KernelRLS (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.regression.KernelRLS

All Implemented Interfaces:

Serializable, Cloneable, Parameterized, Regressor, UpdateableRegressor
```
public class KernelRLS
extends Object
implements UpdateableRegressor, Parameterized
```
Provides an implementation of the Kernel Recursive Least Squares algorithm. This algorithm updates the model one per data point, and induces sparsity by projecting data points down onto a set of basis vectors learned from the data stream.

See: Engel, Y., Mannor, S.,&Meir, R. (2004). The Kernel Recursive Least-Squares Algorithm. IEEE Transactions on Signal Processing, 52(8), 2275–2285. doi:10.1109/TSP.2004.830985

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Modifier	Constructor and Description
`protected`	`KernelRLS(KernelRLS toCopy)` Copy constructor
	`KernelRLS(KernelTrick k, double errorTolerance)` Creates a new Kernel RLS learner

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`KernelRLS`	`clone()`
`void`	`finalizeModel()` Finalizes the model.
`double`	`getErrorTolerance()` Returns the projection approximation tolerance
`int`	`getModelSize()` Returns the number of basis vectors that make up the model
`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`	`setErrorTolerance(double v)` Sets the tolerance for errors in approximating a data point by projecting it onto the set of basis vectors.
`void`	`setUp(CategoricalData[] categoricalAttributes, int numericAttributes)` Prepares the classifier to begin learning from its `UpdateableRegressor.update(jsat.classifiers.DataPoint, double)` method.
`boolean`	`supportsWeightedData()`
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`
`void`	`update(DataPoint dataPoint, double y_t)` Updates the classifier by giving it a new data point to learn from.

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

- Constructor Detail
  - KernelRLS
```
public KernelRLS(KernelTrick k,
                 double errorTolerance)
```
    Creates a new Kernel RLS learner
    
    Parameters:
    
    k - the kernel trick to use
    
    errorTolerance - the tolerance for errors in the projection
  - KernelRLS
```
protected KernelRLS(KernelRLS toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setErrorTolerance
```
public void setErrorTolerance(double v)
```
    Sets the tolerance for errors in approximating a data point by projecting it onto the set of basis vectors. In general: as the tolerance increases the sparsity of the model increases but the accuracy may go down.
    Values in the range 10^x ∀ x ∈ {-1, -2, -3, -4} often work well for this algorithm.
    
    Parameters:
    
    v - the approximation tolerance
  - getErrorTolerance
```
public double getErrorTolerance()
```
    Returns the projection approximation tolerance
    
    Returns:
    
    the projection approximation tolerance
  - getModelSize
```
public int getModelSize()
```
    Returns the number of basis vectors that make up the model
    
    Returns:
    
    the number of basis vectors that make up the model
  - finalizeModel
```
public void finalizeModel()
```
    Finalizes the model. During online training, the the gram matrix and its inverse must be stored to perform updates, at the cost of O(n²) memory. One training is completed, these matrices are no longer needed - and can be removed to reclaim memory by finalizing the model. Once finalized, the model can no longer be updated - unless reset (destroying the model) by calling setUp(jsat.classifiers.CategoricalData[], int)
  - 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 KernelRLS clone()
```
    Specified by:
    
    clone in interface Regressor
    
    Specified by:
    
    clone in interface UpdateableRegressor
    
    Overrides:
    
    clone in class Object
  - setUp
```
public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes)
```
    Description copied from interface: UpdateableRegressor
    
    Prepares the classifier to begin learning from its UpdateableRegressor.update(jsat.classifiers.DataPoint, double) method.
    
    Specified by:
    
    setUp in interface UpdateableRegressor
    
    Parameters:
    
    categoricalAttributes - an array containing the categorical attributes that will be in each data point
    
    numericAttributes - the number of numeric attributes that will be in each data point
  - update
```
public void update(DataPoint dataPoint,
                   double y_t)
```
    Description copied from interface: UpdateableRegressor
    
    Updates the classifier by giving it a new data point to learn from.
    
    Specified by:
    
    update in interface UpdateableRegressor
    
    Parameters:
    
    dataPoint - the data point to learn
    
    y_t - the target value of the data point
  - 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 KernelRLS

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

KernelRLS

KernelRLS

Method Detail

setErrorTolerance

getErrorTolerance

getModelSize

finalizeModel

regress

train

train

supportsWeightedData

clone

setUp

update

getParameters

getParameter