1#!/usr/bin/env python
2
3import unittest
4import random
5import time
6import pickle
7import warnings
8from math import log, exp, pi, fsum, sin
9from functools import reduce
10from test import test_support
11
12class TestBasicOps(unittest.TestCase):
13    # Superclass with tests common to all generators.
14    # Subclasses must arrange for self.gen to retrieve the Random instance
15    # to be tested.
16
17    def randomlist(self, n):
18        """Helper function to make a list of random numbers"""
19        return [self.gen.random() for i in xrange(n)]
20
21    def test_autoseed(self):
22        self.gen.seed()
23        state1 = self.gen.getstate()
24        time.sleep(0.1)
25        self.gen.seed()      # diffent seeds at different times
26        state2 = self.gen.getstate()
27        self.assertNotEqual(state1, state2)
28
29    def test_saverestore(self):
30        N = 1000
31        self.gen.seed()
32        state = self.gen.getstate()
33        randseq = self.randomlist(N)
34        self.gen.setstate(state)    # should regenerate the same sequence
35        self.assertEqual(randseq, self.randomlist(N))
36
37    def test_seedargs(self):
38        for arg in [None, 0, 0L, 1, 1L, -1, -1L, 10**20, -(10**20),
39                    3.14, 1+2j, 'a', tuple('abc')]:
40            self.gen.seed(arg)
41        for arg in [range(3), dict(one=1)]:
42            self.assertRaises(TypeError, self.gen.seed, arg)
43        self.assertRaises(TypeError, self.gen.seed, 1, 2)
44        self.assertRaises(TypeError, type(self.gen), [])
45
46    def test_jumpahead(self):
47        self.gen.seed()
48        state1 = self.gen.getstate()
49        self.gen.jumpahead(100)
50        state2 = self.gen.getstate()    # s/b distinct from state1
51        self.assertNotEqual(state1, state2)
52        self.gen.jumpahead(100)
53        state3 = self.gen.getstate()    # s/b distinct from state2
54        self.assertNotEqual(state2, state3)
55
56        with test_support.check_py3k_warnings(quiet=True):
57            self.assertRaises(TypeError, self.gen.jumpahead)  # needs an arg
58            self.assertRaises(TypeError, self.gen.jumpahead, 2, 3)  # too many
59
60    def test_jumpahead_produces_valid_state(self):
61        # From http://bugs.python.org/issue14591.
62        self.gen.seed(199210368)
63        self.gen.jumpahead(13550674232554645900)
64        for i in range(500):
65            val = self.gen.random()
66            self.assertLess(val, 1.0)
67
68    def test_sample(self):
69        # For the entire allowable range of 0 <= k <= N, validate that
70        # the sample is of the correct length and contains only unique items
71        N = 100
72        population = xrange(N)
73        for k in xrange(N+1):
74            s = self.gen.sample(population, k)
75            self.assertEqual(len(s), k)
76            uniq = set(s)
77            self.assertEqual(len(uniq), k)
78            self.assertTrue(uniq <= set(population))
79        self.assertEqual(self.gen.sample([], 0), [])  # test edge case N==k==0
80
81    def test_sample_distribution(self):
82        # For the entire allowable range of 0 <= k <= N, validate that
83        # sample generates all possible permutations
84        n = 5
85        pop = range(n)
86        trials = 10000  # large num prevents false negatives without slowing normal case
87        def factorial(n):
88            return reduce(int.__mul__, xrange(1, n), 1)
89        for k in xrange(n):
90            expected = factorial(n) // factorial(n-k)
91            perms = {}
92            for i in xrange(trials):
93                perms[tuple(self.gen.sample(pop, k))] = None
94                if len(perms) == expected:
95                    break
96            else:
97                self.fail()
98
99    def test_sample_inputs(self):
100        # SF bug #801342 -- population can be any iterable defining __len__()
101        self.gen.sample(set(range(20)), 2)
102        self.gen.sample(range(20), 2)
103        self.gen.sample(xrange(20), 2)
104        self.gen.sample(str('abcdefghijklmnopqrst'), 2)
105        self.gen.sample(tuple('abcdefghijklmnopqrst'), 2)
106
107    def test_sample_on_dicts(self):
108        self.gen.sample(dict.fromkeys('abcdefghijklmnopqrst'), 2)
109
110        # SF bug #1460340 -- random.sample can raise KeyError
111        a = dict.fromkeys(range(10)+range(10,100,2)+range(100,110))
112        self.gen.sample(a, 3)
113
114        # A followup to bug #1460340:  sampling from a dict could return
115        # a subset of its keys or of its values, depending on the size of
116        # the subset requested.
