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    Class IBSimilarityDescriptor

    Information based model similarity. The algorithm is based on the concept that the information content in any symbolic distribution sequence is primarily determined by the repetitive usage of its basic elements. For written texts this challenge would correspond to comparing the writing styles of different authors.

    Inheritance
    object
    DescriptorBase<IBSimilarityDescriptor, IIBSimilarity>
    IBSimilarityDescriptor
    Implements
    IDescriptor
    IIBSimilarity
    ISimilarity
    Inherited Members
    DescriptorBase<IBSimilarityDescriptor, IIBSimilarity>.Self
    DescriptorBase<IBSimilarityDescriptor, IIBSimilarity>.Assign<TValue>(TValue, Action<IIBSimilarity, TValue>)
    object.Equals(object)
    object.Equals(object, object)
    object.GetHashCode()
    object.GetType()
    object.MemberwiseClone()
    object.ReferenceEquals(object, object)
    object.ToString()
    Namespace: OpenSearch.Client
    Assembly: OpenSearch.Client.dll
    Syntax
    public class IBSimilarityDescriptor : DescriptorBase<IBSimilarityDescriptor, IIBSimilarity>, IDescriptor, IIBSimilarity, ISimilarity

    Methods

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    Distribution(IBDistribution?)

    The distribution

    Declaration
    public IBSimilarityDescriptor Distribution(IBDistribution? distribution)
    Parameters
    Type Name Description
    IBDistribution? distribution
    Returns
    Type Description
    IBSimilarityDescriptor
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    Lambda(IBLambda?)

    The lambda

    Declaration
    public IBSimilarityDescriptor Lambda(IBLambda? lambda)
    Parameters
    Type Name Description
    IBLambda? lambda
    Returns
    Type Description
    IBSimilarityDescriptor
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    NoNormalization()

    The normalization

    Declaration
    public IBSimilarityDescriptor NoNormalization()
    Returns
    Type Description
    IBSimilarityDescriptor
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    NormalizationH1(double?)

    Normalization model that assumes a uniform distribution of the term frequency.

    Declaration
    public IBSimilarityDescriptor NormalizationH1(double? c)
    Parameters
    Type Name Description
    double? c

    hyper-parameter that controls the term frequency normalization with respect to the document length.

    Returns
    Type Description
    IBSimilarityDescriptor
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    NormalizationH2(double?)

    Normalization model in which the term frequency is inversely related to the length.

    Declaration
    public IBSimilarityDescriptor NormalizationH2(double? c)
    Parameters
    Type Name Description
    double? c

    hyper-parameter that controls the term frequency normalization with respect to the document length.

    Returns
    Type Description
    IBSimilarityDescriptor
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    NormalizationH3(double?)

    Dirichlet Priors normalization

    Declaration
    public IBSimilarityDescriptor NormalizationH3(double? mu)
    Parameters
    Type Name Description
    double? mu

    smoothing parameter μ.

    Returns
    Type Description
    IBSimilarityDescriptor
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    NormalizationZ(double?)

    Pareto-Zipf Normalization

    Declaration
    public IBSimilarityDescriptor NormalizationZ(double? z)
    Parameters
    Type Name Description
    double? z

    represents A/(A+1) where A measures the specificity of the language..

    Returns
    Type Description
    IBSimilarityDescriptor

    Implements

    IDescriptor
    IIBSimilarity
    ISimilarity

    Extension Methods

    SuffixExtensions.Suffix(object, string)
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    In this article
    • Methods
      • Distribution(IBDistribution?)
      • Lambda(IBLambda?)
      • NoNormalization()
      • NormalizationH1(double?)
      • NormalizationH2(double?)
      • NormalizationH3(double?)
      • NormalizationZ(double?)
    • Implements
    • Extension Methods
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