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authorRedRuM <44142765+zoj613@users.noreply.github.com>2019-08-17 17:50:56 +0200
committerRedRuM <44142765+zoj613@users.noreply.github.com>2019-08-17 17:50:56 +0200
commite6f9a354ff3e2354d343493f6463328895f702c2 (patch)
tree07db57859b734fab6b49e034d2ec9501fa86111a /numpy/random/tests
parent31de73ba60e7128aced80e65c8e28b28e06bc7f6 (diff)
downloadnumpy-e6f9a354ff3e2354d343493f6463328895f702c2.tar.gz
REV: Undo all changes to codebase
Diffstat (limited to 'numpy/random/tests')
-rw-r--r--numpy/random/tests/test_generator_mt19937.py58
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