Package stallone.doubles

Examples of stallone.doubles.EuclideanDistance

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        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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    {
        this.eps = _epsilon;
        this.minpts = _minpoints;
        this.nclusters = _nclusters;

        neighborSearch = new NeighborSearchTrivial(null,new EuclideanDistance());
    }
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        data.add(Doubles.create.arrayFrom(1.3));
        data.add(Doubles.create.arrayFrom(1.4));

        DensityBasedClusteringOPTICS clustering = new DensityBasedClusteringOPTICS(0.5, 2, 2);
        clustering.setInput(data);
        clustering.setMetric(new EuclideanDistance());
        clustering.perform();
    }
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        int nclusters = str.toInt(args[1]);
        int nrepeats = str.toInt(args[2]);

        CompactRandomClustering C = new CompactRandomClustering(nclusters, nrepeats);
        C.setInput(data);
        C.setMetric(new EuclideanDistance());
        C.perform();

        IIntArray assign = C.getClusterIndexes();
        ints.print(assign,"","\n");
    }
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        return (vd);
    }

    public IDiscretization voronoiDiscretization(IDataSequence centers)
    {
        VoronoiDiscretization vd = new VoronoiDiscretization(centers, new EuclideanDistance());
        return (vd);
    }
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    }

    public IDiscretization coreDiscretization(IDataSequence _centers, double _radius)
    {
        CoreDiscretization disc = new CoreDiscretization(_centers,
            _radius, new EuclideanDistance());
        return (disc);
    }
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        return (vd);
    }

    public IDiscretization regularSelectionDiscretization(IDataSequence data, int k)
    {
        return (regularSelectionDiscretization(data, new EuclideanDistance(), k));
    }
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        return (vd);
    }

    public IDiscretization randomSelectionDiscretization(IDataSequence data, int k)
    {
        return (randomSelectionDiscretization(data, new EuclideanDistance(), k));
    }
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        {
            return new MinimalRMSDistance3D(nRows);
        }
        if (metricID == METRIC_EUCLIDEAN)
        {
            return new EuclideanDistance();
        }
        else
        {
            throw new RuntimeException("No metric.");
        }
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    }

    public IClustering kmeans(int k, int maxIter)
    {
        KMeansClustering clustering = new KMeansClustering();
        kmeans(clustering, new EuclideanDistance(), k, maxIter);
        return (clustering);
    }
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Related Classes of stallone.doubles.EuclideanDistance

  • stallone.api.cluster.ClusterFactory
  • stallone.api.discretization.DiscretizationFactory
  • stallone.api.doubles.IDoubleArray
  • stallone.cluster.CompactRandomClustering
  • stallone.cluster.DensityBasedClusteringOPTICS
  • stallone.cluster.DensityBasedClusteringSimple
  • stallone.hmm.pmm.AdaptiveDiscretization
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