625 lines
24 KiB
Python
625 lines
24 KiB
Python
#!/usr/bin/env python
|
|
|
|
import unittest
|
|
import random
|
|
import time
|
|
import pickle
|
|
import warnings
|
|
from math import log, exp, pi, fsum, sin
|
|
from functools import reduce
|
|
from test import test_support
|
|
|
|
class TestBasicOps(unittest.TestCase):
|
|
# Superclass with tests common to all generators.
|
|
# Subclasses must arrange for self.gen to retrieve the Random instance
|
|
# to be tested.
|
|
|
|
def randomlist(self, n):
|
|
"""Helper function to make a list of random numbers"""
|
|
return [self.gen.random() for i in xrange(n)]
|
|
|
|
def test_autoseed(self):
|
|
self.gen.seed()
|
|
state1 = self.gen.getstate()
|
|
time.sleep(0.1)
|
|
self.gen.seed() # diffent seeds at different times
|
|
state2 = self.gen.getstate()
|
|
self.assertNotEqual(state1, state2)
|
|
|
|
def test_saverestore(self):
|
|
N = 1000
|
|
self.gen.seed()
|
|
state = self.gen.getstate()
|
|
randseq = self.randomlist(N)
|
|
self.gen.setstate(state) # should regenerate the same sequence
|
|
self.assertEqual(randseq, self.randomlist(N))
|
|
|
|
def test_seedargs(self):
|
|
for arg in [None, 0, 0L, 1, 1L, -1, -1L, 10**20, -(10**20),
|
|
3.14, 1+2j, 'a', tuple('abc')]:
|
|
self.gen.seed(arg)
|
|
for arg in [range(3), dict(one=1)]:
|
|
self.assertRaises(TypeError, self.gen.seed, arg)
|
|
self.assertRaises(TypeError, self.gen.seed, 1, 2)
|
|
self.assertRaises(TypeError, type(self.gen), [])
|
|
|
|
def test_jumpahead(self):
|
|
self.gen.seed()
|
|
state1 = self.gen.getstate()
|
|
self.gen.jumpahead(100)
|
|
state2 = self.gen.getstate() # s/b distinct from state1
|
|
self.assertNotEqual(state1, state2)
|
|
self.gen.jumpahead(100)
|
|
state3 = self.gen.getstate() # s/b distinct from state2
|
|
self.assertNotEqual(state2, state3)
|
|
|
|
with test_support.check_py3k_warnings(quiet=True):
|
|
self.assertRaises(TypeError, self.gen.jumpahead) # needs an arg
|
|
self.assertRaises(TypeError, self.gen.jumpahead, 2, 3) # too many
|
|
|
|
def test_jumpahead_produces_valid_state(self):
|
|
# From http://bugs.python.org/issue14591.
|
|
self.gen.seed(199210368)
|
|
self.gen.jumpahead(13550674232554645900)
|
|
for i in range(500):
|
|
val = self.gen.random()
|
|
self.assertLess(val, 1.0)
|
|
|
|
def test_sample(self):
|
|
# For the entire allowable range of 0 <= k <= N, validate that
|
|
# the sample is of the correct length and contains only unique items
|
|
N = 100
|
|
population = xrange(N)
|
|
for k in xrange(N+1):
|
|
s = self.gen.sample(population, k)
|
|
self.assertEqual(len(s), k)
|
|
uniq = set(s)
|
|
self.assertEqual(len(uniq), k)
|
|
self.assertTrue(uniq <= set(population))
|
|
self.assertEqual(self.gen.sample([], 0), []) # test edge case N==k==0
|
|
|
|
def test_sample_distribution(self):
|
|
# For the entire allowable range of 0 <= k <= N, validate that
|
|
# sample generates all possible permutations
|
|
n = 5
|
|
pop = range(n)
|
|
trials = 10000 # large num prevents false negatives without slowing normal case
|
|
def factorial(n):
|
|
return reduce(int.__mul__, xrange(1, n), 1)
|
|
for k in xrange(n):
|
|
expected = factorial(n) // factorial(n-k)
|
|
perms = {}
|
|
for i in xrange(trials):
|
|
perms[tuple(self.gen.sample(pop, k))] = None
|
|
if len(perms) == expected:
|
|
break
|
|
else:
|
|
self.fail()
|
|
|
|
def test_sample_inputs(self):
|
|
# SF bug #801342 -- population can be any iterable defining __len__()
|
|
self.gen.sample(set(range(20)), 2)
|
|
self.gen.sample(range(20), 2)
|
|
self.gen.sample(xrange(20), 2)
|
|
self.gen.sample(str('abcdefghijklmnopqrst'), 2)
|
|
self.gen.sample(tuple('abcdefghijklmnopqrst'), 2)
|
|
|
|
def test_sample_on_dicts(self):
|
|
self.gen.sample(dict.fromkeys('abcdefghijklmnopqrst'), 2)
|
|
|
|
# SF bug #1460340 -- random.sample can raise KeyError
|
|
a = dict.fromkeys(range(10)+range(10,100,2)+range(100,110))
|
|
self.gen.sample(a, 3)
