Module ojalgo

Package org.ojalgo.random.process

Class GaussianProcess

java.lang.Object

org.ojalgo.random.process.GaussianProcess

All Implemented Interfaces:

RandomProcess<Normal>

public final class GaussianProcess
extends Object

A Gaussian process is a RandomProcess where each variable has a normal distribution. In addition, every finite collection of those variables has a multivariate normal distribution.

Prior to calling getDistribution(double) or RandomProcess.simulate(int, int, double) you must call addObservation(Double, double) one or more times.

Author:

apete

Nested Class Summary

Nested classes/interfaces inherited from interface org.ojalgo.random.process.RandomProcess

RandomProcess.SimulationResults

Constructor Summary

Constructors

Constructor	Description
GaussianProcess(GaussianField.Covariance<Double> covarFunc)
GaussianProcess(GaussianField.Mean<Double> meanFunc, GaussianField.Covariance<Double> covarFunc)

Method Summary

Modifier and Type	Method	Description
boolean	addObservation(Double x, double y)
void	calibrate()
Normal	getDistribution(double evaluationPoint)
Normal1D	getDistribution(Double... evaluationPoint)
final double	getExpected()	Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getExpected().
final double	getLowerConfidenceQuantile(double confidence)	The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).
final double	getStandardDeviation()	Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getStandardDeviation().
final double	getUpperConfidenceQuantile(double confidence)	The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).
double	getValue()
final double	getVariance()	Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getVariance().
void	setValue(double newValue)
RandomProcess.SimulationResults	simulate(int numberOfRealisations, int numberOfSteps, double stepSize)	Returns an collection of sample sets.
double	step(double stepSize, double standardGaussianInnovation)

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.ojalgo.random.process.RandomProcess

simulate

Constructor Details

GaussianProcess

public GaussianProcess(GaussianField.Covariance<Double> covarFunc)

GaussianProcess

public GaussianProcess(GaussianField.Mean<Double> meanFunc,
 GaussianField.Covariance<Double> covarFunc)

Method Details

calibrate

public void calibrate()

getDistribution

public Normal getDistribution(double evaluationPoint)

Specified by:

getDistribution in interface RandomProcess<Normal>

Parameters:

evaluationPoint - How far into the future?

Returns:

The distribution for the process value at that future time.

getDistribution

public Normal1D getDistribution(Double... evaluationPoint)

getValue

public double getValue()

setValue

public void setValue(double newValue)

step

public double step(double stepSize,
 double standardGaussianInnovation)

addObservation

public boolean addObservation(Double x,
 double y)

simulate

public RandomProcess.SimulationResults simulate(int numberOfRealisations,
 int numberOfSteps,
 double stepSize)

Description copied from interface: RandomProcess

Returns an collection of sample sets. The array has numberOfSteps elements, and each sample set has numberOfRealisations samples.

Returns:

An array of sample sets. The array has aNumberOfSteps elements, and each sample set has aNumberOfRealisations samples.

getExpected

public final double getExpected()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getExpected().

getLowerConfidenceQuantile

public final double getLowerConfidenceQuantile(double confidence)

The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).

getStandardDeviation

public final double getStandardDeviation()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getStandardDeviation().

getUpperConfidenceQuantile

public final double getUpperConfidenceQuantile(double confidence)

The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).

getVariance

public final double getVariance()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getVariance().