Uses of Interface
at.tuwien.ifs.somtoolbox.layers.metrics.DistanceMetric

Packages that use DistanceMetric
at.tuwien.ifs.somtoolbox.apps.trainer   
at.tuwien.ifs.somtoolbox.clustering.functions   
at.tuwien.ifs.somtoolbox.data Classes in this package implement reading, storing and providing of different data needed for the SOM, e.g. 
at.tuwien.ifs.somtoolbox.data.distance   
at.tuwien.ifs.somtoolbox.layers Provides the basic classes constituting SOM-based neural networks. 
at.tuwien.ifs.somtoolbox.layers.metrics This package contains the metrics used for distance calculation during training and for mapping data onto maps. 
at.tuwien.ifs.somtoolbox.layers.quality Classes in this package implement various quality measures, indicating the quality of the SOM mapping. 
at.tuwien.ifs.somtoolbox.visualization Provides classes creating visualisations of trained SOMs. 
at.tuwien.ifs.somtoolbox.visualization.clustering Contains classes implementing clustering methods on the SOM. 
 

Uses of DistanceMetric in at.tuwien.ifs.somtoolbox.apps.trainer
 

Fields in at.tuwien.ifs.somtoolbox.apps.trainer with type parameters of type DistanceMetric
private  ClassComboBoxModel<DistanceMetric> SOMTrainer.cmbMetricModel
           
 

Methods in at.tuwien.ifs.somtoolbox.apps.trainer that return types with arguments of type DistanceMetric
private  ClassComboBoxModel<DistanceMetric> SOMTrainer.getCmbMetricModel()
           
 

Uses of DistanceMetric in at.tuwien.ifs.somtoolbox.clustering.functions
 

Fields in at.tuwien.ifs.somtoolbox.clustering.functions declared as DistanceMetric
protected  DistanceMetric UnitClusteringFunction.metric
           
protected  DistanceMetric DoubleVector2DDistance.metric
           
 

Constructors in at.tuwien.ifs.somtoolbox.clustering.functions with parameters of type DistanceMetric
DoubleVector2DDistance(DistanceMetric metric)
           
UnitClusteringFunction(DistanceMetric metric)
           
 

Uses of DistanceMetric in at.tuwien.ifs.somtoolbox.data
 

Methods in at.tuwien.ifs.somtoolbox.data with parameters of type DistanceMetric
 InputDatum[] AbstractSOMLibSparseInputData.getByNameDistanceSorted(double[] vector, Collection<String> inputNames, DistanceMetric metric)
          Retrieves the InputDatum corresponding to the given input names, and sorted by their distance to the given vector.
 ArrayList<InputDistance> AbstractSOMLibSparseInputData.getDistances(int inputIndex, DistanceMetric metric)
          Returns the distances to the index of the given vector of the dataset.
 InputDatum[] AbstractSOMLibSparseInputData.getNearestN(double[] vector, DistanceMetric metric, int number)
          Retrieves the given number of InputDatum that are closest to the given vector.
 InputDatum[] AbstractSOMLibSparseInputData.getNearestN(int inputIndex, DistanceMetric metric, int number)
          Returns the n nearest input vectors for the index of the given vector of the dataset.
 InputDatum[] AbstractSOMLibSparseInputData.getNearestNUnsorted(int inputIndex, DistanceMetric metric, int number)
           
 void AbstractSOMLibSparseInputData.initDistanceMatrix(DistanceMetric metric)
          Calculates the AbstractSOMLibSparseInputData.distanceMatrix - careful, this is a lengthy process and should be done only if needed.
 double RandomAccessFileSOMLibInputData.mqe0(DistanceMetric metric)
           
 double InputData.mqe0(DistanceMetric metric)
          Calculates the mean quantisation error of the top-level unit.
 double DataBaseSOMLibSparseInputData.mqe0(DistanceMetric metric)
           
 double SimpleMatrixInputData.mqe0(DistanceMetric metric)
           
 double SOMLibSparseInputData.mqe0(DistanceMetric metric)
           
 void AbstractSOMLibSparseInputData.transformValues(DistanceMetric metric)
          Calculates the matrix of AbstractSOMLibSparseInputData.transformedVectors using transformVector(double[]) of the given metric.
 

