FastICA (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.datatransform.FastICA

All Implemented Interfaces:

Serializable, Cloneable, DataTransform, InvertibleTransform
```
public class FastICA
extends Object
implements InvertibleTransform
```
Provides an implementation of the FastICA algorithm for Independent Component Analysis (ICA). ICA is similar to PCA and Whitening, but assumes that the data is generated from a mixture of some C base components where mixing occurs instantaneously (i.e. produced from some matrix transform of the true components). ICA attempts to find the C components from the raw observations.

See:
- Hyvärinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council, 10(3), 626–34. doi:10.1109/72.761722
- Hyvärinen, a.,&Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), 411–430. doi:10.1016/S0893-6080(00)00026-5
Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`FastICA.DefaultNegEntropyFunc` A set of default negative entropy functions as specified in the original FastICA paper
`static interface`	`FastICA.NegEntropyFunc` The FastICA algorithm requires a function f(x) to be used iteratively in the algorithm, but only makes use of the first and second derivatives of the algorithm.

Constructor Summary

Constructors
Constructor and Description
`FastICA()` Creates a new FastICA transform that will attempt to fit 10 components.
`FastICA(DataSet data, int C)` Creates a new FastICA transform
`FastICA(DataSet data, int C, FastICA.NegEntropyFunc G, boolean preWhitened)` Creates a new FastICA transform
`FastICA(FastICA toCopy)` Copy constructor
`FastICA(int C)` Creates a new FastICA transform
`FastICA(int C, FastICA.NegEntropyFunc G, boolean preWhitened)` Creates a new FastICA transform

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`FastICA`	`clone()`
`void`	`fit(DataSet data)` Fits this transform to the given dataset.
`int`	`getC()`
`FastICA.NegEntropyFunc`	`getNegEntropyFunction()`
`DataPoint`	`inverse(DataPoint dp)` Applies the inverse or "reverse" transform to approximately undo the effect of `DataTransform.transform(jsat.classifiers.DataPoint)` to recover an approximation of the original data point.
`boolean`	`isPreWhitened()`
`void`	`setC(int C)` Sets the number of base components to learn
`void`	`setNegEntropyFunction(FastICA.NegEntropyFunc G)` Sets the Negative Entropy function used to infer the base components.
`void`	`setPreWhitened(boolean preWhitened)` Controls where or not the implementation assumes the input data is already whitened.
`DataPoint`	`transform(DataPoint dp)` Returns a new data point that is a transformation of the original data point.

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

- Constructor Detail
  - FastICA
```
public FastICA()
```
    Creates a new FastICA transform that will attempt to fit 10 components. This is likely not optimal for any particular dataset
  - FastICA
```
public FastICA(int C)
```
    Creates a new FastICA transform
    
    Parameters:
    
    C - the number of base components to assume and try to discover
  - FastICA
```
public FastICA(DataSet data,
               int C)
```
    Creates a new FastICA transform
    
    Parameters:
    
    data - the data set to transform
    
    C - the number of base components to assume and try to discover
  - FastICA
```
public FastICA(int C,
               FastICA.NegEntropyFunc G,
               boolean preWhitened)
```
    Creates a new FastICA transform
    
    Parameters:
    
    data - the data set to transform
    
    C - the number of base components to assume and try to discover
    
    G - the Negative Entropy function to use
    
    preWhitened - true to assume the data has already been whitened before being given to the transform, false and the FastICA implementation will perform its own whitening.
  - FastICA
```
public FastICA(DataSet data,
               int C,
               FastICA.NegEntropyFunc G,
               boolean preWhitened)
```
    Creates a new FastICA transform
    
    Parameters:
    
    data - the data set to transform
    
    C - the number of base components to assume and try to discover
    
    G - the Negative Entropy function to use
    
    preWhitened - true to assume the data has already been whitened before being given to the transform, false and the FastICA implementation will perform its own whitening.
  - FastICA
```
public FastICA(FastICA 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.
    
    Specified by:
    
    fit in interface DataTransform
    
    Parameters:
    
    data - the dataset to fir this transform to
  - setC
```
public void setC(int C)
```
    Sets the number of base components to learn
    
    Parameters:
    
    C - the number of base components to assume and try to discover
  - getC
```
public int getC()
```
    Returns:
    
    the number of base components to assume and try to discover
  - setNegEntropyFunction
```
public void setNegEntropyFunction(FastICA.NegEntropyFunc G)
```
    Sets the Negative Entropy function used to infer the base components.
    
    Parameters:
    
    G - the Negative Entropy function to use
  - getNegEntropyFunction
```
public FastICA.NegEntropyFunc getNegEntropyFunction()
```
    Returns:
    
    the Negative Entropy function to use
  - setPreWhitened
```
public void setPreWhitened(boolean preWhitened)
```
    Controls where or not the implementation assumes the input data is already whitened. Whitening is a requirement for the algorithm to work as intended.
    
    Parameters:
    
    preWhitened - true to assume the data has already been whitened, false for this object to do its own whitening
  - isPreWhitened
```
public boolean isPreWhitened()
```
    Returns:
    
    true if this object will assume the data has already been whitened, false for this object to do its own whitening
  - 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.
    
    Specified by:
    
    transform in interface DataTransform
    
    Parameters:
    
    dp - the data point to apply a transformation to
    
    Returns:
    
    a transformed data point
  - inverse
```
public DataPoint inverse(DataPoint dp)
```
    Description copied from interface: InvertibleTransform
    
    Applies the inverse or "reverse" transform to approximately undo the effect of DataTransform.transform(jsat.classifiers.DataPoint) to recover an approximation of the original data point.
    
    Specified by:
    
    inverse in interface InvertibleTransform
    
    Parameters:
    
    dp - the transformed data point
    
    Returns:
    
    the original data point, or a reasonable approximation
  - clone
```
public FastICA clone()
```
    Specified by:
    
    clone in interface DataTransform
    
    Specified by:
    
    clone in interface InvertibleTransform
    
    Overrides:
    
    clone in class Object

Class FastICA

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

FastICA

FastICA

FastICA

FastICA

FastICA

FastICA

Method Detail

fit

setC

getC

setNegEntropyFunction

getNegEntropyFunction

setPreWhitened

isPreWhitened

transform

inverse

clone