comparison DEPENDENCIES/mingw32/Python27/Lib/site-packages/numpy/random/tests/test_regression.py @ 87:2a2c65a20a8b

Add Python libs and headers

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