diff options
Diffstat (limited to 'tests/stdlib/tmath.nim')
| -rw-r--r-- | tests/stdlib/tmath.nim | 91 |
1 files changed, 78 insertions, 13 deletions
diff --git a/tests/stdlib/tmath.nim b/tests/stdlib/tmath.nim index 581308a7e..bdb5aa332 100644 --- a/tests/stdlib/tmath.nim +++ b/tests/stdlib/tmath.nim @@ -4,6 +4,10 @@ discard """ [Suite] random float +[Suite] cumsum + +[Suite] random sample + [Suite] ^ ''' @@ -11,34 +15,34 @@ discard """ import math, random, os import unittest -import sets +import sets, tables suite "random int": test "there might be some randomness": var set = initSet[int](128) - randomize() + for i in 1..1000: incl(set, random(high(int))) check len(set) == 1000 test "single number bounds work": - randomize() + var rand: int for i in 1..1000: rand = random(1000) check rand < 1000 check rand > -1 test "slice bounds work": - randomize() + var rand: int for i in 1..1000: rand = random(100..1000) check rand < 1000 check rand >= 100 - test "randomize() again gives new numbers": - randomize() + test " again gives new numbers": + var rand1 = random(1000000) os.sleep(200) - randomize() + var rand2 = random(1000000) check rand1 != rand2 @@ -46,32 +50,93 @@ suite "random int": suite "random float": test "there might be some randomness": var set = initSet[float](128) - randomize() + for i in 1..100: incl(set, random(1.0)) check len(set) == 100 test "single number bounds work": - randomize() + var rand: float for i in 1..1000: rand = random(1000.0) check rand < 1000.0 check rand > -1.0 test "slice bounds work": - randomize() + var rand: float for i in 1..1000: rand = random(100.0..1000.0) check rand < 1000.0 check rand >= 100.0 - test "randomize() again gives new numbers": - randomize() + test " again gives new numbers": + var rand1:float = random(1000000.0) os.sleep(200) - randomize() + 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) |