Python numpy.random.RandomState() Examples
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code examples of numpy.random.RandomState().
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Example #1
Source File: init.py From deep_complex_networks with MIT License | 7 votes |
def __call__(self, shape, dtype=None): if self.nb_filters is not None: kernel_shape = tuple(self.kernel_size) + (int(self.input_dim), self.nb_filters) else: kernel_shape = (int(self.input_dim), self.kernel_size[-1]) fan_in, fan_out = initializers._compute_fans( tuple(self.kernel_size) + (self.input_dim, self.nb_filters) ) if self.criterion == 'glorot': s = 1. / (fan_in + fan_out) elif self.criterion == 'he': s = 1. / fan_in else: raise ValueError('Invalid criterion: ' + self.criterion) rng = RandomState(self.seed) modulus = rng.rayleigh(scale=s, size=kernel_shape) phase = rng.uniform(low=-np.pi, high=np.pi, size=kernel_shape) weight_real = modulus * np.cos(phase) weight_imag = modulus * np.sin(phase) weight = np.concatenate([weight_real, weight_imag], axis=-1) return weight
Example #2
Source File: test_algos.py From vnpy_crypto with MIT License | 6 votes |
def test_group_var_generic_1d(self): prng = RandomState(1234) out = (np.nan * np.ones((5, 1))).astype(self.dtype) counts = np.zeros(5, dtype='int64') values = 10 * prng.rand(15, 1).astype(self.dtype) labels = np.tile(np.arange(5), (3, )).astype('int64') expected_out = (np.squeeze(values) .reshape((5, 3), order='F') .std(axis=1, ddof=1) ** 2)[:, np.newaxis] expected_counts = counts + 3 self.algo(out, counts, values, labels) assert np.allclose(out, expected_out, self.rtol) tm.assert_numpy_array_equal(counts, expected_counts)
Example #3
Source File: training_chainer_test.py From knmt with GNU General Public License v3.0 | 6 votes |
def generate_random_dataset(dataset_size, sequence_avg_size): """ Build a dummy data structure with random numbers similar to our usual datasets that is a list of pairs of sequences of numbers: [ ( [1, 2, 10, ...], [3, 5, 6, 7, ...]), ( [5, 3], [34, 23, 44, 1, ...] ), ... ] """ random_generator = RandomState(42) dataset = [] for i in range(0, dataset_size): item = [] # Each item contains 2 sequences. for j in range(0, 2): sequence_length = random_generator.randint(sequence_avg_size - 5, sequence_avg_size + 5) # sequence_length = random_generator.randint(1, 5) sequence = random_generator.randint(0, 100, size=sequence_length) item.append(sequence) item = tuple(item) dataset.append(item) return dataset
Example #4
Source File: test_common.py From vnpy_crypto with MIT License | 6 votes |
def test_random_state(): import numpy.random as npr # Check with seed state = com._random_state(5) assert state.uniform() == npr.RandomState(5).uniform() # Check with random state object state2 = npr.RandomState(10) assert (com._random_state(state2).uniform() == npr.RandomState(10).uniform()) # check with no arg random state assert com._random_state() is np.random # Error for floats or strings with pytest.raises(ValueError): com._random_state('test') with pytest.raises(ValueError): com._random_state(5.5)
Example #5
Source File: test_core.py From permute with BSD 2-Clause "Simplified" License | 6 votes |
def test_two_sample_conf_int(): prng = RandomState(42) # Shift is -1 x = np.array(range(5)) y = np.array(range(1, 6)) res = two_sample_conf_int(x, y, seed=prng) expected_ci = (-3.5, 1.0012461) assert_almost_equal(res, expected_ci) res = two_sample_conf_int(x, y, seed=prng, alternative="upper") expected_ci = (-5, 1) assert_almost_equal(res, expected_ci) res = two_sample_conf_int(x, y, seed=prng, alternative="lower") expected_ci = (-3, 5) assert_almost_equal(res, expected_ci) # Specify shift with a function pair shift = (lambda u, d: u + d, lambda u, d: u - d) res = two_sample_conf_int(x, y, seed=5, shift=shift) assert_almost_equal(res, (-3.5, 1)) # Specify shift with a multiplicative pair shift = (lambda u, d: u * d, lambda u, d: u / d) res = two_sample_conf_int(x, y, seed=5, shift=shift) assert_almost_equal(res, (-1, -1))