Uses of DistanceMetric in at.tuwien.ifs.somtoolbox.data.distance
 

Fields in at.tuwien.ifs.somtoolbox.data.distance declared as DistanceMetric
protected  DistanceMetric InputVectorDistanceMatrix.metric
           
 

Methods in at.tuwien.ifs.somtoolbox.data.distance that return DistanceMetric
 DistanceMetric InputVectorDistanceMatrix.getMetric()
           
 DistanceMetric RandomAccessFileInputVectorDistanceMatrix.getMetric()
           
 

Methods in at.tuwien.ifs.somtoolbox.data.distance with parameters of type DistanceMetric
private static PrintWriter DistanceMatrixWriter.printSOMLibHeader(int numVectors, String fileName, DistanceMetric metric, boolean gzip)
           
static void DistanceMatrixWriter.writeOrangeFileInputVectorDistanceMatrix(InputData data, String fileName, DistanceMetric metric)
          Write input distance matrix to an ASCII file for the Orange data mining toolkit ((http://www.ailab.si/orange/), computing distances on the fly.
static void DistanceMatrixWriter.writePlainFileInputVectorDistanceMatrix(InputData data, String fileName, DistanceMetric metric)
          Write input distance matrix to an ASCII file in plain format, computing distances on the fly.
static void DistanceMatrixWriter.writeRandomAccessFileInputVectorDistanceMatrix(double[][] distances, String fileName, DistanceMetric metric)
          Write pre-calculated input distance matrix to a binary file.
static void DistanceMatrixWriter.writeRandomAccessFileInputVectorDistanceMatrix(InputData data, String fileName, DistanceMetric metric)
          Write input distance matrix to a binary file, computing distances on the fly.
static void DistanceMatrixWriter.writeSOMLibFileInputVectorDistanceMatrix(double[][] distances, String fileName, DistanceMetric metric, boolean gzip)
          Write pre-calculated input distance matrix to an ASCII file in SOMLib format.
static void DistanceMatrixWriter.writeSOMLibFileInputVectorDistanceMatrix(InputData data, String fileName, DistanceMetric metric)
          Write input distance matrix to ASCII file, computing distances on the fly.
static void DistanceMatrixWriter.writeSOMLibFileInputVectorDistanceMatrix(InputData data, String fileName, DistanceMetric metric, boolean gzip)
          Write input distance matrix to ASCII file, computing distances on the fly.
 

Constructors in at.tuwien.ifs.somtoolbox.data.distance with parameters of type DistanceMetric
AbstractMemoryInputVectorDistanceMatrix(InputData data, DistanceMetric metric)
          Constructs the distance matrix by computing the distances on the fly.
FullMemoryInputVectorDistanceMatrix(InputData data, DistanceMetric metric)
           
LeightWeightMemoryInputVectorDistanceMatrix(InputData data, DistanceMetric metric)
           
 

Uses of DistanceMetric in at.tuwien.ifs.somtoolbox.layers
 

Fields in at.tuwien.ifs.somtoolbox.layers declared as DistanceMetric
private  DistanceMetric GrowingCellLayer.metric
          Distance Metric used for GrowingCellStructures
protected  DistanceMetric GrowingLayer.metric
           
 

Methods in at.tuwien.ifs.somtoolbox.layers that return DistanceMetric
 DistanceMetric Layer.getMetric()
          Returns the metric used for distance calculation.
 DistanceMetric GrowingCellLayer.getMetric()
           
 DistanceMetric GrowingLayer.getMetric()
          Calculates and returns the mean quantization error of the map based on the quantization errors of the single units.
 

Methods in at.tuwien.ifs.somtoolbox.layers with parameters of type DistanceMetric
 Unit GrowingLayer.getWinner(InputDatum input, DistanceMetric metric)
          Returns the winner unit for a given input datum specified by argument input.
 Unit[] GrowingLayer.getWinners(InputDatum input, int num, DistanceMetric metric)
          Returns a number of best-matching units sorted by distance (ascending) for a given input datum.
 void AdaptiveCoordinatesVirtualLayer.updateUnitsVirtualSpacePos(Unit[][][] units, DistanceMetric metric, Unit winner, InputDatum input, int curIteration)
          Updates the virtual space position of all map units with respect to the input datum and the according winner unit.
 

Uses of DistanceMetric in at.tuwien.ifs.somtoolbox.layers.metrics
 

Classes in at.tuwien.ifs.somtoolbox.layers.metrics that implement DistanceMetric
 class AbstractMetric
          Implements a static method for metric instantiation and a method for mean vector calculation.
 class AbstractWeightedMetric
           
 class CosineMetric
          Implements the cosine metric, defined for two vectors d1 and d2 as d1xd2 / (|d1|*|d2|).
 class L1Metric
          Implements the L1 or city block metric.
 class L2Metric
          Implements the L2 or Euclidean metric.
 class L2MetricFast
          Implements a fast version of the L2 or Euclidean metric, by not taking the square root.
 class L2MetricSparse
          Implements the L2 or Euclidean metric, considering only those values for the distance calculation that have non-zero values for the first, second or both vectors, depending on the initialisation mode.
 class L2MetricWeighted
           
