smile.stat.distribution

Class KernelDensity

java.lang.Object

smile.stat.distribution.KernelDensity

All Implemented Interfaces:

Distribution

public class KernelDensity
extends Object
implements Distribution

Kernel density estimation is a non-parametric way of estimating the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. It is also known as the Parzen window method.

Author:

Haifeng Li

Constructor Summary

Constructors

Constructor and Description
KernelDensity(double[] x) Constructor.
KernelDensity(double[] x, double h) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
double	bandwidth() Returns the bandwidth of kernel.
double	cdf(double x) Cumulative distribution function.
double	entropy() Shannon entropy.
double	likelihood(double[] x) The likelihood of the samples.
double	logLikelihood(double[] x) The log likelihood of the samples.
double	logp(double x) The density at x in log scale, which may prevents the underflow problem.
double	mean() The mean of distribution.
int	npara() The number of parameters of the distribution.
double	p(double x) The probability density function for continuous distribution or probability mass function for discrete distribution at x.
double	quantile(double p) Inverse of CDF.
double	rand() Random number generator.
double	sd() The standard deviation of distribution.
double	var() The variance of distribution.

Methods inherited from class java.lang.Object

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

Constructor Detail

KernelDensity

public KernelDensity(double[] x)

Constructor. The bandwidth of kernel will be estimated by the rule of thumb.

Parameters:

x - the samples to estimate the density function.

KernelDensity

public KernelDensity(double[] x,
             double h)

Constructor.

Parameters:

x - the samples to estimate the density function.

h - a bandwidth parameter for smoothing.

Method Detail

bandwidth

public double bandwidth()

Returns the bandwidth of kernel.

Returns:

the bandwidth of kernel

npara

public int npara()

Description copied from interface: Distribution

The number of parameters of the distribution.

Specified by:

npara in interface Distribution

mean

public double mean()

Description copied from interface: Distribution

The mean of distribution.

Specified by:

mean in interface Distribution

var

public double var()

Description copied from interface: Distribution

The variance of distribution.

Specified by:

var in interface Distribution

sd

public double sd()

Description copied from interface: Distribution

The standard deviation of distribution.

Specified by:

sd in interface Distribution

entropy

public double entropy()

Shannon entropy. Not supported.

Specified by:

entropy in interface Distribution

rand

public double rand()

Random number generator. Not supported.

Specified by:

rand in interface Distribution

p

public double p(double x)

Description copied from interface: Distribution

The probability density function for continuous distribution or probability mass function for discrete distribution at x.

Specified by:

p in interface Distribution

logp

public double logp(double x)

Description copied from interface: Distribution

The density at x in log scale, which may prevents the underflow problem.

Specified by:

logp in interface Distribution

cdf

public double cdf(double x)

Cumulative distribution function. Not supported.

Specified by:

cdf in interface Distribution

quantile

public double quantile(double p)

Inverse of CDF. Not supported.

Specified by:

quantile in interface Distribution

likelihood

public double likelihood(double[] x)

The likelihood of the samples. Not supported.

Specified by:

likelihood in interface Distribution

logLikelihood

public double logLikelihood(double[] x)

The log likelihood of the samples. Not supported.

Specified by:

logLikelihood in interface Distribution