jsat.distributions

Class LogUniform

java.lang.Object

jsat.distributions.Distribution

jsat.distributions.ContinuousDistribution

jsat.distributions.LogUniform

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable

public class LogUniform
extends ContinuousDistribution

The Log Uniform distribution is such that if X is the distribution, then Y = log(X) is uniformly distributed. Because of this log term, this distribution can only take values in a positive range.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LogUniform() Creates a new Log Uniform distribution between 1e-2 and 1
LogUniform(double min, double max) Creates a new Log Uniform distribution

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.
LogUniform	clone()
double[]	getCurrentVariableValues() Returns an array, where each value contains the value of a parameter in the distribution.
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.
double	invCdf(double p) Computes the inverse Cumulative Density Function (CDF-1) at the given point.
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 x) Computes the value of the Probability Density Function (PDF) at the given point
void	setMinMax(double min, double max) Sets the minimum and maximum values for this distribution
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	variance() Computes the variance of the distribution.

Methods inherited from class jsat.distributions.ContinuousDistribution

getDescriptiveName, logPdf, toString

Methods inherited from class jsat.distributions.Distribution

sample, sampleVec, standardDeviation

Methods inherited from class java.lang.Object

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

Constructor Detail

LogUniform

public LogUniform()

Creates a new Log Uniform distribution between 1e-2 and 1

LogUniform

public LogUniform(double min,
                  double max)

Creates a new Log Uniform distribution

Parameters:

min - the minimum value to be returned by this distribution

max - the maximum value to be returned by this distribution

Method Detail

setMinMax

public void setMinMax(double min,
                      double max)

Sets the minimum and maximum values for this distribution

Parameters:

min - the minimum value, must be positive

max - the maximum value, must be larger than min

pdf

public double pdf(double x)

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:

x - the value to get the PDF

Returns:

the PDF(x)

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

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()

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 LogUniform 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

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 double invCdf(double p)

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:

p - the probability value

Returns:

the value such that the CDF would return p

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.

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.

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.