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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.codelibs.elasticsearch.taste.common;

import java.util.NoSuchElementException;

import org.apache.commons.math3.distribution.PascalDistribution;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.common.RandomWrapper;

import com.google.common.base.Preconditions;

/**
 * Wraps a {@link LongPrimitiveIterator} and returns only some subset of the elements that it would,
 * as determined by a sampling rate parameter.
 */
public final class SamplingLongPrimitiveIterator extends
        AbstractLongPrimitiveIterator {

    private final PascalDistribution geometricDistribution;

    private final LongPrimitiveIterator delegate;

    private long next;

    private boolean hasNext;

    public SamplingLongPrimitiveIterator(final LongPrimitiveIterator delegate,
            final double samplingRate) {
        this(RandomUtils.getRandom(), delegate, samplingRate);
    }

    public SamplingLongPrimitiveIterator(final RandomWrapper random,
            final LongPrimitiveIterator delegate, final double samplingRate) {
        Preconditions.checkNotNull(delegate);
        Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0,
                "Must be: 0.0 < samplingRate <= 1.0");
        // Geometric distribution is special case of negative binomial (aka Pascal) with r=1:
        geometricDistribution = new PascalDistribution(
                random.getRandomGenerator(), 1, samplingRate);
        this.delegate = delegate;
        hasNext = true;
        doNext();
    }

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

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

    @Override
    public long peek() {
        if (hasNext) {
            return next;
        }
        throw new NoSuchElementException();
    }

    private void doNext() {
        final 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(final int n) {
        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;
        }
    }

    public static LongPrimitiveIterator maybeWrapIterator(
            final LongPrimitiveIterator delegate, final double samplingRate) {
        return samplingRate >= 1.0 ? delegate
                : new SamplingLongPrimitiveIterator(delegate, samplingRate);
    }

}