117        N = 30
118        d = dict((i, complex(i, i)) for i in xrange(N))
119        for k in xrange(N+1):
120            samp = self.gen.sample(d, k)
121            # Verify that we got ints back (keys); the values are complex.
122            for x in samp:
123                self.assertTrue(type(x) is int)
124        samp.sort()
125        self.assertEqual(samp, range(N))
126
127    def test_gauss(self):
128        # Ensure that the seed() method initializes all the hidden state.  In
129        # particular, through 2.2.1 it failed to reset a piece of state used
130        # by (and only by) the .gauss() method.
131
132        for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
133            self.gen.seed(seed)
134            x1 = self.gen.random()
135            y1 = self.gen.gauss(0, 1)
136
137            self.gen.seed(seed)
138            x2 = self.gen.random()
139            y2 = self.gen.gauss(0, 1)
140
141            self.assertEqual(x1, x2)
142            self.assertEqual(y1, y2)
143
144    def test_pickling(self):
145        state = pickle.dumps(self.gen)
146        origseq = [self.gen.random() for i in xrange(10)]
147        newgen = pickle.loads(state)
148        restoredseq = [newgen.random() for i in xrange(10)]
149        self.assertEqual(origseq, restoredseq)
150
151    def test_bug_1727780(self):
152        # verify that version-2-pickles can be loaded
153        # fine, whether they are created on 32-bit or 64-bit
154        # platforms, and that version-3-pickles load fine.
155        files = [("randv2_32.pck", 780),
156                 ("randv2_64.pck", 866),
157                 ("randv3.pck", 343)]
158        for file, value in files:
159            f = open(test_support.findfile(file),"rb")
160            r = pickle.load(f)
161            f.close()
162            self.assertEqual(r.randrange(1000), value)
163
164class WichmannHill_TestBasicOps(TestBasicOps):
165    gen = random.WichmannHill()
166
167    def test_setstate_first_arg(self):
168        self.assertRaises(ValueError, self.gen.setstate, (2, None, None))
169
170    def test_strong_jumpahead(self):
171        # tests that jumpahead(n) semantics correspond to n calls to random()
172        N = 1000
173        s = self.gen.getstate()
174        self.gen.jumpahead(N)
175        r1 = self.gen.random()
176        # now do it the slow way
177        self.gen.setstate(s)
178        for i in xrange(N):
179            self.gen.random()
180        r2 = self.gen.random()
181        self.assertEqual(r1, r2)
182
183    def test_gauss_with_whseed(self):
184        # Ensure that the seed() method initializes all the hidden state.  In
185        # particular, through 2.2.1 it failed to reset a piece of state used
186        # by (and only by) the .gauss() method.
187
188        for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
189            self.gen.whseed(seed)
190            x1 = self.gen.random()
191            y1 = self.gen.gauss(0, 1)
192
193            self.gen.whseed(seed)
194            x2 = self.gen.random()
195            y2 = self.gen.gauss(0, 1)
196
197            self.assertEqual(x1, x2)
198            self.assertEqual(y1, y2)
199
200    def test_bigrand(self):
201        # Verify warnings are raised when randrange is too large for random()
202        with warnings.catch_warnings():
203            warnings.filterwarnings("error", "Underlying random")
204            self.assertRaises(UserWarning, self.gen.randrange, 2**60)
205
206class SystemRandom_TestBasicOps(TestBasicOps):
207    gen = random.SystemRandom()
208
209    def test_autoseed(self):
210        # Doesn't need to do anything except not fail
211        self.gen.seed()
212
213    def test_saverestore(self):
214        self.assertRaises(NotImplementedError, self.gen.getstate)
215        self.assertRaises(NotImplementedError, self.gen.setstate, None)
216
217    def test_seedargs(self):
218        # Doesn't need to do anything except not fail
219        self.gen.seed(100)
220
221    def test_jumpahead(self):
222        # Doesn't need to do anything except not fail
223        self.gen.jumpahead(100)
224
225    def test_gauss(self):
226        self.gen.gauss_next = None
227        self.gen.seed(100)
228        self.assertEqual(self.gen.gauss_next, None)
229
230    def test_pickling(self):
231        self.assertRaises(NotImplementedError, pickle.dumps, self.gen)
232
233    def test_53_bits_per_float(self):
234        # This should pass whenever a C double has 53 bit precision.
