## org.apache.commons.math.random
Class EmpiricalDistributionImpl

    java.lang.Object
      org.apache.commons.math.random.EmpiricalDistributionImpl

All Implemented Interfaces:
  Serializable, EmpiricalDistribution

    public class EmpiricalDistributionImpl

extends Object
implements Serializable, EmpiricalDistribution

Implements `EmpiricalDistribution` interface. This implementation uses what amounts to the Variable Kernel Method with Gaussian smoothing:

Digesting the input file

1. Pass the file once to compute min and max.
2. Divide the range from min-max into `binCount` "bins."
3. Pass the data file again, computing bin counts and univariate statistics (mean, std dev.) for each of the bins
4. Divide the interval (0,1) into subintervals associated with the bins, with the length of a bin's subinterval proportional to its count.

Generating random values from the distribution

1. Generate a uniformly distributed value in (0,1)
2. Select the subinterval to which the value belongs.
3. Generate a random Gaussian value with mean = mean of the associated bin and std dev = std dev of associated bin.

USAGE NOTES:

- The `binCount` is set by default to 1000. A good rule of thumb is to set the bin count to approximately the length of the input file divided by 10.
- The input file must be a plain text file containing one valid numeric entry per line.

Version:
  $Revision: 1003886 $ $Date: 2010-10-02 23:04:44 +0200 (sam. 02 oct. 2010) $
See Also:
  Serialized Form

Nested Class Summary
`private class` `EmpiricalDistributionImpl.ArrayDataAdapter` `DataAdapter` for data provided as array of doubles.
`private class` `EmpiricalDistributionImpl.DataAdapter` Provides methods for computing `sampleStats` and `beanStats` abstracting the source of data.
`private class` `EmpiricalDistributionImpl.DataAdapterFactory` Factory of `DataAdapter` objects.
`private class` `EmpiricalDistributionImpl.StreamDataAdapter` `DataAdapter` for data provided through some input stream

Field Summary
`private int` `binCount` number of bins
`private List<SummaryStatistics>` `binStats` List of SummaryStatistics objects characterizing the bins
`private double` `delta` Grid size
`private boolean` `loaded` is the distribution loaded?
`private double` `max` Max loaded value
`private double` `min` Min loaded value
`private RandomData` `randomData` RandomData instance to use in repeated calls to getNext()
`private SummaryStatistics` `sampleStats` Sample statistics
`private static long` `serialVersionUID` Serializable version identifier
`private double[]` `upperBounds` upper bounds of subintervals in (0,1) "belonging" to the bins

Constructor Summary
`EmpiricalDistributionImpl()` Creates a new EmpiricalDistribution with the default bin count.
`EmpiricalDistributionImpl(int binCount)` Creates a new EmpiricalDistribution with the specified bin count.

Method Summary
`private void` `fillBinStats(Object in)` Fills binStats array (second pass through data file).
`private int` `findBin(double value)` Returns the index of the bin to which the given value belongs
`int` `getBinCount()` Returns the number of bins.
`List<SummaryStatistics>` `getBinStats()` Returns a List of `SummaryStatistics` instances containing statistics describing the values in each of the bins.
`double[]` `getGeneratorUpperBounds()` Returns a fresh copy of the array of upper bounds of the subintervals of [0,1] used in generating data from the empirical distribution.
`double` `getNextValue()` Generates a random value from this distribution.
`StatisticalSummary` `getSampleStats()` Returns a `StatisticalSummary` describing this distribution.
`double[]` `getUpperBounds()` Returns a fresh copy of the array of upper bounds for the bins.
`boolean` `isLoaded()` Property indicating whether or not the distribution has been loaded.
`void` `load(double[] in)` Computes the empirical distribution from the provided array of numbers.
`void` `load(File file)` Computes the empirical distribution from the input file.
`void` `load(URL url)` Computes the empirical distribution using data read from a URL.

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

Field Detail

### serialVersionUID

    private static final long serialVersionUID

  Serializable version identifier

  See Also:
    Constant Field Values

### binStats

    private final List<SummaryStatistics> binStats

  List of SummaryStatistics objects characterizing the bins

### sampleStats

    private SummaryStatistics sampleStats

  Sample statistics

### max

    private double max

  Max loaded value

### min

    private double min

  Min loaded value

### delta

    private double delta

  Grid size

### binCount

    private final int binCount

  number of bins

### loaded

    private boolean loaded

  is the distribution loaded?

