diff options
| author | mattip <matti.picus@gmail.com> | 2019-06-26 00:45:41 +0300 |
|---|---|---|
| committer | mattip <matti.picus@gmail.com> | 2019-06-26 01:13:48 +0300 |
| commit | efa35e738027dc833c0d02c8b15f41c9cf547749 (patch) | |
| tree | fd2611d808bd0a76d70dc2648eb1615fc4faefc4 /numpy/random/tests | |
| parent | 8bb4645fe56c6fc107ca5c5bef7a05802112cfdf (diff) | |
| download | numpy-efa35e738027dc833c0d02c8b15f41c9cf547749.tar.gz | |
ENH: use SeedSequence to generate entropy for seeding
Diffstat (limited to 'numpy/random/tests')
| -rw-r--r-- | numpy/random/tests/data/mt19937-testset-1.csv | 2000 | ||||
| -rw-r--r-- | numpy/random/tests/data/mt19937-testset-2.csv | 2000 | ||||
| -rw-r--r-- | numpy/random/tests/data/pcg64-testset-1.csv | 2000 | ||||
| -rw-r--r-- | numpy/random/tests/data/pcg64-testset-2.csv | 2000 | ||||
| -rw-r--r-- | numpy/random/tests/data/philox-testset-1.csv | 2000 | ||||
| -rw-r--r-- | numpy/random/tests/data/philox-testset-2.csv | 2000 | ||||
| -rw-r--r-- | numpy/random/tests/test_direct.py | 157 | ||||
| -rw-r--r-- | numpy/random/tests/test_generator_mt19937.py | 895 | ||||
| -rw-r--r-- | numpy/random/tests/test_generator_mt19937_regressions.py | 35 | ||||
| -rw-r--r-- | numpy/random/tests/test_randomstate.py | 9 | ||||
| -rw-r--r-- | numpy/random/tests/test_seed_sequence.py | 40 | ||||
| -rw-r--r-- | numpy/random/tests/test_smoke.py | 90 |
12 files changed, 6547 insertions, 6679 deletions
diff --git a/numpy/random/tests/data/mt19937-testset-1.csv b/numpy/random/tests/data/mt19937-testset-1.csv index 3d4995840..b97bfa66f 100644 --- a/numpy/random/tests/data/mt19937-testset-1.csv +++ b/numpy/random/tests/data/mt19937-testset-1.csv @@ -1,1001 +1,1001 @@ seed, 0xdeadbeaf -0, 0xca6b2fb1 -1, 0x3fa2a974 -2, 0xc12788fe -3, 0x27c9b64d -4, 0xabea28ce -5, 0x540ac30f -6, 0xb239d7be -7, 0x440e5156 -8, 0xc65c133c -9, 0x7333c5fa -10, 0x8292807d -11, 0x459bdab6 -12, 0x4f9f1306 -13, 0xe261f5f7 -14, 0xdb8b69 -15, 0xdf65ed00 -16, 0x5fc8bb71 -17, 0x35a46ece -18, 0x6344e7ce -19, 0xd4c1c08b -20, 0xb88c9bf1 -21, 0x72292bb3 -22, 0xbfb6a51f -23, 0xbb2ab6f -24, 0x9378d4f3 -25, 0x85bc696 -26, 0x1fa92b2a -27, 0x5816122 -28, 0x54e84469 -29, 0x8cdbea8c -30, 0xefa62749 -31, 0x7b13a32e -32, 0xe0c88cc3 -33, 0x238fba9f -34, 0xbee28cb4 -35, 0xc4a2bbe1 -36, 0x8ebb893f -37, 0x53333007 -38, 0x42c59297 -39, 0x507542da -40, 0x4c89a1d9 -41, 0x28cd06c0 -42, 0x63b1b8ff -43, 0x890947ca -44, 0x73996302 -45, 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a/numpy/random/tests/data/mt19937-testset-2.csv b/numpy/random/tests/data/mt19937-testset-2.csv index d2f6c156c..cdb8e4794 100644 --- a/numpy/random/tests/data/mt19937-testset-2.csv +++ b/numpy/random/tests/data/mt19937-testset-2.csv @@ -1,1001 +1,1001 @@ seed, 0x0 -0, 0x8c7f0aac -1, 0x97c4aa2f -2, 0xb716a675 -3, 0xd821ccc0 -4, 0x9a4eb343 -5, 0xdba252fb -6, 0x8b7d76c3 -7, 0xd8e57d67 -8, 0x6c74a409 -9, 0x9fa1ded3 -10, 0xa5595115 -11, 0x6266d6f2 -12, 0x7005b724 -13, 0x4c2b3a57 -14, 0xe44b3c46 -15, 0xe84bdd8 -16, 0xf6b29a58 -17, 0x45cccd8c -18, 0x6229393a -19, 0x7a4842c1 -20, 0xcaae7de6 -21, 0xcfea4a27 -22, 0x8765a857 -23, 0x7adfc8ae -24, 0x916b5e58 -25, 0x648d8b51 -26, 0xecf3e6a5 -27, 0xd6094219 -28, 0x122f6b4d -29, 0x565f9848 -30, 0x164e1b09 -31, 0xa5ee9794 -32, 0x52d0873 -33, 0x5e4513d0 -34, 0xd52692f3 -35, 0xf5081ec5 -36, 0xc73547fe -37, 0x23ee074f -38, 0xdeb91daf -39, 0xdebe09c0 -40, 0xfa86bb52 -41, 0x793e6063 -42, 0xcc95a7d8 -43, 0xcd087cb1 -44, 0x762382f3 -45, 0x853e031d -46, 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a/numpy/random/tests/data/pcg64-testset-1.csv b/numpy/random/tests/data/pcg64-testset-1.csv index da6d77d40..0c8271fab 100644 --- a/numpy/random/tests/data/pcg64-testset-1.csv +++ b/numpy/random/tests/data/pcg64-testset-1.csv @@ -1,1001 +1,1001 @@ seed, 0xdeadbeaf -0, 0xb174ddf3fe597da6 -1, 0xfc217240c1e61e6f -2, 0x20279da26fec9cbf -3, 0xa5f4ee34651f4e1e -4, 0xb254d7901c68b9db -5, 0x6bf2a64e4052c36f -6, 0xbec4c7418c0f61b6 -7, 0xb03e595a4ef2b2bd -8, 0xc8e051957ccb74c6 -9, 0xf5082df7473c6f62 -10, 0xb72aa3dc22752a38 -11, 0x7a941561514bd680 -12, 0x90a95f42834f347b -13, 0x88e17b9d59def797 -14, 0x18f19c86bfe60368 -15, 0x667f89277be8d1d8 -16, 0x63305475e7aeff27 -17, 0xd7e4d5c09fb0fb4c -18, 0x7dd6deab03a8c26a -19, 0x42bbcb746e2d4b26 -20, 0xe91f2ac4fa90689c -21, 0x2f83458da0af3125 -22, 0x49a43537a00fae89 -23, 0xef02111d1ebc16eb -24, 0x32b3b3019875f3e7 -25, 0x89eb15c48cd02774 -26, 0xa9f1b37865752731 -27, 0xe0eff9dadc47fb3 -28, 0xae859b1c54cc90c2 -29, 0x7b7c5e04d5015dec -30, 0x38faeff5ce3266ff 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0x9bfc93bec116769f +989, 0x2b2ee27c37a3fa5b +990, 0xb25c6ed54006ee38 +991, 0xab04d4a5c69e69a5 +992, 0x6d2f6b45f2d8438f +993, 0x4ad2f32afc82f092 +994, 0x513d718908f709c0 +995, 0x5272aadc4fffca51 +996, 0xeb3f87e66156ef5d +997, 0xf8a3d5a46a86ba85 +998, 0xdb4548a86f27abfd +999, 0x57c05f47ff62380d diff --git a/numpy/random/tests/test_direct.py b/numpy/random/tests/test_direct.py index 0bdfabded..70a731861 100644 --- a/numpy/random/tests/test_direct.py +++ b/numpy/random/tests/test_direct.py @@ -6,7 +6,13 @@ from numpy.testing import (assert_equal, assert_allclose, assert_array_equal, assert_raises) import pytest +<<<<<<< HEAD from numpy.random import (Generator, MT19937, PCG64, Philox, RandomState) +======= +from numpy.random import (Generator, MT19937, PCG64, + Philox, Xoshiro256, Xoshiro512, RandomState, + SeedSequence) +>>>>>>> ENH: use SeedSequence to generate entropy for seeding from numpy.random.common import interface try: @@ -116,6 +122,24 @@ def gauss_from_uint(x, n, bits): return gauss[:n] +def test_seedsequence(): + from numpy.random.bit_generator import (ISeedSequence, + ISpawnableSeedSequence, + SeedlessSeedSequence) + + s1 = SeedSequence(range(10), spawn_key=(1, 2), pool_size=6) + s1.spawn(10) + s2 = SeedSequence(**s1.state) + assert_equal(s1.state, s2.state) + assert_equal(s1.n_children_spawned, s2.n_children_spawned) + + # The interfaces cannot be instantiated themselves. + assert_raises(TypeError, ISeedSequence) + assert_raises(TypeError, ISpawnableSeedSequence) + dummy = SeedlessSeedSequence() + assert_raises(NotImplementedError, dummy.generate_state, 10) + assert len(dummy.spawn(10)) == 10 + class Base(object): dtype = np.uint64 @@ -127,8 +151,8 @@ class Base(object): cls.bits = 64 cls.dtype = np.uint64 cls.seed_error_type = TypeError - cls.invalid_seed_types = [] - cls.invalid_seed_values = [] + cls.invalid_init_types = [] + cls.invalid_init_values = [] @classmethod def _read_csv(cls, filename): @@ -199,39 +223,6 @@ class Base(object): assert_allclose(uniforms, vals) assert_equal(uniforms.dtype, np.float32) - def test_seed_float(self): - # GH #82 - rs = Generator(self.bit_generator(*self.data1['seed'])) - assert_raises(self.seed_error_type, rs.bit_generator.seed, np.pi) - assert_raises(self.seed_error_type, rs.bit_generator.seed, -np.pi) - - def test_seed_float_array(self): - # GH #82 - rs = Generator(self.bit_generator(*self.data1['seed'])) - assert_raises(self.seed_error_type, rs.bit_generator.seed, - np.array([np.pi])) - assert_raises(self.seed_error_type, rs.bit_generator.seed, - np.array([-np.pi])) - assert_raises(ValueError, rs.bit_generator.seed, - np.array([np.pi, -np.pi])) - assert_raises(TypeError, rs.bit_generator.seed, np.array([0, np.pi])) - assert_raises(TypeError, rs.bit_generator.seed, [np.pi]) - assert_raises(TypeError, rs.bit_generator.seed, [0, np.pi]) - - def test_seed_out_of_range(self): - # GH #82 - rs = Generator(self.bit_generator(*self.data1['seed'])) - assert_raises(ValueError, rs.bit_generator.seed, - 2 ** (2 * self.bits + 1)) - assert_raises(ValueError, rs.bit_generator.seed, -1) - - def test_seed_out_of_range_array(self): - # GH #82 - rs = Generator(self.bit_generator(*self.data1['seed'])) - assert_raises(ValueError, rs.bit_generator.seed, - [2 ** (2 * self.bits + 1)]) - assert_raises(ValueError, rs.bit_generator.seed, [-1]) - def test_repr(self): rs = Generator(self.bit_generator(*self.data1['seed'])) assert 'Generator' in repr(rs) @@ -256,6 +247,10 @@ class Base(object): assert bit_generator is not reloaded assert_state_equal(reloaded_state, state) + ss = SeedSequence(100) + aa = pickle.loads(pickle.dumps(ss)) + assert_equal(ss.state, aa.state) + def test_invalid_state_type(self): bit_generator = self.bit_generator(*self.data1['seed']) with pytest.raises(TypeError): @@ -268,17 +263,17 @@ class Base(object): with pytest.raises(ValueError): bit_generator.state = state - def test_invalid_seed_type(self): - bit_generator = self.bit_generator(*self.data1['seed']) - for st in self.invalid_seed_types: + def test_invalid_init_type(self): + bit_generator = self.bit_generator + for st in self.invalid_init_types: with pytest.raises(TypeError): - bit_generator.seed(*st) + bit_generator(*st) - def test_invalid_seed_values(self): - bit_generator = self.bit_generator(*self.data1['seed']) - for st in self.invalid_seed_values: - with pytest.raises(ValueError): - bit_generator.seed(*st) + def test_invalid_init_values(self): + bit_generator = self.bit_generator + for st in self.invalid_init_values: + with pytest.raises((ValueError, OverflowError)): + bit_generator(*st) def test_benchmark(self): bit_generator = self.bit_generator(*self.data1['seed']) @@ -321,9 +316,8 @@ class TestPhilox(Base): cls.data2 = cls._read_csv( join(pwd, './data/philox-testset-2.csv')) cls.seed_error_type = TypeError - cls.invalid_seed_types = [] - cls.invalid_seed_values = [(1, None, 1), (-1,), (2 ** 257 + 1,), - (None, None, 2 ** 257 + 1)] + cls.invalid_init_types = [] + cls.invalid_init_values = [(1, None, 1), (-1,), (None, None, 2 ** 257 + 1)] def test_set_key(self): bit_generator = self.bit_generator(*self.data1['seed']) @@ -341,30 +335,9 @@ class TestPCG64(Base): cls.dtype = np.uint64 cls.data1 = cls._read_csv(join(pwd, './data/pcg64-testset-1.csv')) cls.data2 = cls._read_csv(join(pwd, './data/pcg64-testset-2.csv')) - cls.seed_error_type = TypeError - cls.invalid_seed_types = [(np.array([1, 2]),), (3.2,), - (None, np.zeros(1))] - cls.invalid_seed_values = [(-1,), (2 ** 129 + 1,), (None, -1), - (None, 2 ** 129 + 1)] - - def test_seed_float_array(self): - rs = Generator(self.bit_generator(*self.data1['seed'])) - assert_raises(self.seed_error_type, rs.bit_generator.seed, - np.array([np.pi])) - assert_raises(self.seed_error_type, rs.bit_generator.seed, - np.array([-np.pi])) - assert_raises(self.seed_error_type, rs.bit_generator.seed, - np.array([np.pi, -np.pi])) - assert_raises(self.seed_error_type, rs.bit_generator.seed, - np.array([0, np.pi])) - assert_raises(self.seed_error_type, rs.bit_generator.seed, [np.pi]) - assert_raises(self.seed_error_type, rs.bit_generator.seed, [0, np.pi]) - - def test_seed_out_of_range_array(self): - rs = Generator(self.bit_generator(*self.data1['seed'])) - assert_raises(self.seed_error_type, rs.bit_generator.seed, - [2 ** (2 * self.bits + 1)]) - assert_raises(self.seed_error_type, rs.bit_generator.seed, [-1]) + cls.seed_error_type = (ValueError, TypeError) + cls.invalid_init_types = [(3.2,), ([None],), (1, None)] + cls.invalid_init_values = [(-1,)] def test_advance_symmetry(self): rs = Generator(self.bit_generator(*self.data1['seed'])) @@ -391,42 +364,16 @@ class TestMT19937(Base): cls.data1 = cls._read_csv(join(pwd, './data/mt19937-testset-1.csv')) cls.data2 = cls._read_csv(join(pwd, './data/mt19937-testset-2.csv')) cls.seed_error_type = ValueError - cls.invalid_seed_types = [] - cls.invalid_seed_values = [(-1,), np.array([2 ** 33])] - - def test_seed_out_of_range(self): - # GH #82 - rs = Generator(self.bit_generator(*self.data1['seed'])) - assert_raises(ValueError, rs.bit_generator.seed, 2 ** (self.bits + 1)) - assert_raises(ValueError, rs.bit_generator.seed, -1) - assert_raises(ValueError, rs.bit_generator.seed, - 2 ** (2 * self.bits + 1)) - - def test_seed_out_of_range_array(self): - # GH #82 - rs = Generator(self.bit_generator(*self.data1['seed'])) - assert_raises(ValueError, rs.bit_generator.seed, - [2 ** (self.bits + 1)]) - assert_raises(ValueError, rs.bit_generator.seed, [-1]) - assert_raises(TypeError, rs.bit_generator.seed, - [2 ** (2 * self.bits + 1)]) - - def test_seed_float(self): - # GH #82 - rs = Generator(self.bit_generator(*self.data1['seed'])) - assert_raises(TypeError, rs.bit_generator.seed, np.pi) - assert_raises(TypeError, rs.bit_generator.seed, -np.pi) + cls.invalid_init_types = [] + cls.invalid_init_values = [(-1,)] def test_seed_float_array(self): - # GH #82 - rs = Generator(self.bit_generator(*self.data1['seed'])) - bit_generator = rs.bit_generator - assert_raises(TypeError, bit_generator.seed, np.array([np.pi])) - assert_raises(TypeError, bit_generator.seed, np.array([-np.pi])) - assert_raises(TypeError, bit_generator.seed, np.array([np.pi, -np.pi])) - assert_raises(TypeError, bit_generator.seed, np.array([0, np.pi])) - assert_raises(TypeError, bit_generator.seed, [np.pi]) - assert_raises(TypeError, bit_generator.seed, [0, np.pi]) + assert_raises(TypeError, self.bit_generator, np.array([np.pi])) + assert_raises(TypeError, self.bit_generator, np.array([-np.pi])) + assert_raises(TypeError, self.bit_generator, np.array([np.pi, -np.pi])) + assert_raises(TypeError, self.bit_generator, np.array([0, np.pi])) + assert_raises(TypeError, self.bit_generator, [np.pi]) + assert_raises(TypeError, self.bit_generator, [0, np.pi]) def test_state_tuple(self): rs = Generator(self.bit_generator(*self.data1['seed'])) diff --git a/numpy/random/tests/test_generator_mt19937.py b/numpy/random/tests/test_generator_mt19937.py index 44324c9e3..ef821d46f 100644 --- a/numpy/random/tests/test_generator_mt19937.py +++ b/numpy/random/tests/test_generator_mt19937.py @@ -8,7 +8,7 @@ from numpy.testing import ( assert_warns, assert_no_warnings, assert_array_equal, assert_array_almost_equal, suppress_warnings) -from numpy.random import Generator, MT19937 +from numpy.random import Generator, MT19937, SeedSequence random = Generator(MT19937()) @@ -21,19 +21,23 @@ def endpoint(request): class TestSeed(object): def test_scalar(self): s = Generator(MT19937(0)) - assert_equal(s.integers(1000), 548) + assert_equal(s.integers(1000), 479) s = Generator(MT19937(4294967295)) - assert_equal(s.integers(1000), 97) + assert_equal(s.integers(1000), 324) def test_array(self): s = Generator(MT19937(range(10))) - assert_equal(s.integers(1000), 410) + assert_equal(s.integers(1000), 465) s = Generator(MT19937(np.arange(10))) - assert_equal(s.integers(1000), 410) + assert_equal(s.integers(1000), 465) s = Generator(MT19937([0])) - assert_equal(s.integers(1000), 844) + assert_equal(s.integers(1000), 479) s = Generator(MT19937([4294967295])) - assert_equal(s.integers(1000), 635) + assert_equal(s.integers(1000), 324) + + def test_seedsequence(self): + s = MT19937(SeedSequence(0)) + assert_equal(s.random_raw(1), 2058676884) def test_invalid_scalar(self): # seed must be an unsigned 32 bit integer @@ -41,11 +45,9 @@ class TestSeed(object): assert_raises(ValueError, MT19937, -1) def test_invalid_array(self): - # seed must be an unsigned 32 bit integer + # seed must be an unsigned integer assert_raises(TypeError, MT19937, [-0.5]) assert_raises(ValueError, MT19937, [-1]) - assert_raises(ValueError, MT19937, [4294967296]) - assert_raises(ValueError, MT19937, [1, 2, 4294967296]) assert_raises(ValueError, MT19937, [1, -2, 4294967296]) def test_noninstantized_bitgen(self): @@ -101,14 +103,14 @@ class TestMultinomial(object): def test_invalid_n(self): assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2]) assert_raises(ValueError, random.multinomial, [-1] * 10, [0.8, 0.2]) - + def test_p_non_contiguous(self): p = np.arange(15.) p /= np.sum(p[1::3]) pvals = p[1::3] - random.bit_generator.seed(1432985819) + random = Generator(MT19937(1432985819)) non_contig = random.multinomial(100, pvals=pvals) - random.bit_generator.seed(1432985819) + random = Generator(MT19937(1432985819)) contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals)) assert_array_equal(non_contig, contig) @@ -283,7 +285,7 @@ class TestIntegers(object): def test_in_bounds_fuzz(self, endpoint): # Don't use fixed seed - random.bit_generator.seed() + random = Generator(MT19937()) for dt in self.itype[1:]: for ubnd in [4, 8, 16]: @@ -304,16 +306,16 @@ class TestIntegers(object): ubnd = ubnd - 1 if endpoint else ubnd size = 1000 - random.bit_generator.seed(1234) - scalar = self.rfunc(lbnd, ubnd, size=size, endpoint=endpoint, + random = Generator(MT19937(1234)) + scalar = random.integers(lbnd, ubnd, size=size, endpoint=endpoint, dtype=dt) - random.bit_generator.seed(1234) - scalar_array = self.rfunc([lbnd], [ubnd], size=size, + random = Generator(MT19937(1234)) + scalar_array = random.integers([lbnd], [ubnd], size=size, endpoint=endpoint, dtype=dt) - random.bit_generator.seed(1234) - array = self.rfunc([lbnd] * size, [ubnd] * + random = Generator(MT19937(1234)) + array = random.integers([lbnd] * size, [ubnd] * size, size=size, endpoint=endpoint, dtype=dt) assert_array_equal(scalar, scalar_array) assert_array_equal(scalar, array) @@ -323,33 +325,33 @@ class TestIntegers(object): # We use a md5 hash of generated sequences of 1000 samples # in the range [0, 6) for all but bool, where the range # is [0, 2). Hashes are for little endian numbers. - tgt = {'bool': '7dd3170d7aa461d201a65f8bcf3944b0', - 'int16': '2d26cafb53cb0f5acbb9b3fe86b36991', - 'int32': '54f153d6ae944ce0dde49a66602959bb', - 'int64': '47a068f62fda47f6034aa745e39a1b0d', - 'int8': '1d71d3947cd98598b4f00a77c117d62a', - 'uint16': '2d26cafb53cb0f5acbb9b3fe86b36991', - 'uint32': '54f153d6ae944ce0dde49a66602959bb', - 'uint64': '47a068f62fda47f6034aa745e39a1b0d', - 'uint8': '1d71d3947cd98598b4f00a77c117d62a'} + tgt = {'bool': 'b3300e66d2bb59e493d255d47c3a6cbe', + 'int16': '39624ead49ad67e37545744024d2648b', + 'int32': '5c4810373f979336c6c0c999996e47a1', + 'int64': 'ab126c15edff26f55c50d2b7e37391ac', + 'int8': 'd1746364b48a020dab9ef0568e6c0cd2', + 'uint16': '39624ead49ad67e37545744024d2648b', + 'uint32': '5c4810373f979336c6c0c999996e47a1', + 'uint64': 'ab126c15edff26f55c50d2b7e37391ac', + 'uint8': 'd1746364b48a020dab9ef0568e6c0cd2'} for dt in self.itype[1:]: - random.bit_generator.seed(1234) + random = Generator(MT19937(1234)) # view as little endian for hash if sys.byteorder == 'little': - val = self.rfunc(0, 6 - endpoint, size=1000, endpoint=endpoint, + val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint, dtype=dt) else: - val = self.rfunc(0, 6 - endpoint, size=1000, endpoint=endpoint, + val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint, dtype=dt).byteswap() res = hashlib.md5(val.view(np.int8)).hexdigest() assert_(tgt[np.dtype(dt).name] == res) # bools do not depend on endianness - random.bit_generator.seed(1234) - val = self.rfunc(0, 2 - endpoint, size=1000, endpoint=endpoint, + random = Generator(MT19937(1234)) + val = random.integers(0, 2 - endpoint, size=1000, endpoint=endpoint, dtype=bool).view(np.int8) res = hashlib.md5(val).hexdigest() assert_(tgt[np.dtype(bool).name] == res) @@ -362,18 +364,18 @@ class TestIntegers(object): ubnd = ubnd - 1 if endpoint else ubnd # view as little endian for hash - random.bit_generator.seed(1234) - val = self.rfunc(lbnd, ubnd, size=1000, endpoint=endpoint, + random = Generator(MT19937(1234)) + val = random.integers(lbnd, ubnd, size=1000, endpoint=endpoint, dtype=dt) - random.bit_generator.seed(1234) - val_bc = self.rfunc([lbnd] * 1000, ubnd, endpoint=endpoint, + random = Generator(MT19937(1234)) + val_bc = random.integers([lbnd] * 1000, ubnd, endpoint=endpoint, dtype=dt) assert_array_equal(val, val_bc) - random.bit_generator.seed(1234) - val_bc = self.rfunc([lbnd] * 1000, [ubnd] * 1000, + random = Generator(MT19937(1234)) + val_bc = random.integers([lbnd] * 1000, [ubnd] * 1000, endpoint=endpoint, dtype=dt) assert_array_equal(val, val_bc) @@ -491,27 +493,23 @@ class TestRandomDist(object): self.seed = 1234567890 def