Example #6
Source File: test_kl.py From bayeslite with Apache License 2.0 | 6 votes |
def compute_kullback_leibler_check_statistic(n=100, prngstate=None): """Compute the lowest of the survival function and the CDF of the exact KL divergence KL(N(mu1,s1)||N(mu2,s2)) w.r.t. the sample distribution of the KL divergence drawn by computing log(P(x|N(mu1,s1)))-log(P(x|N(mu2,s2))) over a sample x~N(mu1,s1). If we are computing the KL divergence accurately, the exact value should fall squarely in the sample, and the tail probabilities should be relatively large. """ if prngstate is None: raise TypeError('Must explicitly specify numpy.random.RandomState') mu1 = mu2 = 0 s1 = 1 s2 = 2 exact = gaussian_kl_divergence(mu1, s1, mu2, s2) sample = prngstate.normal(mu1, s1, n) lpdf1 = gaussian_log_pdf(mu1, s1) lpdf2 = gaussian_log_pdf(mu2, s2) estimate, std = kl.kullback_leibler(sample, lpdf1, lpdf2) # This computes the minimum of the left and right tail probabilities of the # exact KL divergence vs a gaussian fit to the sample estimate. There is a # distinct negative skew to the samples used to compute `estimate`, so this # statistic is not uniform. Nonetheless, we do not expect it to get too # small. return erfc(abs(exact - estimate) / std) / 2
Example #7
Source File: test_threshold.py From bayeslite with Apache License 2.0 | 6 votes |
def test_failprob_threshold_basic(): """Sanity check on failprob_threshold: Verify, for a relatively large failure probability, that the failure threshold it returns for a simple test statistic actually results in failures at approximately the right frequency. """ prngstate = RandomState(0) def sample(n): return prngstate.normal(0, 1, n) target_prob = 1e-1 test_sample_size = 6 prob, thresh = failprob_threshold( sample(1000), test_sample_size, target_prob) samples = [all(v < thresh for v in sample(test_sample_size)) for _ in xrange(int(100 / target_prob))] assert 50 < samples.count(True) < 200
Example #8
Source File: test_utils.py From permute with BSD 2-Clause "Simplified" License | 6 votes |
def test_permute_incidence_fixed_sums(): prng = RandomState(42) x0 = prng.randint(2, size=80).reshape((8, 10)) x1 = permute_incidence_fixed_sums(x0) K = 5 m = [] for i in range(1000): x2 = permute_incidence_fixed_sums(x0, k=K) m.append(np.sum(x0 != x2)) np.testing.assert_(max(m) <= K * 4, "Too many swaps occurred") for axis in (0, 1): for test_arr in (x1, x2): np.testing.assert_array_equal(x0.sum(axis=axis), test_arr.sum(axis=axis))
Example #9
Source File: test_common.py From recruit with Apache License 2.0 | 6 votes |
def test_random_state(): import numpy.random as npr # Check with seed state = com.random_state(5) assert state.uniform() == npr.RandomState(5).uniform() # Check with random state object state2 = npr.RandomState(10) assert com.random_state(state2).uniform() == npr.RandomState(10).uniform() # check with no arg random state assert com.random_state() is np.random # Error for floats or strings with pytest.raises(ValueError): com.random_state('test') with pytest.raises(ValueError): com.random_state(5.5)
Example #10
Source File: test_npc.py From permute with BSD 2-Clause "Simplified" License | 6 votes |