|
|
|
|
# A followup to bug #1460340: sampling from a dict could return
|
|
# a subset of its keys or of its values, depending on the size of
|
|
# the subset requested.
|
|
N = 30
|
|
d = dict((i, complex(i, i)) for i in xrange(N))
|
|
for k in xrange(N+1):
|
|
samp = self.gen.sample(d, k)
|
|
# Verify that we got ints back (keys); the values are complex.
|
|
for x in samp:
|
|
self.assertTrue(type(x) is int)
|
|
samp.sort()
|
|
self.assertEqual(samp, range(N))
|
|
|
|
def test_gauss(self):
|
|
# Ensure that the seed() method initializes all the hidden state. In
|
|
# particular, through 2.2.1 it failed to reset a piece of state used
|
|
# by (and only by) the .gauss() method.
|
|
|
|
for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
|
|
self.gen.seed(seed)
|
|
x1 = self.gen.random()
|
|
y1 = self.gen.gauss(0, 1)
|
|
|
|
self.gen.seed(seed)
|
|
x2 = self.gen.random()
|
|
y2 = self.gen.gauss(0, 1)
|
|
|
|
self.assertEqual(x1, x2)
|
|
self.assertEqual(y1, y2)
|
|
|
|
def test_pickling(self):
|
|
state = pickle.dumps(self.gen)
|
|
origseq = [self.gen.random() for i in xrange(10)]
|
|
newgen = pickle.loads(state)
|
|
restoredseq = [newgen.random() for i in xrange(10)]
|
|
self.assertEqual(origseq, restoredseq)
|
|
|
|
def test_bug_1727780(self):
|
|
# verify that version-2-pickles can be loaded
|
|
# fine, whether they are created on 32-bit or 64-bit
|
|
# platforms, and that version-3-pickles load fine.
|
|
files = [("randv2_32.pck", 780),
|
|
("randv2_64.pck", 866),
|
|
("randv3.pck", 343)]
|
|
for file, value in files:
|
|
f = open(test_support.findfile(file),"rb")
|
|
r = pickle.load(f)
|
|
f.close()
|
|
self.assertEqual(r.randrange(1000), value)
|
|
|
|
class WichmannHill_TestBasicOps(TestBasicOps):
|
|
gen = random.WichmannHill()
|
|
|
|
def test_setstate_first_arg(self):
|
|
self.assertRaises(ValueError, self.gen.setstate, (2, None, None))
|
|
|
|
def test_strong_jumpahead(self):
|
|
# tests that jumpahead(n) semantics correspond to n calls to random()
|
|
N = 1000
|
|
s = self.gen.getstate()
|
|
self.gen.jumpahead(N)
|
|
r1 = self.gen.random()
|
|
# now do it the slow way
|
|
self.gen.setstate(s)
|
|
for i in xrange(N):
|
|
self.gen.random()
|
|
r2 = self.gen.random()
|
|
self.assertEqual(r1, r2)
|
|
|
|
def test_gauss_with_whseed(self):
