85 lines
2.0 KiB
Nim
85 lines
2.0 KiB
Nim
import std/math
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import std/sugar
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import std/random
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import std/sequtils
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randomize()
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## Distribution functions
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## Normal
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## <https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform#Basic_form>
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proc ur_normal(): float =
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let u1 = rand(1.0)
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let u2 = rand(1.0)
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let z = sqrt(-2.0 * ln(u1)) * sin(2 * PI * u2)
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return z
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proc normal(mean: float, sigma: float): float =
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return (mean + sigma * ur_normal())
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proc lognormal(logmean: float, logsigma: float): float =
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let answer = pow(E, normal(logmean, logsigma))
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return answer
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proc to(low: float, high: float): float =
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let normal95confidencePoint = 1.6448536269514722
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let loglow = ln(low)
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let loghigh = ln(high)
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let logmean = (loglow + loghigh)/2
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let logsigma = (loghigh - loglow) / (2.0 * normal95confidencePoint)
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return lognormal(logmean, logsigma)
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## echo ur_normal()
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## echo normal(10, 20)
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## echo lognormal(2, 4)
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## echo to(10, 90)
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## Manipulate samples
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proc mixture(fs: seq[proc (): float{.nimcall.}], ps: seq[float], n: int): seq[float] =
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assert fs.len == ps.len
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var ws: seq[float]
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var sum = 0.0
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for i, p in ps:
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sum = sum + p
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ws.add(sum)
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ws = ws.map(w => w/sum)
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var samples: seq[float]
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let rs = toSeq(1..n).map(_=>rand(1.0))
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for i in 0..(n-1):
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let r = rs[i]
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var j = ws.len - 1
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for k, w in ws:
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if r < w:
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j = k
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break
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## only occasion when ^ doesn't assign j
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## is when r is exactly 1
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## which would correspond to choosing the last item in ws
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## which is why j is initialized to ws.len - 1
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let f = fs[j]
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samples.add(f())
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return samples
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## Actual model
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let n = 1000000
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let p_a = 0.8
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let p_b = 0.5
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let p_c = p_a * p_b
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let weights = @[ 1.0 - p_c, p_c/2.0, p_c/4.0, p_c/4.0 ]
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let fs = @[ proc (): float = 0.0, proc (): float = 1.0, proc (): float = to(1.0, 3.0), proc (): float = to(2.0, 10.0)]
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let result = mixture(fs, weights, n)
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let mean_result = foldl(result, a + b, 0.0) / float(result.len)
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# echo result
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echo mean_result
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