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505 changes: 255 additions & 250 deletions
505
src/main/java/edu/emory/clir/clearnlp/bin/PBPostProcess.java
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src/main/java/edu/emory/clir/clearnlp/cluster/AbstractCluster.java
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| /** | ||
| * Copyright 2015, Emory University | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
| package edu.emory.clir.clearnlp.cluster; | ||
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| import java.util.ArrayList; | ||
| import java.util.List; | ||
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| /** | ||
| * @since 3.1.2 | ||
| * @author Jinho D. Choi ({@code jinho.choi@emory.edu}) | ||
| */ | ||
| public abstract class AbstractCluster | ||
| { | ||
| protected List<SparseVector> s_points; | ||
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| public AbstractCluster() | ||
| { | ||
| s_points = new ArrayList<>(); | ||
| } | ||
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| public void addPoint(SparseVector point) | ||
| { | ||
| s_points.add(point); | ||
| } | ||
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| public void setPoints(List<SparseVector> points) | ||
| { | ||
| s_points = points; | ||
| } | ||
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| public SparseVector getPoint(int index) | ||
| { | ||
| return s_points.get(index); | ||
| } | ||
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| public List<SparseVector> getPoints() | ||
| { | ||
| return s_points; | ||
| } | ||
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| public abstract Cluster[] cluster(); | ||
| } |
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src/main/java/edu/emory/clir/clearnlp/cluster/Kmeans.java
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src/main/java/edu/emory/clir/clearnlp/cluster/KmeansPPClustering.java
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| /** | ||
| * Copyright 2014, Emory University | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
| package edu.emory.clir.clearnlp.cluster; | ||
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| /** | ||
| * Kmeans++ algorithm. | ||
| * @since 3.0.3 | ||
| * @author Jinho D. Choi ({@code jinho.choi@emory.edu}) | ||
| */ | ||
| public class KmeansPPClustering extends AbstractCluster | ||
| { | ||
| // final public int K; | ||
| // final public double RSS; | ||
| // | ||
| // /** | ||
| // * @param k the number of clusters to return. | ||
| // * @param rss the threshold for RSS. | ||
| // */ | ||
| // public KmeansPPClustering(int k, double rss) | ||
| // { | ||
| // super(); | ||
| // this.K = k; | ||
| // this.RSS = rss; | ||
| // } | ||
| // | ||
| // @Override | ||
| public Cluster[] cluster() | ||
| { | ||
| // ObjectDoublePair<Cluster[]> previous = null, current = new ObjectDoublePair<Cluster[]>(null, Double.MAX_VALUE); | ||
| // SparseVector[] centroids = initCentroids(); | ||
| // | ||
| // for (int i=s_points.size() / K; i>=0; i--) | ||
| // { | ||
| // BinUtils.LOG.info(String.format("===== Iteration: %d =====\n", i)); | ||
| // | ||
| // previous = current; | ||
| // current = maximization(centroids); | ||
| // expectation(current.o); | ||
| // | ||
| // if (previous.d - current.d < RSS) | ||
| // break; | ||
| // } | ||
| // | ||
| // return current.o; | ||
| return null; | ||
| } | ||
| // | ||
| // private SparseVector[] initCentroids() | ||
| // { | ||
| // SparseVector[] centroids = new SparseVector[K]; | ||
| // int k = 0, N = s_points.size(); | ||
| // Random rand = new Random(1); | ||
