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package spark.util
import java.io.PrintStream
/**
* Util for getting some stats from a small sample of numeric values, with some handy summary functions.
*
* Entirely in memory, not intended as a good way to compute stats over large data sets.
*
* Assumes you are giving it a non-empty set of data
*/
class Distribution(val data: Array[Double], val startIdx: Int, val endIdx: Int) {
require(startIdx < endIdx)
def this(data: Traversable[Double]) = this(data.toArray, 0, data.size)
java.util.Arrays.sort(data, startIdx, endIdx)
val length = endIdx - startIdx
val defaultProbabilities = Array(0,0.25,0.5,0.75,1.0)
/**
* Get the value of the distribution at the given probabilities. Probabilities should be
* given from 0 to 1
* @param probabilities
*/
def getQuantiles(probabilities: Traversable[Double] = defaultProbabilities) = {
probabilities.toIndexedSeq.map{p:Double => data(closestIndex(p))}
}
private def closestIndex(p: Double) = {
math.min((p * length).toInt + startIdx, endIdx - 1)
}
def showQuantiles(out: PrintStream = System.out) = {
out.println("min\t25%\t50%\t75%\tmax")
getQuantiles(defaultProbabilities).foreach{q => out.print(q + "\t")}
out.println
}
def statCounter = StatCounter(data.slice(startIdx, endIdx))
/**
* print a summary of this distribution to the given PrintStream.
* @param out
*/
def summary(out: PrintStream = System.out) {
out.println(statCounter)
showQuantiles(out)
}
}
object Distribution {
def apply(data: Traversable[Double]): Option[Distribution] = {
if (data.size > 0)
Some(new Distribution(data))
else
None
}
def showQuantiles(out: PrintStream = System.out, quantiles: Traversable[Double]) {
out.println("min\t25%\t50%\t75%\tmax")
quantiles.foreach{q => out.print(q + "\t")}
out.println
}
}
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