A C D G R T

A

ArrayFunctions - Class in de.citec.ml.rng

This is a collections of functions on primitive double arrays.

C

checkAssignmentsVector(int, int, int[]) - Static method in class de.citec.ml.rng.CheckFunctions

Checks if the given assignment vector does indeed only map to entries between 0 and C, where C is the number of clusters/prototypes.

checkConvexCoefficients(int, int, double[][]) - Static method in class de.citec.ml.rng.CheckFunctions

Checks if the given matrix contains proper convex coefficient vectors.

checkConvexCoefficients(int, double[]) - Static method in class de.citec.ml.rng.CheckFunctions

Checks if the given vector has the correct size (N), is non-negative and sums up to 1.

checkDissimilaritiesOfDatapointsToPrototypes(double[][]) - Static method in class de.citec.ml.rng.CheckFunctions

Checks if the given matrix is not empty and consistent.

checkDissimilarityMatrix(double[][]) - Static method in class de.citec.ml.rng.CheckFunctions

Checks if the given matrix is quadratic, symmetric and reflexive.

CheckFunctions - Class in de.citec.ml.rng

This is a class for consistency checks on input parameters.

classify(double[][], RNGModel) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Classifies new data according to a given RNGModel.

classify(double[], RNGModel) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Classifies a new data point according to a given RNGModel.

D

de.citec.ml.rng - package de.citec.ml.rng

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering.

G

getAssignments(RNGModel) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Returns the strict assignments of all data points handled in the given Relational Neural Gas model to prototypes.

getClusterMembers(RNGModel) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Returns an array with K entries, where each entry is another array containing the indices of all data points that have been assigned to the respective cluster/prototype.

getClusterMembers(RNGModel, int) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Returns the indices of all data points that have been assigned to the cluster/prototype with index k.

getClusterMembers(RNGModel, int, int[]) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Returns the indices of all data points that have been assigned to the cluster/prototype with index k.

getConvexCoefficients() - Method in interface de.citec.ml.rng.RNGModel

Returns the convex coefficients representing all prototypes.

getConvexCoefficients() - Method in class de.citec.ml.rng.RNGModelImpl

getDistancesToPrototypes(double[][], RNGModel) - Static method in class de.citec.ml.rng.RelationalDistances

Calculates the squared distances of n data points to all prototypes, based on the distances from the test to the training data D and a relational neural gas model.

getDistancesToPrototypes(double[], RNGModel) - Static method in class de.citec.ml.rng.RelationalDistances

Calculates the squared distances of a data point to all prototypes, based on the distances to the training data d and a relational neural gas model.

getDistancesToPrototypes(double[][], double[][], double[]) - Static method in class de.citec.ml.rng.RelationalDistances

Calculates the squared distances of n data points to all prototypes, based on the distances from the test to the training data D, the normalization terms for each prototype Z, and the convex coefficients representing the prototypes Alpha.

getDistancesToPrototypes(double[], double[][], double[]) - Static method in class de.citec.ml.rng.RelationalDistances

Calculates the squared distances of data point to all prototypes, based on the distances to the training data d, the normalization terms for each prototype Z, and the convex coefficients representing the prototypes Alpha.

getDistancesToPrototypes() - Method in interface de.citec.ml.rng.RNGModel

Returns the matrix of distances from all training data points to all prototypes.

getDistancesToPrototypes() - Method in class de.citec.ml.rng.RNGModelImpl

getExamplars(RNGModel) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Returns an array with K entries, where entry k is the index of the data point which is closest to the relational prototype for cluster k.

getLambda(int, int, int) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Returns the λ value for the current training epoch.

getMaxIdx(double[]) - Static method in class de.citec.ml.rng.ArrayFunctions

Returns the index of the maximum element in the given array.

getMinIdx(double[]) - Static method in class de.citec.ml.rng.ArrayFunctions

Returns the index of the minimum element in the given array.

getNormalizationTerms(double[][], double[][]) - Static method in class de.citec.ml.rng.RelationalDistances

Computes the normalization terms

getNormalizationTerms() - Method in interface de.citec.ml.rng.RNGModel

Returns a K x 1 vector containing the normalization terms for for relational distances to each prototype.

getNormalizationTerms() - Method in class de.citec.ml.rng.RNGModelImpl

getNumberOfDatapoints() - Method in interface de.citec.ml.rng.RNGModel

Returns the number of datapoints N.

getNumberOfDatapoints() - Method in class de.citec.ml.rng.RNGModelImpl

getNumberOfEpochs() - Method in interface de.citec.ml.rng.RNGErrorModel

Returns the number of training epochs for this model.

getNumberOfEpochs() - Method in class de.citec.ml.rng.RNGModelImpl

getNumberOfPrototypes() - Method in interface de.citec.ml.rng.RNGModel

Returns the number of clusters/prototypes K.

getNumberOfPrototypes() - Method in class de.citec.ml.rng.RNGModelImpl

getQuantizationErrors() - Method in interface de.citec.ml.rng.RNGErrorModel

Returns the quantization errors in each epoch.

getQuantizationErrors() - Method in class de.citec.ml.rng.RNGModelImpl

R

RelationalDistances - Class in de.citec.ml.rng

This is a helper class for the computation of distances between data points and convex combinations.

RelationalNeuralGas - Class in de.citec.ml.rng

This class implements the batch relational neural gas algorithm, which tries to minimize the soft quantization error for prototypes to data points and anneals the softness over time until the algorithm converges to K-means.

RNGErrorModel - Interface in de.citec.ml.rng

An RNGModel that additionally implements this interface also provides information on the quantization error at each training epoch.

RNGModel - Interface in de.citec.ml.rng

This is an interface for the result of a Relational Neural Gas algorithm.

RNGModelImpl - Class in de.citec.ml.rng

This provides a default implementation of the RNGModel interface.

RNGModelImpl(double[][], double[][], double[]) - Constructor for class de.citec.ml.rng.RNGModelImpl

Initializes an Relational Neural Gas model.

RNGModelImpl(double[][], double[][], double[], double[]) - Constructor for class de.citec.ml.rng.RNGModelImpl

Initializes an Relational Neural Gas model.

T

train(double[][], int) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Trains a relational neural gas model with K prototypes for the given data in terms of a m x m matrix of pairwise distances D, using 30 iterations of the RNG algorithm.

train(double[][], int, int) - Static method in class de.citec.ml.rng.RelationalNeuralGas

Trains a relational neural gas model with K prototypes for the given data in terms of a m x m matrix of pairwise distances D, using T iterations of the RNG algorithm.

A C D G R T