| import unittest |
| import unittest.mock |
| import random |
| import time |
| import pickle |
| import warnings |
| from functools import partial |
| from math import log, exp, pi, fsum, sin |
| from test import support |
| from fractions import Fraction |
| |
| class TestBasicOps: |
| # 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 range(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): |
| # Seed value with a negative hash. |
| class MySeed(object): |
| def __hash__(self): |
| return -1729 |
| for arg in [None, 0, 0, 1, 1, -1, -1, 10**20, -(10**20), |
| 3.14, 1+2j, 'a', tuple('abc'), MySeed()]: |
| self.gen.seed(arg) |
| for arg in [list(range(3)), dict(one=1)]: |
| self.assertRaises(TypeError, self.gen.seed, arg) |
| self.assertRaises(TypeError, self.gen.seed, 1, 2, 3, 4) |
| self.assertRaises(TypeError, type(self.gen), []) |
| |
| @unittest.mock.patch('random._urandom') # os.urandom |
| def test_seed_when_randomness_source_not_found(self, urandom_mock): |
| # Random.seed() uses time.time() when an operating system specific |
| # randomness source is not found. To test this on machines were it |
| # exists, run the above test, test_seedargs(), again after mocking |
| # os.urandom() so that it raises the exception expected when the |
| # randomness source is not available. |
| urandom_mock.side_effect = NotImplementedError |
| self.test_seedargs() |
| |
| def test_shuffle(self): |
| shuffle = self.gen.shuffle |
| lst = [] |
| shuffle(lst) |
| self.assertEqual(lst, []) |
| lst = [37] |
| shuffle(lst) |
| self.assertEqual(lst, [37]) |
| seqs = [list(range(n)) for n in range(10)] |
| shuffled_seqs = [list(range(n)) for n in range(10)] |
| for shuffled_seq in shuffled_seqs: |
| shuffle(shuffled_seq) |
| for (seq, shuffled_seq) in zip(seqs, shuffled_seqs): |
| self.assertEqual(len(seq), len(shuffled_seq)) |
| self.assertEqual(set(seq), set(shuffled_seq)) |
| # The above tests all would pass if the shuffle was a |
| # no-op. The following non-deterministic test covers that. It |
| # asserts that the shuffled sequence of 1000 distinct elements |
| # must be different from the original one. Although there is |
| # mathematically a non-zero probability that this could |
| # actually happen in a genuinely random shuffle, it is |
| # completely negligible, given that the number of possible |
| # permutations of 1000 objects is 1000! (factorial of 1000), |
| # which is considerably larger than the number of atoms in the |
| # universe... |
| lst = list(range(1000)) |
| shuffled_lst = list(range(1000)) |
| shuffle(shuffled_lst) |
| self.assertTrue(lst != shuffled_lst) |
| shuffle(lst) |
| self.assertTrue(lst != shuffled_lst) |
| |
| def test_choice(self): |
| choice = self.gen.choice |
| with self.assertRaises(IndexError): |
| choice([]) |
| self.assertEqual(choice([50]), 50) |
| self.assertIn(choice([25, 75]), [25, 75]) |
| |
| 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 = range(N) |
| for k in range(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 |
| # Exception raised if size of sample exceeds that of population |
| self.assertRaises(ValueError, self.gen.sample, population, N+1) |
| self.assertRaises(ValueError, self.gen.sample, [], -1) |
| |
| 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): |
| if n == 0: |
| return 1 |
| return n * factorial(n - 1) |
| for k in range(n): |
| expected = factorial(n) // factorial(n-k) |
| perms = {} |
| for i in range(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(range(20), 2) |
| self.gen.sample(str('abcdefghijklmnopqrst'), 2) |
| self.gen.sample(tuple('abcdefghijklmnopqrst'), 2) |
| |
| def test_sample_on_dicts(self): |
| self.assertRaises(TypeError, self.gen.sample, dict.fromkeys('abcdef'), 2) |
| |
| def test_choices(self): |
| choices = self.gen.choices |
| data = ['red', 'green', 'blue', 'yellow'] |
| str_data = 'abcd' |
| range_data = range(4) |
| set_data = set(range(4)) |
| |
| # basic functionality |
| for sample in [ |
| choices(data, k=5), |
| choices(data, range(4), k=5), |
| choices(k=5, population=data, weights=range(4)), |