235        span = 2 ** 53
236        cum = 0
237        for i in xrange(100):
238            cum |= int(self.gen.random() * span)
239        self.assertEqual(cum, span-1)
240
241    def test_bigrand(self):
242        # The randrange routine should build-up the required number of bits
243        # in stages so that all bit positions are active.
244        span = 2 ** 500
245        cum = 0
246        for i in xrange(100):
247            r = self.gen.randrange(span)
248            self.assertTrue(0 <= r < span)
249            cum |= r
250        self.assertEqual(cum, span-1)
251
252    def test_bigrand_ranges(self):
253        for i in [40,80, 160, 200, 211, 250, 375, 512, 550]:
254            start = self.gen.randrange(2 ** i)
255            stop = self.gen.randrange(2 ** (i-2))
256            if stop <= start:
257                return
258            self.assertTrue(start <= self.gen.randrange(start, stop) < stop)
259
260    def test_rangelimits(self):
261        for start, stop in [(-2,0), (-(2**60)-2,-(2**60)), (2**60,2**60+2)]:
262            self.assertEqual(set(range(start,stop)),
263                set([self.gen.randrange(start,stop) for i in xrange(100)]))
264
265    def test_genrandbits(self):
266        # Verify ranges
267        for k in xrange(1, 1000):
268            self.assertTrue(0 <= self.gen.getrandbits(k) < 2**k)
269
270        # Verify all bits active
271        getbits = self.gen.getrandbits
272        for span in [1, 2, 3, 4, 31, 32, 32, 52, 53, 54, 119, 127, 128, 129]:
273            cum = 0
274            for i in xrange(100):
275                cum |= getbits(span)
276            self.assertEqual(cum, 2**span-1)
277
278        # Verify argument checking
279        self.assertRaises(TypeError, self.gen.getrandbits)
280        self.assertRaises(TypeError, self.gen.getrandbits, 1, 2)
281        self.assertRaises(ValueError, self.gen.getrandbits, 0)
282        self.assertRaises(ValueError, self.gen.getrandbits, -1)
283        self.assertRaises(TypeError, self.gen.getrandbits, 10.1)
284
285    def test_randbelow_logic(self, _log=log, int=int):
286        # check bitcount transition points:  2**i and 2**(i+1)-1
287        # show that: k = int(1.001 + _log(n, 2))
288        # is equal to or one greater than the number of bits in n
289        for i in xrange(1, 1000):
290            n = 1L << i # check an exact power of two
291            numbits = i+1
292            k = int(1.00001 + _log(n, 2))
293            self.assertEqual(k, numbits)
294            self.assertTrue(n == 2**(k-1))
295
296            n += n - 1      # check 1 below the next power of two
297            k = int(1.00001 + _log(n, 2))
298            self.assertIn(k, [numbits, numbits+1])
299            self.assertTrue(2**k > n > 2**(k-2))
300
301            n -= n >> 15     # check a little farther below the next power of two
302            k = int(1.00001 + _log(n, 2))
303            self.assertEqual(k, numbits)        # note the stronger assertion
304            self.assertTrue(2**k > n > 2**(k-1))   # note the stronger assertion
305
306
307class MersenneTwister_TestBasicOps(TestBasicOps):
308    gen = random.Random()
309
310    def test_setstate_first_arg(self):
311        self.assertRaises(ValueError, self.gen.setstate, (1, None, None))
312
313    def test_setstate_middle_arg(self):
314        # Wrong type, s/b tuple
315        self.assertRaises(TypeError, self.gen.setstate, (2, None, None))
316        # Wrong length, s/b 625
317        self.assertRaises(ValueError, self.gen.setstate, (2, (1,2,3), None))
318        # Wrong type, s/b tuple of 625 ints
