RFF_RBF (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.datatransform.DataTransformBase
- - jsat.datatransform.kernel.RFF_RBF

All Implemented Interfaces:

Serializable, Cloneable, DataTransform, Parameterized
```
public class RFF_RBF
extends DataTransformBase
```
An Implementation of Random Fourier Features for the RBFKernel. It transforms the numerical variables of a feature space to form a new feature space where the dot product between features approximates the RBF Kernel product.

See: Rahimi, A.,&Recht, B. (2007). Random Features for Large-Scale Kernel Machines. Neural Information Processing Systems. Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Modifier	Constructor and Description
	`RFF_RBF()` Creates a new RFF RBF object that will use an transformed feature space with a dimensionality of 512.
	`RFF_RBF(double sigma)` Creates a new RFF RBF object that will use an transformed feature space with a dimensionality of 512.
	`RFF_RBF(double sigma, int dim)` Creates a new RFF RBF object
	`RFF_RBF(double sigma, int dim, boolean inMemory)` Creates a new RFF RBF object
	`RFF_RBF(int featurSize, double sigma, int dim, Random rand, boolean inMemory)` Creates a new RFF RBF object
`protected`	`RFF_RBF(RFF_RBF toCopy)` Copy constructor

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`RFF_RBF`	`clone()`
`void`	`fit(DataSet data)` Fits this transform to the given dataset.
`int`	`getDimensions()` Returns the number of dimensions that will be used in the projected space
`double`	`getSigma()` Returns the σ value used for the RBF kernel approximation.
`Distribution`	`guessSigma(DataSet d)` Guess the distribution to use for the kernel width term `σ` in the RBF kernel being approximated.
`boolean`	`isInMemory()`
`void`	`setDimensions(int dimensions)` Sets the number of dimensions in the new approximate space to use.
`void`	`setInMemory(boolean inMemory)` Sets whether or not the transform matrix is stored explicitly in memory or not.
`void`	`setSigma(double sigma)` Sets the σ parameter of the RBF kernel that is being approximated.
`DataPoint`	`transform(DataPoint dp)` Returns a new data point that is a transformation of the original data point.

Methods inherited from class jsat.datatransform.DataTransformBase
getParameter, getParameters

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

- Constructor Detail
  - RFF_RBF
```
public RFF_RBF()
```
    Creates a new RFF RBF object that will use an transformed feature space with a dimensionality of 512. This constructor should be used with a parameter search to find a good value for sigma
  - RFF_RBF
```
public RFF_RBF(double sigma)
```
    Creates a new RFF RBF object that will use an transformed feature space with a dimensionality of 512.
    
    Parameters:
    
    sigma - the positive sigma value for the RBFKernel
  - RFF_RBF
```
public RFF_RBF(double sigma,
               int dim)
```
    Creates a new RFF RBF object
    
    Parameters:
    
    sigma - the positive sigma value for the RBFKernel
    
    dim - the new feature size dimension to project into.
  - RFF_RBF
```
public RFF_RBF(double sigma,
               int dim,
               boolean inMemory)
```
    Creates a new RFF RBF object
    
    Parameters:
    
    sigma - the positive sigma value for the RBFKernel
    
    dim - the new feature size dimension to project into.
    
    inMemory - true if the internal matrix should be stored in memory. If false, the memory will be re-computed as needed, increasing computation cost but uses no extra memory.
  - RFF_RBF
```
public RFF_RBF(int featurSize,
               double sigma,
               int dim,
               Random rand,
               boolean inMemory)
```
    Creates a new RFF RBF object
    
    Parameters:
    
    featurSize - the number of numeric features in the original feature space
    
    sigma - the positive sigma value for the RBFKernel
    
    dim - the new feature size dimension to project into.
    
    rand - the source of randomness to initialize internal state
    
    inMemory - true if the internal matrix should be stored in memory. If false, the memory will be re-computed as needed, increasing computation cost but uses no extra memory.
  - RFF_RBF
```
protected RFF_RBF(RFF_RBF toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - fit
```
public void fit(DataSet data)
```
    Description copied from interface: DataTransform
    
    Fits this transform to the given dataset. Some transforms can only be learned from classification or regression datasets. If an incompatible dataset type is given, a FailedToFitException exception may be thrown.
    
    Parameters:
    
    data - the dataset to fir this transform to
  - transform
```
public DataPoint transform(DataPoint dp)
```
    Description copied from interface: DataTransform
    
    Returns a new data point that is a transformation of the original data point. This new data point is a different object, but may contain the same references as the original data point. It is not guaranteed that you can mutate the transformed point without having a side effect on the original point.
    
    Parameters:
    
    dp - the data point to apply a transformation to
    
    Returns:
    
    a transformed data point
  - clone
```
public RFF_RBF clone()
```
    Specified by:
    
    clone in interface DataTransform
    
    Specified by:
    
    clone in class DataTransformBase
  - setInMemory
```
public void setInMemory(boolean inMemory)
```
    Sets whether or not the transform matrix is stored explicitly in memory or not. Explicit storage is often faster, but can be prohibitive for large feature sizes
    
    Parameters:
    
    inMemory - true to explicitly store the transform matrix, false to re-create it on the fly as needed
  - isInMemory
```
public boolean isInMemory()
```
    Returns:
    
    true if this object will explicitly store the transform matrix, false to re-create it on the fly as needed
  - setDimensions
```
public void setDimensions(int dimensions)
```
    Sets the number of dimensions in the new approximate space to use. This will be the number of numeric features in the transformed data, and larger values increase the accuracy of the approximation.
    
    Parameters:
    
    dimensions -
  - getDimensions
```
public int getDimensions()
```
    Returns the number of dimensions that will be used in the projected space
    
    Returns:
    
    the number of dimensions that will be used in the projected space
  - setSigma
```
public void setSigma(double sigma)
```
    Sets the σ parameter of the RBF kernel that is being approximated.
    
    Parameters:
    
    sigma - the positive value to use for σ
    
    See Also:
    
    RBFKernel.setSigma(double)
  - getSigma
```
public double getSigma()
```
    Returns the σ value used for the RBF kernel approximation.
    
    Returns:
    
    the σ value used for the RBF kernel approximation.
  - guessSigma
```
public Distribution guessSigma(DataSet d)
```
    Guess the distribution to use for the kernel width term σ in the RBF kernel being approximated.
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the σ parameter in the RBF Kernel

Class RFF_RBF

Constructor Summary

Method Summary

Methods inherited from class jsat.datatransform.DataTransformBase

Methods inherited from class java.lang.Object

Constructor Detail

RFF_RBF

RFF_RBF

RFF_RBF

RFF_RBF

RFF_RBF

RFF_RBF

Method Detail

fit

transform

clone

setInMemory

isInMemory

setDimensions

getDimensions

setSigma

getSigma

guessSigma