gov.sandia.cognition.statistics.distribution
Class UniformDistribution

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.UniformDistribution
All Implemented Interfaces:
Vectorizable, ClosedFormComputableDistribution<Double>, ClosedFormDistribution<Double>, ClosedFormUnivariateDistribution<Double>, ComputableDistribution<Double>, Distribution<Double>, DistributionWithMean<Double>, EstimableDistribution<Double,UniformDistribution>, SmoothUnivariateDistribution, UnivariateDistribution<Double>, CloneableSerializable, Serializable, Cloneable
Direct Known Subclasses:
UniformDistribution.CDF, UniformDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Uniform distribution (continuous)",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Uniform_distribution_(continuous)")
public class UniformDistribution
extends AbstractClosedFormSmoothUnivariateDistribution
implements EstimableDistribution<Double,UniformDistribution>

Contains the (very simple) definition of a continuous Uniform distribution, parameterized between the minimum and maximum bounds.

Since:
3.0
Author:
Kevin R. Dixon
See Also:
Serialized Form

Nested Class Summary
static class UniformDistribution.CDF
          Cumulative Distribution Function of a uniform
static class UniformDistribution.MaximumLikelihoodEstimator
          Maximum Likelihood Estimator of a log-normal distribution.
static class UniformDistribution.PDF
          Probability density function of a Uniform Distribution
 
Field Summary
static double DEFAULT_MAX
          Default max, 1.0.
static double DEFAULT_MIN
          Default min, 0.0.
 
Constructor Summary
UniformDistribution()
          Creates a new instance of UniformDistribution
UniformDistribution(double minSupport, double maxSupport)
          Creates a new instance of UniformDistribution
UniformDistribution(UniformDistribution other)
          Copy constructor
 
Method Summary
 UniformDistribution 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.
 UniformDistribution.CDF getCDF()
          Gets the CDF of a scalar distribution.
 UniformDistribution.MaximumLikelihoodEstimator getEstimator()
          Gets an estimator associated with this 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.
 UniformDistribution.PDF getProbabilityFunction()
          Gets the distribution function associated with this Distribution, either the PDF or PMF.
 double getVariance()
          Gets the variance of the distribution.
 ArrayList<Double> sample(Random random, int numSamples)
          Draws multiple random samples from the distribution.
 void setMaxSupport(double maxSupport)
          Setter for maxSupport
 void setMinSupport(double minSupport)
          Setter for minSupport
 
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_MIN

public static final double DEFAULT_MIN
Default min, 0.0.

See Also:
Constant Field Values

DEFAULT_MAX

public static final double DEFAULT_MAX
Default max, 1.0.

See Also:
Constant Field Values
Constructor Detail

UniformDistribution

public UniformDistribution()
Creates a new instance of UniformDistribution


UniformDistribution

public UniformDistribution(double minSupport,
                           double maxSupport)
Creates a new instance of UniformDistribution

Parameters:
minSupport - Minimum x bound on the distribution
maxSupport - Maximum bound on the distribution

UniformDistribution

public UniformDistribution(UniformDistribution other)
Copy constructor

Parameters:
other - UniformDistribution to copy
Method Detail

clone

public UniformDistribution 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.

getMean

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

Specified by:
getMean in interface DistributionWithMean<Double>
Specified by:
getMean in interface SmoothUnivariateDistribution
Returns:
Mean of the distribution.

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.

Specified by:
getVariance in interface UnivariateDistribution<Double>
Returns:
Variance of the distribution.

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.)

Specified by:
sample in interface Distribution<Double>
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.

getMinSupport

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

Specified by:
getMinSupport in interface UnivariateDistribution<Double>
Returns:
Minimum support.

setMinSupport

public void setMinSupport(double minSupport)
Setter for minSupport

Parameters:
minSupport - Minimum x bound on the distribution

getMaxSupport

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

Specified by:
getMaxSupport in interface UnivariateDistribution<Double>
Returns:
Minimum support.

setMaxSupport

public void setMaxSupport(double maxSupport)
Setter for maxSupport

Parameters:
maxSupport - Maximum x bound on the distribution

convertToVector

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

Specified by:
convertToVector in interface Vectorizable
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.

Specified by:
convertFromVector in interface Vectorizable
Parameters:
parameters - The parameters to incorporate.

getCDF

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

Specified by:
getCDF in interface ClosedFormUnivariateDistribution<Double>
Specified by:
getCDF in interface SmoothUnivariateDistribution
Specified by:
getCDF in interface UnivariateDistribution<Double>
Returns:
CDF of the scalar distribution.

getProbabilityFunction

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

Specified by:
getProbabilityFunction in interface ComputableDistribution<Double>
Specified by:
getProbabilityFunction in interface SmoothUnivariateDistribution
Returns:
Distribution function associated with this Distribution.

getEstimator

public UniformDistribution.MaximumLikelihoodEstimator getEstimator()
Description copied from interface: EstimableDistribution
Gets an estimator associated with this distribution.

Specified by:
getEstimator in interface EstimableDistribution<Double,UniformDistribution>
Returns:
A distribution estimator associated for this distribution.