com.github.marklister.collections.util

WeightedStats

class WeightedStats[A, B] extends AnyRef

A very simple class wrapping an Iterable[Product2[A,B]] and providing basic weighted statistics like weighted mean and weighted variance.

The first element of the Pair is the value and the second element the weight.

There is an implicit conversion from a Seq[Product2[A,B]] to a WeightedStats[A,B] imported with com.github.marklister.collections. So you do not need to do anything to use this class. Simply use the mean, stdDev or variance methods on an appropriate sequence:

import com.github.marklister.collections.io._
import com.github.marklister.collections._
Welcome to Scala version 2.10.1 (OpenJDK Server VM, Java 1.7.0_21).
Type in expressions to have them evaluated.
Type :help for more information.

scala> CollSeq((1,2),(2,1),(3,3)).mean
res1: Double = 2.1666666666666665

I would really prefer to use an external library. If you know of one please raise an issue on Github

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Instance Constructors

  1. new WeightedStats(underlying: Iterable[Product2[A, B]])(implicit numA: Numeric[A], numB: Numeric[B])

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  5. def clone(): AnyRef

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  9. final def getClass(): Class[_]

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  10. def hashCode(): Int

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  11. final def isInstanceOf[T0]: Boolean

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  12. lazy val mean: Double

    The weighted arithmetic mean of all the elements

  13. final def ne(arg0: AnyRef): Boolean

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. lazy val stdDev: Double

    The weighted standard deviation of all the elements

  17. final def synchronized[T0](arg0: ⇒ T0): T0

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  18. def toString(): String

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  19. val underlying: Iterable[Product2[A, B]]

  20. lazy val variance: Double

    The weighted variance of all the elements (the one divided by n not n-1)

  21. final def wait(): Unit

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  22. final def wait(arg0: Long, arg1: Int): Unit

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  23. final def wait(arg0: Long): Unit

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