| choices(k=5, population=data, cum_weights=range(4)), |
| ]: |
| self.assertEqual(len(sample), 5) |
| self.assertEqual(type(sample), list) |
| self.assertTrue(set(sample) <= set(data)) |
| |
| # test argument handling |
| with self.assertRaises(TypeError): # missing arguments |
| choices(2) |
| |
| self.assertEqual(choices(data, k=0), []) # k == 0 |
| self.assertEqual(choices(data, k=-1), []) # negative k behaves like ``[0] * -1`` |
| with self.assertRaises(TypeError): |
| choices(data, k=2.5) # k is a float |
| |
| self.assertTrue(set(choices(str_data, k=5)) <= set(str_data)) # population is a string sequence |
| self.assertTrue(set(choices(range_data, k=5)) <= set(range_data)) # population is a range |
| with self.assertRaises(TypeError): |
| choices(set_data, k=2) # population is not a sequence |
| |
| self.assertTrue(set(choices(data, None, k=5)) <= set(data)) # weights is None |
| self.assertTrue(set(choices(data, weights=None, k=5)) <= set(data)) |
| with self.assertRaises(ValueError): |
| choices(data, [1,2], k=5) # len(weights) != len(population) |
| with self.assertRaises(TypeError): |
| choices(data, 10, k=5) # non-iterable weights |
| with self.assertRaises(TypeError): |
| choices(data, [None]*4, k=5) # non-numeric weights |
| for weights in [ |
| [15, 10, 25, 30], # integer weights |
| [15.1, 10.2, 25.2, 30.3], # float weights |
| [Fraction(1, 3), Fraction(2, 6), Fraction(3, 6), Fraction(4, 6)], # fractional weights |
| [True, False, True, False] # booleans (include / exclude) |
| ]: |
| self.assertTrue(set(choices(data, weights, k=5)) <= set(data)) |
| |
| with self.assertRaises(ValueError): |
| choices(data, cum_weights=[1,2], k=5) # len(weights) != len(population) |
| with self.assertRaises(TypeError): |
| choices(data, cum_weights=10, k=5) # non-iterable cum_weights |
| with self.assertRaises(TypeError): |
| choices(data, cum_weights=[None]*4, k=5) # non-numeric cum_weights |
| with self.assertRaises(TypeError): |
| choices(data, range(4), cum_weights=range(4), k=5) # both weights and cum_weights |
| for weights in [ |
| [15, 10, 25, 30], # integer cum_weights |
| [15.1, 10.2, 25.2, 30.3], # float cum_weights |
| [Fraction(1, 3), Fraction(2, 6), Fraction(3, 6), Fraction(4, 6)], # fractional cum_weights |
| ]: |
| self.assertTrue(set(choices(data, cum_weights=weights, k=5)) <= set(data)) |
| |
| # Test weight focused on a single element of the population |
| self.assertEqual(choices('abcd', [1, 0, 0, 0]), ['a']) |
| self.assertEqual(choices('abcd', [0, 1, 0, 0]), ['b']) |
| self.assertEqual(choices('abcd', [0, 0, 1, 0]), ['c']) |
| self.assertEqual(choices('abcd', [0, 0, 0, 1]), ['d']) |
| |
| # Test consistency with random.choice() for empty population |
| with self.assertRaises(IndexError): |
| choices([], k=1) |
| with self.assertRaises(IndexError): |
| choices([], weights=[], k=1) |
| with self.assertRaises(IndexError): |
| choices([], cum_weights=[], k=5) |
| |
| 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): |
| for proto in range(pickle.HIGHEST_PROTOCOL + 1): |
| state = pickle.dumps(self.gen, proto) |
| origseq = [self.gen.random() for i in range(10)] |
| newgen = pickle.loads(state) |
| restoredseq = [newgen.random() for i in range(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(support.findfile(file),"rb") |
| r = pickle.load(f) |
| f.close() |
| self.assertEqual(int(r.random()*1000), value) |
| |
| def test_bug_9025(self): |
| # Had problem with an uneven distribution in int(n*random()) |
| # Verify the fix by checking that distributions fall within expectations. |
| n = 100000 |
| randrange = self.gen.randrange |
| k = sum(randrange(6755399441055744) % 3 == 2 for i in range(n)) |
| self.assertTrue(0.30 < k/n < .37, (k/n)) |
| |
| try: |
| random.SystemRandom().random() |
| except NotImplementedError: |
| SystemRandom_available = False |
| else: |
| SystemRandom_available = True |
| |
| @unittest.skipUnless(SystemRandom_available, "random.SystemRandom not available") |
| class SystemRandom_TestBasicOps(TestBasicOps, unittest.TestCase): |
| 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_gauss(self): |
| self.gen.gauss_next = None |
