jsat.distributions.discrete

Class Poisson

java.lang.Object

jsat.distributions.Distribution

jsat.distributions.discrete.DiscreteDistribution

jsat.distributions.discrete.Poisson

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable

public class Poisson
extends DiscreteDistribution

The Poisson distribution is for the number of events occurring in a fixed amount of time, where the event has an average rate and all other occurrences are independent.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Poisson() Creates a new Poisson distribution with λ = 1
Poisson(double lambda) Creates a new Poisson distribution

Method Summary

Modifier and Type	Method and Description
double	cdf(int x) Computes the value of the Cumulative Density Function (CDF) at the given point.
Poisson	clone()
double	getLambda()
double	logPmf(int x) Computes the log of the Probability Mass Function.
double	max() The maximum value for which the #pdf(double) is meant to return a value.
double	mean() Computes the mean value of the distribution
double	min() The minimum value for which the #pdf(double) is meant to return a value.
double	mode() Computes the mode of the distribution.
double	pmf(int x)
double[]	sample(int numSamples, java.util.Random rand) This method returns a double array containing the values of random samples from this distribution.
void	setLambda(double lambda) Sets the average rate of the event occurring in a unit of time
double	skewness() Computes the skewness of the distribution.
double	variance() Computes the variance of the distribution.

Methods inherited from class jsat.distributions.discrete.DiscreteDistribution

cdf, invCdf

Methods inherited from class jsat.distributions.Distribution

median, sampleVec, standardDeviation

Methods inherited from class java.lang.Object

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

Constructor Detail

Poisson

public Poisson()

Creates a new Poisson distribution with λ = 1

Poisson

public Poisson(double lambda)

Creates a new Poisson distribution

Parameters:

lambda - the average rate of the event

Method Detail

setLambda

public void setLambda(double lambda)

Sets the average rate of the event occurring in a unit of time

Parameters:

lambda - the average rate of the event occurring

getLambda

public double getLambda()

Returns:

the average rate of the event occurring in a unit of time

logPmf

public double logPmf(int x)

Description copied from class: DiscreteDistribution

Computes the log of the Probability Mass Function. Note, that then the probability is zero, Double.NEGATIVE_INFINITY would be the true value. Instead, this method will always return the negative of Double.MAX_VALUE. This is to avoid propagating bad values through computation.

Overrides:

logPmf in class DiscreteDistribution

Parameters:

x - the value to get the log(PMF) of

Returns:

the value of log(PMF(x))

pmf

public double pmf(int x)

Specified by:

pmf in class DiscreteDistribution

cdf

public double cdf(int x)

Description copied from class: DiscreteDistribution

Computes the value of the Cumulative Density Function (CDF) at the given point. The CDF returns a value in the range [0, 1], indicating what portion of values occur at or below that point.

Specified by:

cdf in class DiscreteDistribution

Parameters:

x - the value to get the CDF of

Returns:

the CDF(x)

sample

public double[] sample(int numSamples,
                       java.util.Random rand)

Description copied from class: Distribution

This method returns a double array containing the values of random samples from this distribution.

Overrides:

sample in class Distribution

Parameters:

numSamples - the number of random samples to take

rand - the source of randomness

Returns:

an array of the random sample values

mean

public double mean()

Description copied from class: Distribution

Computes the mean value of the distribution

Specified by:

mean in class Distribution

Returns:

the mean value of the distribution

mode

public double mode()

Description copied from class: Distribution

Computes the mode of the distribution. Not all distributions have a mode for all parameter values. NaN may be returned if the mode is not defined for the current values of the distribution.

Specified by:

mode in class Distribution

Returns:

the mode of the distribution

variance

public double variance()

Description copied from class: Distribution

Computes the variance of the distribution. Not all distributions have a finite variance for all parameter values. NaN may be returned if the variance is not defined for the current values of the distribution. Infinity is a possible value to be returned by some distributions.

Specified by:

variance in class Distribution

Returns:

the variance of the distribution.

skewness

public double skewness()

Description copied from class: Distribution

Computes the skewness of the distribution. Not all distributions have a finite skewness for all parameter values. NaN may be returned if the skewness is not defined for the current values of the distribution.

Specified by:

skewness in class Distribution

Returns:

the skewness of the distribution.

min

public double min()

Description copied from class: Distribution

The minimum value for which the #pdf(double) is meant to return a value. Note that Double.NEGATIVE_INFINITY is a valid return value.

Specified by:

min in class Distribution

Returns:

the minimum value for which the #pdf(double) is meant to return a value.

max

public double max()

Description copied from class: Distribution

The maximum value for which the #pdf(double) is meant to return a value. Note that Double.POSITIVE_INFINITY is a valid return value.

Specified by:

max in class Distribution

Returns:

the maximum value for which the #pdf(double) is meant to return a value.

clone

public Poisson clone()

Specified by:

clone in class DiscreteDistribution