jsat.distributions

Class Normal

java.lang.Object

jsat.distributions.Distribution

jsat.distributions.ContinuousDistribution

jsat.distributions.Normal

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable

public class Normal
extends ContinuousDistribution

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Normal()
Normal(double mean, double stndDev)

Method Summary

Modifier and Type	Method and Description
double	cdf(double x) Computes the value of the Cumulative Density Function (CDF) at the given point.
static double	cdf(double x, double mu, double sigma)
ContinuousDistribution	clone()
boolean	equals(java.lang.Object obj)
double[]	getCurrentVariableValues() Returns an array, where each value contains the value of a parameter in the distribution.
java.lang.String	getDescriptiveName() The descriptive name of a distribution returns the name of the distribution, followed by the parameters of the distribution and their values.
java.lang.String	getDistributionName() Return the name of the distribution.
java.lang.String[]	getVariables() Returns an array, where each value contains the name of a parameter in the distribution.
int	hashCode()
double	invCdf(double d) Computes the inverse Cumulative Density Function (CDF-1) at the given point.
static double	invcdf(double x, double mu, double sigma)
double	invPdf(double d)
double	logPdf(double x) Computes the log of the Probability Density Function.
static double	logPdf(double x, double mu, double sigma) Computes the log probability of a given value
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	pdf(double d) Computes the value of the Probability Density Function (PDF) at the given point
static double	pdf(double x, double mu, double sigma)
void	setMean(double mean)
void	setStndDev(double stndDev)
void	setUsingData(Vec data) Attempts to set the variables used by this distribution based on population sample data, assuming the sample data is from this type of distribution.
void	setVariable(java.lang.String var, double value) Sets one of the variables of this distribution by the name.
double	skewness() Computes the skewness of the distribution.
double	standardDeviation() Computes the standard deviation of the distribution.
double	variance() Computes the variance of the distribution.
double	zTransform(double x)
static double	zTransform(double x, double mu, double sigma)

Methods inherited from class jsat.distributions.ContinuousDistribution

toString

Methods inherited from class jsat.distributions.Distribution

sample, sampleVec

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

Normal

public Normal()

Normal

public Normal(double mean,
              double stndDev)

Method Detail

setMean

public void setMean(double mean)

setStndDev

public void setStndDev(double stndDev)

cdf

public static double cdf(double x,
                         double mu,
                         double sigma)

cdf

public double cdf(double x)

Description copied from class: Distribution

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.

Overrides:

cdf in class ContinuousDistribution

Parameters:

x - the value to get the CDF of

Returns:

the CDF(x)

invcdf

public static double invcdf(double x,
                            double mu,
                            double sigma)

invCdf

public double invCdf(double d)

Description copied from class: Distribution

Computes the inverse Cumulative Density Function (CDF^-1) at the given point. It takes in a value in the range of [0, 1] and returns the value x, such that CDF(x) = p

Overrides:

invCdf in class ContinuousDistribution

Parameters:

d - the probability value

Returns:

the value such that the CDF would return p

pdf

public static double pdf(double x,
                         double mu,
                         double sigma)

pdf

public double pdf(double d)

Description copied from class: ContinuousDistribution

Computes the value of the Probability Density Function (PDF) at the given point

Specified by:

pdf in class ContinuousDistribution

Parameters:

d - the value to get the PDF

Returns:

the PDF(x)

logPdf

public static double logPdf(double x,
                            double mu,
                            double sigma)

Computes the log probability of a given value

Parameters:

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

mu - the mean of the distribution

sigma - the standard deviation of the distribution

Returns:

the log probability

logPdf

public double logPdf(double x)

Description copied from class: ContinuousDistribution

Computes the log of the Probability Density 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:

logPdf in class ContinuousDistribution

Parameters:

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

Returns:

the value of log(PDF(x))

invPdf

public double invPdf(double d)

zTransform

public static double zTransform(double x,
                                double mu,
                                double sigma)

zTransform

public double zTransform(double x)

getDescriptiveName

public java.lang.String getDescriptiveName()

Description copied from class: ContinuousDistribution

The descriptive name of a distribution returns the name of the distribution, followed by the parameters of the distribution and their values.

Overrides:

getDescriptiveName in class ContinuousDistribution

Returns:

the name of the distribution that includes parameter values

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.

getDistributionName

public java.lang.String getDistributionName()

Description copied from class: ContinuousDistribution

Return the name of the distribution.

Specified by:

getDistributionName in class ContinuousDistribution

Returns:

the name of the distribution.

getVariables

public java.lang.String[] getVariables()

Description copied from class: ContinuousDistribution

Returns an array, where each value contains the name of a parameter in the distribution. The order must always be the same, and match up with the values returned by ContinuousDistribution.getCurrentVariableValues()

Specified by:

getVariables in class ContinuousDistribution

Returns:

a string of the variable names this distribution uses

setVariable

public void setVariable(java.lang.String var,
                        double value)

Description copied from class: ContinuousDistribution

Sets one of the variables of this distribution by the name.

Specified by:

setVariable in class ContinuousDistribution

Parameters:

var - the variable to set

value - the value to set

clone

public ContinuousDistribution clone()

Specified by:

clone in class ContinuousDistribution

setUsingData

public void setUsingData(Vec data)

Description copied from class: ContinuousDistribution

Attempts to set the variables used by this distribution based on population sample data, assuming the sample data is from this type of distribution.

Specified by:

setUsingData in class ContinuousDistribution

Parameters:

data - the data to use to attempt to fit against

getCurrentVariableValues

public double[] getCurrentVariableValues()

Description copied from class: ContinuousDistribution

Returns an array, where each value contains the value of a parameter in the distribution. The order must always be the same, and match up with the values returned by ContinuousDistribution.getVariables()

Specified by:

getCurrentVariableValues in class ContinuousDistribution

Returns:

the current values of the parameters used by this distribution, in the same order as their names are returned by ContinuousDistribution.getVariables()

mean

public double mean()

Description copied from class: Distribution

Computes the mean value of the distribution

Overrides:

mean in class ContinuousDistribution

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.

Overrides:

mode in class ContinuousDistribution

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.

Overrides:

variance in class ContinuousDistribution

Returns:

the variance of the distribution.

standardDeviation

public double standardDeviation()

Description copied from class: Distribution

Computes the standard deviation of the distribution. Not all distributions have a finite standard deviation 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.

Overrides:

standardDeviation in class Distribution

Returns:

the standard deviation 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.

Overrides:

skewness in class ContinuousDistribution

Returns:

the skewness of the distribution.

hashCode

public int hashCode()

Overrides:

hashCode in class java.lang.Object

equals

public boolean equals(java.lang.Object obj)

Overrides:

equals in class java.lang.Object