org.rhwlab.BHCnotused.ThreadedAlgorithm.java Source code

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Here is the source code for org.rhwlab.BHCnotused.ThreadedAlgorithm.java

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package org.rhwlab.BHCnotused;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.concurrent.ForkJoinPool;
import org.apache.commons.math3.dfp.Dfp;
import org.apache.commons.math3.dfp.DfpField;
import org.apache.commons.math3.linear.RealVector;
import org.rhwlab.dispim.datasource.DataSource;
import org.rhwlab.variationalbayesian.MicroClusterDataSource;

/**
 *
 * @author gevirl
 */
public class ThreadedAlgorithm implements Runnable {
    public ThreadedAlgorithm() {
    }

    public void init(int seg) throws Exception {
        clusters = new ArrayList<>();
        // build the initial clusters with one data point in each cluster
        for (int n = 0; n < source.getK(); ++n) {
            RealVector v = source.getCenter(n);
            Dfp[] z = new Dfp[v.getDimension()];
            for (int i = 0; i < z.length; ++i) {
                z[i] = field.newDfp(v.getEntry(i));
            }
            LabeledFieldVector fv = new LabeledFieldVector(z, n);
            Cluster cluster = new Cluster(new GaussianGIWPrior(fv));
            clusters.add(cluster);
        }

        // make all possible pairings of initial clusters
        pairs = new HashMap<>();
        for (int i = 0; i < clusters.size() - 1; ++i) {
            HashMap<Cluster, Cluster> map = new HashMap<>();
            MergeAction merge = new MergeAction(clusters, i, i + 1, map);
            ForkJoinPool pool = new ForkJoinPool();
            pool.invoke(merge);
            Cluster clusterI = clusters.get(i);
            pairs.put(clusterI, map);
            System.out.printf("Cluster %d paired\n", i);
        }
    }

    private Cluster maximumRCluster() {
        Cluster ret = null;
        Dfp maxR = field.getZero();
        for (HashMap<Cluster, Cluster> map : pairs.values()) {
            for (Cluster cl : map.values()) {
                Dfp r = cl.getPosterior();
                if (r.greaterThan(maxR)) {
                    maxR = cl.r;
                    ret = cl;
                }
            }
        }
        return ret;
    }

    @Override
    public void run() {
        while (clusters.size() > 1) {
            System.out.printf("\n%d Clusters\n", clusters.size());
            Cluster T = maximumRCluster();

            if (T == null) {
                return;
            }

            T.printCluster(System.out);

            //            System.out.printf("size=%d,dpm=%e,like=%e,pi=%f,pi*like=%e,r=%.20f,d=%s,Gam=%s,f=%s,d2=%s\n", T.data.getN(),T.dpm,T.data.likelihood(),T.pi,T.pi*T.data.likelihood(),T.r,T.d,T.gammaN,T.f,T.d2);
            // remove the children of the max r pair
            pairs.remove(T.left);
            pairs.remove(T.right);
            for (HashMap<Cluster, Cluster> map : pairs.values()) {
                map.remove(T.left);
                map.remove(T.right);
            }
            clusters.remove(T.left);
            clusters.remove(T.right);

            // make new pairs with all the clusters
            HashMap<Cluster, Cluster> map = new HashMap<>();
            MergeAction merge = new MergeAction(clusters, T, 0, clusters.size() - 1, map);
            ForkJoinPool pool = new ForkJoinPool();
            pool.invoke(merge);
            pairs.put(T, map);
            clusters.add(T);
        }
    }

    public void setSource(DataSource src) {
        this.source = (MicroClusterDataSource) src;
    }

    static public void setDfpField(DfpField fld) {
        field = fld;
    }

    public void saveResultAsXML(String file) throws Exception {
        Cluster.saveClusterListAsXML(file, clusters, 0.5);
    }

    static DfpField field;
    MicroClusterDataSource source;
    List<Cluster> clusters;
    HashMap<Cluster, HashMap<Cluster, Cluster>> pairs;
}