org.ojalgo.random.process

## Class GaussianField<K extends Comparable<? super K>>

• ```public final class GaussianField<K extends Comparable<? super K>>
extends Object```
A Gaussian process is a stochastic process whose realizations consist of random values associated with every point in a range of times (or of space) such that each such random variable has a normal distribution. Moreover, every finite collection of those random variables has a multivariate normal distribution. A random field is a generalization of a stochastic process such that the underlying parameter need no longer be a simple real or integer valued "time", but can instead take values that are multidimensional vectors, or points on some manifold. This GaussianField class is a generalization, as well as the underlying implementation, of GaussianProcess. Prior to calling getDistribution(Comparable...) you must call addObservation(Comparable, double) one or more times.
Author:
apete
• ### Nested Class Summary

Nested Classes
Modifier and Type Class and Description
`static interface ` `GaussianField.Covariance<K extends Comparable<? super K>>`
`static interface ` `GaussianField.Mean<K extends Comparable<? super K>>`
• ### Constructor Summary

Constructors
Constructor and Description
`GaussianField(GaussianField.Covariance<K> covarFunc)`
```GaussianField(GaussianField.Mean<K> meanFunc, GaussianField.Covariance<K> covarFunc)```
• ### Method Summary

All Methods
Modifier and Type Method and Description
`void` ```addObservation(K key, double value)```
`void` `calibrate()`
`Normal1D` ```getDistribution(boolean cleanCovariances, K... evaluationPoint)```
`Normal1D` `getDistribution(K... evaluationPoint)`
• ### Methods inherited from class java.lang.Object

`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Constructor Detail

• #### GaussianField

`public GaussianField(GaussianField.Covariance<K> covarFunc)`
• #### GaussianField

```public GaussianField(GaussianField.Mean<K> meanFunc,
GaussianField.Covariance<K> covarFunc)```
• ### Method Detail

```public void addObservation(K key,
double value)```
• #### calibrate

`public void calibrate()`
• #### getDistribution

```public Normal1D getDistribution(boolean cleanCovariances,
K... evaluationPoint)```
• #### getDistribution

`public Normal1D getDistribution(K... evaluationPoint)`