|
|
# Ensure that the seed() method initializes all the hidden state. In
|
|
# particular, through 2.2.1 it failed to reset a piece of state used
|
|
# by (and only by) the .gauss() method.
|
|
|
|
for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
|
|
self.gen.whseed(seed)
|
|
x1 = self.gen.random()
|
|
y1 = self.gen.gauss(0, 1)
|
|
|
|
self.gen.whseed(seed)
|
|
x2 = self.gen.random()
|
|
y2 = self.gen.gauss(0, 1)
|
|
|
|
self.assertEqual(x1, x2)
|
|
self.assertEqual(y1, y2)
|
|
|
|
def test_bigrand(self):
|
|
# Verify warnings are raised when randrange is too large for random()
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error", "Underlying random")
|
|
self.assertRaises(UserWarning, self.gen.randrange, 2**60)
|
|
|
|
class SystemRandom_TestBasicOps(TestBasicOps):
|
|
gen = random.SystemRandom()
|
|
|
|
def test_autoseed(self):
|
|
# Doesn't need to do anything except not fail
|
|
self.gen.seed()
|
|
|
|
def test_saverestore(self):
|
|
self.assertRaises(NotImplementedError, self.gen.getstate)
|
|
self.assertRaises(NotImplementedError, self.gen.setstate, None)
|
|
|
|
def test_seedargs(self):
|
|
# Doesn't need to do anything except not fail
|
|
self.gen.seed(100)
|
|
|
|
def test_jumpahead(self):
|
|
# Doesn't need to do anything except not fail
|
|
self.gen.jumpahead(100)
|
|
|
|
def test_gauss(self):
|
|
self.gen.gauss_next = None
|
|
self.gen.seed(100)
|
|
self.assertEqual(self.gen.gauss_next, None)
|
|
|
|
def test_pickling(self):
|
|
self.assertRaises(NotImplementedError, pickle.dumps, self.gen)
|
|
|
|
def test_53_bits_per_float(self):
|
|
# This should pass whenever a C double has 53 bit precision.
|
|
span = 2 ** 53
|
|
cum = 0
|
|
for i in xrange(100):
|
|
cum |= int(self.gen.random() * span)
|
|
self.assertEqual(cum, span-1)
|
|
|
|
def test_bigrand(self):
|
|
# The randrange routine should build-up the required number of bits
|
|
# in stages so that all bit positions are active.
|
|
span = 2 ** 500
|
|
cum = 0
|
|
for i in xrange(100):
|
|
r = self.gen.randrange(span)
|
|
self.assertTrue(0 <= r < span)
|
|
cum |= r
|
|
self.assertEqual(cum, span-1)
|
|
|
|
def test_bigrand_ranges(self):
|
|
for i in [40,80, 160, 200, 211, 250, 375, 512, 550]:
|
|
start = self.gen.randrange(2 ** i)
|
|
stop = self.gen.randrange(2 ** (i-2))
|
|
if stop <= start:
|
|
return
|
|
self.assertTrue(start <= self.gen.randrange(start, stop) < stop)
|
|
|
|
def test_rangelimits(self):
|
|
for start, stop in [(-2,0), (-(2**60)-2,-(2**60)), (2**60,2**60+2)]:
|
|
self.assertEqual(set(range(start,stop)),
|
|
set([self.gen.randrange(start,stop) for i in xrange(100)]))
|
|
|
|
def test_genrandbits(self):
|
|
# Verify ranges
|
|
for k in xrange(1, 1000):
|
|
self.assertTrue(0 <= self.gen.getrandbits(k) < 2**k)
|
|
|
|
# Verify all bits active
|
|
getbits = self.gen.getrandbits
|
|
for span in [1, 2, 3, 4, 31, 32, 32, 52, 53, 54, 119, 127, 128, 129]:
|
|
cum = 0
|
|
for i in xrange(100):
|
|
cum |= getbits(span)
|
|
self.assertEqual(cum, 2**span-1)
