SBP (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.svm.SupportVectorLearner
- - jsat.classifiers.svm.SBP

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, Parameterized
```
public class SBP
extends SupportVectorLearner
implements BinaryScoreClassifier, Parameterized
```
Implementation of the Stochastic Batch Perceptron (SBP) algorithm. Despite its name, it solves the kernelized SVM problem. Because it is done stochastically, it may not produce Support Vectors that the standard SVM algorithm learns. It can learn at most one SV per iteration. See:
Cotter, A., Shalev-Shwartz, S.,&Srebro, N. (2012). The Kernelized Stochastic Batch Perceptron. International Conference on Machine Learning. Learning. Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary
- Nested classes/interfaces inherited from class jsat.classifiers.svm.SupportVectorLearner
  SupportVectorLearner.CacheMode

Field Summary
- Fields inherited from class jsat.classifiers.svm.SupportVectorLearner
  accelCache, alphas, cacheEvictions, evalCount, vecs

Constructor Summary

Constructors
Modifier	Constructor and Description
	`SBP(KernelTrick kernel, SupportVectorLearner.CacheMode cacheMode, int iterations, double v)` Creates a new SBP SVM learner
`protected`	`SBP(SBP other)` Copy constructor

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`SBP`	`clone()`
`double`	`getBurnIn()`
`int`	`getIterations()` Returns the number of iterations the algorithm will perform
`double`	`getNu()` Returns the nu SVM parameter
`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.
`void`	`setBurnIn(double burnIn)` Sets the burn in fraction.
`void`	`setIterations(int iterations)` Sets the number of iterations to go through.
`void`	`setNu(double nu)` The nu parameter for this SVM is not the same as the standard nu-SVM formulation, though it plays a similar role.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class jsat.classifiers.svm.SupportVectorLearner
accessingRow, getCacheMode, getCacheValue, getKernel, k, kEval, kEval, kEvalSum, setAlphas, setCacheMode, setCacheSize, setCacheValue, setKernel, sparsify

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

- Constructor Detail
  - SBP
```
public SBP(KernelTrick kernel,
           SupportVectorLearner.CacheMode cacheMode,
           int iterations,
           double v)
```
    Creates a new SBP SVM learner
    
    Parameters:
    
    kernel - the kernel to use
    
    cacheMode - the type of kernel cache to use
  - SBP
```
protected SBP(SBP other)
```
    Copy constructor
    
    Parameters:
    
    other - the object to copy
- Method Detail
  - clone
```
public SBP clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - setIterations
```
public void setIterations(int iterations)
```
    Sets the number of iterations to go through. At most one SV can be learned per iteration. If more iterations are done than there are SVs, it is highly likely that O(n) SVs will be used, making the model very dense. It may take far fewer iterations of the algorithm than there are data points to get good accuracy.
    
    Parameters:
    
    iterations - the number of iterations of the algorithm to perform
  - getIterations
```
public int getIterations()
```
    Returns the number of iterations the algorithm will perform
    
    Returns:
    
    the number of iterations the algorithm will perform
  - setNu
```
public void setNu(double nu)
```
    The nu parameter for this SVM is not the same as the standard nu-SVM formulation, though it plays a similar role. It must be in the range (0, 1), where small values indicate a linearly separable problem (in the kernel space), and large values mean the problem is less separable. If the value is too small for the problem, the SVM may fail to converge or produce good results.
    
    Parameters:
    
    nu - the value between (0, 1)
  - getNu
```
public double getNu()
```
    Returns the nu SVM parameter
    
    Returns:
    
    the nu SVM parameter
  - setBurnIn
```
public void setBurnIn(double burnIn)
```
    Sets the burn in fraction. SBP averages the intermediate solutions from each step as the final solution. The intermediate steps of SBP are highly correlated, and the begging solutions are usually not as meaningful toward the converged solution. To overcome this issue a certain fraction of the iterations are not averaged into the final solution, making them the "burn in" fraction. A value of 0.25 would then be ignoring the initial 25% of solutions.
    
    Parameters:
    
    burnIn - the ratio int [0, 1) initial solutions to ignore
  - getBurnIn
```
public double getBurnIn()
```
    Returns:
    
    the burn in fraction
  - 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
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - 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
  - 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 SBP

Nested Class Summary

Nested classes/interfaces inherited from class jsat.classifiers.svm.SupportVectorLearner

Field Summary

Fields inherited from class jsat.classifiers.svm.SupportVectorLearner

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.svm.SupportVectorLearner

Methods inherited from class java.lang.Object

Constructor Detail

SBP

SBP

Method Detail

clone

setIterations

getIterations

setNu

getNu

setBurnIn

getBurnIn

classify

getScore

trainC

trainC

supportsWeightedData

getParameters

getParameter