ALMA2K (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.BaseUpdateableClassifier
- - jsat.classifiers.linear.kernelized.ALMA2K

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, Parameterized
```
public class ALMA2K
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, Parameterized
```
Provides a kernelized version of the ALMA2 algorithm. It is important to note that the number of "support vectors" ALMA may learn is unbounded.
The averaged output of all previous hyperplanes is supported at almost no overhead, and can be turned on by setting setAveraged(boolean). This information is always collected, and the output can be changed once already learned.

See: Gentile, C. (2002). A New Approximate Maximal Margin Classification Algorithm. The Journal of Machine Learning Research, 2, 213–242. Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Modifier	Constructor and Description
`protected`	`ALMA2K(ALMA2K other)` Copy constructor
	`ALMA2K(KernelTrick kernel, double alpha)` Creates a new kernelized ALMA2 object

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`ALMA2K`	`clone()`
`double`	`getAlpha()` Returns the approximation coefficient used
`double`	`getB()` Returns the B value of the ALMA algorithm
`double`	`getC()`
`KernelTrick`	`getKernelTrick()` Returns the kernel in use
`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`	`getScore(DataPoint dp)` Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
`static Distribution`	`guessAlpha(DataSet d)` Guesses the distribution to use for the α parameter
`boolean`	`isAveraged()` Returns whether or not the averaged or last hypothesis is used
`void`	`setAlpha(double alpha)` Alpha controls the approximation of the large margin formed by ALMA, with larger values causing more updates.
`void`	`setAveraged(boolean averaged)` ALMA2K supports taking the averaged output of all previous hypothesis weighted by the number of successful uses of the hypothesis during training.
`void`	`setB(double B)` Sets the B variable of the ALMA algorithm, this is set automatically by `setAlpha(double)`.
`void`	`setC(double C)` Sets the C value of the ALMA algorithm.
`void`	`setKernelTrick(KernelTrick K)` Sets the kernel to use
`void`	`setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting)` Prepares the classifier to begin learning from its `UpdateableClassifier.update(jsat.classifiers.DataPoint, int)` method.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`update(DataPoint dataPoint, int targetClass)` Updates the classifier by giving it a new data point to learn from.

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier
getEpochs, setEpochs, trainC, trainC, trainEpochs

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

Methods inherited from interface jsat.classifiers.Classifier
trainC, trainC

- Constructor Detail
  - ALMA2K
```
public ALMA2K(KernelTrick kernel,
              double alpha)
```
    Creates a new kernelized ALMA2 object
    
    Parameters:
    
    kernel - the kernel function to use
    
    alpha - the alpha parameter of ALMA
  - ALMA2K
```
protected ALMA2K(ALMA2K other)
```
    Copy constructor
    
    Parameters:
    
    other - the ALMA2K object to copy
- Method Detail
  - clone
```
public ALMA2K clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface UpdateableClassifier
    
    Specified by:
    
    clone in class BaseUpdateableClassifier
  - setAveraged
```
public void setAveraged(boolean averaged)
```
    ALMA2K supports taking the averaged output of all previous hypothesis weighted by the number of successful uses of the hypothesis during training. This effectively reduces the variance of the classifier. It has no impact on the training / update phase, only the classification results are impacted.
    
    Unlike most algorithms, this can be changed at any time without issue - even after the algorithm has been trained the type of output (averaged or last) can be switched on the fly.
    
    Parameters:
    
    averaged - true to use the averaged out, false to only use the last hypothesis
  - isAveraged
```
public boolean isAveraged()
```
    Returns whether or not the averaged or last hypothesis is used
    
    Returns:
    
    whether or not the averaged or last hypothesis is used
  - setKernelTrick
```
public void setKernelTrick(KernelTrick K)
```
    Sets the kernel to use
    
    Parameters:
    
    K - the kernel to use
  - getKernelTrick
```
public KernelTrick getKernelTrick()
```
    Returns the kernel in use
    
    Returns:
    
    the kernel in use
  - setAlpha
```
public void setAlpha(double alpha)
```
    Alpha controls the approximation of the large margin formed by ALMA, with larger values causing more updates. A value of 1.0 will update only on mistakes, while smaller values update if the error was not far enough away from the margin.
    
    NOTE: Whenever alpha is set, the value of B will also be set to an appropriate value. This is not the only possible value that will lead to convergence, and can be set manually after alpha is set to another value.
    
    Parameters:
    
    alpha - the approximation scale in (0.0, 1.0]
  - getAlpha
```
public double getAlpha()
```
    Returns the approximation coefficient used
    
    Returns:
    
    the approximation coefficient used
  - setB
```
public void setB(double B)
```
    Sets the B variable of the ALMA algorithm, this is set automatically by setAlpha(double).
    
    Parameters:
    
    B - the value for B
  - getB
```
public double getB()
```
    Returns the B value of the ALMA algorithm
    
    Returns:
    
    the B value of the ALMA algorithm
  - setC
```
public void setC(double C)
```
    Sets the C value of the ALMA algorithm. The default value is the one suggested in the paper.
    
    Parameters:
    
    C - the C value of ALMA
  - getC
```
public double getC()
```
  - setUp
```
public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes,
                  CategoricalData predicting)
```
    Description copied from interface: UpdateableClassifier
    
    Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
    
    Specified by:
    
    setUp in interface UpdateableClassifier
    
    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
    
    predicting - the information for the target class that will be predicted
  - update
```
public void update(DataPoint dataPoint,
                   int targetClass)
```
    Description copied from interface: UpdateableClassifier
    
    Updates the classifier by giving it a new data point to learn from.
    
    Specified by:
    
    update in interface UpdateableClassifier
    
    Parameters:
    
    dataPoint - the data point to learn
    
    targetClass - the target class of the data point
  - classify
```
public CategoricalResults classify(DataPoint data)
```
    Description copied from interface: Classifier
    
    Performs classification on the given data point.
    
    Specified by:
    
    classify in interface Classifier
    
    Parameters:
    
    data - the data point to classify
    
    Returns:
    
    the results of the classification.
  - getScore
```
public double getScore(DataPoint dp)
```
    Description copied from interface: BinaryScoreClassifier
    
    Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
    
    Specified by:
    
    getScore in interface BinaryScoreClassifier
    
    Parameters:
    
    dp - the data point to predict the class label of
    
    Returns:
    
    the score for the given data point
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Description copied from interface: Classifier
    
    Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
    
    Specified by:
    
    supportsWeightedData in interface Classifier
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - guessAlpha
```
public static Distribution guessAlpha(DataSet d)
```
    Guesses the distribution to use for the α parameter
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the guess for the α parameter
    
    See Also:
    
    setAlpha(double)
  - 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 ALMA2K

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface jsat.classifiers.Classifier

Constructor Detail

ALMA2K

ALMA2K

Method Detail

clone

setAveraged

isAveraged

setKernelTrick

getKernelTrick

setAlpha

getAlpha

setB

getB

setC

getC

setUp

update

classify

getScore

supportsWeightedData

guessAlpha

getParameters

getParameter