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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.apache.spark.util
import org.apache.spark.annotation.Since
/**
* A class for tracking the statistics of a set of numbers (count, mean and variance) in a
* numerically robust way. Includes support for merging two StatCounters. Based on Welford
* and Chan's [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance algorithms]]
* for running variance.
*
* @constructor Initialize the StatCounter with the given values.
*/
class StatCounter(values: TraversableOnce[Double]) extends Serializable {
private var n: Long = 0 // Running count of our values
private var mu: Double = 0 // Running mean of our values
private var m2: Double = 0 // Running variance numerator (sum of (x - mean)^2)
private var maxValue: Double = Double.NegativeInfinity // Running max of our values
private var minValue: Double = Double.PositiveInfinity // Running min of our values
merge(values)
/** Initialize the StatCounter with no values. */
def this() = this(Nil)
/** Add a value into this StatCounter, updating the internal statistics. */
def merge(value: Double): StatCounter = {
val delta = value - mu
n += 1
mu += delta / n
m2 += delta * (value - mu)
maxValue = math.max(maxValue, value)
minValue = math.min(minValue, value)
this
}
/** Add multiple values into this StatCounter, updating the internal statistics. */
def merge(values: TraversableOnce[Double]): StatCounter = {
values.foreach(v => merge(v))
this
}
/** Merge another StatCounter into this one, adding up the internal statistics. */
def merge(other: StatCounter): StatCounter = {
if (other == this) {
merge(other.copy()) // Avoid overwriting fields in a weird order
} else {
if (n == 0) {
mu = other.mu
m2 = other.m2
n = other.n
maxValue = other.maxValue
minValue = other.minValue
} else if (other.n != 0) {
val delta = other.mu - mu
if (other.n * 10 < n) {
mu = mu + (delta * other.n) / (n + other.n)
} else if (n * 10 < other.n) {
mu = other.mu - (delta * n) / (n + other.n)
} else {
mu = (mu * n + other.mu * other.n) / (n + other.n)
}
m2 += other.m2 + (delta * delta * n * other.n) / (n + other.n)
n += other.n
maxValue = math.max(maxValue, other.maxValue)
minValue = math.min(minValue, other.minValue)
}
this
}
}
/** Clone this StatCounter */
def copy(): StatCounter = {
val other = new StatCounter
other.n = n
other.mu = mu
other.m2 = m2
other.maxValue = maxValue
other.minValue = minValue
other
}
def count: Long = n
def mean: Double = mu
def sum: Double = n * mu
def max: Double = maxValue
def min: Double = minValue
/** Return the population variance of the values. */
def variance: Double = popVariance
/**
* Return the population variance of the values.
*/
@Since("2.1.0")
def popVariance: Double = {
if (n == 0) {
Double.NaN
} else {
m2 / n
}
}
/**
* Return the sample variance, which corrects for bias in estimating the variance by dividing
* by N-1 instead of N.
*/
def sampleVariance: Double = {
if (n <= 1) {
Double.NaN
} else {
m2 / (n - 1)
}
}
/** Return the population standard deviation of the values. */
def stdev: Double = popStdev
/**
* Return the population standard deviation of the values.
*/
@Since("2.1.0")
def popStdev: Double = math.sqrt(popVariance)
/**
* Return the sample standard deviation of the values, which corrects for bias in estimating the
* variance by dividing by N-1 instead of N.
*/
def sampleStdev: Double = math.sqrt(sampleVariance)
override def toString: String = {
"(count: %d, mean: %f, stdev: %f, max: %f, min: %f)".format(count, mean, stdev, max, min)
}
}
object StatCounter {
/** Build a StatCounter from a list of values. */
def apply(values: TraversableOnce[Double]): StatCounter = new StatCounter(values)
/** Build a StatCounter from a list of values passed as variable-length arguments. */
def apply(values: Double*): StatCounter = new StatCounter(values)
}
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