DCSVM (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, Parameterized
```
public class DCSVM
extends SupportVectorLearner
implements Classifier, Parameterized, BinaryScoreClassifier
```
This is an implementation of the Divide-and-Conquer Support Vector Machine (DC-SVM). It uses a a combination of clustering and warm-starting to train faster, as well as an early stopping strategy to provide a fast approximate SVM solution. The final accuracy should often be at or near that of a normal SVM, while being faster to train.

The current implementation is based on SVMnoBias, meaning this code does not have a bias term and it only works with normalized kernels. Any non-normalized kernel will be normalized automatically. This is not a problem for the common RBF kernel.

See:
- Hsieh, C.-J., Si, S., & Dhillon, I. S. (2014). A Divide-and-Conquer Solver for Kernel Support Vector Machines. In Proceedings of the 31st International Conference on Machine Learning. Beijing, China.
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
Constructor and Description
`DCSVM()` Creates a new DC-SVM for the RBF kernel
`DCSVM(DCSVM toCopy)` Copy Constructor
`DCSVM(KernelTrick k)` Creates a new DC-SVM for the given kernel

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.
`DCSVM`	`clone()`
`double`	`getC()` Returns the soft margin complexity parameter of the SVM
`int`	`getClusterSampleSize()`
`int`	`getEndLevel()`
`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.
`int`	`getStartLevel()`
`void`	`setC(double C)` Sets the complexity parameter of SVM.
`void`	`setClusterSampleSize(int m)` At each level of the DC-SVM training, a clustering algorithm is used to divide the dataset into sub-groups for independent training.
`void`	`setEndLevel(int l_early)` The DC-SVM algorithm works by creating a hierarchy of levels, and iteratively refining the solution from one level to the next.
`void`	`setStartLevel(int l_max)` The DC-SVM algorithm works by creating a hierarchy of levels, and iteratively refining the solution from one level to the next.
`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
  - DCSVM
```
public DCSVM(KernelTrick k)
```
    Creates a new DC-SVM for the given kernel
    
    Parameters:
    
    k - the kernel to use
  - DCSVM
```
public DCSVM()
```
    Creates a new DC-SVM for the RBF kernel
  - DCSVM
```
public DCSVM(DCSVM toCopy)
```
    Copy Constructor
    
    Parameters:
    
    toCopy - object to copy
- Method Detail
  - setStartLevel
```
public void setStartLevel(int l_max)
```
    The DC-SVM algorithm works by creating a hierarchy of levels, and iteratively refining the solution from one level to the next. Level 0 corresponds to the exact SVM solution, and higher levels are courser approximations. This method controls which level the training starts at.
    
    Parameters:
    
    l_max - which level to start the training at.
  - getStartLevel
```
public int getStartLevel()
```
    Returns:
    
    which level to start the training at.
  - setEndLevel
```
public void setEndLevel(int l_early)
```
    The DC-SVM algorithm works by creating a hierarchy of levels, and iteratively refining the solution from one level to the next. Level 0 corresponds to the exact SVM solution, and higher levels are courser approximations. This method controls which level the training stops at, with 0 being the latest it can stop. The default stopping level is 3.
    
    Parameters:
    
    l_early - which level to stop the training at, and use for classification.
  - getEndLevel
```
public int getEndLevel()
```
    Returns:
    
    which level to stop the training at, and use for classification.
  - setClusterSampleSize
```
public void setClusterSampleSize(int m)
```
    At each level of the DC-SVM training, a clustering algorithm is used to divide the dataset into sub-groups for independent training. Increasing the number of points used for clustering improves model accuracy, but also increases training time. The default value is 2000. This value may need to be increased if using a higher staring level.
    
    Parameters:
    
    m - the number of data points to sample for each cluster size
  - getClusterSampleSize
```
public int getClusterSampleSize()
```
  - 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
  - clone
```
public DCSVM clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - 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
  - 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 DCSVM

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

DCSVM

DCSVM

DCSVM

Method Detail

setStartLevel

getStartLevel

setEndLevel

getEndLevel

setClusterSampleSize

getClusterSampleSize

classify

getScore

trainC

trainC

supportsWeightedData

clone

setC

getC

getParameters

getParameter