OrdinaryKriging (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.regression.OrdinaryKriging

All Implemented Interfaces:

Serializable, Cloneable, Parameterized, Regressor
```
public class OrdinaryKriging
extends Object
implements Regressor, Parameterized
```
An implementation of Ordinary Kriging with support for a uniform error measurement. When an error value is applied, Kriging becomes equivalent to Gaussian Processes Regression.

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class OrdinaryKriging.PowVariogram

static interface OrdinaryKriging.Variogram

Nested Classes
Modifier and Type	Class and Description
`static class`	`OrdinaryKriging.PowVariogram`
`static interface`	`OrdinaryKriging.Variogram`

Field Summary

Fields
Modifier and Type Field and Description

static double DEFAULT_ERROR
The default error value is DEFAULT_ERROR

static double DEFAULT_NUGGET
The default nugget value is 0.1

Fields
Modifier and Type	Field and Description
`static double`	`DEFAULT_ERROR` The default error value is `DEFAULT_ERROR`
`static double`	`DEFAULT_NUGGET` The default nugget value is 0.1

Constructor Summary

Constructors
Constructor and Description
`OrdinaryKriging()` Creates a new Ordinary Kriging with a small error value using the `power` variogram.
`OrdinaryKriging(OrdinaryKriging.Variogram vari)` Creates a new Ordinary Kriging with a small error value
`OrdinaryKriging(OrdinaryKriging.Variogram vari, double error)` Creates a new Ordinary Kriging
`OrdinaryKriging(OrdinaryKriging.Variogram vari, double error, double nugget)` Creates a new Ordinary Kriging.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`OrdinaryKriging`	`clone()`
`double`	`getMeasurementError()` Returns the measurement error used for Kriging, which is equivalent to altering the diagonal values of the covariance.
`double`	`getNugget()` Returns the nugget value passed to the variogram during training.
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`double`	`regress(DataPoint data)`
`void`	`setMeasurementError(double error)` Sets the measurement error used for Kriging, which is equivalent to altering the diagonal values of the covariance.
`void`	`setNugget(double nugget)` Sets the nugget value passed to the variogram during training.
`boolean`	`supportsWeightedData()`
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - DEFAULT_NUGGET
```
public static final double DEFAULT_NUGGET
```
    The default nugget value is 0.1
    
    See Also:
    
    Constant Field Values
  - DEFAULT_ERROR
```
public static final double DEFAULT_ERROR
```
    The default error value is DEFAULT_ERROR
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - OrdinaryKriging
```
public OrdinaryKriging(OrdinaryKriging.Variogram vari,
                       double error,
                       double nugget)
```
    Creates a new Ordinary Kriging.
    
    Parameters:
    
    vari - the variogram to fit to the data
    
    error - the global measurement error
    
    nugget - the nugget value to add to the variogram
  - OrdinaryKriging
```
public OrdinaryKriging(OrdinaryKriging.Variogram vari,
                       double error)
```
    Creates a new Ordinary Kriging
    
    Parameters:
    
    vari - the variogram to fit to the data
    
    error - the global measurement error
  - OrdinaryKriging
```
public OrdinaryKriging(OrdinaryKriging.Variogram vari)
```
    Creates a new Ordinary Kriging with a small error value
    
    Parameters:
    
    vari - the variogram to fit to the data
  - OrdinaryKriging
```
public OrdinaryKriging()
```
    Creates a new Ordinary Kriging with a small error value using the power variogram.
- Method Detail
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Specified by:
    
    supportsWeightedData in interface Regressor
  - clone
```
public OrdinaryKriging clone()
```
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class Object
  - setMeasurementError
```
public void setMeasurementError(double error)
```
    Sets the measurement error used for Kriging, which is equivalent to altering the diagonal values of the covariance. While the measurement errors could be per data point, this implementation provides only a global error. If the error is set to zero, it will perfectly interpolate all data points.
    Increasing the error smooths the interpolation, and has a large impact on the regression results.
    
    Parameters:
    
    error - the measurement error for all data points
  - getMeasurementError
```
public double getMeasurementError()
```
    Returns the measurement error used for Kriging, which is equivalent to altering the diagonal values of the covariance. While the measurement errors could be per data point, this implementation provides only a global error. If the error is set to zero, it will perfectly interpolate all data points.
    
    Returns:
    
    the global error used for the data
  - setNugget
```
public void setNugget(double nugget)
```
    Sets the nugget value passed to the variogram during training. The nugget allows the variogram to start from a non-zero value, and is equivalent to alerting the off diagonal values of the covariance.
    Altering the nugget value has only a minor impact on the output
    
    Parameters:
    
    nugget - the new nugget value
    
    Throws:
    
    ArithmeticException - if a negative nugget value is provided
  - getNugget
```
public double getNugget()
```
    Returns the nugget value passed to the variogram during training. The nugget allows the variogram to start from a non-zero value, and is equivalent to alerting the off diagonal values of the covariance.
    
    Returns:
    
    the nugget added to the variogram
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class OrdinaryKriging

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_NUGGET

DEFAULT_ERROR

Constructor Detail

OrdinaryKriging

OrdinaryKriging

OrdinaryKriging

OrdinaryKriging

Method Detail

regress

train

train

supportsWeightedData

clone

setMeasurementError

getMeasurementError

setNugget

getNugget

getParameters

getParameter