|
|
|
|
# Verify argument checking
|
|
self.assertRaises(TypeError, self.gen.getrandbits)
|
|
self.assertRaises(TypeError, self.gen.getrandbits, 1, 2)
|
|
self.assertRaises(ValueError, self.gen.getrandbits, 0)
|
|
self.assertRaises(ValueError, self.gen.getrandbits, -1)
|
|
self.assertRaises(TypeError, self.gen.getrandbits, 10.1)
|
|
|
|
def test_randbelow_logic(self, _log=log, int=int):
|
|
# check bitcount transition points: 2**i and 2**(i+1)-1
|
|
# show that: k = int(1.001 + _log(n, 2))
|
|
# is equal to or one greater than the number of bits in n
|
|
for i in xrange(1, 1000):
|
|
n = 1L << i # check an exact power of two
|
|
numbits = i+1
|
|
k = int(1.00001 + _log(n, 2))
|
|
self.assertEqual(k, numbits)
|
|
self.assertTrue(n == 2**(k-1))
|
|
|
|
n += n - 1 # check 1 below the next power of two
|
|
k = int(1.00001 + _log(n, 2))
|
|
self.assertIn(k, [numbits, numbits+1])
|
|
self.assertTrue(2**k > n > 2**(k-2))
|
|
|
|
n -= n >> 15 # check a little farther below the next power of two
|
|
k = int(1.00001 + _log(n, 2))
|
|
self.assertEqual(k, numbits) # note the stronger assertion
|
|
self.assertTrue(2**k > n > 2**(k-1)) # note the stronger assertion
|
|
|
|
|
|
class MersenneTwister_TestBasicOps(TestBasicOps):
|
|
gen = random.Random()
|
|
|
|
def test_setstate_first_arg(self):
|
|
self.assertRaises(ValueError, self.gen.setstate, (1, None, None))
|
|
|
|
def test_setstate_middle_arg(self):
|
|
# Wrong type, s/b tuple
|
|
self.assertRaises(TypeError, self.gen.setstate, (2, None, None))
|
|
# Wrong length, s/b 625
|
|
self.assertRaises(ValueError, self.gen.setstate, (2, (1,2,3), None))
|
|
# Wrong type, s/b tuple of 625 ints
|
|
self.assertRaises(TypeError, self.gen.setstate, (2, ('a',)*625, None))
|
|
# Last element s/b an int also
|
|
self.assertRaises(TypeError, self.gen.setstate, (2, (0,)*624+('a',), None))
|
|
|
|
def test_referenceImplementation(self):
|
|
# Compare the python implementation with results from the original
|
|
# code. Create 2000 53-bit precision random floats. Compare only
|
|
# the last ten entries to show that the independent implementations
|
|
# are tracking. Here is the main() function needed to create the
|
|
# list of expected random numbers:
|
|
# void main(void){
|
|
# int i;
|
|
# unsigned long init[4]={61731, 24903, 614, 42143}, length=4;
|
|
# init_by_array(init, length);
|
|
# for (i=0; i<2000; i++) {
|
|
# printf("%.15f ", genrand_res53());
|
|
# if (i%5==4) printf("\n");
|
|
# }
|
|
# }
|
|
expected = [0.45839803073713259,
|
|
0.86057815201978782,
|
|
0.92848331726782152,
|
|
0.35932681119782461,
|
|
0.081823493762449573,
|
|
0.14332226470169329,
|
|
0.084297823823520024,
|
|
0.53814864671831453,
|
|
0.089215024911993401,
|
|
0.78486196105372907]
|
|
|
|
self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96))
|
|
actual = self.randomlist(2000)[-10:]
|
|
for a, e in zip(actual, expected):
|
|
self.assertAlmostEqual(a,e,places=14)
|
|
|
|
def test_strong_reference_implementation(self):
