jsat.distributions.discrete

Class Binomial

java.lang.Object

jsat.distributions.Distribution

jsat.distributions.discrete.DiscreteDistribution

jsat.distributions.discrete.Binomial

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable

public class Binomial
extends DiscreteDistribution

The Binomial distribution is the distribution for the number of successful, independent, trials with a specific probability of success

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Binomial() Creates a new Binomial distribution for 1 trial with a 0.5 probability of success
Binomial(int trials, double p) Creates a new Binomial 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.
Binomial	clone()
double	getP()
int	getTrials()
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	median() Computes the median 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)
void	setP(double p) Sets the probability of a trial being a success
void	setTrials(int trials) The number of trials for the distribution
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

sample, sampleVec, standardDeviation

Methods inherited from class java.lang.Object

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

Constructor Detail

Binomial

public Binomial()

Creates a new Binomial distribution for 1 trial with a 0.5 probability of success

Binomial

public Binomial(int trials,
                double p)

Creates a new Binomial distribution

Parameters:

trials - the number of independent trials

p - the probability of success

Method Detail

setTrials

public void setTrials(int trials)

The number of trials for the distribution

Parameters:

trials - the number of trials to perform

getTrials

public int getTrials()

setP

public void setP(double p)

Sets the probability of a trial being a success

Parameters:

p - the probability of success for each trial

getP

public double getP()

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)

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

median

public double median()

Description copied from class: Distribution

Computes the median value of the distribution

Overrides:

median in class Distribution

Returns:

the median 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 Binomial clone()

Specified by:

clone in class DiscreteDistribution