gov.sandia.cognition.statistics.distribution
Class LogisticDistribution

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.statistics.AbstractDistribution<NumberType>
          extended by gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<Double>
              extended by gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution
                  extended by gov.sandia.cognition.statistics.distribution.LogisticDistribution
All Implemented Interfaces:
Vectorizable, ClosedFormComputableDistribution<Double>, ClosedFormDistribution<Double>, ClosedFormUnivariateDistribution<Double>, ComputableDistribution<Double>, Distribution<Double>, DistributionWithMean<Double>, SmoothUnivariateDistribution, UnivariateDistribution<Double>, CloneableSerializable, Serializable, Cloneable
Direct Known Subclasses:
LogisticDistribution.CDF, LogisticDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Logistic distribution",
                      type=WebPage,
                      year=2011,
                      url="http://en.wikipedia.org/wiki/Logistic_distribution")
public class LogisticDistribution
extends AbstractClosedFormSmoothUnivariateDistribution

A implementation of the scalar logistic distribution, which measures the log-odds of a binary event.

Since:
3.2.1
Author:
Kevin R. Dixon
See Also:
Serialized Form

Nested Class Summary
static class LogisticDistribution.CDF
          CDF of the LogisticDistribution
static class LogisticDistribution.PDF
          PDF of the LogisticDistribution
 
Field Summary
static double DEFAULT_MEAN
          Default mean, 0.0.
static double DEFAULT_SCALE
          Default scale, 1.0.
protected  double mean
          Mean (median and mode) of the distribution.
protected  double scale
          Scale of the distribution, proportionate to the standard deviation, must be greater than zero.
 
Constructor Summary
LogisticDistribution()
          Default constructor
LogisticDistribution(double mean, double scale)
          Creates a new instance of LogisticDistribution
LogisticDistribution(LogisticDistribution other)
          Copy constructor
 
Method Summary
 LogisticDistribution clone()
          This makes public the clone method on the Object class and removes the exception that it throws.
 void convertFromVector(Vector parameters)
          Converts the object from a Vector of parameters.
 Vector convertToVector()
          Converts the object to a vector.
 LogisticDistribution.CDF getCDF()
          Gets the CDF of a scalar distribution.
 Double getMaxSupport()
          Gets the minimum support (domain or input) of the distribution.
 Double getMean()
          Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
 Double getMinSupport()
          Gets the minimum support (domain or input) of the distribution.
 LogisticDistribution.PDF getProbabilityFunction()
          Gets the distribution function associated with this Distribution, either the PDF or PMF.
 double getScale()
          Getter for scale
 double getVariance()
          Gets the variance of the distribution.
 ArrayList<Double> sample(Random random, int numSamples)
          Draws multiple random samples from the distribution.
 void setMean(double mean)
          Setter for mean
 void setScale(double scale)
          Setter for scale
 
Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution
sample
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 
Methods inherited from interface gov.sandia.cognition.statistics.Distribution
sample
 

Field Detail

DEFAULT_MEAN

public static final double DEFAULT_MEAN
Default mean, 0.0.

See Also:
Constant Field Values

DEFAULT_SCALE

public static final double DEFAULT_SCALE
Default scale, 1.0.

See Also:
Constant Field Values

mean

protected double mean
Mean (median and mode) of the distribution.


scale

protected double scale
Scale of the distribution, proportionate to the standard deviation, must be greater than zero.

Constructor Detail

LogisticDistribution

public LogisticDistribution()
Default constructor


LogisticDistribution

public LogisticDistribution(double mean,
                            double scale)
Creates a new instance of LogisticDistribution

Parameters:
mean - Mean (median and mode) of the distribution.
scale - Scale of the distribution, proportionate to the standard deviation, must be greater than zero.

LogisticDistribution

public LogisticDistribution(LogisticDistribution other)
Copy constructor

Parameters:
other - LogisticDistribution to copy
Method Detail

clone

public LogisticDistribution clone()
Description copied from class: AbstractCloneableSerializable
This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:
clone in interface Vectorizable
Specified by:
clone in interface CloneableSerializable
Overrides:
clone in class AbstractClosedFormUnivariateDistribution<Double>
Returns:
A clone of this object.

sample

public ArrayList<Double> sample(Random random,
                                int numSamples)
Description copied from interface: Distribution
Draws multiple random samples from the distribution. It is generally more efficient to use this multiple-sample method than multiple calls of the single-sample method. (But not always.)

Parameters:
random - Random-number generator to use in order to generate random numbers.
numSamples - Number of samples to draw from the distribution.
Returns:
Samples drawn according to this distribution.

convertToVector

public Vector convertToVector()
Description copied from interface: Vectorizable
Converts the object to a vector.

Returns:
The Vector form of the object.

convertFromVector

public void convertFromVector(Vector parameters)
Description copied from interface: Vectorizable
Converts the object from a Vector of parameters.

Parameters:
parameters - The parameters to incorporate.

getMinSupport

public Double getMinSupport()
Description copied from interface: UnivariateDistribution
Gets the minimum support (domain or input) of the distribution.

Returns:
Minimum support.

getMaxSupport

public Double getMaxSupport()
Description copied from interface: UnivariateDistribution
Gets the minimum support (domain or input) of the distribution.

Returns:
Minimum support.

getVariance

public double getVariance()
Description copied from interface: UnivariateDistribution
Gets the variance of the distribution. This is sometimes called the second central moment by more pedantic people, which is equivalent to the square of the standard deviation.

Returns:
Variance of the distribution.

getProbabilityFunction

public LogisticDistribution.PDF getProbabilityFunction()
Description copied from interface: ComputableDistribution
Gets the distribution function associated with this Distribution, either the PDF or PMF.

Returns:
Distribution function associated with this Distribution.

getCDF

public LogisticDistribution.CDF getCDF()
Description copied from interface: UnivariateDistribution
Gets the CDF of a scalar distribution.

Returns:
CDF of the scalar distribution.

getMean

public Double getMean()
Description copied from interface: DistributionWithMean
Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.

Returns:
Mean of the distribution.

setMean

public void setMean(double mean)
Setter for mean

Parameters:
mean - Mean (median and mode) of the distribution.

getScale

public double getScale()
Getter for scale

Returns:
Scale of the distribution, proportionate to the standard deviation, must be greater than zero.

setScale

public void setScale(double scale)
Setter for scale

Parameters:
scale - Scale of the distribution, proportionate to the standard deviation, must be greater than zero.