diff options
| -rw-r--r-- | changelog.md | 2 | ||||
| -rw-r--r-- | lib/pure/random.nim | 43 |
2 files changed, 43 insertions, 2 deletions
diff --git a/changelog.md b/changelog.md index 9ab6ebfda..151eafcd5 100644 --- a/changelog.md +++ b/changelog.md @@ -113,7 +113,7 @@ - Add `rstgen.rstToLatex` convenience proc for `renderRstToOut` and `initRstGenerator` with `outLatex` output. - Add `os.normalizeExe`, eg: `koch` => `./koch`. - `macros.newLit` now preserves named vs unnamed tuples; use `-d:nimHasWorkaround14720` to keep old behavior - +- Add `random.gauss`, that uses the ratio of uniforms method of sampling from a Gaussian distribution. ## Language changes - In the newruntime it is now allowed to assign discriminator field without restrictions as long as case object doesn't have custom destructor. Discriminator value doesn't have to be a constant either. If you have custom destructor for case object and you do want to freely assign discriminator fields, it is recommended to refactor object into 2 objects like this: diff --git a/lib/pure/random.nim b/lib/pure/random.nim index e794a4dde..7c5d51ceb 100644 --- a/lib/pure/random.nim +++ b/lib/pure/random.nim @@ -78,7 +78,8 @@ ## <lib.html#pure-libraries-hashing>`_ ## in the standard library -import algorithm #For upperBound +import algorithm, math +import std/private/since include "system/inclrtl" {.push debugger: off.} @@ -516,6 +517,33 @@ proc sample*[T, U](a: openArray[T]; cdf: openArray[U]): T = doAssert sample(marbles, cdf) == "blue" state.sample(a, cdf) +proc gauss*(r: var Rand; mu = 0.0; sigma = 1.0): float {.since: (1, 3).} = + ## Returns a Gaussian random variate, + ## with mean ``mu`` and standard deviation ``sigma`` + ## using the given state. + # Ratio of uniforms method for normal + # http://www2.econ.osaka-u.ac.jp/~tanizaki/class/2013/econome3/13.pdf + const K = sqrt(2 / E) + var + a = 0.0 + b = 0.0 + while true: + a = rand(r, 1.0) + b = (2.0 * rand(r, 1.0) - 1.0) * K + if b * b <= -4.0 * a * a * ln(a): break + result = mu + sigma * (b / a) + +proc gauss*(mu = 0.0, sigma = 1.0): float {.since: (1, 3).} = + ## Returns a Gaussian random variate, + ## with mean ``mu`` and standard deviation ``sigma``. + ## + ## If `randomize<#randomize>`_ has not been called, the order of outcomes + ## from this proc will always be the same. + ## + ## This proc uses the default random number generator. Thus, it is **not** + ## thread-safe. + result = gauss(state, mu, sigma) + proc initRand*(seed: int64): Rand = ## Initializes a new `Rand<#Rand>`_ state using the given seed. ## @@ -619,6 +647,8 @@ when not defined(nimscript) and not defined(standalone): {.pop.} when isMainModule: + import stats + proc main = var occur: array[1000, int] @@ -632,6 +662,17 @@ when isMainModule: elif oc > 150: doAssert false, "too many occurrences of " & $i + when false: + var rs: RunningStat + for j in 1..5: + for i in 1 .. 1_000: + rs.push(gauss()) + echo("mean: ", rs.mean, + " stdDev: ", rs.standardDeviation(), + " min: ", rs.min, + " max: ", rs.max) + rs.clear() + var a = [0, 1] shuffle(a) doAssert a[0] == 1 |