|
|
# Like test_referenceImplementation, but checks for exact bit-level
|
|
# equality. This should pass on any box where C double contains
|
|
# at least 53 bits of precision (the underlying algorithm suffers
|
|
# no rounding errors -- all results are exact).
|
|
from math import ldexp
|
|
|
|
expected = [0x0eab3258d2231fL,
|
|
0x1b89db315277a5L,
|
|
0x1db622a5518016L,
|
|
0x0b7f9af0d575bfL,
|
|
0x029e4c4db82240L,
|
|
0x04961892f5d673L,
|
|
0x02b291598e4589L,
|
|
0x11388382c15694L,
|
|
0x02dad977c9e1feL,
|
|
0x191d96d4d334c6L]
|
|
self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96))
|
|
actual = self.randomlist(2000)[-10:]
|
|
for a, e in zip(actual, expected):
|
|
self.assertEqual(long(ldexp(a, 53)), e)
|
|
|
|
def test_long_seed(self):
|
|
# This is most interesting to run in debug mode, just to make sure
|
|
# nothing blows up. Under the covers, a dynamically resized array
|
|
# is allocated, consuming space proportional to the number of bits
|
|
# in the seed. Unfortunately, that's a quadratic-time algorithm,
|
|
# so don't make this horribly big.
|
|
seed = (1L << (10000 * 8)) - 1 # about 10K bytes
|
|
self.gen.seed(seed)
|
|
|
|
def test_53_bits_per_float(self):
|
|
# This should pass whenever a C double has 53 bit precision.
|
|
span = 2 ** 53
|
|
cum = 0
|
|
for i in xrange(100):
|
|
cum |= int(self.gen.random() * span)
|
|
self.assertEqual(cum, span-1)
|
|
|
|
def test_bigrand(self):
|
|
# The randrange routine should build-up the required number of bits
|
|
# in stages so that all bit positions are active.
|
|
span = 2 ** 500
|
|
cum = 0
|
|
for i in xrange(100):
|
|
r = self.gen.randrange(span)
|
|
self.assertTrue(0 <= r < span)
|
|
cum |= r
|
|
self.assertEqual(cum, span-1)
|
|
|
|
def test_bigrand_ranges(self):
|
|
for i in [40,80, 160, 200, 211, 250, 375, 512, 550]:
|
|
start = self.gen.randrange(2 ** i)
|
|
stop = self.gen.randrange(2 ** (i-2))
|
|
if stop <= start:
|
|
return
|
|
self.assertTrue(start <= self.gen.randrange(start, stop) < stop)
|
|
|
|
def test_rangelimits(self):
|
|
for start, stop in [(-2,0), (-(2**60)-2,-(2**60)), (2**60,2**60+2)]:
|
|
self.assertEqual(set(range(start,stop)),
|
|
set([self.gen.randrange(start,stop) for i in xrange(100)]))
|
|
|
|
def test_genrandbits(self):
|
|
# Verify cross-platform repeatability
|
|
self.gen.seed(1234567)
|
|
self.assertEqual(self.gen.getrandbits(100),
|
|
97904845777343510404718956115L)
|
|
# Verify ranges
|
|
for k in xrange(1, 1000):
|
|
self.assertTrue(0 <= self.gen.getrandbits(k) < 2**k)
|
|
|
|
# Verify all bits active
|
|
getbits = self.gen.getrandbits
|
|
for span in [1, 2, 3, 4, 31, 32, 32, 52, 53, 54, 119, 127, 128, 129]:
|
|
cum = 0
|
|
for i in xrange(100):
|
|
cum |= getbits(span)
|
|
self.assertEqual(cum, 2**span-1)
|
|
|
|
# Verify argument checking
|
|
self.assertRaises(TypeError, self.gen.getrandbits)
|
|