test_integers(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.integers(-99, 99, size=(3, 2)) - desired = np.array([[23, -32], - [18, -70], - [76, -53]]) + desired = np.array([[-80, -56], [41, 37], [-83, -16]]) assert_array_equal(actual, desired) def test_integers_masked(self): # Test masked rejection sampling algorithm to generate array of # uint32 in an interval. - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.integers(0, 99, size=(3, 2), dtype=np.uint32) - desired = np.array([[61, 33], - [58, 14], - [87, 23]], dtype=np.uint32) + desired = np.array([[9, 21], [70, 68], [8, 41]], dtype=np.uint32) assert_array_equal(actual, desired) def test_integers_closed(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.integers(-99, 99, size=(3, 2), endpoint=True) - desired = np.array([[24, -32], [18, -70], [77, -53]]) + desired = np.array([[-80, -56], [ 41, 38], [-83, -15]]) assert_array_equal(actual, desired) def test_integers_max_int(self): @@ -527,74 +525,74 @@ class TestRandomDist(object): assert_equal(actual, desired) def test_random(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.random((3, 2)) - desired = np.array([[0.61879477158567997, 0.59162362775974664], - [0.88868358904449662, 0.89165480011560816], - [0.4575674820298663, 0.7781880808593471]]) + desired = np.array([[0.096999199829214, 0.707517457682192], + [0.084364834598269, 0.767731206553125], + [0.665069021359413, 0.715487190596693]]) assert_array_almost_equal(actual, desired, decimal=15) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.random() assert_array_almost_equal(actual, desired[0, 0], decimal=15) def test_random_float(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.random((3, 2)) - desired = np.array([[0.6187948, 0.5916236], - [0.8886836, 0.8916548], - [0.4575675, 0.7781881]]) + desired = np.array([[0.0969992 , 0.70751746], + [0.08436483, 0.76773121], + [0.66506902, 0.71548719]]) assert_array_almost_equal(actual, desired, decimal=7) def test_random_float_scalar(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.random(dtype=np.float32) - desired = 0.6187948 + desired = 0.0969992 assert_array_almost_equal(actual, desired, decimal=7) def test_random_unsupported_type(self): assert_raises(TypeError, random.random, dtype='int32') def test_choice_uniform_replace(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.choice(4, 4) - desired = np.array([2, 1, 2, 0], dtype=np.int64) + desired = np.array([0, 0, 2, 2], dtype=np.int64) assert_array_equal(actual, desired) def test_choice_nonuniform_replace(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1]) - desired = np.array([1, 1, 2, 2], dtype=np.int64) + desired = np.array([0, 1, 0, 1], dtype=np.int64) assert_array_equal(actual, desired) def test_choice_uniform_noreplace(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.choice(4, 3, replace=False) - desired = np.array([0, 2, 3], dtype=np.int64) + desired = np.array([0, 1, 3], dtype=np.int64) assert_array_equal(actual, desired) def test_choice_nonuniform_noreplace(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1]) - desired = np.array([2, 3, 1], dtype=np.int64) + desired = np.array([0, 2, 3], dtype=np.int64) assert_array_equal(actual, desired) def test_choice_noninteger(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.choice(['a', 'b', 'c', 'd'], 4) - desired = np.array(['c', 'b', 'c', 'a']) + desired = np.array(['a', 'a', 'c', 'c']) assert_array_equal(actual, desired) def test_choice_multidimensional_default_axis(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 3) - desired = np.array([[4, 5], [2, 3], [4, 5]]) + desired = np.array([[0, 1], [0, 1], [4, 5]]) assert_array_equal(actual, desired) def test_choice_multidimensional_custom_axis(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 1, axis=1) - desired = np.array([[1], [3], [5], [7]]) + desired = np.array([[0], [2], [4], [6]]) assert_array_equal(actual, desired) def test_choice_exceptions(self): @@ -665,13 +663,13 @@ class TestRandomDist(object): a = np.array([42, 1, 2]) p = [None, None, None] assert_raises(ValueError, random.choice, a, p=p) - + def test_choice_p_non_contiguous(self): p = np.ones(10) / 5 p[1::2] = 3.0 - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) non_contig = random.choice(5, 3, p=p[::2]) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2])) assert_array_equal(non_contig, contig) @@ -690,8 +688,8 @@ class TestRandomDist(object): def test_choice_large_sample(self): import hashlib - choice_hash = '6395868be877d27518c832213c17977c' - random.bit_generator.seed(self.seed) + choice_hash = '5ca163da624c938bb3bc93e89a7dec4c' + random = Generator(MT19937(self.seed)) actual = random.choice(10000, 5000, replace=False) if sys.byteorder != 'little': actual = actual.byteswap() @@ -699,9 +697,9 @@ class TestRandomDist(object): assert_(choice_hash == res) def test_bytes(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.bytes(10) - desired = b'\x82Ui\x9e\xff\x97+Wf\xa5' + desired = b'\x86\xf0\xd4\x18\xe1\x81\t8%\xdd' assert_equal(actual, desired) def test_shuffle(self): @@ -724,11 +722,11 @@ class TestRandomDist(object): lambda x: np.asarray([(i, i) for i in x], [("a", object, (1,)), ("b", np.int32, (1,))])]: - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]) random.shuffle(alist) actual = alist - desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3]) + desired = conv([4, 1, 9, 8, 0, 5, 3, 6, 2, 7]) assert_array_equal(actual, desired) def test_shuffle_masked(self): @@ -746,62 +744,62 @@ class TestRandomDist(object): sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask])) def test_permutation(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] actual = random.permutation(alist) - desired = [0, 1, 9, 6, 2, 4, 5, 8, 7, 3] + desired = [4, 1, 9, 8, 0, 5, 3, 6, 2, 7] assert_array_equal(actual, desired) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T actual = random.permutation(arr_2d) assert_array_equal(actual, np.atleast_2d(desired).T) def test_beta(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.beta(.1, .9, size=(3, 2)) desired = np.array( - [[1.45341850513746058e-02, 5.31297615662868145e-04], - [1.85366619058432324e-06, 4.19214516800110563e-03], - [1.58405155108498093e-04, 1.26252891949397652e-04]]) + [[1.083029353267698e-10, 2.449965303168024e-11], + [2.397085162969853e-02, 3.590779671820755e-08], + [2.830254190078299e-04, 1.744709918330393e-01]]) assert_array_almost_equal(actual, desired, decimal=15) def test_binomial(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.binomial(100.123, .456, size=(3, 2)) - desired = np.array([[37, 43], + desired = np.array([[42, 41], [42, 48], - [46, 45]]) + [44, 50]]) assert_array_equal(actual, desired) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.binomial(100.123, .456) - desired = 37 + desired = 42 assert_array_equal(actual, desired) def test_chisquare(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.chisquare(50, size=(3, 2)) - desired = np.array([[22.2534560369812, 46.9302393710074], - [52.9974164611614, 85.3559029505718], - [46.1580841240719, 36.1933148548090]]) + desired = np.array([[32.9850547060149, 39.0219480493301], + [56.2006134779419, 57.3474165711485], + [55.4243733880198, 55.4209797925213]]) assert_array_almost_equal(actual, desired, decimal=13) def test_dirichlet(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) alpha = np.array([51.72840233779265162, 39.74494232180943953]) actual = random.dirichlet(alpha, size=(3, 2)) - desired = np.array([[[0.444382290764855, 0.555617709235145], - [0.468440809291970, 0.531559190708030]], - [[0.613461427360549, 0.386538572639451], - [0.529103072088183, 0.470896927911817]], - [[0.513490650101800, 0.486509349898200], - [0.558550925712797, 0.441449074287203]]]) + desired = np.array([[[0.5439892869558927, 0.45601071304410745], + [0.5588917345860708, 0.4411082654139292 ]], + [[0.5632074165063435, 0.43679258349365657], + [0.54862581112627, 0.45137418887373015]], + [[0.49961831357047226, 0.5003816864295278 ], + [0.52374806183482, 0.47625193816517997]]]) assert_array_almost_equal(actual, desired, decimal=15) bad_alpha = np.array([5.4e-01, -1.0e-16]) assert_raises(ValueError, random.dirichlet, bad_alpha) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) alpha = np.array([51.72840233779265162, 39.74494232180943953]) actual = random.dirichlet(alpha) assert_array_almost_equal(actual, desired[0, 0], decimal=15) @@ -822,23 +820,23 @@ class TestRandomDist(object): # gh-2089 alpha = np.array([5.4e-01, -1.0e-16]) assert_raises(ValueError, random.dirichlet, alpha) - + def test_dirichlet_alpha_non_contiguous(self): a = np.array([51.72840233779265162, -1.0, 39.74494232180943953]) alpha = a[::2] - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) non_contig = random.dirichlet(alpha, size=(3, 2)) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) contig = random.dirichlet(np.ascontiguousarray(alpha), size=(3, 2)) assert_array_almost_equal(non_contig, contig) def test_exponential(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.exponential(1.1234, size=(3, 2)) - desired = np.array([[5.350682337747634, 1.152307441755771], - [3.867015473358779, 1.538765912839396], - [0.347846818048527, 2.715656549872026]]) + desired = np.array([[0.098845481066258, 1.560752510746964], + [0.075730916041636, 1.769098974710777], + [1.488602544592235, 2.49684815275751 ]]) assert_array_almost_equal(actual, desired, decimal=15) def test_exponential_0(self): @@ -846,19 +844,19 @@ class TestRandomDist(object): assert_raises(ValueError, random.exponential, scale=-0.) def test_f(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.f(12, 77, size=(3, 2)) - desired = np.array([[0.809498839488467, 2.867222762455471], - [0.588036831639353, 1.012185639664636], - [1.147554281917365, 1.150886518432105]]) + desired = np.array([[0.461720027077085, 1.100441958872451], + [1.100337455217484, 0.91421736740018 ], + [0.500811891303113, 0.826802454552058]]) assert_array_almost_equal(actual, desired, decimal=15) def