MCL/sf/adapt/qemu: comparison symbian-qemu-0.9.1-12/python-2.6.1/Lib/test/test

equal deleted inserted replaced

-:ffa851df0825
+:2fb8b9db1c86
+#!/usr/bin/env python
+import unittest
+import random
+import time
+import pickle
+import warnings
+from math import log, exp, sqrt, pi, fsum as msum
+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)
+self.assertRaises(TypeError, self.gen.jumpahead)  # needs an arg
+self.assertRaises(TypeError, self.gen.jumpahead, "ick")  # wrong type
+self.assertRaises(TypeError, self.gen.jumpahead, 2.3)  # wrong type
+self.assertRaises(TypeError, self.gen.jumpahead, 2, 3)  # too many
+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.failUnless(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.assert_(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.assert_(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.assert_(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.assert_(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.assert_(n == 2**(k-1))
+n += n - 1      # check 1 below the next power of two
+k = int(1.00001 + _log(n, 2))
+self.assert_(k in [numbits, numbits+1])
+self.assert_(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.assert_(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.assert_(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.assert_(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.assert_(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.assert_(n == 2**(k-1))
+n += n - 1      # check 1 below the next power of two
+k = int(1.00001 + _log(n, 2))
+self.assert_(k in [numbits, numbits+1])
+self.assert_(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.assert_(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.assert_(stop < x <= start)
+self.assertEqual((x+stop)%step, 0)
+_gammacoeff = (0.9999999999995183, 676.5203681218835, -1259.139216722289,
+771.3234287757674,  -176.6150291498386, 12.50734324009056,
+-0.1385710331296526, 0.9934937113930748e-05, 0.1659470187408462e-06)
+def gamma(z, cof=_gammacoeff, g=7):
+z -= 1.0
+s = msum([cof[0]] + [cof[i] / (z+i) for i in range(1,len(cof))])
+z += 0.5
+return (z+g)**z / exp(z+g) * sqrt(2.0*pi) * s
+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.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.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, 2)
+self.assertAlmostEqual(s2/(N-1), sigmasqrd, 2)
+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.failUnless(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)