LSSVM (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, WarmClassifier, Parameterized, Regressor, WarmRegressor
```
public class LSSVM
extends SupportVectorLearner
implements BinaryScoreClassifier, Regressor, Parameterized, WarmRegressor, WarmClassifier
```
The Least Squares Support Vector Machine (LS-SVM) is an alternative to the standard SVM classifier for regression and binary classification problems. It can be faster to train, but is usually significantly slower to perform predictions with. This is because the LS-SVM solution is dense, so all training points become support vectors.

The LS-SVM algorithm may be warm started only from another LS-SVM object trained on the same data set.

NOTE: A SMO implementation similar to PlattSMO is used. This is done because is can easily operate without explicitly forming the whole kernel matrix. However it is recommended to use the LS-SVM when the problem size is small enough such that SupportVectorLearner.CacheMode.FULL can be used.

If N is the number of data points:
- Training complexity is roughly O(n^3), but can be lower for small C
- Prediction complexity is O(n)
- This implementation is multi-threaded, but scales best when there are several thousand data points per core. For smaller problems, especially when full cache mode can be used, there may be negative speedups when using the parallel training methods
See:
- Suykens, J.,&Vandewalle, J. (1999). Least Squares Support Vector Machine Classifiers. Neural processing letters, 9(3), 293–298. doi:10.1023/A:1018628609742
- Keerthi, S. S.,&Shevade, S. K. (2003). SMO algorithm for Least Squares SVM. In Proceedings of the International Joint Conference on Neural Networks (Vol. 3, pp. 2088–2093). IEEE. doi:10.1109/IJCNN.2003.1223730
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 double b

protected double b_low

protected double b_up
- Fields inherited from class jsat.classifiers.svm.SupportVectorLearner
  accelCache, alphas, cacheEvictions, evalCount, vecs

Fields
Modifier and Type	Field and Description
`protected double`	`b`
`protected double`	`b_low`
`protected double`	`b_up`

Constructor Summary

Constructors
Constructor and Description
`LSSVM()` Creates a new LS-SVM learner that uses a linear model and does not use a cache
`LSSVM(KernelTrick kernel)` Creates a new LS-SVM learner that does not use a cache
`LSSVM(KernelTrick kernel, SupportVectorLearner.CacheMode cacheMode)` Creates a new LS-SVM learner
`LSSVM(LSSVM toCopy)` Creates a deep copy of another LS-SVM

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.
`LSSVM`	`clone()`
`double`	`getC()` Returns the regularization parameter value used
`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`	`guessC(DataSet d)` Guess the distribution to use for the regularization term `C` in a LS-SVM.
`double`	`regress(DataPoint data)`
`void`	`setC(double C)` Sets the regularization constant when training.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`
`void`	`train(RegressionDataSet dataSet, Regressor warmSolution)` Trains the regressor and constructs a model for regression using the given data set.
`void`	`train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool)` Trains the regressor and constructs a model for regression using the given data set.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, Classifier warmSolution)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, Classifier warmSolution, ExecutorService threadPool)` 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.
`boolean`	`warmFromSameDataOnly()` Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from.

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

- Field Detail
  - b
```
protected double b
```
  - b_low
```
protected double b_low
```
  - b_up
```
protected double b_up
```
- Constructor Detail
  - LSSVM
```
public LSSVM()
```
    Creates a new LS-SVM learner that uses a linear model and does not use a cache
  - LSSVM
```
public LSSVM(KernelTrick kernel)
```
    Creates a new LS-SVM learner that does not use a cache
    
    Parameters:
    
    kernel - the kernel method to use
  - LSSVM
```
public LSSVM(KernelTrick kernel,
             SupportVectorLearner.CacheMode cacheMode)
```
    Creates a new LS-SVM learner
    
    Parameters:
    
    kernel - the kernel method to use
    
    cacheMode - the caching scheme to use for kernel evaluations
  - LSSVM
```
public LSSVM(LSSVM toCopy)
```
    Creates a deep copy of another LS-SVM
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setC
```
public void setC(double C)
```
    Sets the regularization constant when training. Lower values correspond to higher amounts of regularization.
    
    Parameters:
    
    C - the positive regularization parameter
  - getC
```
public double getC()
```
    Returns the regularization parameter value used
    
    Returns:
    
    the regularization parameter value
  - warmFromSameDataOnly
```
public boolean warmFromSameDataOnly()
```
    Description copied from interface: WarmRegressor
    
    Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from. If this is the case true will be returned. The behavior for training on a different data set when this is defined is undefined. It may cause an error, or it may cause the algorithm to take longer or reach a worse solution.
    When true, it is important that the data set be unaltered - this includes mutating the values stored or re-arranging the data points within the data set.
    
    Specified by:
    
    warmFromSameDataOnly in interface WarmClassifier
    
    Specified by:
    
    warmFromSameDataOnly in interface WarmRegressor
    
    Returns:
    
    true if the algorithm can only be warm started from the model trained on the exact same data set.
  - 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
  - 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,
                   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
  - train
```
public void train(RegressionDataSet dataSet,
                  Regressor warmSolution,
                  ExecutorService threadPool)
```
    Description copied from interface: WarmRegressor
    
    Trains the regressor and constructs a model for regression 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:
    
    train in interface WarmRegressor
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
    
    threadPool - the source of threads to use.
  - train
```
public void train(RegressionDataSet dataSet,
                  Regressor warmSolution)
```
    Description copied from interface: WarmRegressor
    
    Trains the regressor and constructs a model for regression using the given data set.
    
    Specified by:
    
    train in interface WarmRegressor
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   Classifier warmSolution,
                   ExecutorService threadPool)
```
    Description copied from interface: WarmClassifier
    
    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 WarmClassifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   Classifier warmSolution)
```
    Description copied from interface: WarmClassifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface WarmClassifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
  - 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
    
    Specified by:
    
    supportsWeightedData in interface Regressor
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - clone
```
public LSSVM clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class Object
  - 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.
  - guessC
```
public static Distribution guessC(DataSet d)
```
    Guess the distribution to use for the regularization term C in a LS-SVM.
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the C parameter in the LS-SVM

Class LSSVM

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

b

b_low

b_up

Constructor Detail

LSSVM

LSSVM

LSSVM

LSSVM

Method Detail

setC

getC

warmFromSameDataOnly

getScore

classify

trainC

trainC

train

train

trainC

trainC

supportsWeightedData

regress

train

train

clone

getParameters

getParameter

guessC