test_gamma(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.gamma(5, 3, size=(3, 2)) - desired = np.array([[12.46569350177219, 16.46580642087044], - [43.65744473309084, 11.98722785682592], - [6.50371499559955, 7.48465689751638]]) + desired = np.array([[ 5.03850858902096, 7.9228656732049 ], + [18.73983605132985, 19.57961681699238], + [18.17897755150825, 18.17653912505234]]) assert_array_almost_equal(actual, desired, decimal=14) def test_gamma_0(self): @@ -866,11 +864,11 @@ class TestRandomDist(object): assert_raises(ValueError, random.gamma, shape=-0., scale=-0.) def test_geometric(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.geometric(.123456789, size=(3, 2)) - desired = np.array([[8, 7], - [17, 17], - [5, 12]]) + desired = np.array([[ 1, 10], + [ 1, 12], + [ 9, 10]]) assert_array_equal(actual, desired) def test_geometric_exceptions(self): @@ -883,11 +881,11 @@ class TestRandomDist(object): assert_raises(ValueError, random.geometric, [np.nan] * 10) def test_gumbel(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2)) - desired = np.array([[0.19591898743416816, 0.34405539668096674], - [-1.4492522252274278, -1.47374816298446865], - [1.10651090478803416, -0.69535848626236174]]) + desired = np.array([[ 4.688397515056245, -0.289514845417841], + [ 4.981176042584683, -0.633224272589149], + [-0.055915275687488, -0.333962478257953]]) assert_array_almost_equal(actual, desired, decimal=15) def test_gumbel_0(self): @@ -895,11 +893,11 @@ class TestRandomDist(object): assert_raises(ValueError, random.gumbel, scale=-0.) def test_hypergeometric(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2)) - desired = np.array([[9, 9], - [10, 9], - [9, 10]]) + desired = np.array([[ 9, 9], + [ 9, 9], + [10, 9]]) assert_array_equal(actual, desired) # Test nbad = 0 @@ -921,11 +919,11 @@ class TestRandomDist(object): assert_array_equal(actual, desired) def test_laplace(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2)) - desired = np.array([[0.66599721112760157, 0.52829452552221945], - [3.12791959514407125, 3.18202813572992005], - [-0.05391065675859356, 1.74901336242837324]]) + desired = np.array([[-3.156353949272393, 1.195863024830054], + [-3.435458081645966, 1.656882398925444], + [ 0.924824032467446, 1.251116432209336]]) assert_array_almost_equal(actual, desired, decimal=15) def test_laplace_0(self): @@ -933,19 +931,19 @@ class TestRandomDist(object): assert_raises(ValueError, random.laplace, scale=-0.) def test_logistic(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2)) - desired = np.array([[1.09232835305011444, 0.8648196662399954], - [4.27818590694950185, 4.33897006346929714], - [-0.21682183359214885, 2.63373365386060332]]) + desired = np.array([[-4.338584631510999, 1.890171436749954], + [-4.64547787337966 , 2.514545562919217], + [ 1.495389489198666, 1.967827627577474]]) assert_array_almost_equal(actual, desired, decimal=15) def test_lognormal(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2)) - desired = np.array([[1.0894838661036e-03, 9.0990021488311e-01], - [6.9178869932225e-01, 2.7672077560016e-01], - [2.3248645126975e+00, 1.4609997951330e+00]]) + desired = np.array([[ 0.0268252166335, 13.9534486483053], + [ 0.1204014788936, 2.2422077497792], + [ 4.2484199496128, 12.0093343977523]]) assert_array_almost_equal(actual, desired, decimal=13) def test_lognormal_0(self): @@ -953,11 +951,11 @@ class TestRandomDist(object): assert_raises(ValueError, random.lognormal, sigma=-0.) def test_logseries(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.logseries(p=.923456789, size=(3, 2)) - desired = np.array([[2, 2], - [6, 17], - [3, 6]]) + desired = np.array([[14, 17], + [3, 18], + [5, 1]]) assert_array_equal(actual, desired) def test_logseries_exceptions(self): @@ -966,34 +964,34 @@ class TestRandomDist(object): assert_raises(ValueError, random.logseries, [np.nan] * 10) def test_multinomial(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2)) - desired = np.array([[[4, 3, 5, 4, 2, 2], - [5, 2, 8, 2, 2, 1]], - [[3, 4, 3, 6, 0, 4], - [2, 1, 4, 3, 6, 4]], - [[4, 4, 2, 5, 2, 3], - [4, 3, 4, 2, 3, 4]]]) + desired = np.array([[[1, 5, 1, 6, 4, 3], + [4, 2, 6, 2, 4, 2]], + [[5, 3, 2, 6, 3, 1], + [4, 4, 0, 2, 3, 7]], + [[6, 3, 1, 5, 3, 2], + [5, 5, 3, 1, 2, 4]]]) assert_array_equal(actual, desired) def test_multivariate_normal(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) mean = (.123456789, 10) cov = [[1, 0], [0, 1]] size = (3, 2) actual = random.multivariate_normal(mean, cov, size) - desired = np.array([[[-3.34929721161096100, 9.891061435770858], - [-0.12250896439641100, 9.295898449738300]], - [[0.48355927611635563, 10.127832101772366], - [3.11093021424924300, 10.283109168794352]], - [[-0.20332082341774727, 9.868532121697195], - [-1.33806889550667330, 9.813657233804179]]]) + desired = np.array([[[-1.747478062846581, 11.25613495182354 ], + [-0.9967333370066214, 10.342002097029821 ]], + [[ 0.7850019631242964, 11.181113712443013 ], + [ 0.8901349653255224, 8.873825399642492 ]], + [[ 0.7130260107430003, 9.551628690083056 ], + [ 0.7127098726541128, 11.991709234143173 ]]]) assert_array_almost_equal(actual, desired, decimal=15) # Check for default size, was raising deprecation warning actual = random.multivariate_normal(mean, cov) - desired = np.array([-1.097443117192574, 10.535787051184261]) + desired = np.array([0.233278563284287, 9.424140804347195]) assert_array_almost_equal(actual, desired, decimal=15) # Check that non positive-semidefinite covariance warns with @@ -1028,11 +1026,11 @@ class TestRandomDist(object): mu, np.eye(3)) def test_negative_binomial(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.negative_binomial(n=100, p=.12345, size=(3, 2)) - desired = np.array([[521, 736], - [665, 690], - [723, 751]]) + desired = np.array([[543, 727], + [775, 760], + [600, 674]]) assert_array_equal(actual, desired) def test_negative_binomial_exceptions(self): @@ -1042,46 +1040,46 @@ class TestRandomDist(object): [np.nan] * 10) def test_noncentral_chisquare(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2)) - desired = np.array([[9.47783251920357, 10.02066178260461], - [3.15869984192364, 10.5581565031544], - [5.01652540543548, 13.7689551218441]]) + desired = np.array([[ 1.70561552362133, 15.97378184942111], + [13.71483425173724, 20.17859633310629], + [11.3615477156643 , 3.67891108738029]]) assert_array_almost_equal(actual, desired, decimal=14) actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2)) - desired = np.array([[0.00145153051285, 0.22432468724778], - [0.02956713468556, 0.00207192946898], - [1.41985055641800, 0.15451287602753]]) + desired = np.array([[9.41427665607629e-04, 1.70473157518850e-04], + [1.14554372041263e+00, 1.38187755933435e-03], + [1.90659181905387e+00, 1.21772577941822e+00]]) assert_array_almost_equal(actual, desired, decimal=14) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2)) - desired = np.array([[3.64881368071039, 5.48224544747803], - [20.41999842025404, 3.44075915187367], - [1.29765160605552, 1.64125033268606]]) + desired = np.array([[0.82947954590419, 1.80139670767078], + [6.58720057417794, 7.00491463609814], + [6.31101879073157, 6.30982307753005]]) assert_array_almost_equal(actual, desired, decimal=14) def test_noncentral_f(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1, size=(3, 2)) - desired = np.array([[1.22680230963236, 2.56457837623956], - [2.7653304499494, 7.4336268865443], - [1.16362730891403, 2.54104276581491]]) + desired = np.array([[0.060310671139 , 0.23866058175939], + [0.86860246709073, 0.2668510459738 ], + [0.23375780078364, 1.88922102885943]]) assert_array_almost_equal(actual, desired, decimal=14) def test_noncentral_f_nan(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan) assert np.isnan(actual) def test_normal(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2)) - desired = np.array([[-6.822051212221923, -0.094420339458285], - [-0.368474717792823, -1.284746311523402], - [0.843661763232711, 0.379120992544734]]) + desired = np.array([[-3.618412914693162, 2.635726692647081], + [-2.116923463013243, 0.807460983059643], + [ 1.446547137248593, 2.485684213886024]]) assert_array_almost_equal(actual, desired, decimal=15) def test_normal_0(self): @@ -1089,11 +1087,11 @@ class TestRandomDist(object): assert_raises(ValueError, random.normal, scale=-0.) def test_pareto(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.pareto(a=.123456789, size=(3, 2)) - desired = np.array([[5.6883528121891552e+16, 4.0569373841667057e+03], - [1.2854967019379475e+12, 6.5833156486851483e+04], - [1.1281132447159091e+01, 3.1895968171107006e+08]]) + desired = np.array([[1.0394926776069018e+00, 7.7142534343505773e+04], + [7.2640150889064703e-01, 3.4650454783825594e+05], + [4.5852344481994740e+04, 6.5851383009539105e+07]]) # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this # matrix differs by 24 nulps. Discussion: # https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html @@ -1103,10 +1101,10 @@ class TestRandomDist(object): np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30) def test_poisson(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.poisson(lam=.123456789, size=(3, 2)) desired = np.array([[0, 0], - [1, 0], + [0, 0], [0, 0]]) assert_array_equal(actual, desired) @@ -1122,19 +1120,19 @@ class TestRandomDist(object): assert_raises(ValueError, random.poisson, [np.nan] * 10) def test_power(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.power(a=.123456789, size=(3, 2)) - desired = np.array([[9.328833342693975e-01, 2.742250409261003e-02], - [7.684513237993961e-01, 9.297548209160028e-02], - [2.214811188828573e-05, 4.693448360603472e-01]]) + desired = np.array([[1.977857368842754e-09, 9.806792196620341e-02], + [2.482442984543471e-10, 1.527108843266079e-01], + [8.188283434244285e-02, 3.950547209346948e-01]]) assert_array_almost_equal(actual, desired, decimal=15) def test_rayleigh(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.rayleigh(scale=10, size=(3, 