org.ojalgo.random

Class LogNormal

java.lang.Object

java.lang.Number

org.ojalgo.random.RandomNumber

org.ojalgo.random.LogNormal

All Implemented Interfaces:

Serializable, DoubleSupplier, Supplier<Double>, BasicFunction, NullaryFunction<Double>, ContinuousDistribution, Distribution

public class LogNormal
extends RandomNumber

A continuous distribution in which the logarithm of a variable has a normal distribution. A log normal distribution results if the variable is the product of a large number of independent, identically-distributed variables in the same way that a normal distribution results if the variable is the sum of a large number of independent, identically-distributed variables.

Author:

apete

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.ojalgo.function.BasicFunction

BasicFunction.Differentiable<N extends Number,F extends BasicFunction>, BasicFunction.Integratable<N extends Number,F extends BasicFunction>, BasicFunction.PlainUnary<T,R>

Constructor Summary

Constructors

Constructor and Description
LogNormal()
LogNormal(double aMean, double aStdDev) The aMean and aStdDev parameters are the mean and standard deviation of the variable's logarithm (by definition, the variable's logarithm is normally distributed).

Method Summary

Modifier and Type	Method and Description
static LogNormal	estimate(Access1D<?> rawSamples)
protected double	generate()
double	getDensity(double value) In probability theory, a probability density function (pdf), or density of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a given point.
double	getDistribution(double value) In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x.
double	getExpected()
double	getGeometricMean() The geometric mean is also the median
double	getGeometricStandardDeviation()
double	getLowerConfidenceQuantile(double confidence)
double	getQuantile(double probability) The quantile function, for any distribution, is defined for real variables between zero and one and is mathematically the inverse of the cumulative distribution function.
double	getUpperConfidenceQuantile(double confidence)
double	getVariance() Subclasses must override either getStandardDeviation() or getVariance()!
static LogNormal	make(double aExpected, double aVariance)
void	setSeed(long seed)

Methods inherited from class org.ojalgo.random.RandomNumber

checkProbabilty, doubleValue, floatValue, getStandardDeviation, intValue, invoke, longValue, newSampleSet, random, toString

Methods inherited from class java.lang.Number

byteValue, shortValue

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.ojalgo.random.ContinuousDistribution

getProbability

Methods inherited from interface org.ojalgo.random.Distribution

getStandardDeviation

Methods inherited from interface org.ojalgo.function.NullaryFunction

andThen, get, getAsDouble

Constructor Detail

LogNormal

public LogNormal()

LogNormal

public LogNormal(double aMean,
                 double aStdDev)

The aMean and aStdDev parameters are the mean and standard deviation of the variable's logarithm (by definition, the variable's logarithm is normally distributed).

Method Detail

estimate

public static LogNormal estimate(Access1D<?> rawSamples)

make

public static LogNormal make(double aExpected,
                             double aVariance)

getDensity

public double getDensity(double value)

Description copied from interface: ContinuousDistribution

In probability theory, a probability density function (pdf), or density of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a given point. The probability for the random variable to fall within a particular region is given by the integral of this variable's density over the region. The probability density function is nonnegative everywhere, and its integral over the entire space is equal to one. WikipediA

Parameters:

value - x

Returns:

P(x)

getDistribution

public double getDistribution(double value)

Description copied from interface: ContinuousDistribution

In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. Intuitively, it is the "area so far" function of the probability distribution. Cumulative distribution functions are also used to specify the distribution of multivariate random variables. WikipediA

Parameters:

value - x

Returns:

P(≤x)

getExpected

public double getExpected()

getGeometricMean

public double getGeometricMean()

The geometric mean is also the median

getGeometricStandardDeviation

public double getGeometricStandardDeviation()

getQuantile

public double getQuantile(double probability)

Description copied from interface: ContinuousDistribution

The quantile function, for any distribution, is defined for real variables between zero and one and is mathematically the inverse of the cumulative distribution function. WikipediA The input probability absolutely has to be [0.0, 1.0], but values close to 0.0 and 1.0 may be problematic

Parameters:

probability - P(<=x)

Returns:

x

getVariance

public double getVariance()

Description copied from class: RandomNumber

Subclasses must override either getStandardDeviation() or getVariance()!

Specified by:

getVariance in interface Distribution

Overrides:

getVariance in class RandomNumber

See Also:

Distribution.getStandardDeviation(), Distribution.getVariance()

setSeed

public void setSeed(long seed)

Overrides:

setSeed in class RandomNumber

generate

protected double generate()

getLowerConfidenceQuantile

public final double getLowerConfidenceQuantile(double confidence)

getUpperConfidenceQuantile

public final double getUpperConfidenceQuantile(double confidence)