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https://github.com/nim-lang/Nim.git
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e1d5356ae9
* Add the ability to sample elements from an openArray according to a parallel array of weights/unnormalized probabilities (any sort of histogram, basically). Also add a non-thread safe version for convenience. * Address Araq comments on https://github.com/nim-lang/Nim/pull/10072 * import at top of file and space after '#'. * Put in a check for non-zero total weight. * Clarify constraint on `w`. * Rename `rand(openArray[T])` to `sample(openArray[T])` to `sample`, deprecating old name and name new (openArray[T], openArray[U]) variants `sample`. * Rename caller-provided state version of rand(openArray[T]) and also clean up doc comments. * Add test for new non-uniform array sampler. 3 sd bound makes it 99% likely that it will still pass in the future if the random number generator changes. We cannot both have a tight bound to check distribution *and* loose check to ensure resilience to RNG changes. (We cannot *guarantee* resilience, anyway. There's always a small chance any test hits a legitimate random fluctuation.)
108 lines
2.8 KiB
Nim
108 lines
2.8 KiB
Nim
discard """
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action: run
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output: '''[Suite] random int
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[Suite] random float
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[Suite] random sample
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[Suite] ^
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'''
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"""
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import math, random, os
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import unittest
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import sets, tables
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suite "random int":
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test "there might be some randomness":
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var set = initSet[int](128)
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randomize()
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for i in 1..1000:
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incl(set, random(high(int)))
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check len(set) == 1000
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test "single number bounds work":
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randomize()
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var rand: int
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for i in 1..1000:
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rand = random(1000)
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check rand < 1000
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check rand > -1
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test "slice bounds work":
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randomize()
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var rand: int
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for i in 1..1000:
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rand = random(100..1000)
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check rand < 1000
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check rand >= 100
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test "randomize() again gives new numbers":
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randomize()
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var rand1 = random(1000000)
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os.sleep(200)
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randomize()
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var rand2 = random(1000000)
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check rand1 != rand2
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suite "random float":
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test "there might be some randomness":
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var set = initSet[float](128)
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randomize()
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for i in 1..100:
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incl(set, random(1.0))
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check len(set) == 100
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test "single number bounds work":
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randomize()
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var rand: float
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for i in 1..1000:
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rand = random(1000.0)
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check rand < 1000.0
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check rand > -1.0
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test "slice bounds work":
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randomize()
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var rand: float
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for i in 1..1000:
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rand = random(100.0..1000.0)
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check rand < 1000.0
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check rand >= 100.0
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test "randomize() again gives new numbers":
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randomize()
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var rand1:float = random(1000000.0)
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os.sleep(200)
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randomize()
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var rand2:float = random(1000000.0)
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check rand1 != rand2
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suite "random sample":
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test "non-uniform array sample":
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let values = [ 10, 20, 30, 40, 50 ] # values
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let weight = [ 4, 3, 2, 1, 0 ] # weights aka unnormalized probabilities
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let weightSum = 10.0 # sum of weights
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var histo = initCountTable[int]()
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for v in sample(values, weight, 5000):
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histo.inc(v)
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check histo.len == 4 # number of non-zero in `weight`
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# Any one bin is a binomial random var for n samples, each with prob p of
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# adding a count to k; E[k]=p*n, Var k=p*(1-p)*n, approximately Normal for
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# big n. So, P(abs(k - p*n)/sqrt(p*(1-p)*n))>3.0) =~ 0.0027, while
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# P(wholeTestFails) =~ 1 - P(binPasses)^4 =~ 1 - (1-0.0027)^4 =~ 0.01.
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for i, w in weight:
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if w == 0:
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check values[i] notin histo
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continue
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let p = float(w) / float(weightSum)
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let n = 5000.0
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let expected = p * n
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let stdDev = sqrt(n * p * (1.0 - p))
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check abs(float(histo[values[i]]) - expected) <= 3.0 * stdDev
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suite "^":
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test "compiles for valid types":
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check: compiles(5 ^ 2)
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check: compiles(5.5 ^ 2)
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check: compiles(5.5 ^ 2.int8)
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check: compiles(5.5 ^ 2.uint)
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check: compiles(5.5 ^ 2.uint8)
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check: not compiles(5.5 ^ 2.2) |