gov.sandia.cognition.statistics.bayesian

Class AdaptiveRejectionSampling.UpperEnvelope

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.math.AbstractScalarFunction<java.lang.Double>

gov.sandia.cognition.math.AbstractUnivariateScalarFunction

gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope

gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.UpperEnvelope

All Implemented Interfaces:

Evaluator<java.lang.Double,java.lang.Double>, ScalarFunction<java.lang.Double>, UnivariateScalarFunction, ComputableDistribution<java.lang.Double>, Distribution<java.lang.Double>, ProbabilityFunction<java.lang.Double>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

AdaptiveRejectionSampling

public class AdaptiveRejectionSampling.UpperEnvelope
extends AdaptiveRejectionSampling.AbstractEnvelope
implements ProbabilityFunction<java.lang.Double>

Constructs the upper envelope for sampling.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected double[]	segmentCDF Cumulative sums of the normalized weights of the lines...

Fields inherited from class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope

lines

Constructor Summary

Constructors

Constructor and Description
UpperEnvelope() Default constructor

Method Summary

Modifier and Type	Method and Description
AdaptiveRejectionSampling.UpperEnvelope	clone() This makes public the clone method on the Object class and removes the exception that it throws.
protected void	computeLines() Recomputes the line segments that comprise the upper envelope
java.lang.Double	getMean() Gets the mean, which is not a supported operation.
AdaptiveRejectionSampling.UpperEnvelope	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
java.lang.Double	sample(java.util.Random random) Draws a single random sample from the distribution.
java.util.ArrayList<java.lang.Double>	sample(java.util.Random random, int numSamples) Draws multiple random samples from the distribution.
double	sampleAsDouble(java.util.Random random) Samples from this distribution as a double.
void	sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super java.lang.Double> output) Draws multiple random samples from the distribution and puts the result into the given collection.

Methods inherited from class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.AbstractEnvelope

evaluate, findLineSegment, getLines, logEvaluate, resetLines

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.ProbabilityFunction

logEvaluate

Methods inherited from interface gov.sandia.cognition.evaluator.Evaluator

evaluate

Methods inherited from interface gov.sandia.cognition.math.UnivariateScalarFunction

evaluate, evaluateAsDouble

Field Detail

segmentCDF

protected double[] segmentCDF

Cumulative sums of the normalized weights of the lines... This is automatically computed by computeSegments method.

Constructor Detail

UpperEnvelope

public UpperEnvelope()

Default constructor

Method Detail

clone

public AdaptiveRejectionSampling.UpperEnvelope 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 CloneableSerializable

Overrides:

clone in class AdaptiveRejectionSampling.AbstractEnvelope

Returns:

A clone of this object.

getProbabilityFunction

public AdaptiveRejectionSampling.UpperEnvelope 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<java.lang.Double>

Returns:

Distribution function associated with this Distribution.

getMean

public java.lang.Double getMean()

Gets the mean, which is not a supported operation. An exception is thrown.

Returns:

Nothing.

sampleAsDouble

public double sampleAsDouble(java.util.Random random)

Samples from this distribution as a double.

Parameters:

random - The random number generator to use.

Returns:

A sample from this distribution.

sample

public java.lang.Double sample(java.util.Random random)

Description copied from interface: Distribution

Draws a single random sample from the distribution.

Specified by:

sample in interface Distribution<java.lang.Double>

Parameters:

random - Random-number generator to use in order to generate random numbers.

Returns:

Sample drawn according to this distribution.

sample

public java.util.ArrayList<java.lang.Double> sample(java.util.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<java.lang.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.

sampleInto

public void sampleInto(java.util.Random random,
                       int sampleCount,
                       java.util.Collection<? super java.lang.Double> output)

Description copied from interface: Distribution

Draws multiple random samples from the distribution and puts the result into the given collection.

Specified by:

sampleInto in interface Distribution<java.lang.Double>

Parameters:

random - Random number generator to use.

sampleCount - The number of samples to draw. Cannot be negative.

output - The collection to add the samples into.

computeLines

protected void computeLines()

Recomputes the line segments that comprise the upper envelope

Specified by:

computeLines in class AdaptiveRejectionSampling.AbstractEnvelope