com.cloudera.oryx.common.collection.SamplingLongPrimitiveIterator.java Source code

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Here is the source code for com.cloudera.oryx.common.collection.SamplingLongPrimitiveIterator.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.
 */

/*
 * 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 com.cloudera.oryx.common.collection;

import java.util.NoSuchElementException;

import com.google.common.base.Preconditions;
import org.apache.commons.math3.distribution.IntegerDistribution;
import org.apache.commons.math3.distribution.PascalDistribution;
import org.apache.commons.math3.random.RandomGenerator;

import com.cloudera.oryx.common.iterator.AbstractLongPrimitiveIterator;
import com.cloudera.oryx.common.iterator.LongPrimitiveIterator;

/**
 * Wraps a {@link LongPrimitiveIterator} and returns only some subset of the elements that it would,
 * as determined by a sampling rate parameter.
 * 
 * Adapted from the same class in Mahout 0.8.
 * 
 * @author Sean Owen
 * @author Mahout
 */
public final class SamplingLongPrimitiveIterator extends AbstractLongPrimitiveIterator {

    private final IntegerDistribution geometricDistribution;
    private final LongPrimitiveIterator delegate;
    private long next;
    private boolean hasNext;

    /**
     *
     * @param random random number generator to re-use
     * @param delegate iterator to sample from
     * @param samplingRate sampling rate in (0,1]
     */
    public SamplingLongPrimitiveIterator(RandomGenerator random, LongPrimitiveIterator delegate,
            double samplingRate) {
        Preconditions.checkNotNull(random);
        Preconditions.checkNotNull(delegate);
        Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0);
        // Geometric distribution is special case of negative binomial (aka Pascal) with r=1:
        geometricDistribution = new PascalDistribution(random, 1, samplingRate);
        this.delegate = delegate;
        this.hasNext = true;
        doNext();
    }

    @Override
    public boolean hasNext() {
        return hasNext;
    }

    @Override
    public long nextLong() {
        if (hasNext) {
            long result = next;
            doNext();
            return result;
        }
        throw new NoSuchElementException();
    }

    private void doNext() {
        int toSkip = geometricDistribution.sample();
        delegate.skip(toSkip);
        if (delegate.hasNext()) {
            next = delegate.next();
        } else {
            hasNext = false;
        }
    }

    /**
     * @throws UnsupportedOperationException
     */
    @Override
    public void remove() {
        throw new UnsupportedOperationException();
    }

    @Override
    public void skip(int n) {
        if (n < 1) {
            return;
        }
        int toSkip = 0;
        for (int i = 0; i < n; i++) {
            toSkip += geometricDistribution.sample();
        }
        delegate.skip(toSkip);
        if (delegate.hasNext()) {
            next = delegate.next();
        } else {
            hasNext = false;
        }
    }

}