Examples of EuclideanDistance

cascading.assembly.EuclideanDistance
Computes the euclidean distance between every unique set of first fields, with using the label and value of each element.
Expects on input three values: item, label, value
com.heatonresearch.aifh.distance.EuclideanDistance
eatonresearch.com/wiki/Euclidean_Distance
org.apache.commons.math3.ml.distance.EuclideanDistance
Calculates the L₂ (Euclidean) distance between two points. @since 3.2
stallone.doubles.EuclideanDistance
@author noe

weka.core.EuclideanDistance

kipedia.org/wiki/Euclidean_distance.

BibTeX:

 @misc{missing_id, author = {Wikipedia}, title = {Euclidean distance}, URL = {http://en.wikipedia.org/wiki/Euclidean_distance} }

Valid options are:

 -D Turns off the normalization of attribute  values in distance calculation.

 -R <col1,col2-col4,...> Specifies list of columns to used in the calculation of the  distance. 'first' and 'last' are valid indices. (default: first-last)

 -V Invert matching sense of column indices.

@author Gabi Schmidberger (gabi@cs.waikato.ac.nz) @author Ashraf M. Kibriya (amk14@cs.waikato.ac.nz) @author FracPete (fracpete at waikato dot ac dot nz) @version $Revision: 1.13 $

Examples of org.apache.commons.math3.ml.distance.EuclideanDistance


    private ClusterEvaluator<DoublePoint> evaluator;


    @Before
    public void setUp() {
        evaluator = new SumOfClusterVariances<DoublePoint>(new EuclideanDistance());
    }

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Examples of org.apache.commons.math3.ml.distance.EuclideanDistance

    /**
     * Creates a new cluster evaluator with an {@link EuclideanDistance}
     * as distance measure.
     */
    public ClusterEvaluator() {
        this(new EuclideanDistance());
    }

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Examples of org.apache.commons.math3.ml.distance.EuclideanDistance

                position4 += numberOfVariables;
            }


            for (int n = 2; n < 27; ++n) {
                KMeansPlusPlusClusterer<DoublePoint> transformer =
                    new KMeansPlusPlusClusterer<DoublePoint>(n, 100, new EuclideanDistance(), random, strategy);


                List<? extends Cluster<DoublePoint>> clusters =
                        transformer.cluster(Arrays.asList(breakingPoints));


                Assert.assertEquals(n, clusters.size());

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Examples of org.apache.commons.math3.ml.distance.EuclideanDistance

     * 2 variables cannot be clustered into 3 clusters. See issue MATH-436.
     */
    @Test(expected=NumberIsTooSmallException.class)
    public void testPerformClusterAnalysisToManyClusters() {
        KMeansPlusPlusClusterer<DoublePoint> transformer = 
            new KMeansPlusPlusClusterer<DoublePoint>(3, 1, new EuclideanDistance(), random);
        
        DoublePoint[] points = new DoublePoint[] {
            new DoublePoint(new int[] {
                1959, 325100
            }), new DoublePoint(new int[] {

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Examples of org.apache.commons.math3.ml.distance.EuclideanDistance

        final FeatureInitializer init
            = new OffsetFeatureInitializer(FeatureInitializerFactory.uniform(-0.1, 0.1));
        final FeatureInitializer[] initArray = { init };


        final Network net = new NeuronString(3, false, initArray).getNetwork();
        final DistanceMeasure dist = new EuclideanDistance();


        final Set<Neuron> allBest = new HashSet<Neuron>();
        final Set<Neuron> best = new HashSet<Neuron>();
        double[][] features;

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Examples of org.apache.commons.math3.ml.distance.EuclideanDistance

     * @param minPts minimum number of points needed for a cluster
     * @throws NotPositiveException if {@code eps < 0.0} or {@code minPts < 0}
     */
    public DBSCANClusterer(final double eps, final int minPts)
        throws NotPositiveException {
        this(eps, minPts, new EuclideanDistance());
    }

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Examples of org.apache.commons.math3.ml.distance.EuclideanDistance

     * @param k the number of clusters to split the data into
     * @param maxIterations the maximum number of iterations to run the algorithm for.
     *   If negative, no maximum will be used.
     */
    public KMeansPlusPlusClusterer(final int k, final int maxIterations) {
        this(k, maxIterations, new EuclideanDistance());
    }

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Examples of org.apache.commons.math3.ml.distance.EuclideanDistance

                position4 = position4 + numberOfVariables;
            }


            for (int n = 2; n < 27; ++n) {
                KMeansPlusPlusClusterer<DoublePoint> transformer =
                    new KMeansPlusPlusClusterer<DoublePoint>(n, 100, new EuclideanDistance(), random, strategy);


                List<? extends Cluster<DoublePoint>> clusters =
                        transformer.cluster(Arrays.asList(breakingPoints));


                Assert.assertEquals(n, clusters.size());

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Examples of org.apache.commons.math3.ml.distance.EuclideanDistance

     * 2 variables cannot be clustered into 3 clusters. See issue MATH-436.
     */
    @Test(expected=NumberIsTooSmallException.class)
    public void testPerformClusterAnalysisToManyClusters() {
        KMeansPlusPlusClusterer<DoublePoint> transformer = 
            new KMeansPlusPlusClusterer<DoublePoint>(3, 1, new EuclideanDistance(), random);
        
        DoublePoint[] points = new DoublePoint[] {
            new DoublePoint(new int[] {
                1959, 325100
            }), new DoublePoint(new int[] {

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Examples of stallone.doubles.EuclideanDistance


        for (double d=0; d<4; d+=0.1)
        {
                DensityBasedClusteringSimple clustering = new DensityBasedClusteringSimple(d, 3);
                clustering.setInput(data);
                clustering.setMetric(new EuclideanDistance());
                clustering.perform();
                System.out.println(d+"\t"+clustering.getNumberOfClusters());
        }


    }

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