| self.gen.seed(100) |
| self.assertEqual(self.gen.gauss_next, None) |
| |
| def test_pickling(self): |
| for proto in range(pickle.HIGHEST_PROTOCOL + 1): |
| self.assertRaises(NotImplementedError, pickle.dumps, self.gen, proto) |
| |
| 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 range(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 range(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-2)) |
| stop = self.gen.randrange(2 ** i) |
| if stop <= start: |
| continue |
| 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 range(100)])) |
| |
| def test_randrange_nonunit_step(self): |
| rint = self.gen.randrange(0, 10, 2) |
| self.assertIn(rint, (0, 2, 4, 6, 8)) |
| rint = self.gen.randrange(0, 2, 2) |
| self.assertEqual(rint, 0) |
| |
| def test_randrange_errors(self): |
| raises = partial(self.assertRaises, ValueError, self.gen.randrange) |
| # Empty range |
| raises(3, 3) |
| raises(-721) |
| raises(0, 100, -12) |
| # Non-integer start/stop |
| raises(3.14159) |
| raises(0, 2.71828) |
| # Zero and non-integer step |
| raises(0, 42, 0) |
| raises(0, 42, 3.14159) |
| |
| def test_genrandbits(self): |
| # Verify ranges |
| for k in range(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 range(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 range(1, 1000): |
| n = 1 << i # check an exact power of two |
| numbits = i+1 |
| k = int(1.00001 + _log(n, 2)) |
| self.assertEqual(k, numbits) |
| self.assertEqual(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, unittest.TestCase): |
| gen = random.Random() |
| |
| def test_guaranteed_stable(self): |
| # These sequences are guaranteed to stay the same across versions of python |
| self.gen.seed(3456147, version=1) |
| self.assertEqual([self.gen.random().hex() for i in range(4)], |
| ['0x1.ac362300d90d2p-1', '0x1.9d16f74365005p-1', |
| '0x1.1ebb4352e4c4dp-1', '0x1.1a7422abf9c11p-1']) |
| self.gen.seed("the quick brown fox", version=2) |
| self.assertEqual([self.gen.random().hex() for i in range(4)], |
| ['0x1.1239ddfb11b7cp-3', '0x1.b3cbb5c51b120p-4', |
| '0x1.8c4f55116b60fp-1', '0x1.63eb525174a27p-1']) |
| |
| def test_bug_27706(self): |
| # Verify that version 1 seeds are unaffected by hash randomization |
| |
| self.gen.seed('nofar', version=1) # hash('nofar') == 5990528763808513177 |
| self.assertEqual([self.gen.random().hex() for i in range(4)], |
| ['0x1.8645314505ad7p-1', '0x1.afb1f82e40a40p-5', |
| '0x1.2a59d2285e971p-1', '0x1.56977142a7880p-6']) |
| |
| self.gen.seed('rachel', version=1) # hash('rachel') == -9091735575445484789 |
| self.assertEqual([self.gen.random().hex() for i in range(4)], |
| ['0x1.0b294cc856fcdp-1', '0x1.2ad22d79e77b8p-3', |
| '0x1.3052b9c072678p-2', '0x1.578f332106574p-3']) |
| |
| self.gen.seed('', version=1) # hash('') == 0 |
| self.assertEqual([self.gen.random().hex() for i in range(4)], |
| ['0x1.b0580f98a7dbep-1', '0x1.84129978f9c1ap-1', |
| '0x1.aeaa51052e978p-2', '0x1.092178fb945a6p-2']) |
| |
| 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)) |
| # Last element s/b between 0 and 624 |
| with self.assertRaises((ValueError, OverflowError)): |
| self.gen.setstate((2, (1,)*624+(625,), None)) |
| with self.assertRaises((ValueError, OverflowError)): |
| self.gen.setstate((2, (1,)*624+(-1,), None)) |
| |
| # Little trick to make "tuple(x % (2**32) for x in internalstate)" |
| # raise ValueError. I cannot think of a simple way to achieve this, so |
| # I am opting for using a generator as the middle argument of setstate |
| # which attempts to cast a NaN to integer. |
| state_values = self.gen.getstate()[1] |
| state_values = list(state_values) |
| state_values[-1] = float('nan') |
| state = (int(x) for x in state_values) |
| self.assertRaises(TypeError, self.gen.setstate, (2, state, 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(61731 + (24903<<32) + (614<<64) + (42143<<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 = [0x0eab3258d2231f, |
| 0x1b89db315277a5, |
| 0x1db622a5518016, |
| 0x0b7f9af0d575bf, |
| 0x029e4c4db82240, |
| 0x04961892f5d673, |
| 0x02b291598e4589, |
| 0x11388382c15694, |
| 0x02dad977c9e1fe, |
