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ConditionalRandomFieldT Class

Linear-Chain Conditional Random Field (CRF).
Inheritance Hierarchy
SystemObject
  Accord.MachineLearningTransformBaseT, Int32
    Accord.MachineLearningTaggerBaseT
      Accord.Statistics.Models.FieldsConditionalRandomFieldT

Namespace:  Accord.Statistics.Models.Fields
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.7.0
Syntax
[SerializableAttribute]
public class ConditionalRandomField<T> : TaggerBase<T>
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Type Parameters

T

The ConditionalRandomFieldT type exposes the following members.

Constructors
  NameDescription
Public methodConditionalRandomFieldT
Initializes a new instance of the ConditionalRandomFieldT class.
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Properties
Methods
  NameDescription
Public methodClone
Creates a new object that is a copy of the current instance.
Public methodCompute
Computes the most likely state labels for the given observations, returning the overall sequence probability for this model.
Public methodDecide(TInput)
Computes class-label decisions for the given input.
(Inherited from TaggerBaseTInput.)
Public methodDecide(TInput)
Computes class-label decisions for the given input.
(Inherited from TaggerBaseTInput.)
Public methodDecide(TInput, Int32)
Computes class-label decisions for the given input.
(Inherited from TaggerBaseTInput.)
Public methodDecide(T, Int32)
Computes class-label decisions for the given input.
(Overrides TaggerBaseTInputDecide(TInput, Int32).)
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 methodStatic memberLoad(Stream) Obsolete.
Loads a random field from a stream.
Public methodStatic memberLoad(String) Obsolete.
Loads a random field from a file.
Public methodLogLikelihood(T, Int32)
Computes the log-likelihood of the model for the given observations. This method is equivalent to the HiddenMarkovModel.LogLikelihood(TObservation[], int[]) method.
Public methodLogLikelihood(T, Int32)
Computes the most likely state labels for the given observations, returning the overall sequence log-likelihood for this model.
Public methodLogPartition
Computes the Log of the partition function.
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodPartition
Computes the partition function, as known as Z(x), for the specified observations.
Public methodSave(Stream) Obsolete.
Saves the random field to a stream.
Public methodSave(String) Obsolete.
Saves the random field to a stream.
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
Public methodTransform(TInput)
Applies the transformation to an input, producing an associated output.
(Inherited from TaggerBaseTInput.)
Public methodTransform(TInput)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from TransformBaseTInput, TOutput.)
Public methodTransform(TInput, TOutput)
Applies the transformation to an input, producing an associated output.
(Inherited from TransformBaseTInput, TOutput.)
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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 MethodToT
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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Remarks

A conditional random field (CRF) is a type of discriminative undirected probabilistic graphical model. It is most often used for labeling or parsing of sequential data, such as natural language text or biological sequences and computer vision.

This implementation is currently experimental.

See Also