Chris@87: from __future__ import division, absolute_import, print_function
Chris@87: 
Chris@87: from numpy.testing import (TestCase, run_module_suite, assert_,
Chris@87:                            assert_array_equal)
Chris@87: from numpy import random
Chris@87: from numpy.compat import long
Chris@87: import numpy as np
Chris@87: 
Chris@87: 
Chris@87: class TestRegression(TestCase):
Chris@87: 
Chris@87:     def test_VonMises_range(self):
Chris@87:         # Make sure generated random variables are in [-pi, pi].
Chris@87:         # Regression test for ticket #986.
Chris@87:         for mu in np.linspace(-7., 7., 5):
Chris@87:             r = random.mtrand.vonmises(mu, 1, 50)
Chris@87:             assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
Chris@87: 
Chris@87:     def test_hypergeometric_range(self):
Chris@87:         # Test for ticket #921
Chris@87:         assert_(np.all(np.random.hypergeometric(3, 18, 11, size=10) < 4))
Chris@87:         assert_(np.all(np.random.hypergeometric(18, 3, 11, size=10) > 0))
Chris@87: 
Chris@87:     def test_logseries_convergence(self):
Chris@87:         # Test for ticket #923
Chris@87:         N = 1000
Chris@87:         np.random.seed(0)
Chris@87:         rvsn = np.random.logseries(0.8, size=N)
Chris@87:         # these two frequency counts should be close to theoretical
Chris@87:         # numbers with this large sample
Chris@87:         # theoretical large N result is 0.49706795
Chris@87:         freq = np.sum(rvsn == 1) / float(N)
Chris@87:         msg = "Frequency was %f, should be > 0.45" % freq
Chris@87:         assert_(freq > 0.45, msg)
Chris@87:         # theoretical large N result is 0.19882718
Chris@87:         freq = np.sum(rvsn == 2) / float(N)
Chris@87:         msg = "Frequency was %f, should be < 0.23" % freq
Chris@87:         assert_(freq < 0.23, msg)
Chris@87: 
Chris@87:     def test_permutation_longs(self):
Chris@87:         np.random.seed(1234)
Chris@87:         a = np.random.permutation(12)
Chris@87:         np.random.seed(1234)
Chris@87:         b = np.random.permutation(long(12))
Chris@87:         assert_array_equal(a, b)
Chris@87: 
Chris@87:     def test_randint_range(self):
Chris@87:         # Test for ticket #1690
Chris@87:         lmax = np.iinfo('l').max
Chris@87:         lmin = np.iinfo('l').min
Chris@87:         try:
Chris@87:             random.randint(lmin, lmax)
Chris@87:         except:
Chris@87:             raise AssertionError
Chris@87: 
Chris@87:     def test_shuffle_mixed_dimension(self):
Chris@87:         # Test for trac ticket #2074
Chris@87:         for t in [[1, 2, 3, None],
Chris@87:                   [(1, 1), (2, 2), (3, 3), None],
Chris@87:                   [1, (2, 2), (3, 3), None],
Chris@87:                   [(1, 1), 2, 3, None]]:
Chris@87:             np.random.seed(12345)
Chris@87:             shuffled = list(t)
Chris@87:             random.shuffle(shuffled)
Chris@87:             assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]])
Chris@87: 
Chris@87:     def test_call_within_randomstate(self):
Chris@87:         # Check that custom RandomState does not call into global state
Chris@87:         m = np.random.RandomState()
Chris@87:         res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
Chris@87:         for i in range(3):
Chris@87:             np.random.seed(i)
Chris@87:             m.seed(4321)
Chris@87:             # If m.state is not honored, the result will change
Chris@87:             assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
Chris@87: 
Chris@87:     def test_multivariate_normal_size_types(self):
Chris@87:         # Test for multivariate_normal issue with 'size' argument.
Chris@87:         # Check that the multivariate_normal size argument can be a
Chris@87:         # numpy integer.
Chris@87:         np.random.multivariate_normal([0], [[0]], size=1)
Chris@87:         np.random.multivariate_normal([0], [[0]], size=np.int_(1))
Chris@87:         np.random.multivariate_normal([0], [[0]], size=np.int64(1))
Chris@87: 
Chris@87: if __name__ == "__main__":
Chris@87:     run_module_suite()