GaussianClusterCollection Methods

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.

Gets the deviance of the points in relation to the model.

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.

Initializes the model with initial values obtained through a run of the K-Means clustering algorithm.

Initializes the model with initial values.

Initializes the model with initial values.

Initializes the model with initial values.

Initializes the model with initial values.

Initializes the model with initial values.

Initializes the model with initial values.

Computes the log-likelihood that the given input vector belongs to its most plausible class.

Computes the log-likelihood that the given input vectors belongs to each of the possible classes.

Computes the log-likelihood that the given input vector belongs to the specified classIndex.

Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.

Computes the log-likelihood that the given input vectors belongs to each of the possible classes.

Computes the log-likelihood that the given input vector belongs to the specified classIndex.

Computes the log-likelihood that the given input vector belongs to the specified classIndex.

Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class.

Computes the log-likelihood that the given input vector belongs to the specified classIndex.

Computes the log-likelihood that the given input vector belongs to the specified classIndex.

Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class.

Computes the log-likelihood that the given input vector belongs to each of the possible classes.

Computes the log-likelihood that the given input vectors belongs to each of the possible classes.

Computes the log-likelihood that the given input vector belongs to each of the possible classes.

Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class.

Computes the log-likelihood that the given input vector belongs to each of the possible classes.

Computes the log-likelihood that the given input vector belongs to the specified classIndex.

Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class.

Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class.

Computes the log-likelihood that the given input vector belongs to the specified classIndex.

Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class.

Creates a shallow copy of the current Object.

Computes the probabilities that the given input vector belongs to each of the possible classes.

Computes the probabilities that the given input vector belongs to each of the possible classes.

Computes the probabilities that the given input vector belongs to each of the possible classes.

Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class.

Computes the probabilities that the given input vector belongs to each of the possible classes.

Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class.

Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class.

Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class.

Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.

Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.

Computes the probability that the given input vector belongs to the specified classIndex.

Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.

Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.

Computes the probability that the given input vector belongs to the specified classIndex.

Computes the probability that the given input vector belongs to the specified classIndex.

Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class.

Computes the probability that the given input vector belongs to the specified classIndex.

Computes the probability that the given input vector belongs to the specified classIndex.

Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted 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 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.

Gets a copy of the mixture distribution modeled by this Gaussian Mixture Model.

Views this instance as a multi-class generative classifier.

Views this instance as a multi-label generative 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.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

Compares two enumerables for set equality. Two enumerables are set equal if they contain the same elements, but not necessarily in the same order.

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.

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.