org.apache.mahout.knn.means.ThreadedKmeans.java Source code

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Here is the source code for org.apache.mahout.knn.means.ThreadedKmeans.java

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/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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 org.apache.mahout.knn.means;

import com.google.common.collect.Iterables;
import com.google.common.collect.Lists;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.knn.search.Searcher;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.MatrixSlice;

import java.util.List;
import java.util.Random;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;

public class ThreadedKmeans {

    public Searcher cluster(final DistanceMeasure distance, List<Iterable<MatrixSlice>> data, final int maxClusters,
            final int threads, final StreamingKmeans.CentroidFactory centroidFactory)
            throws InterruptedException, ExecutionException {
        // initialize scale
        int i = 0;
        final int width = data.get(0).iterator().next().vector().size();
        Matrix sample = new DenseMatrix(100, width);
        for (Iterable<MatrixSlice> split : data) {
            sample = sampleRows(sample, i, Iterables.limit(split, 1000), 100);
            i += 100;
        }

        List<Callable<Searcher>> tasks = Lists.newArrayList();
        ExecutorService pool = Executors.newFixedThreadPool(threads);
        for (final Iterable<MatrixSlice> split : data) {
            tasks.add(new Callable<Searcher>() {
                @Override
                public Searcher call() {
                    return new StreamingKmeans().cluster(split, maxClusters, centroidFactory);
                }
            });
        }
        List<Future<Searcher>> results = pool.invokeAll(tasks);
        pool.shutdown();

        List<MatrixSlice> raw = Lists.newArrayList();
        for (Future<Searcher> result : results) {
            Iterables.addAll(raw, result.get());
        }

        return new StreamingKmeans().cluster(raw, data.size() * maxClusters, centroidFactory);
    }

    public static List<Iterable<MatrixSlice>> split(Iterable<MatrixSlice> data, int threads) {
        List<Iterable<MatrixSlice>> r = Lists.newArrayList();
        int size = Iterables.size(data);
        int block = (size + threads - 1) / threads;

        for (int start = 0; start < size; start += block) {
            final Iterable<MatrixSlice> split = Iterables.limit(Iterables.skip(data, start),
                    (Math.min(start + block, size) - start));
            r.add(split);
        }
        return r;
    }

    private Matrix sampleRows(Matrix r, int soFar, Iterable<MatrixSlice> data, int n) {
        Random rand = new Random();
        int i = soFar;
        for (MatrixSlice row : data) {
            if (i < n) {
                r.viewRow(i).assign(row.vector());
            } else {
                int k = rand.nextInt(n);
                if (k < n) {
                    r.viewRow(k).assign(row.vector());
                }
            }
            i++;
        }

        return r;
    }
}