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author | RedRuM <44142765+zoj613@users.noreply.github.com> | 2019-08-17 17:50:56 +0200 |
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committer | RedRuM <44142765+zoj613@users.noreply.github.com> | 2019-08-17 17:50:56 +0200 |
commit | e6f9a354ff3e2354d343493f6463328895f702c2 (patch) | |
tree | 07db57859b734fab6b49e034d2ec9501fa86111a /numpy/random/tests | |
parent | 31de73ba60e7128aced80e65c8e28b28e06bc7f6 (diff) | |
download | numpy-e6f9a354ff3e2354d343493f6463328895f702c2.tar.gz |
REV: Undo all changes to codebase
Diffstat (limited to 'numpy/random/tests')
-rw-r--r-- | numpy/random/tests/test_generator_mt19937.py | 58 |
1 files changed, 8 insertions, 50 deletions
diff --git a/numpy/random/tests/test_generator_mt19937.py b/numpy/random/tests/test_generator_mt19937.py index b720a7b39..a962fe84e 100644 --- a/numpy/random/tests/test_generator_mt19937.py +++ b/numpy/random/tests/test_generator_mt19937.py @@ -3,7 +3,6 @@ import sys import pytest import numpy as np -from numpy.linalg import LinAlgError from numpy.testing import ( assert_, assert_raises, assert_equal, assert_warns, assert_no_warnings, assert_array_equal, @@ -983,16 +982,7 @@ class TestRandomDist(object): mean = (.123456789, 10) cov = [[1, 0], [0, 1]] size = (3, 2) - actual_svd = random.multivariate_normal(mean, cov, size) - # the seed needs to be reset for each method - random = Generator(MT19937(self.seed)) - actual_eigh = random.multivariate_normal(mean, cov, size, method='eigh') - random = Generator(MT19937(self.seed)) - actual_chol = random.multivariate_normal(mean, cov, size, - method='cholesky') - # the factor matrix is the same for all methods - random = Generator(MT19937(self.seed)) - actual_factor = random.multivariate_normal.from_factor(mean, cov, size) + actual = random.multivariate_normal(mean, cov, size) desired = np.array([[[-1.747478062846581, 11.25613495182354 ], [-0.9967333370066214, 10.342002097029821 ]], [[ 0.7850019631242964, 11.181113712443013 ], @@ -1000,62 +990,30 @@ class TestRandomDist(object): [[ 0.7130260107430003, 9.551628690083056 ], [ 0.7127098726541128, 11.991709234143173 ]]]) - assert_array_almost_equal(actual_svd, desired, decimal=15) - assert_array_almost_equal(actual_eigh, desired, decimal=15) - assert_array_almost_equal(actual_chol, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) # Check for default size, was raising deprecation warning - random = Generator(MT19937(self.seed)) - actual_svd = random.multivariate_normal(mean, cov) - random = Generator(MT19937(self.seed)) - actual_eigh = random.multivariate_normal(mean, cov, method='eigh') - random = Generator(MT19937(self.seed)) - actual_chol = random.multivariate_normal(mean, cov, method='cholesky') - # the factor matrix is the same for all methods - random = Generator(MT19937(self.seed)) - actual_factor = random.multivariate_normal.from_factor(mean, cov) - desired = np.array([-1.747478062846581, 11.25613495182354]) - assert_array_almost_equal(actual_svd, desired, decimal=15) - assert_array_almost_equal(actual_eigh, desired, decimal=15) - assert_array_almost_equal(actual_chol, desired, decimal=15) - assert_array_almost_equal(actual_factor, desired, decimal=15) - - # test if raises ValueError when non-square factor is used - non_square_factor = [[1, 0], [0, 1], [1, 0]] - assert_raises(ValueError, random.multivariate_normal.from_factor, - mean, non_square_factor) - - # Check that non symmetric covariance input raises exception when - # check_valid='raises' if using default svd method. - mean = [0, 0] - cov = [[1, 2], [1, 2]] - assert_raises(ValueError, random.multivariate_normal, mean, cov, - check_valid='raise') + actual = random.multivariate_normal(mean, cov) + desired = np.array([0.233278563284287, 9.424140804347195]) + assert_array_almost_equal(actual, desired, decimal=15) # Check that non positive-semidefinite covariance warns with # RuntimeWarning + mean = [0, 0] cov = [[1, 2], [2, 1]] - assert_warns(RuntimeWarning, random.multivariate_normal.__call__, mean, cov) - assert_warns(RuntimeWarning, random.multivariate_normal.__call__, mean, cov, - method='eigh') - assert_raises(LinAlgError, random.multivariate_normal, mean, cov, - method='cholesky') + assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov) # and that it doesn't warn with RuntimeWarning check_valid='ignore' - assert_no_warnings(random.multivariate_normal.__call__, mean, cov, + assert_no_warnings(random.multivariate_normal, mean, cov, check_valid='ignore') # and that it raises with RuntimeWarning check_valid='raises' assert_raises(ValueError, random.multivariate_normal, mean, cov, check_valid='raise') - assert_raises(ValueError, random.multivariate_normal, mean, cov, - check_valid='raise', method='eigh') cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32) with suppress_warnings() as sup: random.multivariate_normal(mean, cov) - random.multivariate_normal(mean, cov, method='eigh') - random.multivariate_normal(mean, cov, method='cholesky') w = sup.record(RuntimeWarning) assert len(w) == 0 |