com.imsl.datamining.supportvectormachine

Class SVOneClass

java.lang.Object

com.imsl.datamining.PredictiveModel

com.imsl.datamining.supportvectormachine.SupportVectorMachine

com.imsl.datamining.supportvectormachine.SVOneClass

All Implemented Interfaces:

Serializable, Cloneable

public class SVOneClass
extends SupportVectorMachine
implements Serializable, Cloneable

Specifies a support vector machine for the one class problem. The one class SVM estimates the support (the range of values with positive density) of a high-dimensional distribution.

The one-class SVM algorithm estimates the support of a high-dimensional distribution without any class information. The primal problem of one-class SVM is

$$\min_ {w, \xi, \rho} \frac{1}{2} w^Tw-\rho+\frac{1}{\nu l}\sum_{i=1}^{l} \xi _i$$

$$\text{subject to} \, \, (w^T \phi (x_i)) \geq \rho - \xi _i,$$

$$ \xi _i \geq 0, i=1, \, \ldots \,,\,l$$

See Also:

Example, Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class com.imsl.datamining.supportvectormachine.SupportVectorMachine

SupportVectorMachine.ReflectiveOperationException

Nested classes/interfaces inherited from class com.imsl.datamining.PredictiveModel

PredictiveModel.CloneNotSupportedException, PredictiveModel.PredictiveModelException, PredictiveModel.StateChangeException, PredictiveModel.SumOfProbabilitiesNotOneException, PredictiveModel.VariableType

Constructor Summary

Constructors

Constructor and Description
SVOneClass(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType) Constructs a one class support vector machine.
SVOneClass(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType, Kernel k) Constructs a one class support vector machine.
SVOneClass(SVOneClass oneClassModel) Constructs a copy of the input SVOneClass predictive model.

Method Summary

Modifier and Type	Method and Description
SVOneClass	clone() Clones an SVOneClass predictive model.
protected SVModel	optimize(DataNode[][] x, double[] y, double[] w, int len, Kernel kernel) Performs the one class support vector machine optimization problem.
protected double[]	predictValues(SVModel model, double[][] attributeData) Generates the predicted values on the attribute data using the given support vector machine model.

Methods inherited from class com.imsl.datamining.supportvectormachine.SupportVectorMachine

fitModel, getConvergenceTolerance, getKernel, getKernelParameters, getModel, getNuParameter, getRegularizationParameter, getWorkingArraySize, isNuFormulation, isProbability, isShrinking, predict, predict, setConfiguration, setConvergenceTolerance, setKernel, setKernelParameters, setNuFormulation, setNuParameter, setProbability, setRegularizationParameter, setShrinking, setWorkArraySize

Methods inherited from class com.imsl.datamining.PredictiveModel

getClassCounts, getClassErrors, getClassLabels, getClassProbabilities, getCostMatrix, getMaxNumberOfCategories, getMaxNumberOfIterations, getNumberOfClasses, getNumberOfColumns, getNumberOfMissing, getNumberOfPredictors, getNumberOfRows, getNumberOfUniquePredictorValues, getPredictorIndexes, getPredictorTypes, getPrintLevel, getPriorProbabilities, getRandomObject, getResponseColumnIndex, getResponseVariableAverage, getResponseVariableMostFrequentClass, getResponseVariableType, getTotalWeight, getVariableType, getWeights, getXY, isConstantSeries, isMustFitModel, isUserFixedNClasses, predict, setClassCounts, setClassLabels, setClassProbabilities, setCostMatrix, setMaxNumberOfCategories, setMaxNumberOfIterations, setMustFitModel, setNumberOfClasses, setPredictorIndex, setPredictorTypes, setPrintLevel, setPriorProbabilities, setRandomObject, setResponseColumnIndex, setTrainingData, setVariableType, setWeights

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SVOneClass

public SVOneClass(double[][] xy,
                  int responseColumnIndex,
                  PredictiveModel.VariableType[] varType)

Constructs a one class support vector machine.

Parameters:

xy - a double matrix containing the training data and associated response values by the number of variables

responseColumnIndex - an int, the column index of the response variable

varType - a PredictiveModel.VariableType array of length equal to xy[0].length containing the type of each variable

SVOneClass

public SVOneClass(double[][] xy,
                  int responseColumnIndex,
                  PredictiveModel.VariableType[] varType,
                  Kernel k)

Constructs a one class support vector machine.

Parameters:

xy - a double matrix containing the training data and associated response values

responseColumnIndex - an int, the column index of the response variable

varType - a PredictiveModel.VariableType array of length equal to xy[0].length containing the type of each variable

k - a Kernel, the kernel function

SVOneClass

public SVOneClass(SVOneClass oneClassModel)

Constructs a copy of the input SVOneClass predictive model.

Parameters:

oneClassModel - an SVOneClass predictive model

Method Detail

clone

public SVOneClass clone()

Clones an SVOneClass predictive model.

Specified by:

clone in class PredictiveModel

Returns:

a clone of the SVOneClass predictive model

optimize

protected SVModel optimize(DataNode[][] x,
                           double[] y,
                           double[] w,
                           int len,
                           Kernel kernel)
                    throws NoSuchMethodException,
                           InstantiationException,
                           IllegalAccessException,
                           InvocationTargetException

Performs the one class support vector machine optimization problem.

Specified by:

optimize in class SupportVectorMachine

Parameters:

x - a DataNode matrix containing the attribute data

y - a double array containing the response variable

len - an int, the total possible number of support vectors

w - a double array containing the observation weights

kernel - a Kernel object

Returns:

an SVModel structure containing the fitted model

Throws:

NoSuchMethodException - thrown when a particular method cannot be found

InstantiationException - thrown when an application tries to create an instance of a class using the newInstance method in class Class, but the specified class object cannot be instantiated.

IllegalAccessException - thrown when an application tries to reflectively create an instance (other than an array), set or get a field, or invoke a method, but the currently executing method does not have access to the definition of the specified class, field, method or constructor

InvocationTargetException - a checked exception that wraps an exception thrown by an invoked method or constructor

predictValues

protected double[] predictValues(SVModel model,
                                 double[][] attributeData)
                          throws PredictiveModel.SumOfProbabilitiesNotOneException

Generates the predicted values on the attribute data using the given support vector machine model.

Specified by:

predictValues in class SupportVectorMachine

Parameters:

model - a fitted SVModel object

attributeData - a double matrix containing the attribute (or predictor) data

Returns:

a double array containing the predictions for each row in the input attribute data

Throws:

PredictiveModel.SumOfProbabilitiesNotOneException - the sum of probabilities is not approximately one