def test_npc(): prng = RandomState(55) pvalues = np.linspace(0.05, 0.9, num=5) distr = prng.uniform(low=0, high=10, size=500).reshape(100, 5) res = npc(pvalues, distr, "fisher", "greater", plus1=False) np.testing.assert_almost_equal(res, 0.33) res = npc(pvalues, distr, "fisher", "less", plus1=False) np.testing.assert_almost_equal(res, 0.33) res = npc(pvalues, distr, "fisher", "two-sided", plus1=False) np.testing.assert_almost_equal(res, 0.31) res = npc(pvalues, distr, "liptak", "greater", plus1=False) np.testing.assert_almost_equal(res, 0.35) res = npc(pvalues, distr, "tippett", "greater", plus1=False) np.testing.assert_almost_equal(res, 0.25) res = npc(pvalues, distr, "fisher", alternatives=np.array(["less", "greater", "less", "greater", "two-sided"]), plus1=False) np.testing.assert_almost_equal(res, 0.38)
Example #11
Source File: test_random.py From vnpy_crypto with MIT License | 6 votes |
def check_function(self, function, sz): from threading import Thread out1 = np.empty((len(self.seeds),) + sz) out2 = np.empty((len(self.seeds),) + sz) # threaded generation t = [Thread(target=function, args=(np.random.RandomState(s), o)) for s, o in zip(self.seeds, out1)] [x.start() for x in t] [x.join() for x in t] # the same serial for s, o in zip(self.seeds, out2): function(np.random.RandomState(s), o) # these platforms change x87 fpu precision mode in threads if np.intp().dtype.itemsize == 4 and sys.platform == "win32": assert_array_almost_equal(out1, out2) else: assert_array_equal(out1, out2)
Example #12
Source File: test_algos.py From recruit with Apache License 2.0 | 6 votes |
def test_group_var_generic_1d(self): prng = RandomState(1234) out = (np.nan * np.ones((5, 1))).astype(self.dtype) counts = np.zeros(5, dtype='int64') values = 10 * prng.rand(15, 1).astype(self.dtype) labels = np.tile(np.arange(5), (3, )).astype('int64') expected_out = (np.squeeze(values) .reshape((5, 3), order='F') .std(axis=1, ddof=1) ** 2)[:, np.newaxis] expected_counts = counts + 3 self.algo(out, counts, values, labels) assert np.allclose(out, expected_out, self.rtol) tm.assert_numpy_array_equal(counts, expected_counts)
Example #13
Source File: algorithm.py From NiaPy with MIT License | 6 votes |
def defaultNumPyInit(task, NP, rnd=rand, **kwargs): r"""Initialize starting population that is represented with `numpy.ndarray` with shape `{NP, task.D}`. Args: task (Task): Optimization task. NP (int): Number of individuals in population. rnd (Optional[mtrand.RandomState]): Random number generator. kwargs (Dict[str, Any]): Additional arguments. Returns: Tuple[numpy.ndarray, numpy.ndarray[float]]: 1. New population with shape `{NP, task.D}`. 2. New population function/fitness values. """ pop = task.Lower + rnd.rand(NP, task.D) * task.bRange fpop = apply_along_axis(task.eval, 1, pop) return pop, fpop
Example #14
Source File: algorithm.py From NiaPy with MIT License | 6 votes |
def defaultIndividualInit(task, NP, rnd=rand, itype=None, **kwargs): r"""Initialize `NP` individuals of type `itype`. Args: task (Task): Optimization task. NP (int): Number of individuals in population. rnd (Optional[mtrand.RandomState]): Random number generator. itype (Optional[Individual]): Class of individual in population. kwargs (Dict[str, Any]): Additional arguments. Returns: Tuple[numpy.ndarray[Individual], numpy.ndarray[float]: 1. Initialized individuals. 2. Initialized individuals function/fitness values. """ pop = objects2array([itype(task=task, rnd=rnd, e=True) for _ in range(NP)]) return pop, asarray([x.f for x in pop])
Example #15
Source File: test_arima.py From pmdarima with MIT License | 6 votes |
