#!/usr/bin/env python # Copyright (c) 2017-2019, Intel Corporation # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of Intel Corporation nor the names of its contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from __future__ import division, absolute_import, print_function import sys from numpy.testing import (TestCase, run_module_suite, assert_, assert_array_equal, assert_raises) import mkl_random as rnd from numpy.compat import long import numpy as np class TestRegression_Intel(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 = rnd.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(rnd.hypergeometric(3, 18, 11, size=10) < 4)) assert_(np.all(rnd.hypergeometric(18, 3, 11, size=10) > 0)) # Test for ticket #5623 args = [ (2**20 - 2, 2**20 - 2, 2**20 - 2), # Check for 32-bit systems (2 ** 30 - 1, 2 ** 30 - 2, 2 ** 30 - 1) ] for arg in args: assert_(rnd.hypergeometric(*arg) > 0) def test_logseries_convergence(self): # Test for ticket #923 N = 1000 rnd.seed(0) rvsn = rnd.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): rnd.seed(1234) a = rnd.permutation(12) rnd.seed(1234) b = rnd.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: rnd.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]]: rnd.seed(12345, brng='MT2203') shuffled = list(t) rnd.shuffle(shuffled) assert_array_equal(shuffled, [t[0], t[2], t[1], t[3]]) def test_call_within_randomstate(self): # Check that custom RandomState does not call into global state m = rnd.RandomState() res = np.array([5, 7, 5, 4, 5, 5, 6, 9, 6, 1]) for i in range(3): rnd.seed(i) m.seed(4321, brng='SFMT19937') # 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. rnd.multivariate_normal([0], [[0]], size=1) rnd.multivariate_normal([0], [[0]], size=np.int_(1)) rnd.multivariate_normal([0], [[0]], size=np.int64(1)) 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 rnd.seed(1234567890) x = rnd.beta(0.0001, 0.0001, size=100) assert_(not np.any(np.isnan(x)), 'Nans in rnd.beta') def test_choice_sum_of_probs_tolerance(self): # 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. rnd.seed(1234) a = [1, 2, 3] counts = [4, 4, 2] for dt in np.float16, np.float32, np.float64: probs = np.array(counts, dtype=dt) / sum(counts) c = rnd.choice(a, p=probs) assert_(c in a) assert_raises(ValueError, rnd.choice, a, p=probs*0.9) def test_shuffle_of_array_of_different_length_strings(self): # Test that permuting an array of different length strings # will not cause a segfault on garbage collection # Tests gh-7710 rnd.seed(1234) a = np.array(['a', 'a' * 1000]) for _ in range(100): rnd.shuffle(a) # Force Garbage Collection - should not segfault. import gc gc.collect() def test_shuffle_of_array_of_objects(self): # Test that permuting an array of objects will not cause # a segfault on garbage collection. # See gh-7719 rnd.seed(1234) a = np.array([np.arange(1), np.arange(4)]) for _ in range(1000): rnd.shuffle(a) # Force Garbage Collection - should not segfault. import gc gc.collect() if __name__ == "__main__": run_module_suite()