| // |
| // ======================================================================== |
| // Copyright (c) 1995-2014 Mort Bay Consulting Pty. Ltd. |
| // ------------------------------------------------------------------------ |
| // All rights reserved. This program and the accompanying materials |
| // are made available under the terms of the Eclipse Public License v1.0 |
| // and Apache License v2.0 which accompanies this distribution. |
| // |
| // The Eclipse Public License is available at |
| // http://www.eclipse.org/legal/epl-v10.html |
| // |
| // The Apache License v2.0 is available at |
| // http://www.opensource.org/licenses/apache2.0.php |
| // |
| // You may elect to redistribute this code under either of these licenses. |
| // ======================================================================== |
| // |
| |
| package org.eclipse.jetty.util.statistic; |
| |
| import java.util.concurrent.atomic.AtomicLong; |
| |
| import org.eclipse.jetty.util.Atomics; |
| |
| |
| /* ------------------------------------------------------------ */ |
| /** |
| * SampledStatistics |
| * <p> |
| * Provides max, total, mean, count, variance, and standard |
| * deviation of continuous sequence of samples. |
| * <p> |
| * Calculates estimates of mean, variance, and standard deviation |
| * characteristics of a sample using a non synchronized |
| * approximation of the on-line algorithm presented |
| * in Donald Knuth's Art of Computer Programming, Volume 2, |
| * Seminumerical Algorithms, 3rd edition, page 232, |
| * Boston: Addison-Wesley. that cites a 1962 paper by B.P. Welford |
| * that can be found by following the link http://www.jstor.org/pss/1266577 |
| * <p> |
| * This algorithm is also described in Wikipedia at |
| * http://en.wikipedia.org/w/index.php?title=Algorithms_for_calculating_variance§ion=4#On-line_algorithm |
| */ |
| public class SampleStatistic |
| { |
| protected final AtomicLong _max = new AtomicLong(); |
| protected final AtomicLong _total = new AtomicLong(); |
| protected final AtomicLong _count = new AtomicLong(); |
| protected final AtomicLong _totalVariance100 = new AtomicLong(); |
| |
| public void reset() |
| { |
| _max.set(0); |
| _total.set(0); |
| _count.set(0); |
| _totalVariance100.set(0); |
| } |
| |
| public void set(final long sample) |
| { |
| long total = _total.addAndGet(sample); |
| long count = _count.incrementAndGet(); |
| |
| if (count>1) |
| { |
| long mean10 = total*10/count; |
| long delta10 = sample*10 - mean10; |
| _totalVariance100.addAndGet(delta10*delta10); |
| } |
| |
| Atomics.updateMax(_max, sample); |
| } |
| |
| /** |
| * @return the max value |
| */ |
| public long getMax() |
| { |
| return _max.get(); |
| } |
| |
| public long getTotal() |
| { |
| return _total.get(); |
| } |
| |
| public long getCount() |
| { |
| return _count.get(); |
| } |
| |
| public double getMean() |
| { |
| return (double)_total.get()/_count.get(); |
| } |
| |
| public double getVariance() |
| { |
| final long variance100 = _totalVariance100.get(); |
| final long count = _count.get(); |
| |
| return count>1?((double)variance100)/100.0/(count-1):0.0; |
| } |
| |
| public double getStdDev() |
| { |
| return Math.sqrt(getVariance()); |
| } |
| } |