| // | ||
| // | ||
| // | ||
| // IntHashSet set = new IntHashSet(); | ||
| // while (set.size() < K) set.add(rand.nextInt(N)); | ||
| // | ||
| // for (IntCursor c : set) | ||
| // centroids[k++] = s_points.get(c.value); | ||
| // | ||
| // return centroids; | ||
| // } | ||
| // | ||
| // private double[] D2(IntHashSet centroids) | ||
| // { | ||
| // int i, N = s_points.size(); | ||
| // double[] d2 = new double[N]; | ||
| // SparseVector point; | ||
| // | ||
| // for (i=0; i<N; i++) | ||
| // { | ||
| // if (centroids.contains(i)) continue; | ||
| // | ||
| // for (IntCursor c : centroids) | ||
| // { | ||
| // | ||
| // } | ||
| // } | ||
| // } | ||
| // | ||
| // private ObjectDoublePair<List<Cluster>> maximization(List<SparseVector> centroids) | ||
| // { | ||
| // List<Cluster> clusters = centroids.stream().map(c -> new Cluster()).collect(Collectors.toCollection(ArrayList::new)); | ||
| // double[] centNorms = euclideanNorms(centroids); | ||
| // double[] rss = new double[centNorms.length]; | ||
| // DoubleIntPair max = new DoubleIntPair(0, 0); | ||
| // | ||
| // BinUtils.LOG.info("Maximizing:"); | ||
| // | ||
| // for (int i=s_points.size()-1; i>=0; i--) | ||
| // { | ||
| // if (i%10000 == 0) BinUtils.LOG.info("."); | ||
| // max = max(centroids, centNorms, s_points.get(i)); | ||
| // clusters.get(max.i).addPoint(i); | ||
| // rss[max.i] += max.d; | ||
| // } BinUtils.LOG.info("\n"); | ||
| // | ||
| // | ||
| // double d = MathUtils.sum(rss); | ||
| // int k = 0; | ||
| // | ||
| // clusters.stream().forEach(cluster -> BinUtils.LOG.info(String.format("%4d: size = %6d, avg-rss = %5.4f\n", k++, cluster.size(), rss[k]/cluster.size()))); | ||
| // BinUtils.LOG.info(String.format("%4s: size = %6d, sum-rss = %5.4f\n", "ALL", s_points.size(), d)); | ||
| // return new ObjectDoublePair<List<Cluster>>(clusters, d); | ||
| // } | ||
| // | ||
| // private List<SparseVector> expectation(List<Cluster> clusters) | ||
| // { | ||
| // BinUtils.LOG.info("Calculating centroids: "+clusters.size()+"\n"); | ||
| // List<SparseVector> centroids = clusters.stream().map(cluster -> computeCentroid(cluster)).collect(Collectors.toCollection(ArrayList::new)); | ||
| // BinUtils.LOG.info("\n"); | ||
| // return centroids; | ||
| // } | ||
| // | ||
| // private SparseVector computeCentroid(Cluster cluster) | ||
| // { | ||
| // SparseVector centroid = new SparseVector(-1); | ||
| // BinUtils.LOG.info("."); | ||
| // | ||
| // for (IntCursor c : cluster.getPointSet()) | ||
| // centroid.add(s_points.get(c.value)); | ||
| // | ||
| // centroid.divide(cluster.size()); | ||
| // return centroid; | ||
| // } | ||
| // | ||
| // private double[] euclideanNorms(List<SparseVector> points) | ||
| // { | ||
| // return points.stream().mapToDouble(point -> point.euclideanNorm()).toArray(); | ||
| // } | ||
| // | ||
| // private DoubleIntPair max(List<SparseVector> centroids, double[] centNorms, SparseVector point) | ||
| // { | ||
| // DoubleIntPair max = new DoubleIntPair(-10000d, 0); | ||
| // double d, pointNorm = point.euclideanNorm(); | ||
| // | ||
| // for (int k=centNorms.length-1; k>=0; k--) | ||
| // { | ||
| // d = cosineSimilarity(centroids.get(k), centNorms[k], point, pointNorm); | ||
| // if (d > max.d) max.set(d, k); | ||
| // } | ||
| // | ||
| // return max; | ||
| // } | ||
| // | ||
| // private double cosineSimilarity(SparseVector centroid, double centNorm, SparseVector point, double pointNorm) | ||
| // { | ||
| // return centroid.dotProduct(point) / (centNorm * pointNorm); | ||
| // } | ||
| } |
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