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Merge pull request #2537 from jsanjuas/devel

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Generalize mean to other types
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Varriount 2015-05-07 12:59:01 -04:00
commit ace11f08aa
1 changed files with 12 additions and 7 deletions
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19
lib/pure/math.nim
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@ -114,18 +114,23 @@ proc sum*[T](x: openArray[T]): T {.noSideEffect.} =
## If `x` is empty, 0 is returned.
for i in items(x): result = result + i
proc mean*(x: openArray[float]): float {.noSideEffect.} =
## computes the mean of the elements in `x`.
## If `x` is empty, NaN is returned.
result = sum(x) / toFloat(len(x))
template toFloat(f: float): float = f
proc variance*(x: openArray[float]): float {.noSideEffect.} =
proc mean*[T](x: openArray[T]): float {.noSideEffect.} =
## computes the mean of the elements in `x`, which are first converted to floats.
## If `x` is empty, NaN is returned.
## ``toFloat(x: T): float`` must be defined.
for i in items(x): result = result + toFloat(i)
result = result / toFloat(len(x))
proc variance*[T](x: openArray[T]): float {.noSideEffect.} =
## computes the variance of the elements in `x`.
## If `x` is empty, NaN is returned.
## ``toFloat(x: T): float`` must be defined.
result = 0.0
var m = mean(x)
for i in 0 .. high(x):
var diff = x[i] - m
for i in items(x):
var diff = toFloat(i) - m
result = result + diff*diff
result = result / toFloat(len(x))