319        self.assertRaises(TypeError, self.gen.setstate, (2, ('a',)*625, None))
320        # Last element s/b an int also
321        self.assertRaises(TypeError, self.gen.setstate, (2, (0,)*624+('a',), None))
322
323    def test_referenceImplementation(self):
324        # Compare the python implementation with results from the original
325        # code.  Create 2000 53-bit precision random floats.  Compare only
326        # the last ten entries to show that the independent implementations
327        # are tracking.  Here is the main() function needed to create the
328        # list of expected random numbers:
329        #    void main(void){
330        #         int i;
331        #         unsigned long init[4]={61731, 24903, 614, 42143}, length=4;
332        #         init_by_array(init, length);
333        #         for (i=0; i<2000; i++) {
334        #           printf("%.15f ", genrand_res53());
335        #           if (i%5==4) printf("\n");
336        #         }
337        #     }
338        expected = [0.45839803073713259,
339                    0.86057815201978782,
340                    0.92848331726782152,
341                    0.35932681119782461,
342                    0.081823493762449573,
343                    0.14332226470169329,
344                    0.084297823823520024,
345                    0.53814864671831453,
346                    0.089215024911993401,
347                    0.78486196105372907]
348
349        self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96))
350        actual = self.randomlist(2000)[-10:]
351        for a, e in zip(actual, expected):
352            self.assertAlmostEqual(a,e,places=14)
353
354    def test_strong_reference_implementation(self):
355        # Like test_referenceImplementation, but checks for exact bit-level
356        # equality.  This should pass on any box where C double contains
357        # at least 53 bits of precision (the underlying algorithm suffers
358        # no rounding errors -- all results are exact).
359        from math import ldexp
360
361        expected = [0x0eab3258d2231fL,
362                    0x1b89db315277a5L,
363                    0x1db622a5518016L,
364                    0x0b7f9af0d575bfL,
365                    0x029e4c4db82240L,
366                    0x04961892f5d673L,
367                    0x02b291598e4589L,
368                    0x11388382c15694L,
369                    0x02dad977c9e1feL,
370                    0x191d96d4d334c6L]
371        self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96))
372        actual = self.randomlist(2000)[-10:]
373        for a, e in zip(actual, expected):
374            self.assertEqual(long(ldexp(a, 53)), e)
375
376    def test_long_seed(self):
377        # This is most interesting to run in debug mode, just to make sure
378        # nothing blows up.  Under the covers, a dynamically resized array
379        # is allocated, consuming space proportional to the number of bits
380        # in the seed.  Unfortunately, that's a quadratic-time algorithm,
381        # so don't make this horribly big.
382        seed = (1L << (10000 * 8)) - 1  # about 10K bytes
383        self.gen.seed(seed)
384
385    def test_53_bits_per_float(self):
386        # This should pass whenever a C double has 53 bit precision.
387        span = 2 ** 53
388        cum = 0
389        for i in xrange(100):
390            cum |= int(self.gen.random() * span)
391        self.assertEqual(cum, span-1)
392
393    def test_bigrand(self):
394        # The randrange routine should build-up the required number of bits
395        # in stages so that all bit positions are active.