def test_issue_30(): # From the issue: vec = np.array([33., 44., 58., 49., 46., 98., 97.]) arm = AutoARIMA(out_of_sample_size=1, seasonal=False, suppress_warnings=True) arm.fit(vec) # This is a way to force it: ARIMA(order=(0, 1, 0), out_of_sample_size=1).fit(vec) # Want to make sure it works with exog arrays as well exog = np.random.RandomState(1).rand(vec.shape[0], 2) auto_arima(vec, exogenous=exog, out_of_sample_size=1, seasonal=False, suppress_warnings=True) # This is a way to force it: ARIMA(order=(0, 1, 0), out_of_sample_size=1).fit(vec, exogenous=exog)
Example #16
Source File: test_npc.py From permute with BSD 2-Clause "Simplified" License | 5 votes |
def test_minp_one_pvalue(): prng = RandomState(55) pvalues = np.array([1]) distr = prng.uniform(low=0, high=10, size=20).reshape(20, 1) npc(pvalues, distr, "fisher", "greater") # TODO: more fwer_minp tests
Example #17
Source File: test_arima.py From pmdarima with MIT License | 5 votes |
def test_oob_for_issue_29(): dta = sm.datasets.sunspots.load_pandas().data dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008')) del dta["YEAR"] xreg = np.random.RandomState(1).rand(dta.shape[0], 3) # Try for cv on/off, various D levels, and various Xregs for d in (0, 1): for cv in (0, 3): for exog in (xreg, None): # surround with try/except so we can log the failing combo try: model = ARIMA(order=(2, d, 0), out_of_sample_size=cv).fit(dta, exogenous=exog) # If exogenous is defined, we need to pass n_periods of # exogenous rows to the predict function. Otherwise we'll # just leave it at None if exog is not None: xr = exog[:3, :] else: xr = None _, _ = model.predict(n_periods=3, return_conf_int=True, exogenous=xr) # Statsmodels can be fragile with ARMA coefficient # computation. If we encounter that, pass: # ValueError: The computed initial MA coefficients are # not invertible. You should induce invertibility, # choose a different model order, or ... except Exception as ex: # print("Failing combo: d=%i, cv=%i, exog=%r" # % (d, cv, exog)) if "invertibility" in pytest_error_str(ex): pass else: raise
Example #18
Source File: test_arima.py From pmdarima with MIT License | 5 votes |
def test_with_oob_and_exog(as_pd): endog = hr exog = np.random.RandomState(1).rand(hr.shape[0], 3) if as_pd: exog = pd.DataFrame.from_records(exog) endog = pd.Series(hr) arima = ARIMA(order=(2, 1, 2), suppress_warnings=True, scoring='mse', out_of_sample_size=10).fit(y=endog, exogenous=exog) # show we can get oob score and preds arima.oob()
Example #19
Source File: test_npc.py From permute with BSD 2-Clause "Simplified" License | 5 votes |
def test_npc_bad_alternative(): prng = RandomState(55) pvalues = np.linspace(0.05, 0.9, num=5) distr = prng.uniform(low=0, high=10, size=50).reshape(10, 5) npc(pvalues, distr, "fisher", np.array(["greater", "less"]))
Example #20
Source File: test_npc.py From permute with BSD 2-Clause "Simplified" License | 5 votes |
def test_minp_bad_distr(): prng = RandomState(55) pvalues = np.linspace(0.05, 0.9, num=5) distr = prng.uniform(low=0, high=10, size=20).reshape(10, 2) fwer_minp(pvalues, distr, "fisher", "greater")
Example #21
Source File: test_morestats.py From Computable with MIT License | 5 votes |
def test_normal(self): rs = RandomState(1234567890) x1 = rs.standard_exponential(size=50) x2 = rs.standard_normal(size=50) A,crit,sig = stats.anderson(x1) assert_array_less(crit[:-1], A) A,crit,sig = stats.anderson(x2) assert_array_less(A, crit[-2:])
Example #22
Source File: test_algos.py From vnpy_crypto with MIT License | 5 votes |
