SVMnoBias (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier
```
public class SVMnoBias
extends SupportVectorLearner
implements BinaryScoreClassifier
```
This class implements a version of the Support Vector Machine without a bias term. In addition, the current implementation requires that the Kernel Trick used be a normalized kernel. If the given kernel is not normalized, this class will automatically wrap it to become normalized.

Because there is no bias term, this class should never be used with the Linear kernel. But for the more common RBF kernel the lack of bias term should have minimal impact on accuracy.

See: Steinwart, I., Hush, D., & Scovel, C. (2011). Training SVMs Without Offset. The Journal of Machine Learning Research, 12, 141–202.

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
Modifier and Type	Field and Description
`protected short[]`	`label` Stores the true label value (-1 or +1) of the data point
`protected Vec`	`weights` Weight values to apply to each data point

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

Constructor Summary

Constructors
Constructor and Description

SVMnoBias(KernelTrick kf)
Creates a new SVM object that uses no cache mode.

SVMnoBias(SVMnoBias toCopy)

Constructors
Constructor and Description
`SVMnoBias(KernelTrick kf)` Creates a new SVM object that uses no cache mode.
`SVMnoBias(SVMnoBias toCopy)`

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.
`SVMnoBias`	`clone()`
`double`	`getC()` Returns the soft margin complexity parameter of the SVM
`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.
`double`	`getTolerance()` Returns the solution tolerance
`void`	`setC(double C)` Sets the complexity parameter of SVM.
`void`	`setKernel(KernelTrick kernel)` Sets the kernel trick to use
`void`	`setTolerance(double tolerance)` Sets the tolerance for the solution.
`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.
`protected void`	`trainC(ClassificationDataSet dataSet, double[] warm_start)`
`protected void`	`trainC(ClassificationDataSet dataSet, double[] warm_start, ExecutorService ex)`
`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, sparsify

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

- Field Detail
  - label
```
protected short[] label
```
    Stores the true label value (-1 or +1) of the data point
  - weights
```
protected Vec weights
```
    Weight values to apply to each data point
- Constructor Detail
  - SVMnoBias
```
public SVMnoBias(KernelTrick kf)
```
    Creates a new SVM object that uses no cache mode.
    
    Parameters:
    
    kf - the kernel trick to use
  - SVMnoBias
```
public SVMnoBias(SVMnoBias toCopy)
```
- Method Detail
  - setKernel
```
public void setKernel(KernelTrick kernel)
```
    Description copied from class: SupportVectorLearner
    
    Sets the kernel trick to use
    
    Overrides:
    
    setKernel in class SupportVectorLearner
    
    Parameters:
    
    kernel - the kernel trick to use
  - 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
  - clone
```
public SVMnoBias clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - 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.
  - 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
  - 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
```
protected void trainC(ClassificationDataSet dataSet,
                      double[] warm_start)
```
    Parameters:
    
    dataSet - the dataset to train on
    
    warm_start - Array of initial alpha values to use for support vectors. The absolute value of the inputs will be used. may be longer than the number of data points.
  - trainC
```
protected void trainC(ClassificationDataSet dataSet,
                      double[] warm_start,
                      ExecutorService ex)
```
  - 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
  - setC
```
public void setC(double C)
```
    Sets the complexity parameter of SVM. The larger the C value the harder the margin SVM will attempt to find. Lower values of C allow for more misclassification errors.
    
    Parameters:
    
    C - the soft margin parameter
  - getC
```
public double getC()
```
    Returns the soft margin complexity parameter of the SVM
    
    Returns:
    
    the complexity parameter of the SVM
  - setTolerance
```
public void setTolerance(double tolerance)
```
    Sets the tolerance for the solution. Higher values converge to worse solutions, but do so faster
    
    Parameters:
    
    tolerance - the tolerance for the solution
  - getTolerance
```
public double getTolerance()
```
    Returns the solution tolerance
    
    Returns:
    
    the solution tolerance

Class SVMnoBias

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

Field Detail

label

weights

Constructor Detail

SVMnoBias

SVMnoBias

Method Detail

setKernel

getScore

clone

classify

trainC

trainC

trainC

trainC

supportsWeightedData

setC

getC

setTolerance

getTolerance