionHandler

    public class DistributionUtils

extends java.lang.Object
implements RevisionHandler

Class with some simple methods acting on `CumulativeDiscreteDistribution.` All of the methods in this class are very easily implemented and the main use of this class is to gather all these methods in a single place. It could be argued that some of the methods should be implemented in the class `CumulativeDiscreteDistribution` itself.

This implementation is part of the master's thesis: "Studie en implementatie van instantie-gebaseerde algoritmen voor gesuperviseerd rangschikken", Stijn Lievens, Ghent University, 2004.

Version:
  $Revision: 5922 $
Author:
  Stijn Lievens (stijn.lievens@ugent.be)

Constructor Summary
`DistributionUtils()`

Method Summary
`static double[]` | `getDistributionArray(DiscreteEstimator df)` Converts a `DiscreteEstimator` to an array of doubles.
`static CumulativeDiscreteDistribution` | `getMaximalCumulativeDiscreteDistribution(int numClasses)` Get the maximal `CumulativeDiscreteDistribution` over `numClasses` elements.
`static CumulativeDiscreteDistribution` | `getMinimalCumulativeDiscreteDistribution(int numClasses)` Get the minimal `CumulativeDiscreteDistribution` over `numClasses` elements.
`java.lang.String` | `getRevision()` Returns the revision string.
`static CumulativeDiscreteDistribution` | `interpolate(CumulativeDiscreteDistribution cdf1, CumulativeDiscreteDistribution cdf2, double s)` Compute a linear interpolation between the two given `CumulativeDiscreteDistribution.`
`static CumulativeDiscreteDistribution` | `interpolate(CumulativeDiscreteDistribution cdf1, CumulativeDiscreteDistribution cdf2, double[] s)` Compute a linear interpolation between the two given `CumulativeDiscreteDistribution.`
`static DiscreteDistribution` | `interpolate(DiscreteDistribution ddf1, DiscreteDistribution ddf2, double s)` Compute a linear interpolation between the two given `DiscreteDistribution.`
`static CumulativeDiscreteDistribution` | `takeMax(CumulativeDiscreteDistribution cdf1, CumulativeDiscreteDistribution cdf2)` Create a new `CumulativeDiscreteDistribution` that is the maximum of the two given `CumulativeDiscreteDistribution.`
`static CumulativeDiscreteDistribution` | `takeMin(CumulativeDiscreteDistribution cdf1, CumulativeDiscreteDistribution cdf2)` Create a new `CumulativeDiscreteDistribution` that is the minimum of the two given `CumulativeDiscreteDistribution.`

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

Constructor Detail

### DistributionUtils

    public DistributionUtils()

Method Detail

### interpolate

    public static CumulativeDiscreteDistribution interpolate(CumulativeDiscreteDistribution cdf1,
                                                             CumulativeDiscreteDistribution cdf2,
                                                             double s)
                                                      throws java.lang.IllegalArgumentException

  Compute a linear interpolation between the two given `CumulativeDiscreteDistribution.`

  Parameters:
    `cdf1` - the first `CumulativeDiscreteDistribution`
    `cdf2` - the second `CumulativeDiscreteDistribution`
    `s` - the interpolation parameter
  Returns:
    (1 - s) × cdf1 + s × cdf2
  Throws:
    `java.lang.IllegalArgumentException` - if the two distributions don't have the same size or if the parameter `s` is not in the range [0,1]

### interpolate

    public static CumulativeDiscreteDistribution interpolate(CumulativeDiscreteDistribution cdf1,
                                                             CumulativeDiscreteDistribution cdf2,
                                                             double[] s)
                                                      throws java.lang.IllegalArgumentException

  Compute a linear interpolation between the two given `CumulativeDiscreteDistribution.`

  Parameters:
    `cdf1` - the first `CumulativeDiscreteDistribution`
    `cdf2` - the second `CumulativeDiscreteDistribution`
    `s` - the interpolation parameters, only the relevant number of entries is used, so the array may be longer than the common length of `cdf1` and `cdf2`
  Returns:
    (1 - s) × cdf1 + s × cdf2, or more specifically a distribution cd such that `cd.getCumulativeProbability(i) = (1-s[i]) × cdf1.getCumulativeProbability(i) + s[i] × cdf2.getCumulativeProbability(i)`
  Throws:
    `java.lang.IllegalArgumentException` - if the two distributions don't have the same size or if the array `s` contains parameters not in the range `[0,1]`