2)) - desired = np.array([[13.8882496494248393, 13.383318339044731], - [20.95413364294492098, 21.08285015800712614], - [11.06066537006854311, 17.35468505778271009]]) + desired = np.array([[ 4.51734079831581, 15.6802442485758 ], + [ 4.19850651287094, 17.08718809823704], + [14.7907457708776 , 15.85545333419775]]) assert_array_almost_equal(actual, desired, decimal=14) def test_rayleigh_0(self): @@ -1142,56 +1140,56 @@ class TestRandomDist(object): assert_raises(ValueError, random.rayleigh, scale=-0.) def test_standard_cauchy(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.standard_cauchy(size=(3, 2)) - desired = np.array([[31.87809592667601, 0.349332782046838], - [2.816995747731641, 10.552372563459114], - [2.485608017991235, 7.843211273201831]]) + desired = np.array([[-1.489437778266206, -3.275389641569784], + [ 0.560102864910406, -0.680780916282552], + [-1.314912905226277, 0.295852965660225]]) assert_array_almost_equal(actual, desired, decimal=15) def test_standard_exponential(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.standard_exponential(size=(3, 2), method='inv') - desired = np.array([[0.96441739162374596, 0.89556604882105506], - [2.1953785836319808, 2.22243285392490542], - [0.6116915921431676, 1.50592546727413201]]) + desired = np.array([[0.102031839440643, 1.229350298474972], + [0.088137284693098, 1.459859985522667], + [1.093830802293668, 1.256977002164613]]) assert_array_almost_equal(actual, desired, decimal=15) def test_standard_expoential_type_error(self): assert_raises(TypeError, random.standard_exponential, dtype=np.int32) def test_standard_gamma(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.standard_gamma(shape=3, size=(3, 2)) - desired = np.array([[2.28483515569645, 3.29899524967824], - [11.12492298902645, 2.16784417297277], - [0.92121813690910, 1.12853552328470]]) + desired = np.array([[0.62970724056362, 1.22379851271008], + [3.899412530884 , 4.12479964250139], + [3.74994102464584, 3.74929307690815]]) assert_array_almost_equal(actual, desired, decimal=14) def test_standard_gammma_scalar_float(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.standard_gamma(3, dtype=np.float32) - desired = 1.3877466 + desired = 2.9242148399353027 assert_array_almost_equal(actual, desired, decimal=6) def test_standard_gamma_float(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.standard_gamma(shape=3, size=(3, 2)) - desired = np.array([[2.2848352, 3.2989952], - [11.124923, 2.1678442], - [0.9212181, 1.1285355]]) + desired = np.array([[0.62971, 1.2238 ], + [3.89941, 4.1248 ], + [3.74994, 3.74929]]) assert_array_almost_equal(actual, desired, decimal=5) def test_standard_gammma_float_out(self): actual = np.zeros((3, 2), dtype=np.float32) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) random.standard_gamma(10.0, out=actual, dtype=np.float32) - desired = np.array([[6.9824033, 7.3731737], - [14.860578, 7.5327270], - [11.767487, 6.2320185]], dtype=np.float32) + desired = np.array([[10.14987, 7.87012], + [ 9.46284, 12.56832], + [13.82495, 7.81533]], dtype=np.float32) assert_array_almost_equal(actual, desired, decimal=5) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) random.standard_gamma(10.0, out=actual, size=(3, 2), dtype=np.float32) assert_array_almost_equal(actual, desired, decimal=5) @@ -1211,39 +1209,39 @@ class TestRandomDist(object): assert_raises(ValueError, random.standard_gamma, shape=-0.) def test_standard_normal(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.standard_normal(size=(3, 2)) - desired = np.array([[-3.472754000610961, -0.108938564229143], - [-0.245965753396411, -0.704101550261701], - [0.360102487116356, 0.127832101772367]]) + desired = np.array([[-1.870934851846581, 1.25613495182354 ], + [-1.120190126006621, 0.342002097029821], + [ 0.661545174124296, 1.181113712443012]]) assert_array_almost_equal(actual, desired, decimal=15) def test_standard_normal_unsupported_type(self): assert_raises(TypeError, random.standard_normal, dtype=np.int32) def test_standard_t(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.standard_t(df=10, size=(3, 2)) - desired = np.array([[-3.68722108185508, -0.672031186266171], - [2.900224996448669, -0.199656996187739], - [-1.12179956985969, 1.85668262342106]]) + desired = np.array([[-1.484666193042647, 0.30597891831161 ], + [ 1.056684299648085, -0.407312602088507], + [ 0.130704414281157, -2.038053410490321]]) assert_array_almost_equal(actual, desired, decimal=15) def test_triangular(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.triangular(left=5.12, mode=10.23, right=20.34, size=(3, 2)) - desired = np.array([[12.68117178949215784, 12.4129206149193152], - [16.20131377335158263, 16.25692138747600524], - [11.20400690911820263, 14.4978144835829923]]) + desired = np.array([[ 7.86664070590917, 13.6313848513185 ], + [ 7.68152445215983, 14.36169131136546], + [13.16105603911429, 13.72341621856971]]) assert_array_almost_equal(actual, desired, decimal=14) def test_uniform(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.uniform(low=1.23, high=10.54, size=(3, 2)) - desired = np.array([[6.99097932346268003, 6.73801597444323974], - [9.50364421400426274, 9.53130618907631089], - [5.48995325769805476, 8.47493103280052118]]) + desired = np.array([[2.13306255040998 , 7.816987531021207], + [2.015436610109887, 8.377577533009589], + [7.421792588856135, 7.891185744455209]]) assert_array_almost_equal(actual, desired, decimal=15) def test_uniform_range_bounds(self): @@ -1285,51 +1283,51 @@ class TestRandomDist(object): assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1) def test_vonmises(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2)) - desired = np.array([[2.28567572673902042, 2.89163838442285037], - [0.38198375564286025, 2.57638023113890746], - [1.19153771588353052, 1.83509849681825354]]) + desired = np.array([[ 1.107972248690106, 2.841536476232361], + [ 1.832602376042457, 1.945511926976032], + [-0.260147475776542, 2.058047492231698]]) assert_array_almost_equal(actual, desired, decimal=15) def test_vonmises_small(self): # check infinite loop, gh-4720 - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6) assert_(np.isfinite(r).all()) def test_vonmises_nan(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) r = random.vonmises(mu=0., kappa=np.nan) assert_(np.isnan(r)) def test_wald(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.wald(mean=1.23, scale=1.54, size=(3, 2)) - desired = np.array([[0.10653278160339, 0.98771068102461], - [0.89276055317879, 0.13640126419923], - [0.9194319091599, 0.36037816317472]]) + desired = np.array([[0.26871721804551, 3.2233942732115 ], + [2.20328374987066, 2.40958405189353], + [2.07093587449261, 0.73073890064369]]) assert_array_almost_equal(actual, desired, decimal=14) def test_weibull(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.weibull(a=1.23, size=(3, 2)) - desired = np.array([[3.557276979846361, 1.020870580998542], - [2.731847777612348, 1.29148068905082], - [0.385531483942839, 2.049551716717254]]) + desired = np.array([[0.138613914769468, 1.306463419753191], + [0.111623365934763, 1.446570494646721], + [1.257145775276011, 1.914247725027957]]) assert_array_almost_equal(actual, desired, decimal=15) def test_weibull_0(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) assert_equal(random.weibull(a=0, size=12), np.zeros(12)) assert_raises(ValueError, random.weibull, a=-0.) def test_zipf(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.zipf(a=1.23, size=(3, 2)) - desired = np.array([[66, 29], - [1, 1], - [3, 13]]) + desired = np.array([[ 1, 1], + [ 10, 867], + [354, 2]]) assert_array_equal(actual, desired) @@ -1339,88 +1337,75 @@ class TestBroadcast(object): def setup(self): self.seed = 123456789 - def set_seed(self): - random.bit_generator.seed(self.seed) def test_uniform(self): + random = Generator(MT19937(self.seed)) low = [0] high = [1] uniform = random.uniform - desired = np.array([0.53283302478975902, - 0.53413660089041659, - 0.50955303552646702]) + desired = np.array([0.16693771389729, 0.19635129550675, 0.75563050964095]) - self.set_seed() - actual = uniform(low * 3, high) + random = Generator(MT19937(self.seed)) + actual = random.uniform(low * 3, high) assert_array_almost_equal(actual, desired, decimal=14) - self.set_seed() - actual = uniform(low, high * 3) + random = Generator(MT19937(self.seed)) + actual = random.uniform(low, high * 3) assert_array_almost_equal(actual, desired, decimal=14) def test_normal(self): loc = [0] scale = [1] bad_scale = [-1] - normal = random.normal - desired = np.array([0.454879818179180, - -0.62749179463661, - -0.06063266769872]) + random = Generator(MT19937(self.seed)) + desired = np.array([-0.38736406738527, 0.79594375042255, 0.0197076236097]) - self.set_seed() - actual = normal(loc * 3, scale) + random = Generator(MT19937(self.seed)) + actual = random.normal(loc * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, normal, loc * 3, bad_scale) assert_raises(ValueError, random.normal, loc * 3, bad_scale) - self.set_seed() + random = Generator(MT19937(self.seed)) + normal = random.normal actual = normal(loc, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, normal, loc, bad_scale * 3) - assert_raises(ValueError, random.normal, loc, bad_scale * 3) def test_beta(self): a = [1] b = [2] bad_a = [-1] bad_b = [-2] - beta = random.beta - desired = np.array([0.63222080311226, - 0.33310522220774, - 0.64494078460190]) + desired = np.array([0.18719338682602, 0.73234824491364, 0.17928615186455]) - self.set_seed() + random = Generator(MT19937(self.seed)) + beta = random.beta actual = beta(a * 3, b) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, beta, bad_a * 3, b) assert_raises(ValueError, beta, a * 3, bad_b) - self.set_seed() - actual = beta(a, b * 3) + random = Generator(MT19937(self.seed)) + actual = random.beta(a, b * 3) assert_array_almost_equal(actual, desired, decimal=14) def test_exponential(self): scale = [1] bad_scale = [-1] - exponential = random.exponential - desired = np.array([1.68591211640990, - 3.14186859487914, - 0.67717375919228]) + desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629]) - self.set_seed() - actual = exponential(scale * 3) + random = Generator(MT19937(self.seed)) + actual = random.exponential(scale * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, exponential, bad_scale * 3) + assert_raises(ValueError, random.exponential, bad_scale * 3) def test_standard_gamma(self): shape = [1] bad_shape = [-1] - std_gamma = random.standard_gamma - desired = np.array([1.68591211640990, - 3.14186859487914, - 0.67717375919228]) + desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629]) - self.set_seed() + random = Generator(MT19937(self.seed)) + std_gamma = random.standard_gamma actual = std_gamma(shape * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, std_gamma, bad_shape * 3) @@ -1430,18 +1415,17 @@ class TestBroadcast(object): scale = [2] bad_shape = [-1] bad_scale = [-2] - gamma = random.gamma - desired = np.array([3.37182423281980, - 6.28373718975827, - 1.35434751838456]) + desired = np.array([1.34491986425611, 0.42760990636187, 1.4355697857258]) - self.set_seed() + random = Generator(MT19937(self.seed)) + gamma = random.gamma actual = gamma(shape * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, gamma, bad_shape * 3, scale) assert_raises(ValueError, gamma, shape * 3, bad_scale) - self.set_seed() + random = Generator(MT19937(self.seed)) + gamma = random.gamma actual = gamma(shape, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, gamma, bad_shape, scale * 3) @@ -1452,18 +1436,17 @@ class TestBroadcast(object): dfden = [2] bad_dfnum = [-1] bad_dfden = [-2] - f = random.f - desired = np.array([0.84207044881810, - 3.08607209903483, - 3.12823105933169]) + desired = np.array([0.07765056244107, 7.72951397913186, 0.05786093891763]) - self.set_seed() + random = Generator(MT19937(self.seed)) + f = random.f actual = f(dfnum * 3, dfden) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, f, bad_dfnum * 3, dfden) assert_raises(ValueError, f, dfnum * 3, bad_dfden) - self.set_seed() + random = Generator(MT19937(self.seed)) + f = random.f actual = f(dfnum, dfden * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, f, bad_dfnum, dfden * 3) @@ -1476,12 +1459,10 @@ class TestBroadcast(object): bad_dfnum = [0] bad_dfden = [-1] bad_nonc = [-2] - nonc_f = random.noncentral_f - desired = np.array([3.83710578542563, - 8.74926819712029, - 0.48892943835401]) + desired = np.array([2.02434240411421, 12.91838601070124, 1.24395160354629]) - self.set_seed() + random = Generator(MT19937(self.seed)) + nonc_f = random.noncentral_f actual = nonc_f(dfnum * 3, dfden, nonc) assert_array_almost_equal(actual, desired, decimal=14) assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3))) @@ -1490,14 +1471,16 @@ class TestBroadcast(object): assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc) assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc) - self.set_seed() + random = Generator(MT19937(self.seed)) + nonc_f = random.noncentral_f actual = nonc_f(dfnum, dfden * 3, nonc) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc) assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc) assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc) - self.set_seed() + random = Generator(MT19937(self.seed)) + nonc_f = random.noncentral_f actual = nonc_f(dfnum, dfden, nonc * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3) @@ -1505,41 +1488,37 @@ class TestBroadcast(object): assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3) def test_noncentral_f_small_df(self): - self.set_seed() - desired = np.array([21.57878070681719, 1.17110217503908]) + random = Generator(MT19937(self.seed)) + desired = np.array([0.04714867120827, 0.1239390327694]) actual = random.noncentral_f(0.9, 0.9, 2, size=2) assert_array_almost_equal(actual, desired, decimal=14) def test_chisquare(self): df = [1] bad_df = [-1] - chisquare = random.chisquare - desired = np.array([0.57022801133088286, - 0.51947702108840776, - 0.1320969254923558]) + desired = np.array([0.05573640064251, 1.47220224353539, 2.9469379318589]) - self.set_seed() - actual = chisquare(df * 3) + random = Generator(MT19937(self.seed)) + actual = random.chisquare(df * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, chisquare, bad_df * 3) + assert_raises(ValueError, random.chisquare, bad_df * 3) def test_noncentral_chisquare(self): df = [1] nonc = [2] bad_df = [-1] bad_nonc = [-2] - nonc_chi = random.noncentral_chisquare - desired = np.array([2.20478739452297, - 1.45177405755115, - 1.00418921695354]) + desired = np.array([0.07710766249436, 5.27829115110304, 0.630732147399]) - self.set_seed() + random = Generator(MT19937(self.seed)) + nonc_chi = random.noncentral_chisquare actual = nonc_chi(df * 3, nonc) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, nonc_chi, bad_df * 3, nonc) assert_raises(ValueError, nonc_chi, df * 3, bad_nonc) - self.set_seed() + random = Generator(MT19937(self.seed)) + nonc_chi = random.noncentral_chisquare actual = nonc_chi(df, nonc * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, nonc_chi, bad_df, nonc * 3) @@ -1548,93 +1527,73 @@ class TestBroadcast(object): def test_standard_t(self): df = [1] bad_df = [-1] - t = random.standard_t - desired = np.array([0.60081050724244, - -0.90380889829210, - -0.64499590504117]) + desired = np.array([-1.39498829447098, -1.23058658835223, 0.17207021065983]) - self.set_seed() - actual = t(df * 3) + random = Generator(MT19937(self.seed)) + actual = random.standard_t(df * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, t, bad_df * 3) assert_raises(ValueError, random.standard_t, bad_df * 3) def test_vonmises(self): mu = [2] kappa = [1] bad_kappa = [-1] - vonmises = random.vonmises - desired = np.array([2.9883443664201312, - -2.7064099483995943, - -1.8672476700665914]) + desired = np.array([2.25935584988528, 2.23326261461399, -2.84152146503326]) - self.set_seed() - actual = vonmises(mu * 3, kappa) + random = Generator(MT19937(self.seed)) + actual = random.vonmises(mu * 3, kappa) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, vonmises, mu * 3, bad_kappa) + assert_raises(ValueError, random.vonmises, mu * 3, bad_kappa) - self.set_seed() - actual = vonmises(mu, kappa * 3) + random = Generator(MT19937(self.seed)) + actual = random.vonmises(mu, kappa * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, vonmises, mu, bad_kappa * 3) + assert_raises(ValueError, random.vonmises, mu, bad_kappa * 3) def test_pareto(self): a = [1] bad_a = [-1] - pareto = random.pareto - desired = np.array([4.397371719158540, - 22.14707898642946, - 0.968306954322200]) + desired = np.array([0.95905052946317, 0.2383810889437 , 1.04988745750013]) - self.set_seed() - actual = pareto(a * 3) + random = Generator(MT19937(self.seed)) + actual = random.pareto(a * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, pareto, bad_a * 3) assert_raises(ValueError, random.pareto, bad_a * 3) def test_weibull(self): a = [1] bad_a = [-1] - weibull = random.weibull - desired = np.array([1.68591211640990, - 3.14186859487914, - 0.67717375919228]) + desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629]) - self.set_seed() - actual = weibull(a * 3) + random = Generator(MT19937(self.seed)) + actual = random.weibull(a * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, weibull, bad_a * 3) assert_raises(ValueError, random.weibull, bad_a * 3) def test_power(self): a = [1] bad_a = [-1] - power = random.power - desired = np.array([0.81472463783615, - 0.95679800459547, - 0.49194916077287]) + desired = np.array([0.48954864361052, 0.19249412888486, 0.51216834058807]) - self.set_seed() - actual = power(a * 3) + random = Generator(MT19937(self.seed)) + actual = random.power(a * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, power, bad_a * 3) assert_raises(ValueError, random.power, bad_a * 3) def test_laplace(self): loc = [0] scale = [1] bad_scale = [-1] - laplace = random.laplace - desired = np.array([0.067921356028507157, - 0.070715642226971326, - 0.019290950698972624]) + desired = np.array([-1.09698732625119, -0.93470271947368, 0.71592671378202]) - self.set_seed() + random = Generator(MT19937(self.seed)) + laplace = random.laplace actual = laplace(loc * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, laplace, loc * 3, bad_scale) - self.set_seed() + random = Generator(MT19937(self.seed)) + laplace = random.laplace actual = laplace(loc, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, laplace, loc, bad_scale * 3) @@ -1643,17 +1602,16 @@ class TestBroadcast(object): loc = [0] scale = [1] bad_scale = [-1] - gumbel = random.gumbel - desired = np.array([0.2730318639556768, - 0.26936705726291116, - 0.33906220393037939]) + desired = np.array([1.70020068231762, 1.52054354273631, -0.34293267607081]) - self.set_seed() + random = Generator(MT19937(self.seed)) + gumbel = random.gumbel actual = gumbel(loc * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, gumbel, loc * 3, bad_scale) - self.set_seed() + random = Generator(MT19937(self.seed)) + gumbel = random.gumbel actual = gumbel(loc, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, gumbel, loc, bad_scale * 3) @@ -1662,79 +1620,61 @@ class TestBroadcast(object): loc = [0] scale = [1] bad_scale = [-1] - logistic = random.logistic - desired = np.array([0.13152135837586171, - 0.13675915696285773, - 0.038216792802833396]) + desired = np.array([-1.607487640433, -1.40925686003678, 1.12887112820397]) - self.set_seed() - actual = logistic(loc * 3, scale) + random = Generator(MT19937(self.seed)) + actual = random.logistic(loc * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, logistic, loc * 3, bad_scale) + assert_raises(ValueError, random.logistic, loc * 3, bad_scale) - self.set_seed() - actual = logistic(loc, scale * 3) + random = Generator(MT19937(self.seed)) + actual = random.logistic(loc, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, logistic, loc, bad_scale * 3) + assert_raises(ValueError, random.logistic, loc, bad_scale * 3) assert_equal(random.logistic(1.0, 0.0), 1.0) def test_lognormal(self): mean = [0] sigma = [1] bad_sigma = [-1] - lognormal = random.lognormal - desired = np.array([1.57598396702930, - 0.53392932731280, - 0.94116889802361]) + desired = np.array([0.67884390500697, 2.21653186290321, 1.01990310084276]) - self.set_seed() + random = Generator(MT19937(self.seed)) + lognormal = random.lognormal actual = lognormal(mean * 3, sigma) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, lognormal, mean * 3, bad_sigma) - assert_raises(ValueError, random.lognormal, mean * 3, bad_sigma) - self.set_seed() - actual = lognormal(mean, sigma * 3) - assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, lognormal, mean, bad_sigma * 3) + random = Generator(MT19937(self.seed)) + actual = random.lognormal(mean, sigma * 3) assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3) def test_rayleigh(self): scale = [1] bad_scale = [-1] - rayleigh = random.rayleigh - desired = np.array([1.2337491937897689, - 1.2360119924878694, - 1.1936818095781789]) + desired = np.array([0.60439534475066, 0.66120048396359, 1.67873398389499]) - self.set_seed() - actual = rayleigh(scale * 3) + random = Generator(MT19937(self.seed)) + actual = random.rayleigh(scale * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, rayleigh, bad_scale * 3) + assert_raises(ValueError, random.rayleigh, bad_scale * 3) def test_wald(self): mean = [0.5] scale = [1] bad_mean = [0] bad_scale = [-2] - wald = random.wald - desired = np.array([0.36297361471752, - 0.52190135028254, - 0.55111022040727]) + desired = np.array([0.38052407392905, 0.50701641508592, 0.484935249864]) - self.set_seed() - actual = wald(mean * 3, scale) + random = Generator(MT19937(self.seed)) + actual = random.wald(mean * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, wald, bad_mean * 3, scale) - assert_raises(ValueError, wald, mean * 3, bad_scale) assert_raises(ValueError, random.wald, bad_mean * 3, scale) assert_raises(ValueError, random.wald, mean * 3, bad_scale) - self.set_seed() - actual = wald(mean, scale * 3) + random = Generator(MT19937(self.seed)) + actual = random.wald(mean, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) - assert_raises(ValueError, wald, bad_mean, scale * 3) - assert_raises(ValueError, wald, mean, bad_scale * 3) assert_raises(ValueError, random.wald, bad_mean, scale * 3) assert_raises(ValueError, random.wald, mean, bad_scale * 3) @@ -1745,12 +1685,10 @@ class TestBroadcast(object): bad_left_one = [3] bad_mode_one = [4] bad_left_two, bad_mode_two = right * 2 - triangular = random.triangular - desired = np.array([2.03339048710429, - 2.0347400359389356, - 2.0095991069536208]) + desired = np.array([1.57781954604754, 1.62665986867957, 2.30090130831326]) - self.set_seed() + random = Generator(MT19937(self.seed)) + triangular = random.triangular actual = triangular(left * 3, mode, right) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, triangular, bad_left_one * 3, mode, right) @@ -1758,7 +1696,8 @@ class TestBroadcast(object): assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two, right) - self.set_seed() + random = Generator(MT19937(self.seed)) + triangular = random.triangular actual = triangular(left, mode * 3, right) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, triangular, bad_left_one, mode * 3, right) @@ -1766,7 +1705,8 @@ class TestBroadcast(object): assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3, right) - self.set_seed() + random = Generator(MT19937(self.seed)) + triangular = random.triangular actual = triangular(left, mode, right * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, triangular, bad_left_one, mode, right * 3) @@ -1784,18 +1724,18 @@ class TestBroadcast(object): bad_n = [-1] bad_p_one = [-1] bad_p_two = [1.5] - binom = random.binomial - desired = np.array([1, 1, 1]) + desired = np.array([0, 0, 1]) - self.set_seed() + random = Generator(MT19937(self.seed)) + binom = random.binomial actual = binom(n * 3, p) assert_array_equal(actual, desired) assert_raises(ValueError, binom, bad_n * 3, p) assert_raises(ValueError, binom, n * 3, bad_p_one) assert_raises(ValueError, binom, n * 3, bad_p_two) - self.set_seed() - actual = binom(n, p * 3) + random = Generator(MT19937(self.seed)) + actual = random.binomial(n, p * 3) assert_array_equal(actual, desired) assert_raises(ValueError, binom, bad_n, p * 3) assert_raises(ValueError, binom, n, bad_p_one * 3) @@ -1807,17 +1747,18 @@ class TestBroadcast(object): bad_n = [-1] bad_p_one = [-1] bad_p_two = [1.5] - neg_binom = random.negative_binomial - desired = np.array([3, 1, 2], dtype=np.int64) + desired = np.array([0, 2, 1], dtype=np.int64) - self.set_seed() + random = Generator(MT19937(self.seed)) + neg_binom = random.negative_binomial actual = neg_binom(n * 3, p) assert_array_equal(actual, desired) assert_raises(ValueError, neg_binom, bad_n * 3, p) assert_raises(ValueError, neg_binom, n * 3, bad_p_one) assert_raises(ValueError, neg_binom, n * 3, bad_p_two) - self.set_seed() + random = Generator(MT19937(self.seed)) + neg_binom = random.negative_binomial actual = neg_binom(n, p * 3) assert_array_equal(actual, desired) assert_raises(ValueError, neg_binom, bad_n, p * 3) @@ -1825,15 +1766,15 @@ class TestBroadcast(object): assert_raises(ValueError, neg_binom, n, bad_p_two * 3) def test_poisson(self): - max_lam = random._poisson_lam_max lam = [1] bad_lam_one = [-1] + desired = np.array([0, 0, 3]) + + random = Generator(MT19937(self.seed)) + max_lam = random._poisson_lam_max bad_lam_two = [max_lam * 2] poisson = random.poisson - desired = np.array([1, 1, 0]) - - self.set_seed() actual = poisson(lam * 3) assert_array_equal(actual, desired) assert_raises(ValueError, poisson, bad_lam_one * 3) @@ -1842,10 +1783,10 @@ class TestBroadcast(object): def test_zipf(self): a = [2] bad_a = [0] - zipf = random.zipf - desired = np.array([2, 2, 1]) + desired = np.array([1, 8, 1]) - self.set_seed() + random = Generator(MT19937(self.seed)) + zipf = random.zipf actual = zipf(a * 3) assert_array_equal(actual, desired) assert_raises(ValueError, zipf, bad_a * 3) @@ -1857,14 +1798,14 @@ class TestBroadcast(object): p = [0.5] bad_p_one = [-1] bad_p_two = [1.5] - geom = random.geometric - desired = np.array([2, 2, 2]) + desired = np.array([1, 1, 3]) - self.set_seed() - actual = geom(p * 3) + random = Generator(MT19937(self.seed)) + geometric = random.geometric + actual = geometric(p * 3) assert_array_equal(actual, desired) - assert_raises(ValueError, geom, bad_p_one * 3) - assert_raises(ValueError, geom, bad_p_two * 3) + assert_raises(ValueError, geometric, bad_p_one * 3) + assert_raises(ValueError, geometric, bad_p_two * 3) def test_hypergeometric(self): ngood = [1] @@ -1874,26 +1815,26 @@ class TestBroadcast(object): bad_nbad = [-2] bad_nsample_one = [-1] bad_nsample_two = [4] - hypergeom = random.hypergeometric desired = np.array([0, 0, 1]) - self.set_seed() - actual = hypergeom(ngood * 3, nbad, nsample) + random = Generator(MT19937(self.seed)) + actual = random.hypergeometric(ngood * 3, nbad, nsample) assert_array_equal(actual, desired) - assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample) - assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample) - assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one) - assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two) + assert_raises(ValueError, random.hypergeometric, bad_ngood * 3, nbad, nsample) + assert_raises(ValueError, random.hypergeometric, ngood * 3, bad_nbad, nsample) + assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_one) + assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_two) - self.set_seed() - actual = hypergeom(ngood, nbad * 3, nsample) + random = Generator(MT19937(self.seed)) + actual = random.hypergeometric(ngood, nbad * 3, nsample) assert_array_equal(actual, desired) - assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample) - assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample) - assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one) - assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two) + assert_raises(ValueError, random.hypergeometric, bad_ngood, nbad * 3, nsample) + assert_raises(ValueError, random.hypergeometric, ngood, bad_nbad * 3, nsample) + assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_one) + assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_two) - self.set_seed() + random = Generator(MT19937(self.seed)) + hypergeom = random.hypergeometric actual = hypergeom(ngood, nbad, nsample * 3) assert_array_equal(actual, desired) assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3) @@ -1915,30 +1856,30 @@ class TestBroadcast(object): p = [0.5] bad_p_one = [2] bad_p_two = [-1] - logseries = random.logseries desired = np.array([1, 1, 1]) - self.set_seed() + random = Generator(MT19937(self.seed)) + logseries = random.logseries actual = logseries(p * 3) assert_array_equal(actual, desired) assert_raises(ValueError, logseries, bad_p_one * 3) assert_raises(ValueError, logseries, bad_p_two * 3) def test_multinomial(self): - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.multinomial([5, 20], [1 / 6.] * 6, size=(3, 2)) - desired = np.array([[[1, 1, 1, 1, 0, 1], - [4, 5, 1, 4, 3, 3]], - [[1, 1, 1, 0, 0, 2], - [2, 0, 4, 3, 7, 4]], - [[1, 2, 0, 0, 2, 0], - [3, 2, 3, 4, 2, 6]]], dtype=np.int64) + desired = np.array([[[0, 0, 2, 1, 2, 0], + [2, 3, 6, 4, 2, 3]], + [[1, 0, 1, 0, 2, 1], + [7, 2, 2, 1, 4, 4]], + [[0, 2, 0, 1, 2, 0], + [3, 2, 3, 3, 4, 5]]], dtype=np.int64) assert_array_equal(actual, desired) - random.bit_generator.seed(self.seed) + random = Generator(MT19937(self.seed)) actual = random.multinomial([5, 20], [1 / 6.] * 6) - desired = np.array([[1, 1, 1, 1, 0, 1], - [4, 5, 1, 4, 3, 3]], dtype=np.int64) + desired = np.array([[0, 0, 2, 1, 2, 0], + [2, 3, 6, 4, 2, 