| 0x191d96d4d334c6] |
| self.gen.seed(61731 + (24903<<32) + (614<<64) + (42143<<96)) |
| actual = self.randomlist(2000)[-10:] |
| for a, e in zip(actual, expected): |
| self.assertEqual(int(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 = (1 << (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 range(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 range(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-2)) |
| stop = self.gen.randrange(2 ** i) |
| if stop <= start: |
| continue |
| 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 range(100)])) |
| |
| def test_genrandbits(self): |
| # Verify cross-platform repeatability |
| self.gen.seed(1234567) |
| self.assertEqual(self.gen.getrandbits(100), |
| 97904845777343510404718956115) |
| # Verify ranges |
| for k in range(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 range(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 range(1, 1000): |
| n = 1 << i # check an exact power of two |
| numbits = i+1 |
| k = int(1.00001 + _log(n, 2)) |
| self.assertEqual(k, numbits) |
| self.assertEqual(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 |
| |
| @unittest.mock.patch('random.Random.random') |
| def test_randbelow_overridden_random(self, random_mock): |
| # Random._randbelow() can only use random() when the built-in one |
| # has been overridden but no new getrandbits() method was supplied. |
| random_mock.side_effect = random.SystemRandom().random |
| maxsize = 1<<random.BPF |
| with warnings.catch_warnings(): |
| warnings.simplefilter("ignore", UserWarning) |
| # Population range too large (n >= maxsize) |
| self.gen._randbelow(maxsize+1, maxsize = maxsize) |
| self.gen._randbelow(5640, maxsize = maxsize) |
| |
| # This might be going too far to test a single line, but because of our |
| # noble aim of achieving 100% test coverage we need to write a case in |
| # which the following line in Random._randbelow() gets executed: |
| # |
| # rem = maxsize % n |
| # limit = (maxsize - rem) / maxsize |
| # r = random() |
| # while r >= limit: |
| # r = random() # <== *This line* <==< |
| # |
| # Therefore, to guarantee that the while loop is executed at least |
| # once, we need to mock random() so that it returns a number greater |
| # than 'limit' the first time it gets called. |
| |
| n = 42 |
| epsilon = 0.01 |
| limit = (maxsize - (maxsize % n)) / maxsize |
| random_mock.side_effect = [limit + epsilon, limit - epsilon] |
| self.gen._randbelow(n, maxsize = maxsize) |
| |
| 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 test_choices_algorithms(self): |
| # The various ways of specifying weights should produce the same results |
| choices = self.gen.choices |
| n = 104729 |
| |
| self.gen.seed(8675309) |
| a = self.gen.choices(range(n), k=10000) |
| |
| self.gen.seed(8675309) |
| b = self.gen.choices(range(n), [1]*n, k=10000) |
| self.assertEqual(a, b) |
| |
| self.gen.seed(8675309) |
| c = self.gen.choices(range(n), cum_weights=range(1, n+1), k=10000) |
| self.assertEqual(a, c) |
| |
| # Amerian Roulette |
| population = ['Red', 'Black', 'Green'] |
| weights = [18, 18, 2] |
| cum_weights = [18, 36, 38] |
| expanded_population = ['Red'] * 18 + ['Black'] * 18 + ['Green'] * 2 |
| |
| self.gen.seed(9035768) |
| a = self.gen.choices(expanded_population, k=10000) |
| |
| self.gen.seed(9035768) |
| b = self.gen.choices(population, weights, k=10000) |
| self.assertEqual(a, b) |
| |
| self.gen.seed(9035768) |
| c = self.gen.choices(population, cum_weights=cum_weights, k=10000) |
| self.assertEqual(a, c) |
| |
| 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 range(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 range(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 range(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) |
| |
| def test_gammavariate_errors(self): |
| # Both alpha and beta must be > 0.0 |
| self.assertRaises(ValueError, random.gammavariate, -1, 3) |
| self.assertRaises(ValueError, random.gammavariate, 0, 2) |
| self.assertRaises(ValueError, random.gammavariate, 2, 0) |
| self.assertRaises(ValueError, random.gammavariate, 1, -3) |