### interpolate

    public static DiscreteDistribution interpolate(DiscreteDistribution ddf1,
                                                   DiscreteDistribution ddf2,
                                                   double s)
                                            throws java.lang.IllegalArgumentException

  Compute a linear interpolation between the two given `DiscreteDistribution.`

  Parameters:
    `ddf1` - the first `DiscreteDistribution`
    `ddf2` - the second `DiscreteDistribution`
    `s` - the interpolation parameter
  Returns:
    `(1 - s) × ddf1 + s × ddf2`
  Throws:
    `java.lang.IllegalArgumentException` - if the two distributions don't have the same size or if the parameter `s` is not in the range [0,1]

### takeMin

    public static CumulativeDiscreteDistribution takeMin(CumulativeDiscreteDistribution cdf1,
                                                         CumulativeDiscreteDistribution cdf2)
                                                  throws java.lang.IllegalArgumentException

  Create a new `CumulativeDiscreteDistribution` that is the minimum of the two given `CumulativeDiscreteDistribution.` Each component of the resulting probability distribution is the minimum of the two corresponding components.
  Note: despite of its name, the returned cumulative probability distribution dominates both the arguments of this method.

  Parameters:
    `cdf1` - first `CumulativeDiscreteDistribution`
    `cdf2` - second `CumulativeDiscreteDistribution`
  Returns:
    the minimum of the two distributions
  Throws:
    `java.lang.IllegalArgumentException` - if the two distributions dont't have the same length

### takeMax

    public static CumulativeDiscreteDistribution takeMax(CumulativeDiscreteDistribution cdf1,
                                                         CumulativeDiscreteDistribution cdf2)
                                                  throws java.lang.IllegalArgumentException

  Create a new `CumulativeDiscreteDistribution` that is the maximum of the two given `CumulativeDiscreteDistribution.` Each component of the resulting probability distribution is the maximum of the two corresponding components. Note: despite of its name, the returned cumulative probability distribution is dominated by both the arguments of this method.

  Parameters:
    `cdf1` - first `CumulativeDiscreteDistribution`
    `cdf2` - second `CumulativeDiscreteDistribution`
  Returns:
    the maximum of the two distributions
  Throws:
    `java.lang.IllegalArgumentException` - if the two distributions dont't have the same length

### getDistributionArray

    public static double[] getDistributionArray(DiscreteEstimator df)

  Converts a `DiscreteEstimator` to an array of doubles.

  Parameters:
    `df` - the `DiscreteEstimator` to be converted
  Returns:
    an array of doubles representing the `DiscreteEstimator`

### getMinimalCumulativeDiscreteDistribution

    public static CumulativeDiscreteDistribution getMinimalCumulativeDiscreteDistribution(int numClasses)
                                                                                   throws java.lang.IllegalArgumentException

  Get the minimal `CumulativeDiscreteDistribution` over `numClasses` elements. This means that a probability of one is assigned to the first element.

  Parameters:
    `numClasses` - the number of elements
  Returns:
    the minimal `CumulativeDiscreteDistribution` over the requested number of elements
  Throws:
    `java.lang.IllegalArgumentException` - if `numClasses` is smaller or equal than 0

### getMaximalCumulativeDiscreteDistribution

    public static CumulativeDiscreteDistribution getMaximalCumulativeDiscreteDistribution(int numClasses)
                                                                                   throws java.lang.IllegalArgumentException

  Get the maximal `CumulativeDiscreteDistribution` over `numClasses` elements. This means that a probability of one is assigned to the last class.

  Parameters:
    `numClasses` - the number of elements
  Returns:
    the maximal `CumulativeDiscreteDistribution` over the requested number of elements
  Throws:
    `java.lang.IllegalArgumentException` - if `numClasses` is smaller or equal than 0

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

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

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