self.assertRaises(TypeError, self.gen.getrandbits, 'a')
|
|
self.assertRaises(TypeError, self.gen.getrandbits, 1, 2)
|
|
self.assertRaises(ValueError, self.gen.getrandbits, 0)
|
|
self.assertRaises(ValueError, self.gen.getrandbits, -1)
|
|
|
|
def test_randbelow_logic(self, _log=log, int=int):
|
|
# check bitcount transition points: 2**i and 2**(i+1)-1
|
|
# show that: k = int(1.001 + _log(n, 2))
|
|
# is equal to or one greater than the number of bits in n
|
|
for i in xrange(1, 1000):
|
|
n = 1L << i # check an exact power of two
|
|
numbits = i+1
|
|
k = int(1.00001 + _log(n, 2))
|
|
self.assertEqual(k, numbits)
|
|
self.assertTrue(n == 2**(k-1))
|
|
|
|
n += n - 1 # check 1 below the next power of two
|
|
k = int(1.00001 + _log(n, 2))
|
|
self.assertIn(k, [numbits, numbits+1])
|
|
self.assertTrue(2**k > n > 2**(k-2))
|
|
|
|
n -= n >> 15 # check a little farther below the next power of two
|
|
k = int(1.00001 + _log(n, 2))
|
|
self.assertEqual(k, numbits) # note the stronger assertion
|
|
self.assertTrue(2**k > n > 2**(k-1)) # note the stronger assertion
|
|
|
|
def test_randrange_bug_1590891(self):
|
|
start = 1000000000000
|
|
stop = -100000000000000000000
|
|
step = -200
|
|
x = self.gen.randrange(start, stop, step)
|
|
self.assertTrue(stop < x <= start)
|
|
self.assertEqual((x+stop)%step, 0)
|
|
|
|
def gamma(z, sqrt2pi=(2.0*pi)**0.5):
|
|
# Reflection to right half of complex plane
|
|
if z < 0.5:
|
|
return pi / sin(pi*z) / gamma(1.0-z)
|
|
# Lanczos approximation with g=7
|
|
az = z + (7.0 - 0.5)
|
|
return az ** (z-0.5) / exp(az) * sqrt2pi * fsum([
|
|
0.9999999999995183,
|
|
676.5203681218835 / z,
|
|
-1259.139216722289 / (z+1.0),
|
|
771.3234287757674 / (z+2.0),
|
|
-176.6150291498386 / (z+3.0),
|
|
12.50734324009056 / (z+4.0),
|
|
-0.1385710331296526 / (z+5.0),
|
|
0.9934937113930748e-05 / (z+6.0),
|
|
0.1659470187408462e-06 / (z+7.0),
|
|
])
|
|
|
|
class TestDistributions(unittest.TestCase):
|
|
def test_zeroinputs(self):
|
|
# Verify that distributions can handle a series of zero inputs'
|
|
g = random.Random()
|
|
x = [g.random() for i in xrange(50)] + [0.0]*5
|
|
g.random = x[:].pop; g.uniform(1,10)
|
|
g.random = x[:].pop; g.paretovariate(1.0)
|
|
g.random = x[:].pop; g.expovariate(1.0)
|
|
g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
|
|
g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
|
|
g.random = x[:].pop; g.normalvariate(0.0, 1.0)
|
|
g.random = x[:].pop; g.gauss(0.0, 1.0)
|
|
g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
|
|
g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
|
|
g.random = x[:].pop; g.gammavariate(0.01, 1.0)
|
|
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
|
|
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
|
|
g.random = x[:].pop; g.betavariate(3.0, 3.0)
|
|
g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
|
|
|
|
def test_avg_std(self):
