MeanShiftClusterCollection Class

Mean shift cluster collection.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningTransformBaseDouble, Int32
    Accord.MachineLearningClassifierBaseDouble, Int32
      Accord.MachineLearningMulticlassClassifierBaseDouble
        Accord.MachineLearningMeanShiftClusterCollection

Namespace: Accord.MachineLearning
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

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[SerializableAttribute]
public class MeanShiftClusterCollection : MulticlassClassifierBase<double[]>, 
	IClusterCollectionEx<double[], MeanShiftClusterCollectionMeanShiftCluster>, IEnumerable

<SerializableAttribute>
Public Class MeanShiftClusterCollection
	Inherits MulticlassClassifierBase(Of Double())
	Implements IClusterCollectionEx(Of Double(), MeanShiftClusterCollectionMeanShiftCluster), 
	IEnumerable

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The MeanShiftClusterCollection type exposes the following members.

Constructors

	Name	Description
	MeanShiftClusterCollection	Initializes a new instance of the MeanShiftClusterCollection class.

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Properties

	Name	Description
	Clusters	Gets the collection of clusters currently modeled by the clustering algorithm.
	Count	Gets the number of clusters in the collection.
	Item	Gets the MeanShiftClusterCollectionMeanShiftCluster at the specified index.
	Modes	Gets the cluster modes.
	NumberOfClasses	Gets the number of classes expected and recognized by the classifier. (Inherited from ClassifierBaseTInput, TClasses.)
	NumberOfInputs	Gets the number of inputs accepted by the model. (Inherited from TransformBaseTInput, TOutput.)
	NumberOfOutputs	Gets the number of outputs generated by the model. (Inherited from TransformBaseTInput, TOutput.)
	Proportions	Gets the proportion of samples in each cluster.

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Methods

	Name	Description
	Decide(TInput)	Computes class-label decisions for a given set of input vectors. (Inherited from ClassifierBaseTInput, TClasses.)
	Decide(Double)	Computes a class-label decision for a given input. (Overrides ClassifierBaseTInput, TClassesDecide(TInput).)
	Decide(TInput, TClasses)	Computes a class-label decision for a given input. (Inherited from ClassifierBaseTInput, TClasses.)
	Decide(TInput, Boolean)	Computes class-label decisions for the given input. (Inherited from MulticlassClassifierBaseTInput.)
	Decide(TInput, Double)	Computes class-label decisions for the given input. (Inherited from MulticlassClassifierBaseTInput.)
	Decide(TInput, Int32)	Computes class-label decisions for the given input. (Inherited from MulticlassClassifierBaseTInput.)
	Decide(TInput, Double)	Computes a class-label decision for a given input. (Inherited from MulticlassClassifierBaseTInput.)
	Distortion	Calculates the average square distance from the data points to the nearest clusters' centroids.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetEnumerator	Returns an enumerator that iterates through the collection.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToMultilabel	Views this instance as a multi-label classifier, giving access to more advanced methods, such as the prediction of one-hot vectors. (Inherited from MulticlassClassifierBaseTInput.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	Transform(TInput)	Applies the transformation to an input, producing an associated output. (Inherited from ClassifierBaseTInput, TClasses.)
	Transform(TInput)	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from TransformBaseTInput, TOutput.)
	Transform(TInput, TClasses)	Applies the transformation to an input, producing an associated output. (Inherited from ClassifierBaseTInput, TClasses.)
	Transform(TInput, Boolean)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Double)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Int32)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Boolean)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Double)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Double)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Int32)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(Double, Int32, Double, Double)	Transform data points into feature vectors containing the distance between each point and each of the clusters.

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	SetEqualsMeanShiftClusterCollectionMeanShiftCluster	Compares two enumerables for set equality. Two enumerables are set equal if they contain the same elements, but not necessarily in the same order. (Defined by Matrix.)
	To(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.)
	ToT	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.)

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Reference

Accord.MachineLearning Namespace

Accord.MachineLearningMeanShift