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OneclassSupportVectorLearningTKernel, TInput Class
One-class Support Vector Machine learning algorithm.
Inheritance Hierarchy
SystemObject
  Accord.MachineLearning.VectorMachines.LearningBaseOneclassSupportVectorLearningSupportVectorMachineTKernel, TInput, TKernel, TInput
    Accord.MachineLearning.VectorMachines.LearningOneclassSupportVectorLearningTKernel, TInput

Namespace:  Accord.MachineLearning.VectorMachines.Learning
Assembly:  Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0
Syntax
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public class OneclassSupportVectorLearning<TKernel, TInput> : BaseOneclassSupportVectorLearning<SupportVectorMachine<TKernel, TInput>, TKernel, TInput>
where TKernel : Object, IKernel<TInput>
where TInput : ICloneable
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Type Parameters

TKernel
TInput

The OneclassSupportVectorLearningTKernel, TInput type exposes the following members.

Constructors
  NameDescription
Public methodOneclassSupportVectorLearningTKernel, TInput
Initializes a new instance of the OneclassSupportVectorLearningTKernel, TInput class
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Properties
  NameDescription
Public propertyKernel
Gets or sets the kernel function use to create a kernel Support Vector Machine. If this property is set, UseKernelEstimation will be set to false.
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
Public propertyLagrange
Gets the value for the Lagrange multipliers (alpha) for every observation vector.
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
Public propertyModel
Gets or sets the classifier being learned.
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
Public propertyNu
Controls the number of outliers accepted by the algorithm. This value provides an upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Default is 0.5
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
Public propertyShrinking
Gets or sets a value indicating whether to use shrinking heuristics during learning. Default is true.
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
Public propertyToken
Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
Public propertyTolerance
Convergence tolerance. Default value is 1e-2.
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
Public propertyUseKernelEstimation
Gets or sets whether initial values for some kernel parameters should be estimated from the data, if possible. Default is true.
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
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Methods
  NameDescription
Protected methodCreate
Creates an instance of the model to be learned. Inheritors of this abstract class must define this method so new models can be created from the training data.
(Overrides BaseOneclassSupportVectorLearningTModel, TKernel, TInputCreate(Int32, TKernel).)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodLearn
Learns a model that can map the given inputs to the desired outputs.
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodRun Obsolete.
Obsolete.
(Inherited from BaseOneclassSupportVectorLearningTModel, TKernel, TInput.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(Type)Overloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
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Examples

The following example shows how to use an one-class SVM.

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// Ensure that results are reproducible
Accord.Math.Random.Generator.Seed = 0;

// Generate some data to be learned
double[][] inputs =
{
    new double[] { +1.0312479734420776  },
    new double[] { +0.99444115161895752 },
    new double[] { +0.21835240721702576 },
    new double[] { +0.47197291254997253 },
    new double[] { +0.68701112270355225 },
    new double[] { -0.58556461334228516 },
    new double[] { -0.64154046773910522 },
    new double[] { -0.66485315561294556 },
    new double[] { +0.37940266728401184 },
    new double[] { -0.61046308279037476 }
};


// Create a new One-class SVM learning algorithm
var teacher = new OneclassSupportVectorLearning<Linear>()
{
    Kernel = new Linear(), // or, for example, 'new Gaussian(0.9)'
    Nu = 0.1
};

// Learn a support vector machine
var svm = teacher.Learn(inputs);

// Test the machine
double[] prediction = svm.Score(inputs);

// Compute the log-likelihood of the answer
double ll = new LogLikelihoodLoss().Loss(prediction);
See Also