WhitenedPCA (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.datatransform.DataTransformBase
- - jsat.datatransform.WhitenedPCA

All Implemented Interfaces:

Serializable, Cloneable, DataTransform, Parameterized

Direct Known Subclasses:

WhitenedZCA
```
public class WhitenedPCA
extends DataTransformBase
```
An extension of PCA that attempts to capture the variance, and make the variables in the output space independent from each-other. An of equal scale, so that the covariance is equal to I. The results may be further from the identity matrix than desired as the target dimension shrinks

The Whitened PCA is more computational expensive than the normal PCA algorithm, but transforming the data takes the same time.

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected int`	`dimensions` The number of dimensions to project down to
`protected double`	`regularization` Regularization parameter
`protected Matrix`	`transform` The final transformation matrix, that will create new points `y` = `transform` * x

Constructor Summary

Constructors
Constructor and Description
`WhitenedPCA()` Creates a new WhitenedPCA transform that uses up to 50 dimensions for the transformed space.
`WhitenedPCA(DataSet dataSet)` Creates a new WhitenedPCA.
`WhitenedPCA(DataSet dataSet, double regularization)` Creates a new WhitenedPCA, the dimensions will be chosen so that the subset of dimensions is of full rank.
`WhitenedPCA(DataSet dataSet, double regularization, int dims)` Creates a new WhitenedPCA from the given dataset
`WhitenedPCA(DataSet dataSet, int dims)` Creates a new WhitenedPCA.
`WhitenedPCA(double regularization, int dims)` Creates a new WhitenedPCA transform
`WhitenedPCA(int dims)` Creates a new WhitenedPCA transform

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`DataTransform`	`clone()`
`void`	`fit(DataSet dataSet)` Fits this transform to the given dataset.
`int`	`getDimensions()` Returns the number of dimensions to project down to
`double`	`getRegularization()`
`static Distribution`	`guessDimensions(DataSet d)`
`void`	`setDimensions(int dimensions)` Sets the number of dimensions to project down to
`void`	`setRegularization(double regularization)`
`protected void`	`setUpTransform(SingularValueDecomposition svd)` Creates the `transform matrix` to be used when converting data points.
`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

- Field Detail
  - regularization
```
protected double regularization
```
    Regularization parameter
  - dimensions
```
protected int dimensions
```
    The number of dimensions to project down to
  - transform
```
protected Matrix transform
```
    The final transformation matrix, that will create new points y = transform * x
- Constructor Detail
  - WhitenedPCA
```
public WhitenedPCA()
```
    Creates a new WhitenedPCA transform that uses up to 50 dimensions for the transformed space. This may not be optimal for any given dataset.
    
    Parameters:
    
    dims - the number of dimensions to project down to
  - WhitenedPCA
```
public WhitenedPCA(int dims)
```
    Creates a new WhitenedPCA transform
    
    Parameters:
    
    dims - the number of dimensions to project down to
  - WhitenedPCA
```
public WhitenedPCA(double regularization,
                   int dims)
```
    Creates a new WhitenedPCA transform
    
    Parameters:
    
    regularization - the amount of regularization to add, avoids numerical instability
    
    dims - the number of dimensions to project down to
  - WhitenedPCA
```
public WhitenedPCA(DataSet dataSet,
                   double regularization,
                   int dims)
```
    Creates a new WhitenedPCA from the given dataset
    
    Parameters:
    
    dataSet - the data set to whiten
    
    regularization - the amount of regularization to add, avoids numerical instability
    
    dims - the number of dimensions to project down to
  - WhitenedPCA
```
public WhitenedPCA(DataSet dataSet,
                   double regularization)
```
    Creates a new WhitenedPCA, the dimensions will be chosen so that the subset of dimensions is of full rank.
    
    Parameters:
    
    dataSet - the data set to whiten
    
    regularization - the amount of regularization to add, avoids numerical instability
  - WhitenedPCA
```
public WhitenedPCA(DataSet dataSet)
```
    Creates a new WhitenedPCA. The dimensions will be chosen so that the subset of dimensions is of full rank. The regularization parameter will be chosen as the log of the condition of the covariance.
    
    Parameters:
    
    dataSet - the data set to whiten
  - WhitenedPCA
```
public WhitenedPCA(DataSet dataSet,
                   int dims)
```
    Creates a new WhitenedPCA. The regularization parameter will be chosen as the log of the condition of the covariance.
    
    Parameters:
    
    dataSet - the data set to whiten
    
    dims - the number of dimensions to project down to
- Method Detail
  - fit
```
public void fit(DataSet dataSet)
```
    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:
    
    dataSet - the dataset to fir this transform to
  - setUpTransform
```
protected void setUpTransform(SingularValueDecomposition svd)
```
    Creates the transform matrix to be used when converting data points. It is called in the constructor after all values are set.
    
    Parameters:
    
    svd - the SVD of the covariance of the source data set
  - 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
  - setRegularization
```
public void setRegularization(double regularization)
```
    Parameters:
    
    regularization - the regularization to apply to the diagonal of the decomposition. This can improve numeric stability and reduces noise.
  - getRegularization
```
public double getRegularization()
```
    Returns:
    
    the amount of regularization to apply
  - clone
```
public DataTransform clone()
```
    Specified by:
    
    clone in interface DataTransform
    
    Specified by:
    
    clone in class DataTransformBase
  - setDimensions
```
public void setDimensions(int dimensions)
```
    Sets the number of dimensions to project down to
    
    Parameters:
    
    dimensions - the feature size to project down to
  - getDimensions
```
public int getDimensions()
```
    Returns the number of dimensions to project down to
    
    Returns:
    
    the number of dimensions to project down to
  - guessDimensions
```
public static Distribution guessDimensions(DataSet d)
```

Class WhitenedPCA

Field Summary

Constructor Summary

Method Summary

Methods inherited from class jsat.datatransform.DataTransformBase

Methods inherited from class java.lang.Object

Field Detail

regularization

dimensions

transform

Constructor Detail

WhitenedPCA

WhitenedPCA

WhitenedPCA

WhitenedPCA

WhitenedPCA

WhitenedPCA

WhitenedPCA

Method Detail

fit

setUpTransform

transform

setRegularization

getRegularization

clone

setDimensions

getDimensions

guessDimensions