| |
| @unittest.mock.patch('random.Random.random') |
| def test_gammavariate_full_code_coverage(self, random_mock): |
| # There are three different possibilities in the current implementation |
| # of random.gammavariate(), depending on the value of 'alpha'. What we |
| # are going to do here is to fix the values returned by random() to |
| # generate test cases that provide 100% line coverage of the method. |
| |
| # #1: alpha > 1.0: we want the first random number to be outside the |
| # [1e-7, .9999999] range, so that the continue statement executes |
| # once. The values of u1 and u2 will be 0.5 and 0.3, respectively. |
| random_mock.side_effect = [1e-8, 0.5, 0.3] |
| returned_value = random.gammavariate(1.1, 2.3) |
| self.assertAlmostEqual(returned_value, 2.53) |
| |
| # #2: alpha == 1: first random number less than 1e-7 to that the body |
| # of the while loop executes once. Then random.random() returns 0.45, |
| # which causes while to stop looping and the algorithm to terminate. |
| random_mock.side_effect = [1e-8, 0.45] |
| returned_value = random.gammavariate(1.0, 3.14) |
| self.assertAlmostEqual(returned_value, 2.507314166123803) |
| |
| # #3: 0 < alpha < 1. This is the most complex region of code to cover, |
| # as there are multiple if-else statements. Let's take a look at the |
| # source code, and determine the values that we need accordingly: |
| # |
| # while 1: |
| # u = random() |
| # b = (_e + alpha)/_e |
| # p = b*u |
| # if p <= 1.0: # <=== (A) |
| # x = p ** (1.0/alpha) |
| # else: # <=== (B) |
| # x = -_log((b-p)/alpha) |
| # u1 = random() |
| # if p > 1.0: # <=== (C) |
| # if u1 <= x ** (alpha - 1.0): # <=== (D) |
| # break |
| # elif u1 <= _exp(-x): # <=== (E) |
| # break |
| # return x * beta |
| # |
| # First, we want (A) to be True. For that we need that: |
| # b*random() <= 1.0 |
| # r1 = random() <= 1.0 / b |
| # |
| # We now get to the second if-else branch, and here, since p <= 1.0, |
| # (C) is False and we take the elif branch, (E). For it to be True, |
| # so that the break is executed, we need that: |
| # r2 = random() <= _exp(-x) |
| # r2 <= _exp(-(p ** (1.0/alpha))) |
| # r2 <= _exp(-((b*r1) ** (1.0/alpha))) |
| |
| _e = random._e |
| _exp = random._exp |
| _log = random._log |
| alpha = 0.35 |
| beta = 1.45 |
| b = (_e + alpha)/_e |
| epsilon = 0.01 |
| |
| r1 = 0.8859296441566 # 1.0 / b |
| r2 = 0.3678794411714 # _exp(-((b*r1) ** (1.0/alpha))) |
| |
| # These four "random" values result in the following trace: |
| # (A) True, (E) False --> [next iteration of while] |
| # (A) True, (E) True --> [while loop breaks] |
| random_mock.side_effect = [r1, r2 + epsilon, r1, r2] |
| returned_value = random.gammavariate(alpha, beta) |
| self.assertAlmostEqual(returned_value, 1.4499999999997544) |
| |
| # Let's now make (A) be False. If this is the case, when we get to the |
| # second if-else 'p' is greater than 1, so (C) evaluates to True. We |
| # now encounter a second if statement, (D), which in order to execute |
| # must satisfy the following condition: |
| # r2 <= x ** (alpha - 1.0) |
| # r2 <= (-_log((b-p)/alpha)) ** (alpha - 1.0) |
| # r2 <= (-_log((b-(b*r1))/alpha)) ** (alpha - 1.0) |
| r1 = 0.8959296441566 # (1.0 / b) + epsilon -- so that (A) is False |
| r2 = 0.9445400408898141 |
| |
| # And these four values result in the following trace: |
| # (B) and (C) True, (D) False --> [next iteration of while] |
| # (B) and (C) True, (D) True [while loop breaks] |
| random_mock.side_effect = [r1, r2 + epsilon, r1, r2] |
| returned_value = random.gammavariate(alpha, beta) |
| self.assertAlmostEqual(returned_value, 1.5830349561760781) |
| |
| @unittest.mock.patch('random.Random.gammavariate') |
| def test_betavariate_return_zero(self, gammavariate_mock): |
| # betavariate() returns zero when the Gamma distribution |
| # that it uses internally returns this same value. |
| gammavariate_mock.return_value = 0.0 |
| self.assertEqual(0.0, random.betavariate(2.71828, 3.14159)) |
| |
| 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) |
| |
| |
| if __name__ == "__main__": |
| unittest.main() |