public sealed class PearsonCorrelationSimilarity : AbstractSimilarity

public sealed class PearsonCorrelationSimilarity : AbstractSimilarity

Public NotInheritable Class PearsonCorrelationSimilarity 
	Inherits AbstractSimilarity

Public NotInheritable Class PearsonCorrelationSimilarity 
	Inherits AbstractSimilarity

public ref class PearsonCorrelationSimilarity sealed : public AbstractSimilarity

public ref class PearsonCorrelationSimilarity sealed : public AbstractSimilarity

[<SealedAttribute>]
type PearsonCorrelationSimilarity =  
    class 
        inherit AbstractSimilarity 
    end

[<SealedAttribute>]
type PearsonCorrelationSimilarity =  
    class 
        inherit AbstractSimilarity 
    end

The PearsonCorrelationSimilarity type exposes the following members.

	Name	Description
	PearsonCorrelationSimilarity(IDataModel)	Initializes a new instance of the PearsonCorrelationSimilarity class
	PearsonCorrelationSimilarity(IDataModel, Weighting)	Initializes a new instance of the PearsonCorrelationSimilarity class

	Name	Description
	AllSimilarItemIDs	(Inherited from AbstractItemSimilarity.)
	Equals	Determines whether the specified Object is equal to the current Object. (Inherited from Object.)
	GetHashCode	Serves as a hash function for a particular type. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	ItemSimilarities	(Inherited from AbstractSimilarity.)
	ItemSimilarity	(Inherited from AbstractSimilarity.)
	Refresh	(Inherited from AbstractSimilarity.)
	SetPreferenceInferrer	(Inherited from AbstractSimilarity.)
	ToString	(Inherited from AbstractSimilarity.)
	UserSimilarity	(Inherited from AbstractSimilarity.)

• sumX2: sum of the square of all X's preference values
• sumY2: sum of the square of all Y's preference values
• sumXY: sum of the product of X and Y's preference value for all items for which both X and Y express a preference

sumXY / sqrt(sumX2 * sumY2)

sumXY / sqrt(sumX2 * sumY2)

Note that this correlation "centers" its data, shifts the user's preference values so that each of their means is 0. This is necessary to achieve expected behavior on all data sets.

This correlation implementation is equivalent to the cosine similarity since the data it receives is assumed to be centered -- mean is 0. The correlation may be interpreted as the cosine of the angle between the two vectors defined by the users' preference values.

For cosine similarity on uncentered data, see UncenteredCosineSimilarity.