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discard """
action: run
output: '''[Suite] random int
[Suite] random float
[Suite] cumsum
[Suite] random sample
[Suite] ^
'''
"""
import math, random, os
import unittest
import sets, tables
suite "random int":
test "there might be some randomness":
var set = initHashSet[int](128)
for i in 1..1000:
incl(set, random(high(int)))
check len(set) == 1000
test "single number bounds work":
var rand: int
for i in 1..1000:
rand = random(1000)
check rand < 1000
check rand > -1
test "slice bounds work":
var rand: int
for i in 1..1000:
rand = random(100..1000)
check rand < 1000
check rand >= 100
test " again gives new numbers":
var rand1 = random(1000000)
os.sleep(200)
var rand2 = random(1000000)
check rand1 != rand2
suite "random float":
test "there might be some randomness":
var set = initSet[float](128)
for i in 1..100:
incl(set, random(1.0))
check len(set) == 100
test "single number bounds work":
var rand: float
for i in 1..1000:
rand = random(1000.0)
check rand < 1000.0
check rand > -1.0
test "slice bounds work":
var rand: float
for i in 1..1000:
rand = random(100.0..1000.0)
check rand < 1000.0
check rand >= 100.0
test " again gives new numbers":
var rand1:float = random(1000000.0)
os.sleep(200)
var rand2:float = random(1000000.0)
check rand1 != rand2
suite "cumsum":
test "cumsum int seq return":
let counts = [ 1, 2, 3, 4 ]
check counts.cumsummed == [ 1, 3, 6, 10 ]
test "cumsum float seq return":
let counts = [ 1.0, 2.0, 3.0, 4.0 ]
check counts.cumsummed == [ 1.0, 3.0, 6.0, 10.0 ]
test "cumsum int in-place":
var counts = [ 1, 2, 3, 4 ]
counts.cumsum
check counts == [ 1, 3, 6, 10 ]
test "cumsum float in-place":
var counts = [ 1.0, 2.0, 3.0, 4.0 ]
counts.cumsum
check counts == [ 1.0, 3.0, 6.0, 10.0 ]
suite "random sample":
test "non-uniform array sample unnormalized int CDF":
let values = [ 10, 20, 30, 40, 50 ] # values
let counts = [ 4, 3, 2, 1, 0 ] # weights aka unnormalized probabilities
var histo = initCountTable[int]()
let cdf = counts.cumsummed # unnormalized CDF
for i in 0 ..< 5000:
histo.inc(sample(values, cdf))
check histo.len == 4 # number of non-zero in `counts`
# Any one bin is a binomial random var for n samples, each with prob p of
# adding a count to k; E[k]=p*n, Var k=p*(1-p)*n, approximately Normal for
# big n. So, P(abs(k - p*n)/sqrt(p*(1-p)*n))>3.0) =~ 0.0027, while
# P(wholeTestFails) =~ 1 - P(binPasses)^4 =~ 1 - (1-0.0027)^4 =~ 0.01.
for i, c in counts:
if c == 0:
check values[i] notin histo
continue
let p = float(c) / float(cdf[^1])
let n = 5000.0
let expected = p * n
let stdDev = sqrt(n * p * (1.0 - p))
check abs(float(histo[values[i]]) - expected) <= 3.0 * stdDev
test "non-uniform array sample normalized float CDF":
let values = [ 10, 20, 30, 40, 50 ] # values
let counts = [ 0.4, 0.3, 0.2, 0.1, 0 ] # probabilities
var histo = initCountTable[int]()
let cdf = counts.cumsummed # normalized CDF
for i in 0 ..< 5000:
histo.inc(sample(values, cdf))
check histo.len == 4 # number of non-zero in ``counts``
for i, c in counts:
if c == 0:
check values[i] notin histo
continue
let p = float(c) / float(cdf[^1])
let n = 5000.0
let expected = p * n
let stdDev = sqrt(n * p * (1.0 - p))
# NOTE: like unnormalized int CDF test, P(wholeTestFails) =~ 0.01.
check abs(float(histo[values[i]]) - expected) <= 3.0 * stdDev
suite "^":
test "compiles for valid types":
check: compiles(5 ^ 2)
check: compiles(5.5 ^ 2)
check: compiles(5.5 ^ 2.int8)
check: compiles(5.5 ^ 2.uint)
check: compiles(5.5 ^ 2.uint8)
check: not compiles(5.5 ^ 2.2)
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