@CodeReview(reviewer="Kevin R. Dixon", date="2009-10-20", changesNeeded=true, comments={"Fixed some missing javadoc.","General style fixes.","Added task to figure out a way to avoid storing weights in matrix.","Generally looks good.","Some argument checks need to be more complete"}, response=@CodeReviewResponse(date="2009-10-20",respondent="Dan Morrow",comments={"added additional test coverage","added more argument checks"},moreChangesNeeded=false)) @PublicationReference(author="Wikipedia", title="Mixture Model", type=WebPage, year=2009, url="http://en.wikipedia.org/wiki/Mixture_density") public class ScalarMixtureDensityModel extends LinearMixtureModel<java.lang.Double,SmoothUnivariateDistribution> implements SmoothUnivariateDistribution
Modifier and Type | Class and Description |
---|---|
static class |
ScalarMixtureDensityModel.CDF
CDFof the SMDM
|
static class |
ScalarMixtureDensityModel.EMLearner
An EM learner that estimates a mixture model from data
|
static class |
ScalarMixtureDensityModel.PDF
PDF of the SMDM
|
distributions, priorWeights
Constructor and Description |
---|
ScalarMixtureDensityModel()
Creates a new instance of ScalarMixtureDensityModel
|
ScalarMixtureDensityModel(java.util.Collection<? extends SmoothUnivariateDistribution> distributions)
Creates a new instance of ScalarMixtureDensityModel
|
ScalarMixtureDensityModel(java.util.Collection<? extends SmoothUnivariateDistribution> distributions,
double[] priorWeights)
Creates a new instance of ScalarMixtureDensityModel
|
ScalarMixtureDensityModel(ScalarMixtureDensityModel other)
Copy constructor
|
ScalarMixtureDensityModel(SmoothUnivariateDistribution... distributions)
Creates a new instance of ScalarMixtureDensityModel
|
Modifier and Type | Method and Description |
---|---|
ScalarMixtureDensityModel |
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.
|
ScalarMixtureDensityModel.CDF |
getCDF()
Gets the CDF of a scalar distribution.
|
java.lang.Double |
getMaxSupport()
Gets the minimum support (domain or input) of the distribution.
|
java.lang.Double |
getMean()
Gets the arithmetic mean, or "first central moment" or "expectation",
of the distribution.
|
double |
getMeanAsDouble()
Gets the mean of the distribution as a double.
|
java.lang.Double |
getMinSupport()
Gets the minimum support (domain or input) of the distribution.
|
ScalarMixtureDensityModel.PDF |
getProbabilityFunction()
Gets the distribution function associated with this Distribution,
either the PDF or PMF.
|
double |
getVariance()
Gets the variance of the distribution.
|
double |
sampleAsDouble(java.util.Random random)
Samples a value from this distribution as a double.
|
double[] |
sampleAsDoubles(java.util.Random random,
int count)
Samples values from this distribution as an array of doubles.
|
void |
sampleInto(java.util.Random random,
double[] output,
int start,
int length)
Samples values from this distribution as an array of doubles.
|
getDistributionCount, getDistributions, getPriorWeights, getPriorWeightSum, sample, sampleInto, setDistributions, setPriorWeights, toString
sample
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
sample, sample, sampleInto
public ScalarMixtureDensityModel()
public ScalarMixtureDensityModel(SmoothUnivariateDistribution... distributions)
distributions
- Distributions that comprise the SMDM with equal prior weightpublic ScalarMixtureDensityModel(java.util.Collection<? extends SmoothUnivariateDistribution> distributions)
distributions
- Distributions that comprise the SMDM with equal prior weightpublic ScalarMixtureDensityModel(java.util.Collection<? extends SmoothUnivariateDistribution> distributions, double[] priorWeights)
distributions
- Distributions that comprise the SMDMpriorWeights
- Weights proportionate by which the distributions are sampledpublic ScalarMixtureDensityModel(ScalarMixtureDensityModel other)
other
- SMDM to copypublic ScalarMixtureDensityModel clone()
AbstractCloneableSerializable
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.clone
in interface Vectorizable
clone
in interface CloneableSerializable
clone
in class LinearMixtureModel<java.lang.Double,SmoothUnivariateDistribution>
public Vector convertToVector()
Vectorizable
convertToVector
in interface Vectorizable
public void convertFromVector(Vector parameters)
Vectorizable
convertFromVector
in interface Vectorizable
parameters
- The parameters to incorporate.public java.lang.Double getMinSupport()
UnivariateDistribution
getMinSupport
in interface UnivariateDistribution<java.lang.Double>
public java.lang.Double getMaxSupport()
UnivariateDistribution
getMaxSupport
in interface UnivariateDistribution<java.lang.Double>
public java.lang.Double getMean()
DistributionWithMean
getMean
in interface DistributionWithMean<java.lang.Double>
getMean
in interface SmoothUnivariateDistribution
public double getMeanAsDouble()
UnivariateDistribution
getMeanAsDouble
in interface UnivariateDistribution<java.lang.Double>
@PublicationReference(author="Wikipedia", title="Mixture Model", type=WebPage, year=2009, url="http://en.wikipedia.org/wiki/Mixture_density") public double getVariance()
UnivariateDistribution
getVariance
in interface UnivariateDistribution<java.lang.Double>
public double sampleAsDouble(java.util.Random random)
SmoothUnivariateDistribution
sampleAsDouble
in interface SmoothUnivariateDistribution
random
- Random number generator to use.public double[] sampleAsDoubles(java.util.Random random, int count)
SmoothUnivariateDistribution
sampleAsDoubles
in interface SmoothUnivariateDistribution
random
- Random number generator to use.count
- The number of values to sample. Cannot be negativepublic void sampleInto(java.util.Random random, double[] output, int start, int length)
SmoothUnivariateDistribution
sampleInto
in interface SmoothUnivariateDistribution
random
- Random number generator to use.output
- The array to write the result into. Cannot be null.start
- The offset in the array to start writing at. Cannot be negative.length
- The number of values to sample. Cannot be negative.public ScalarMixtureDensityModel.PDF getProbabilityFunction()
ComputableDistribution
getProbabilityFunction
in interface ComputableDistribution<java.lang.Double>
getProbabilityFunction
in interface SmoothUnivariateDistribution
public ScalarMixtureDensityModel.CDF getCDF()
UnivariateDistribution
getCDF
in interface ClosedFormUnivariateDistribution<java.lang.Double>
getCDF
in interface SmoothUnivariateDistribution
getCDF
in interface UnivariateDistribution<java.lang.Double>