|
|
# Use integration to test distribution average and standard deviation.
|
|
# Only works for distributions which do not consume variates in pairs
|
|
g = random.Random()
|
|
N = 5000
|
|
x = [i/float(N) for i in xrange(1,N)]
|
|
for variate, args, mu, sigmasqrd in [
|
|
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
|
|
(g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
|
|
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
|
|
(g.vonmisesvariate, (1.23, 0), pi, pi**2/3),
|
|
(g.paretovariate, (5.0,), 5.0/(5.0-1),
|
|
5.0/((5.0-1)**2*(5.0-2))),
|
|
(g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0),
|
|
gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]:
|
|
g.random = x[:].pop
|
|
y = []
|
|
for i in xrange(len(x)):
|
|
try:
|
|
y.append(variate(*args))
|
|
except IndexError:
|
|
pass
|
|
s1 = s2 = 0
|
|
for e in y:
|
|
s1 += e
|
|
s2 += (e - mu) ** 2
|
|
N = len(y)
|
|
self.assertAlmostEqual(s1/N, mu, places=2,
|
|
msg='%s%r' % (variate.__name__, args))
|
|
self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2,
|
|
msg='%s%r' % (variate.__name__, args))
|
|
|
|
def test_constant(self):
|
|
g = random.Random()
|
|
N = 100
|
|
for variate, args, expected in [
|
|
(g.uniform, (10.0, 10.0), 10.0),
|
|
(g.triangular, (10.0, 10.0), 10.0),
|
|
#(g.triangular, (10.0, 10.0, 10.0), 10.0),
|
|
(g.expovariate, (float('inf'),), 0.0),
|
|
(g.vonmisesvariate, (3.0, float('inf')), 3.0),
|
|
(g.gauss, (10.0, 0.0), 10.0),
|
|
(g.lognormvariate, (0.0, 0.0), 1.0),
|
|
(g.lognormvariate, (-float('inf'), 0.0), 0.0),
|
|
(g.normalvariate, (10.0, 0.0), 10.0),
|
|
(g.paretovariate, (float('inf'),), 1.0),
|
|
(g.weibullvariate, (10.0, float('inf')), 10.0),
|
|
(g.weibullvariate, (0.0, 10.0), 0.0),
|
|
]:
|
|
for i in range(N):
|
|
self.assertEqual(variate(*args), expected)
|
|
|
|
def test_von_mises_range(self):
|
|
# Issue 17149: von mises variates were not consistently in the
|
|
# range [0, 2*PI].
|
|
g = random.Random()
|
|
N = 100
|
|
for mu in 0.0, 0.1, 3.1, 6.2:
|
|
for kappa in 0.0, 2.3, 500.0:
|
|
for _ in range(N):
|
|
sample = g.vonmisesvariate(mu, kappa)
|
|
self.assertTrue(
|
|
0 <= sample <= random.TWOPI,
|
|
msg=("vonmisesvariate({}, {}) produced a result {} out"
|
|
" of range [0, 2*pi]").format(mu, kappa, sample))
|
|
|
|
def test_von_mises_large_kappa(self):
|
|
# Issue #17141: vonmisesvariate() was hang for large kappas
|
|
random.vonmisesvariate(0, 1e15)
|
|
random.vonmisesvariate(0, 1e100)
|
|
|
|
|
|
class TestModule(unittest.TestCase):
|
|
def testMagicConstants(self):
|
|
self.assertAlmostEqual(random.NV_MAGICCONST, 1.71552776992141)
|
|
self.assertAlmostEqual(random.TWOPI, 6.28318530718)
|
|
self.assertAlmostEqual(random.LOG4, 1.38629436111989)
|
|
self.assertAlmostEqual(random.SG_MAGICCONST, 2.50407739677627)
|
|
|
|
def test__all__(self):
|
|
# tests validity but not completeness of the __all__ list
|
|
self.assertTrue(set(random.__all__) <= set(dir(random)))
|
|
|
|
def test_random_subclass_with_kwargs(self):
|
|
# SF bug #1486663 -- this used to erroneously raise a TypeError
|
|
class Subclass(random.Random):
|
|
def __init__(self, newarg=None):
|
|
random.Random.__init__(self)
|
|
Subclass(newarg=1)
|
|
|
|
|
|
def test_main(verbose=None):
|
|
testclasses = [WichmannHill_TestBasicOps,
|
|
MersenneTwister_TestBasicOps,
|
|
TestDistributions,
|
|
TestModule]
|
|
|
|
try:
|
|
random.SystemRandom().random()
|
|
except NotImplementedError:
|
|
pass
|
|
else:
|
|
testclasses.append(SystemRandom_TestBasicOps)
|
|
|
|
test_support.run_unittest(*testclasses)
|
|
|
|
# verify reference counting
|
|
import sys
|
|
if verbose and hasattr(sys, "gettotalrefcount"):
|
|
counts = [None] * 5
|
|
for i in xrange(len(counts)):
|
|
test_support.run_unittest(*testclasses)
|
|
counts[i] = sys.gettotalrefcount()
|
|
print counts
|
|
|
|
if __name__ == "__main__":
|
|
test_main(verbose=True)
|