def test_group_var_large_inputs(self): prng = RandomState(1234) out = np.array([[np.nan]], dtype=self.dtype) counts = np.array([0], dtype='int64') values = (prng.rand(10 ** 6) + 10 ** 12).astype(self.dtype) values.shape = (10 ** 6, 1) labels = np.zeros(10 ** 6, dtype='int64') self.algo(out, counts, values, labels) assert counts[0] == 10 ** 6 tm.assert_almost_equal(out[0, 0], 1.0 / 12, check_less_precise=True)
Example #23
Source File: test_algos.py From vnpy_crypto with MIT License | 5 votes |
def test_group_var_generic_2d_all_finite(self): prng = RandomState(1234) out = (np.nan * np.ones((5, 2))).astype(self.dtype) counts = np.zeros(5, dtype='int64') values = 10 * prng.rand(10, 2).astype(self.dtype) labels = np.tile(np.arange(5), (2, )).astype('int64') expected_out = np.std(values.reshape(2, 5, 2), ddof=1, axis=0) ** 2 expected_counts = counts + 2 self.algo(out, counts, values, labels) assert np.allclose(out, expected_out, self.rtol) tm.assert_numpy_array_equal(counts, expected_counts)
Example #24
Source File: test_algos.py From vnpy_crypto with MIT License | 5 votes |
def test_group_var_generic_1d_flat_labels(self): prng = RandomState(1234) out = (np.nan * np.ones((1, 1))).astype(self.dtype) counts = np.zeros(1, dtype='int64') values = 10 * prng.rand(5, 1).astype(self.dtype) labels = np.zeros(5, dtype='int64') expected_out = np.array([[values.std(ddof=1) ** 2]]) expected_counts = counts + 5 self.algo(out, counts, values, labels) assert np.allclose(out, expected_out, self.rtol) tm.assert_numpy_array_equal(counts, expected_counts)
Example #25
Source File: test_random.py From vnpy_crypto with MIT License | 5 votes |
def test_poisson(self): max_lam = np.random.RandomState().poisson_lam_max lam = [1] bad_lam_one = [-1] bad_lam_two = [max_lam * 2] poisson = np.random.poisson desired = np.array([1, 1, 0]) self.setSeed() actual = poisson(lam * 3) assert_array_equal(actual, desired) assert_raises(ValueError, poisson, bad_lam_one * 3) assert_raises(ValueError, poisson, bad_lam_two * 3)
Example #26
Source File: test_random.py From vnpy_crypto with MIT License | 5 votes |
def test_invalid_array_shape(self): # gh-9832 assert_raises(ValueError, np.random.RandomState, np.array([], dtype=np.int64)) assert_raises(ValueError, np.random.RandomState, [[1, 2, 3]]) assert_raises(ValueError, np.random.RandomState, [[1, 2, 3], [4, 5, 6]])
Example #27
Source File: test_random.py From vnpy_crypto with MIT License | 5 votes |
def test_invalid_array(self): # seed must be an unsigned 32 bit integer assert_raises(TypeError, np.random.RandomState, [-0.5]) assert_raises(ValueError, np.random.RandomState, [-1]) assert_raises(ValueError, np.random.RandomState, [4294967296]) assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296]) assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296])
Example #28
Source File: test_random.py From vnpy_crypto with MIT License | 5 votes |
def test_invalid_scalar(self): # seed must be an unsigned 32 bit integer assert_raises(TypeError, np.random.RandomState, -0.5) assert_raises(ValueError, np.random.RandomState, -1)
Example #29
Source File: test_random.py From vnpy_crypto with MIT License | 5 votes |
def test_array(self): s = np.random.RandomState(range(10)) assert_equal(s.randint(1000), 468) s = np.random.RandomState(np.arange(10)) assert_equal(s.randint(1000), 468) s = np.random.RandomState([0]) assert_equal(s.randint(1000), 973) s = np.random.RandomState([4294967295]) assert_equal(s.randint(1000), 265)
Example #30
Source File: test_random.py From vnpy_crypto with MIT License | 5 votes |
def test_scalar(self): s = np.random.RandomState(0) assert_equal(s.randint(1000), 684) s = np.random.RandomState(4294967295) assert_equal(s.randint(1000), 419)