com.aspectran.core.util.statistic.SampleStatistic Maven / Gradle / Ivy
/*
* Copyright (c) 2008-2023 The Aspectran Project
*
* Licensed 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.
*/
//
// ========================================================================
// Copyright (c) 1995-2017 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.
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//
package com.aspectran.core.util.statistic;
import com.aspectran.core.util.ToStringBuilder;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.LongAccumulator;
import java.util.concurrent.atomic.LongAdder;
/**
* This class is a clone of org.eclipse.jetty.util.statistic.SampleStatistic
*
* Statistics on a sampled value.
* Provides max, total, mean, count, variance, and standard deviation of continuous sequence of samples.
* 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,
* Semi numerical Algorithms, 3rd edition, page 232, Boston: Addison-Wesley. That cites a 1962 paper by B.P. Welford:
* Note on a Method for Calculating Corrected Sums of Squares and Products
* This algorithm is also described in Wikipedia in the section "Online algorithm":
* Algorithms for calculating variance.
*/
public class SampleStatistic {
protected final LongAccumulator max = new LongAccumulator(Math::max,0L);
protected final AtomicLong total = new AtomicLong();
protected final AtomicLong count = new AtomicLong();
protected final LongAdder totalVariance100 = new LongAdder();
/**
* Resets the statistics.
*/
public void reset() {
max.reset();
total.set(0);
count.set(0);
totalVariance100.reset();
}
/**
* Records a sample value.
* @param sample the value to record.
*/
public void record(long sample) {
long total = this.total.addAndGet(sample);
long count = this.count.incrementAndGet();
if (count > 1) {
long mean10 = total * 10 / count;
long delta10 = sample * 10 - mean10;
totalVariance100.add(delta10 * delta10);
}
max.accumulate(sample);
}
/**
* @return the max value of the recorded samples
*/
public long getMax() {
return max.get();
}
/**
* @return the sum of all the recorded samples
*/
public long getTotal() {
return total.get();
}
/**
* @return the number of samples recorded
*/
public long getCount() {
return count.get();
}
/**
* @return the average value of the samples recorded, or zero if there are no samples
*/
public double getMean() {
long count = getCount();
return (count > 0 ? (double)this.total.get() / this.count.get() : 0.0D);
}
/**
* @return the variance of the samples recorded, or zero if there are less than 2 samples
*/
public double getVariance() {
long variance100 = totalVariance100.sum();
long count = getCount();
return (count > 1 ? variance100 / 100.0D / (count - 1) : 0.0D);
}
/**
* @return the standard deviation of the samples recorded
*/
public double getStdDev() {
return Math.sqrt(getVariance());
}
@Override
public String toString() {
ToStringBuilder tsb = new ToStringBuilder(String.format("%s@%x", getClass().getSimpleName(), hashCode()));
tsb.append("count", getCount());
tsb.append("max", getMax());
tsb.append("total", getTotal());
tsb.append("stddev", getStdDev());
return tsb.toString();
}
}