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Showing content from http://accord-framework.net/docs/html/T_Accord_MachineLearning_KMeansClusterCollection.htm below:

Computes class-label decisions for a given set of input vectors.

Computes a class-label decision for a given input.

Computes a class-label decision for a given input.

Computes class-label decisions for the given input.

Computes class-label decisions for the given input.

Computes class-label decisions for the given input.

Computes a class-label decision for a given input.

Calculates the average square distance from the data points to the nearest clusters' centroids.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Returns an enumerator that iterates through the collection.

Serves as the default hash function.

Gets the Type of the current instance.

Creates a shallow copy of the current Object.

Randomizes the clusters inside a dataset.

Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier).

Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier).

Computes a numerical score measuring the association between the given input vector and a given classIndex.

Predicts a class label for the input vector, returning a numerical score measuring the strength of association of the input vector to its most strongly related class.

Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier).

Computes a numerical score measuring the association between the given input vector and a given classIndex.

Computes a numerical score measuring the association between the given input vector and a given classIndex.

Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class.

Computes a numerical score measuring the association between the given input vector and a given classIndex.

Computes a numerical score measuring the association between the given input vector and a given classIndex.

Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class.

Computes a numerical score measuring the association between the given input vector and each class.

Computes a numerical score measuring the association between the given input vector and each class.

Computes a numerical score measuring the association between the given input vector and each class.

Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.

Computes a numerical score measuring the association between the given input vector and each class.

Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.

Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.

Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.

Views this instance as a multi-class generative classifier.

Views this instance as a multi-label distance classifier, giving access to more advanced methods, such as the prediction of one-hot vectors.

Returns a string that represents the current object.

Applies the transformation to an input, producing an associated output.

Applies the transformation to a set of input vectors, producing an associated set of output vectors.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Transform data points into feature vectors containing the distance between each point and each of the clusters.

Transform data points into feature vectors containing the distance between each point and each of the clusters.

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