396        span = 2 ** 500
397        cum = 0
398        for i in xrange(100):
399            r = self.gen.randrange(span)
400            self.assertTrue(0 <= r < span)
401            cum |= r
402        self.assertEqual(cum, span-1)
403
404    def test_bigrand_ranges(self):
405        for i in [40,80, 160, 200, 211, 250, 375, 512, 550]:
406            start = self.gen.randrange(2 ** i)
407            stop = self.gen.randrange(2 ** (i-2))
408            if stop <= start:
409                return
410            self.assertTrue(start <= self.gen.randrange(start, stop) < stop)
411
412    def test_rangelimits(self):
413        for start, stop in [(-2,0), (-(2**60)-2,-(2**60)), (2**60,2**60+2)]:
414            self.assertEqual(set(range(start,stop)),
415                set([self.gen.randrange(start,stop) for i in xrange(100)]))
416
417    def test_genrandbits(self):
418        # Verify cross-platform repeatability
419        self.gen.seed(1234567)
420        self.assertEqual(self.gen.getrandbits(100),
421                         97904845777343510404718956115L)
422        # Verify ranges
423        for k in xrange(1, 1000):
424            self.assertTrue(0 <= self.gen.getrandbits(k) < 2**k)
425
426        # Verify all bits active
427        getbits = self.gen.getrandbits
428        for span in [1, 2, 3, 4, 31, 32, 32, 52, 53, 54, 119, 127, 128, 129]:
429            cum = 0
430            for i in xrange(100):
431                cum |= getbits(span)
432            self.assertEqual(cum, 2**span-1)
433
434        # Verify argument checking
435        self.assertRaises(TypeError, self.gen.getrandbits)
436        self.assertRaises(TypeError, self.gen.getrandbits, 'a')
437        self.assertRaises(TypeError, self.gen.getrandbits, 1, 2)
438        self.assertRaises(ValueError, self.gen.getrandbits, 0)
439        self.assertRaises(ValueError, self.gen.getrandbits, -1)
440
441    def test_randbelow_logic(self, _log=log, int=int):
442        # check bitcount transition points:  2**i and 2**(i+1)-1
443        # show that: k = int(1.001 + _log(n, 2))
444        # is equal to or one greater than the number of bits in n
445        for i in xrange(1, 1000):
446            n = 1L << i # check an exact power of two
447            numbits = i+1
448            k = int(1.00001 + _log(n, 2))
449            self.assertEqual(k, numbits)
450            self.assertTrue(n == 2**(k-1))
451
452            n += n - 1      # check 1 below the next power of two
453            k = int(1.00001 + _log(n, 2))
454            self.assertIn(k, [numbits, numbits+1])
455            self.assertTrue(2**k > n > 2**(k-2))
456
457            n -= n >> 15     # check a little farther below the next power of two
458            k = int(1.00001 + _log(n, 2))
459            self.assertEqual(k, numbits)        # note the stronger assertion
460            self.assertTrue(2**k > n > 2**(k-1))   # note the stronger assertion
461
462    def test_randrange_bug_1590891(self):
463        start = 1000000000000
464        stop = -100000000000000000000
465        step = -200
466        x = self.gen.randrange(start, stop, step)
467        self.assertTrue(stop < x <= start)
468        self.assertEqual((x+stop)%step, 0)
469
470def gamma(z, sqrt2pi=(2.0*pi)**0.5):
471    # Reflection to right half of complex plane
472    if z < 0.5:
473        return pi / sin(pi*z) / gamma(1.0-z)
474    # Lanczos approximation with g=7
475    az = z + (7.0 - 0.5)
476    return az ** (z-0.5) / exp(az) * sqrt2pi * fsum([
477        0.9999999999995183,
478        676.5203681218835 / z,
479        -1259.139216722289 / (z+1.0),
480        771.3234287757674 / (z+2.0),
481        -176.6150291498386 / (z+3.0),
482        12.50734324009056 / (z+4.0),
483        -0.1385710331296526 / (z+5.0),
484        0.9934937113930748e-05 / (z+6.0),
485        0.1659470187408462e-06 / (z+7.0),
486    ])
487
488class TestDistributions(unittest.TestCase):
489    def test_zeroinputs(self):
490        # Verify that distributions can handle a series of zero inputs'
491        g = random.Random()
492        x = [g.random() for i in xrange(50)] + [0.0]*5
493        g.random = x[:].pop; g.uniform(1,10)
494        g.random = x[:].pop; g.paretovariate(1.0)
495        g.random = x[:].pop; g.expovariate(1.0)
496        g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
497        g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
498        g.random = x[:].pop; g.normalvariate(0.0, 1.0)
499        g.random = x[:].pop; g.gauss(0.0, 1.0)
500        g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
501        g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
502        g.random = x[:].pop; g.gammavariate(0.01, 1.0)
503        g.random = x[:].pop; g.gammavariate(1.0, 1.0)
504        g.random = x[:].pop; g.gammavariate(200.0, 1.0)
505        g.random = x[:].pop; g.betavariate(3.0, 3.0)
506        g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
507
508    def test_avg_std(self):
509        # Use integration to test distribution average and standard deviation.