### upperBounds

    private double[] upperBounds

  upper bounds of subintervals in (0,1) "belonging" to the bins

### randomData

    private final RandomData randomData

  RandomData instance to use in repeated calls to getNext()

Constructor Detail

### EmpiricalDistributionImpl

    public EmpiricalDistributionImpl()

  Creates a new EmpiricalDistribution with the default bin count.

### EmpiricalDistributionImpl

    public EmpiricalDistributionImpl(int binCount)

  Creates a new EmpiricalDistribution with the specified bin count.

  Parameters:
    `binCount` - number of bins

Method Detail

### load

    public void load(double[] in)

  Computes the empirical distribution from the provided array of numbers.

  Specified by:
    `load` in interface `EmpiricalDistribution`
  Parameters:
    `in` - the input data array

### load

    public void load(URL url)
              throws IOException

  Computes the empirical distribution using data read from a URL.

  Specified by:
    `load` in interface `EmpiricalDistribution`
  Parameters:
    `url` - url of the input file
  Throws:
    `IOException` - if an IO error occurs

### load

    public void load(File file)
              throws IOException

  Computes the empirical distribution from the input file.

  Specified by:
    `load` in interface `EmpiricalDistribution`
  Parameters:
    `file` - the input file
  Throws:
    `IOException` - if an IO error occurs

### fillBinStats

    private void fillBinStats(Object in)
                       throws IOException

  Fills binStats array (second pass through data file).

  Parameters:
    `in` - object providing access to the data
  Throws:
    `IOException` - if an IO error occurs

### findBin

    private int findBin(double value)

  Returns the index of the bin to which the given value belongs

  Parameters:
    `value` - the value whose bin we are trying to find
  Returns:
    the index of the bin containing the value

### getNextValue

    public double getNextValue()
                        throws IllegalStateException

  Generates a random value from this distribution.

  Specified by:
    `getNextValue` in interface `EmpiricalDistribution`
  Returns:
    the random value.
  Throws:
    `IllegalStateException` - if the distribution has not been loaded

### getSampleStats

    public StatisticalSummary getSampleStats()

  Returns a `StatisticalSummary` describing this distribution. Preconditions:
  - the distribution must be loaded before invoking this method

  Specified by:
    `getSampleStats` in interface `EmpiricalDistribution`
  Returns:
    the sample statistics
  Throws:
    `IllegalStateException` - if the distribution has not been loaded

### getBinCount

    public int getBinCount()

  Returns the number of bins.

  Specified by:
    `getBinCount` in interface `EmpiricalDistribution`
  Returns:
    the number of bins.

### getBinStats

    public List<SummaryStatistics> getBinStats()

  Returns a List of `SummaryStatistics` instances containing statistics describing the values in each of the bins. The list is indexed on the bin number.

  Specified by:
    `getBinStats` in interface `EmpiricalDistribution`
  Returns:
    List of bin statistics.

### getUpperBounds

    public double[] getUpperBounds()

  Returns a fresh copy of the array of upper bounds for the bins. Bins are:
  [min,upperBounds[0]],(upperBounds[0],upperBounds[1]],..., (upperBounds[binCount-2], upperBounds[binCount-1] = max].

  Note: In versions 1.0-2.0 of commons-math, this method incorrectly returned the array of probability generator upper bounds now returned by `getGeneratorUpperBounds()`.

  Specified by:
    `getUpperBounds` in interface `EmpiricalDistribution`
  Returns:
    array of bin upper bounds
  Since:
    2.1

### getGeneratorUpperBounds

    public double[] getGeneratorUpperBounds()

  Returns a fresh copy of the array of upper bounds of the subintervals of [0,1] used in generating data from the empirical distribution. Subintervals correspond to bins with lengths proportional to bin counts.

  In versions 1.0-2.0 of commons-math, this array was (incorrectly) returned by `getUpperBounds()`.

  Returns:
    array of upper bounds of subintervals used in data generation
  Since:
    2.1

### isLoaded

    public boolean isLoaded()

  Property indicating whether or not the distribution has been loaded.

  Specified by:
    `isLoaded` in interface `EmpiricalDistribution`
  Returns:
    true if the distribution has been loaded

Copyright (c) 2003-2013 Apache Software Foundation