summaryrefslogtreecommitdiffstats
path: root/Lib/test/test_random.py
blob: 3796c3b9bf491481c69ebb0588430b67cb5f0d22 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
#!/usr/bin/env python

import unittest
import random
import time
import pickle
import warnings
from math import log, exp, sqrt, pi
from sets import Set
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)

    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__()
        from sets import Set
        self.gen.sample(Set(range(20)), 2)
        self.gen.sample(range(20), 2)
        self.gen.sample(xrange(20), 2)
        self.gen.sample(dict.fromkeys('abcdefghijklmnopqrst'), 2)
        self.gen.sample(str('abcdefghijklmnopqrst'), 2)
        self.gen.sample(tuple('abcdefghijklmnopqrst'), 2)

    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)

class WichmannHill_TestBasicOps(TestBasicOps):
    gen = random.WichmannHill()

    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()
        oldfilters = warnings.filters[:]
        warnings.filterwarnings("error", "Underlying random")
        self.assertRaises(UserWarning, self.gen.randrange, 2**60)
        warnings.filters[:] = oldfilters

class MersenneTwister_TestBasicOps(TestBasicOps):
    gen = random.Random()

    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)

    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

_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
    sum = cof[0]
    for i in xrange(1,len(cof)):
        sum += cof[i] / (z+i)
    z += 0.5
    return (z+g)**z / exp(z+g) * sqrt(2*pi) * sum

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)

    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.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_main(verbose=None):
    testclasses =    (WichmannHill_TestBasicOps,
                      MersenneTwister_TestBasicOps,
                      TestDistributions,
                      TestModule)
    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)