 class LInfinityMetric
          Implements the L-Infinity metric,defined for two vectors x and y as max( |xi-yi| ), i = 1,...,|x|.
 class LnAlphaMetric
           
 class LnMetric
          Generic Ln metric.
 class MahalanobisMetric
          Implements the Mahalanobis distance metric.
 class MissingValueMetricWrapper
          A wrapper class around other distance metrics, modifying the distance computation in such a way that only vector attributes that are not missing (indicated by InputData.MISSING_VALUE are considered.
When instantiating using the empty constructor MissingValueMetricWrapper.MissingValueMetricWrapper() the default metric MissingValueMetricWrapper.DEFAULT_METRIC is used.
 class MnemonicSOMMetric
          A metric for mnemonic SOMs.
 

Fields in at.tuwien.ifs.somtoolbox.layers.metrics declared as DistanceMetric
private  DistanceMetric MissingValueMetricWrapper.metric
           
private static DistanceMetric[] Metrics.singleton
           
 

Methods in at.tuwien.ifs.somtoolbox.layers.metrics that return DistanceMetric
static DistanceMetric[] Metrics.getAvailableMetrics()
           
static DistanceMetric AbstractMetric.instantiate(String mName)
          Instantiates a certain distance metric class specified by argument mName.
Note: for backwards compatibility, if the metric name contains the package prefix at.ec3.somtoolbox, this will be replaced by at.tuwien.ifs.somtoolbox.
static DistanceMetric AbstractMetric.instantiateNice(String metricName)
          Same as AbstractMetric.instantiate(String), but tries to get the metric with the specified name, and then with the package prefix, and throwing only a SOMToolboxException with the root cause nested.
 

Methods in at.tuwien.ifs.somtoolbox.layers.metrics with parameters of type DistanceMetric
 int AbstractMetric.compareTo(DistanceMetric o)
           
protected static void AbstractMetric.performanceTest(DistanceMetric metric, int dim)
          Can be used to do some performance testing to compare colt vs.
 void MissingValueMetricWrapper.setMetric(DistanceMetric metric)
           
 

Constructors in at.tuwien.ifs.somtoolbox.layers.metrics with parameters of type DistanceMetric
MissingValueMetricWrapper(DistanceMetric metric)
           
 

Uses of DistanceMetric in at.tuwien.ifs.somtoolbox.layers.quality
 

Fields in at.tuwien.ifs.somtoolbox.layers.quality declared as DistanceMetric
(package private)  DistanceMetric Trustworthiness_NeighborhoodPreservation.metric
           
(package private)  DistanceMetric TopographicProduct.metric
           
 

Uses of DistanceMetric in at.tuwien.ifs.somtoolbox.visualization
 

Fields in at.tuwien.ifs.somtoolbox.visualization declared as DistanceMetric
private  DistanceMetric NeighbourhoodGraph.metric
           
 

Uses of DistanceMetric in at.tuwien.ifs.somtoolbox.visualization.clustering
 

Fields in at.tuwien.ifs.somtoolbox.visualization.clustering declared as DistanceMetric
private  DistanceMetric Cluster.distanceFunction
           
 

Methods in at.tuwien.ifs.somtoolbox.visualization.clustering with parameters of type DistanceMetric
private  void KMeans.initClustersEqualNumbers(DistanceMetric distanceFunction)
          cluster centres are initialised by equally sized random chunks of the input data when there's 150 instances, we assign 50 chosen randomly to each cluster and calculate its centre from these (the last cluster might be larger if numInstances mod k < 0)
private  void KMeans.initClustersLinearly(DistanceMetric distanceFunction)
          This one does linear initialisation.
private  void KMeans.initClustersLinearlyOnInstances(DistanceMetric distanceFunction)
          like KMeans.initClustersLinearly(DistanceMetric), but after computing the exact linear point, rather finds & uses the closest instance from the data set as centroid.
private  void KMeans.initClustersRandomly(DistanceMetric distanceFunction)
          Calculate random centroids for each cluster.
private  void KMeans.initClustersRandomlyOnInstances(DistanceMetric distanceFunction)
          Take random points from the input data as centroids.
 

Constructors in at.tuwien.ifs.somtoolbox.visualization.clustering with parameters of type DistanceMetric
Cluster(DistanceMetric distanceFunction)
           
Cluster(double[] centroid, DistanceMetric distanceFunction)
           
KMeans(int k, double[][] data, KMeans.InitType initialisation, DistanceMetric distanceFunction)
          Construct a new K-Means bugger.