510        # Only works for distributions which do not consume variates in pairs
511        g = random.Random()
512        N = 5000
513        x = [i/float(N) for i in xrange(1,N)]
514        for variate, args, mu, sigmasqrd in [
515                (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
516                (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
517                (g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
518                (g.vonmisesvariate, (1.23, 0), pi, pi**2/3),
519                (g.paretovariate, (5.0,), 5.0/(5.0-1),
520                                  5.0/((5.0-1)**2*(5.0-2))),
521                (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0),
522                                  gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]:
523            g.random = x[:].pop
524            y = []
525            for i in xrange(len(x)):
526                try:
527                    y.append(variate(*args))
528                except IndexError:
529                    pass
530            s1 = s2 = 0
531            for e in y:
532                s1 += e
533                s2 += (e - mu) ** 2
534            N = len(y)
535            self.assertAlmostEqual(s1/N, mu, places=2,
536                                   msg='%s%r' % (variate.__name__, args))
537            self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2,
538                                   msg='%s%r' % (variate.__name__, args))
539
540    def test_constant(self):
541        g = random.Random()
542        N = 100
543        for variate, args, expected in [
544                (g.uniform, (10.0, 10.0), 10.0),
545                (g.triangular, (10.0, 10.0), 10.0),
546                #(g.triangular, (10.0, 10.0, 10.0), 10.0),
547                (g.expovariate, (float('inf'),), 0.0),
548                (g.vonmisesvariate, (3.0, float('inf')), 3.0),
549                (g.gauss, (10.0, 0.0), 10.0),
550                (g.lognormvariate, (0.0, 0.0), 1.0),
551                (g.lognormvariate, (-float('inf'), 0.0), 0.0),
552                (g.normalvariate, (10.0, 0.0), 10.0),
553                (g.paretovariate, (float('inf'),), 1.0),
554                (g.weibullvariate, (10.0, float('inf')), 10.0),
555                (g.weibullvariate, (0.0, 10.0), 0.0),
556            ]:
557            for i in range(N):
558                self.assertEqual(variate(*args), expected)
559
560    def test_von_mises_range(self):
561        # Issue 17149: von mises variates were not consistently in the
562        # range [0, 2*PI].
563        g = random.Random()
564        N = 100
565        for mu in 0.0, 0.1, 3.1, 6.2:
566            for kappa in 0.0, 2.3, 500.0:
567                for _ in range(N):
568                    sample = g.vonmisesvariate(mu, kappa)
569                    self.assertTrue(
570                        0 <= sample <= random.TWOPI,
571                        msg=("vonmisesvariate({}, {}) produced a result {} out"
572                             " of range [0, 2*pi]").format(mu, kappa, sample))
573
574    def test_von_mises_large_kappa(self):
575        # Issue #17141: vonmisesvariate() was hang for large kappas
576        random.vonmisesvariate(0, 1e15)
577        random.vonmisesvariate(0, 1e100)
578
579
580class TestModule(unittest.TestCase):
581    def testMagicConstants(self):
582        self.assertAlmostEqual(random.NV_MAGICCONST, 1.71552776992141)
583        self.assertAlmostEqual(random.TWOPI, 6.28318530718)
584        self.assertAlmostEqual(random.LOG4, 1.38629436111989)
585        self.assertAlmostEqual(random.SG_MAGICCONST, 2.50407739677627)
586
587    def test__all__(self):
588        # tests validity but not completeness of the __all__ list
589        self.assertTrue(set(random.__all__) <= set(dir(random)))
590
591    def test_random_subclass_with_kwargs(self):
592        # SF bug #1486663 -- this used to erroneously raise a TypeError
593        class Subclass(random.Random):
594            def __init__(self, newarg=None):
595                random.Random.__init__(self)
596        Subclass(newarg=1)
597
598
599def test_main(verbose=None):
600    testclasses =    [WichmannHill_TestBasicOps,
601                      MersenneTwister_TestBasicOps,
602                      TestDistributions,
603                      TestModule]
604
605    try:
606        random.SystemRandom().random()
607    except NotImplementedError:
608        pass
609    else:
610        testclasses.append(SystemRandom_TestBasicOps)
611
612    test_support.run_unittest(*testclasses)
613
614    # verify reference counting
615    import sys
616    if verbose and hasattr(sys, "gettotalrefcount"):
617        counts = [None] * 5
618        for i in xrange(len(counts)):
619            test_support.run_unittest(*testclasses)
620            counts[i] = sys.gettotalrefcount()
621        print counts
622
623if __name__ == "__main__":
624    test_main(verbose=True)
625