com.cloudera.oryx.kmeans.common.MiniBatchUpdateStrategy.java Source code

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Here is the source code for com.cloudera.oryx.kmeans.common.MiniBatchUpdateStrategy.java

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/*
 * Copyright (c) 2013, Cloudera, Inc. All Rights Reserved.
 *
 * Cloudera, Inc. 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
 *
 * This software 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 com.cloudera.oryx.kmeans.common;

import java.util.List;

import com.cloudera.oryx.common.random.RandomManager;

import com.google.common.collect.Lists;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.random.RandomGenerator;

public final class MiniBatchUpdateStrategy implements KMeansUpdateStrategy {

    private final int numIterations;
    private final int miniBatchSize;
    private final RandomGenerator random;

    public MiniBatchUpdateStrategy(int numIterations, int miniBatchSize, RandomGenerator random) {
        this.numIterations = numIterations;
        this.miniBatchSize = miniBatchSize;
        this.random = (random == null) ? RandomManager.getRandom() : random;
    }

    @Override
    public <W extends Weighted<RealVector>> Centers update(List<W> points, Centers centers) {
        int[] perCenterStepCounts = new int[centers.size()];
        WeightedSampler<RealVector, W> sampler = new WeightedSampler<RealVector, W>(points, random);
        for (int iter = 0; iter < numIterations; iter++) {
            // Compute closest cent for each mini-batch
            List<List<RealVector>> centerAssignments = Lists.newArrayList();
            for (int i = 0; i < centers.size(); i++) {
                centerAssignments.add(Lists.<RealVector>newArrayList());
            }
            for (int i = 0; i < miniBatchSize; i++) {
                RealVector sample = sampler.sample();
                int closestId = centers.getDistance(sample).getClosestCenterId();
                centerAssignments.get(closestId).add(sample);
            }
            // Apply the mini-batch
            List<RealVector> nextCenters = Lists.newArrayList();
            for (int i = 0; i < centerAssignments.size(); i++) {
                RealVector currentCenter = centers.get(i);
                for (int j = 0; j < centerAssignments.get(i).size(); j++) {
                    double eta = 1.0 / (++perCenterStepCounts[i] + 1.0);
                    currentCenter = currentCenter.mapMultiply(1.0 - eta);
                    currentCenter = currentCenter.add(centerAssignments.get(i).get(j).mapMultiply(eta));
                }
                nextCenters.add(currentCenter);
            }
            centers = new Centers(nextCenters);
        }
        return centers;
    }
}