jsat.distributions.multivariate

Class MultivariateKDE

java.lang.Object

jsat.distributions.multivariate.MultivariateDistributionSkeleton

jsat.distributions.multivariate.MultivariateKDE

All Implemented Interfaces:

Serializable, Cloneable, MultivariateDistribution

Direct Known Subclasses:

MetricKDE, ProductKDE

public abstract class MultivariateKDE
extends MultivariateDistributionSkeleton

There are several methods of generalizing the KernelDensityEstimator to the multivariate case. This class provides a contract for implementations that provide a generalization of the KDE.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
MultivariateKDE()

Method Summary

Modifier and Type	Method and Description
abstract MultivariateKDE	clone()
abstract KernelFunction	getKernelFunction()
abstract List<? extends VecPaired<VecPaired<Vec,Integer>,Double>>	getNearby(Vec x) Returns the list of vectors that have a non zero contribution to the density of the query point x.
abstract List<? extends VecPaired<VecPaired<Vec,Integer>,Double>>	getNearbyRaw(Vec x) Returns the list of vectors that have a non zero contribution to the density of the query point x.
abstract void	scaleBandwidth(double scale) A caller may want to increase or decrease the bandwidth after training has been completed to get smoother model, or decrease it to observe behavior.

Methods inherited from class jsat.distributions.multivariate.MultivariateDistributionSkeleton

logPdf, logPdf, pdf, setUsingData, setUsingData, setUsingData, setUsingDataList

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface jsat.distributions.multivariate.MultivariateDistribution

pdf, sample, setUsingData, setUsingDataList

Constructor Detail

MultivariateKDE

public MultivariateKDE()

Method Detail

getNearby

public abstract List<? extends VecPaired<VecPaired<Vec,Integer>,Double>> getNearby(Vec x)

Returns the list of vectors that have a non zero contribution to the density of the query point x. Each vector is paired with its integer index from the original constructing list vectors, and a double indicating its weight given the kernel function in use.

Parameters:

x - the query point

Returns:

the list of near by vectors and their weights

getNearbyRaw

public abstract List<? extends VecPaired<VecPaired<Vec,Integer>,Double>> getNearbyRaw(Vec x)

Returns the list of vectors that have a non zero contribution to the density of the query point x. Each vector is paired with its integer index from the original constructing list vectors, and a double indicating its distance from the query point divided by the bandwidth of the point.

Parameters:

x - the query point

Returns:

the list of near by vectors and their weights

getKernelFunction

public abstract KernelFunction getKernelFunction()

Returns:

the kernel function used

scaleBandwidth

public abstract void scaleBandwidth(double scale)

A caller may want to increase or decrease the bandwidth after training has been completed to get smoother model, or decrease it to observe behavior. This method will scaled the bandwidth of each data point by the given factor

Parameters:

scale - the value to scale the bandwidth used

clone

public abstract MultivariateKDE clone()

Specified by:

clone in interface MultivariateDistribution

Specified by:

clone in class MultivariateDistributionSkeleton