3]], dtype=np.int64) assert_array_equal(actual, desired) diff --git a/numpy/random/tests/test_generator_mt19937_regressions.py b/numpy/random/tests/test_generator_mt19937_regressions.py index 7dca65071..3a937f997 100644 --- a/numpy/random/tests/test_generator_mt19937_regressions.py +++ b/numpy/random/tests/test_generator_mt19937_regressions.py @@ -29,7 +29,7 @@ class TestRegression(object): def test_logseries_convergence(self): # Test for ticket #923 N = 1000 - mt19937.bit_generator.seed(0) + mt19937 = Generator(MT19937(0)) rvsn = mt19937.logseries(0.8, size=N) # these two frequency counts should be close to theoretical # numbers with this large sample @@ -43,9 +43,9 @@ class TestRegression(object): assert_(freq < 0.23, msg) def test_permutation_longs(self): - mt19937.bit_generator.seed(1234) + mt19937 = Generator(MT19937(1234)) a = mt19937.permutation(12) - mt19937.bit_generator.seed(1234) + mt19937 = Generator(MT19937(1234)) b = mt19937.permutation(long(12)) assert_array_equal(a, b) @@ -55,18 +55,17 @@ class TestRegression(object): [(1, 1), (2, 2), (3, 3), None], [1, (2, 2), (3, 3), None], [(1, 1), 2, 3, None]]: - mt19937.bit_generator.seed(12345) + mt19937 = Generator(MT19937(12345)) shuffled = list(t) mt19937.shuffle(shuffled) - assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]]) + assert_array_equal(shuffled, [t[2], t[0], t[3], t[1]]) def test_call_within_randomstate(self): - # Check that custom RandomState does not call into global state - m = Generator(MT19937()) # mt19937.RandomState() - res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3]) + # Check that custom BitGenerator does not call into global state + res = np.array([1, 8, 0, 1, 5, 3, 3, 8, 1, 4]) for i in range(3): - mt19937.bit_generator.seed(i) - m.bit_generator.seed(4321) + mt19937 = Generator(MT19937(i)) + m = Generator(MT19937(4321)) # If m.state is not honored, the result will change assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res) @@ -81,7 +80,7 @@ class TestRegression(object): def test_beta_small_parameters(self): # Test that beta with small a and b parameters does not produce # NaNs due to roundoff errors causing 0 / 0, gh-5851 - mt19937.bit_generator.seed(1234567890) + mt19937 = Generator(MT19937(1234567890)) x = mt19937.beta(0.0001, 0.0001, size=100) assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta') @@ -89,7 +88,7 @@ class TestRegression(object): # The sum of probs should be 1.0 with some tolerance. # For low precision dtypes the tolerance was too tight. # See numpy github issue 6123. - mt19937.bit_generator.seed(1234) + mt19937 = Generator(MT19937(1234)) a = [1, 2, 3] counts = [4, 4, 2] for dt in np.float16, np.float32, np.float64: @@ -103,7 +102,7 @@ class TestRegression(object): # Test that permuting an array of different length strings # will not cause a segfault on garbage collection # Tests gh-7710 - mt19937.bit_generator.seed(1234) + mt19937 = Generator(MT19937(1234)) a = np.array(['a', 'a' * 1000]) @@ -118,7 +117,7 @@ class TestRegression(object): # Test that permuting an array of objects will not cause # a segfault on garbage collection. # See gh-7719 - mt19937.bit_generator.seed(1234) + mt19937 = Generator(MT19937(1234)) a = np.array([np.arange(1), np.arange(4)]) for _ in range(1000): @@ -132,10 +131,10 @@ class TestRegression(object): class N(np.ndarray): pass - mt19937.bit_generator.seed(1) + mt19937 = Generator(MT19937(1)) orig = np.arange(3).view(N) perm = mt19937.permutation(orig) - assert_array_equal(perm, np.array([0, 2, 1])) + assert_array_equal(perm, np.array([2, 0, 1])) assert_array_equal(orig, np.arange(3).view(N)) class M(object): @@ -144,10 +143,10 @@ class TestRegression(object): def __array__(self): return self.a - mt19937.bit_generator.seed(1) + mt19937 = Generator(MT19937(1)) m = M() perm = mt19937.permutation(m) - assert_array_equal(perm, np.array([2, 1, 4, 0, 3])) + assert_array_equal(perm, np.array([4, 1, 3, 0, 2])) assert_array_equal(m.__array__(), np.arange(5)) def test_gamma_0(self): diff --git a/numpy/random/tests/test_randomstate.py b/numpy/random/tests/test_randomstate.py index 4acdff114..3b5a279a3 100644 --- a/numpy/random/tests/test_randomstate.py +++ b/numpy/random/tests/test_randomstate.py @@ -98,11 +98,10 @@ class TestSeed(object): assert_raises(ValueError, random.RandomState, [[1, 2, 3], [4, 5, 6]]) - def test_seed_equivalency(self): - rs = random.RandomState(0) - rs2 = random.RandomState(MT19937(0)) - assert_mt19937_state_equal(rs.get_state(legacy=False), - rs2.get_state(legacy=False)) + def test_cannot_seed(self): + rs = random.RandomState(PCG64(0)) + with assert_raises(TypeError): + rs.seed(1234) def test_invalid_initialization(self): assert_raises(ValueError, random.RandomState, MT19937) diff --git a/numpy/random/tests/test_seed_sequence.py b/numpy/random/tests/test_seed_sequence.py new file mode 100644 index 000000000..34278375a --- /dev/null +++ b/numpy/random/tests/test_seed_sequence.py @@ -0,0 +1,40 @@ +import numpy as np +from numpy.testing import assert_array_equal + +from numpy.random.bit_generator import SeedSequence + + +def test_reference_data(): + """ Check that SeedSequence generates data the same as the C++ reference. + + https://gist.github.com/imneme/540829265469e673d045 + """ + inputs = [ + [3735928559, 195939070, 229505742, 305419896], + [3668361503, 4165561550, 1661411377, 3634257570], + [164546577, 4166754639, 1765190214, 1303880213], + [446610472, 3941463886, 522937693, 1882353782], + [1864922766, 1719732118, 3882010307, 1776744564], + [4141682960, 3310988675, 553637289, 902896340], + [1134851934, 2352871630, 3699409824, 2648159817], + [1240956131, 3107113773, 1283198141, 1924506131], + [2669565031, 579818610, 3042504477, 2774880435], + [2766103236, 2883057919, 4029656435, 862374500], + ] + outputs = [ + [3914649087, 576849849, 3593928901, 2229911004], + [2240804226, 3691353228, 1365957195, 2654016646], + [3562296087, 3191708229, 1147942216, 3726991905], + [1403443605, 3591372999, 1291086759, 441919183], + [1086200464, 2191331643, 560336446, 3658716651], + [3249937430, 2346751812, 847844327, 2996632307], + [2584285912, 4034195531, 3523502488, 169742686], + [959045797, 3875435559, 1886309314, 359682705], + [3978441347, 432478529, 3223635119, 138903045], + [296367413, 4262059219, 13109864, 3283683422], + ] + for seed, expected in zip(inputs, outputs): + expected = np.array(expected, dtype=np.uint32) + ss = SeedSequence(seed) + state = ss.generate_state(len(expected)) + assert_array_equal(state, expected) diff --git a/numpy/random/tests/test_smoke.py b/numpy/random/tests/test_smoke.py index c09c6db82..c512cb26c 100644 --- a/numpy/random/tests/test_smoke.py +++ b/numpy/random/tests/test_smoke.py @@ -456,56 +456,34 @@ class RNG(object): else: dtype = np.uint64 seed = np.array([1], dtype=dtype) - self.rg.bit_generator.seed(seed) - state1 = self.rg.bit_generator.state - self.rg.bit_generator.seed(1) - state2 = self.rg.bit_generator.state + bg = self.bit_generator(seed) + state1 = bg.state + bg = self.bit_generator(1) + state2 = bg.state assert_(comp_state(state1, state2)) seed = np.arange(4, dtype=dtype) - self.rg.bit_generator.seed(seed) - state1 = self.rg.bit_generator.state - self.rg.bit_generator.seed(seed[0]) - state2 = self.rg.bit_generator.state + bg = self.bit_generator(seed) + state1 = bg.state + bg = self.bit_generator(seed[0]) + state2 = bg.state assert_(not comp_state(state1, state2)) seed = np.arange(1500, dtype=dtype) - self.rg.bit_generator.seed(seed) - state1 = self.rg.bit_generator.state - self.rg.bit_generator.seed(seed[0]) - state2 = self.rg.bit_generator.state + bg = self.bit_generator(seed) + state1 = bg.state + bg = self.bit_generator(seed[0]) + state2 = bg.state assert_(not comp_state(state1, state2)) seed = 2 ** np.mod(np.arange(1500, dtype=dtype), self.seed_vector_bits - 1) + 1 - self.rg.bit_generator.seed(seed) - state1 = self.rg.bit_generator.state - self.rg.bit_generator.seed(seed[0]) - state2 = self.rg.bit_generator.state + bg = self.bit_generator(seed) + state1 = bg.state + bg = self.bit_generator(seed[0]) + state2 = bg.state assert_(not comp_state(state1, state2)) - def test_seed_array_error(self): - if self.seed_vector_bits == 32: - out_of_bounds = 2 ** 32 - else: - out_of_bounds = 2 ** 64 - - seed = -1 - with pytest.raises(ValueError): - self.rg.bit_generator.seed(seed) - - seed = np.array([-1], dtype=np.int32) - with pytest.raises(ValueError): - self.rg.bit_generator.seed(seed) - - seed = np.array([1, 2, 3, -5], dtype=np.int32) - with pytest.raises(ValueError): - self.rg.bit_generator.seed(seed) - - seed = np.array([1, 2, 3, out_of_bounds]) - with pytest.raises(ValueError): - self.rg.bit_generator.seed(seed) - def test_uniform_float(self): rg = Generator(self.bit_generator(12345)) warmup(rg) @@ -792,39 +770,3 @@ class TestEntropy(object): e2 = entropy.random_entropy(source='fallback') assert_((e1 != e2)) - -class TestPCG64(RNG): - @classmethod - def setup_class(cls): - cls.bit_generator = PCG64 - cls.advance = 2 ** 96 + 2 ** 48 + 2 ** 21 + 2 ** 16 + 2 ** 5 + 1 - cls.seed = [2 ** 96 + 2 ** 48 + 2 ** 21 + 2 ** 16 + 2 ** 5 + 1, - 2 ** 21 + 2 ** 16 + 2 ** 5 + 1] - cls.rg = Generator(cls.bit_generator(*cls.seed)) - cls.initial_state = cls.rg.bit_generator.state - cls.seed_vector_bits = None - cls._extra_setup() - - def test_seed_array_error(self): - # GH #82 for error type changes - if self.seed_vector_bits == 32: - out_of_bounds = 2 ** 32 - else: - out_of_bounds = 2 ** 64 - - seed = -1 - with pytest.raises(ValueError): - self.rg.bit_generator.seed(seed) - - error_type = ValueError if self.seed_vector_bits else TypeError - seed = np.array([-1], dtype=np.int32) - with pytest.raises(error_type): - self.rg.bit_generator.seed(seed) - - seed = np.array([1, 2, 3, -5], dtype=np.int32) - with pytest.raises(error_type): - self.rg.bit_generator.seed(seed) - - seed = np.array([1, 2, 3, out_of_bounds]) - with pytest.raises(error_type): - self.rg.bit_generator.seed(seed) |