本文整理匯總了Python中numpy.linalg.cond方法的典型用法代碼示例。如果您正苦於以下問題：Python linalg.cond方法的具體用法？Python linalg.cond怎麽用？Python linalg.cond使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy.linalg的用法示例。
在下文中一共展示了linalg.cond方法的10個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_nan
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def test_nan(self):
# nans should be passed through, not converted to infs
ps = [None, 1, -1, 2, -2, 'fro']
p_pos = [None, 1, 2, 'fro']
A = np.ones((2, 2))
A[0,1] = np.nan
for p in ps:
c = linalg.cond(A, p)
assert_(isinstance(c, np.float_))
assert_(np.isnan(c))
A = np.ones((3, 2, 2))
A[1,0,1] = np.nan
for p in ps:
c = linalg.cond(A, p)
assert_(np.isnan(c[1]))
if p in p_pos:
assert_(c[0] > 1e15)
assert_(c[2] > 1e15)
else:
assert_(not np.isnan(c[0]))
assert_(not np.isnan(c[2])) 示例2: do
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def do(self, a, b, tags):
c = asarray(a) # a might be a matrix
if 'size-0' in tags:
assert_raises(LinAlgError, linalg.cond, c)
return
# +-2 norms
s = linalg.svd(c, compute_uv=False)
assert_almost_equal(
linalg.cond(a), s[..., 0] / s[..., -1],
single_decimal=5, double_decimal=11)
assert_almost_equal(
linalg.cond(a, 2), s[..., 0] / s[..., -1],
single_decimal=5, double_decimal=11)
assert_almost_equal(
linalg.cond(a, -2), s[..., -1] / s[..., 0],
single_decimal=5, double_decimal=11)
# Other norms
cinv = np.linalg.inv(c)
assert_almost_equal(
linalg.cond(a, 1),
abs(c).sum(-2).max(-1) * abs(cinv).sum(-2).max(-1),
single_decimal=5, double_decimal=11)
assert_almost_equal(
linalg.cond(a, -1),
abs(c).sum(-2).min(-1) * abs(cinv).sum(-2).min(-1),
single_decimal=5, double_decimal=11)
assert_almost_equal(
linalg.cond(a, np.inf),
abs(c).sum(-1).max(-1) * abs(cinv).sum(-1).max(-1),
single_decimal=5, double_decimal=11)
assert_almost_equal(
linalg.cond(a, -np.inf),
abs(c).sum(-1).min(-1) * abs(cinv).sum(-1).min(-1),
single_decimal=5, double_decimal=11)
assert_almost_equal(
linalg.cond(a, 'fro'),
np.sqrt((abs(c)**2).sum(-1).sum(-1)
* (abs(cinv)**2).sum(-1).sum(-1)),
single_decimal=5, double_decimal=11) 示例3: test_basic_nonsvd
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def test_basic_nonsvd(self):
# Smoketest the non-svd norms
A = array([[1., 0, 1], [0, -2., 0], [0, 0, 3.]])
assert_almost_equal(linalg.cond(A, inf), 4)
assert_almost_equal(linalg.cond(A, -inf), 2/3)
assert_almost_equal(linalg.cond(A, 1), 4)
assert_almost_equal(linalg.cond(A, -1), 0.5)
assert_almost_equal(linalg.cond(A, 'fro'), np.sqrt(265 / 12)) 示例4: test_singular
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def test_singular(self):
# Singular matrices have infinite condition number for
# positive norms, and negative norms shouldn't raise
# exceptions
As = [np.zeros((2, 2)), np.ones((2, 2))]
p_pos = [None, 1, 2, 'fro']
p_neg = [-1, -2]
for A, p in itertools.product(As, p_pos):
# Inversion may not hit exact infinity, so just check the
# number is large
assert_(linalg.cond(A, p) > 1e15)
for A, p in itertools.product(As, p_neg):
linalg.cond(A, p) 示例5: test_stacked_singular
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def test_stacked_singular(self):
# Check behavior when only some of the stacked matrices are
# singular
np.random.seed(1234)
A = np.random.rand(2, 2, 2, 2)
A[0,0] = 0
A[1,1] = 0
for p in (None, 1, 2, 'fro', -1, -2):
c = linalg.cond(A, p)
assert_equal(c[0,0], np.inf)
assert_equal(c[1,1], np.inf)
assert_(np.isfinite(c[0,1]))
assert_(np.isfinite(c[1,0])) 示例6: do
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def do(self, a, b):
c = asarray(a) # a might be a matrix
s = linalg.svd(c, compute_uv=False)
old_assert_almost_equal(
s[..., 0] / s[..., -1], linalg.cond(a), decimal=5) 示例7: test_stacked_arrays_explicitly
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def test_stacked_arrays_explicitly(self):
A = np.array([[1., 2., 1.], [0, -2., 0], [6., 2., 3.]])
assert_equal(linalg.cond(A), linalg.cond(A[None, ...])[0]) 示例8: test
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def test(self):
A = array([[1., 0, 0], [0, -2., 0], [0, 0, 3.]])
assert_almost_equal(linalg.cond(A, inf), 3.) 示例9: do
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def do(self, a, b, tags):
c = asarray(a) # a might be a matrix
if 'size-0' in tags:
assert_raises(LinAlgError, linalg.svd, c, compute_uv=False)
return
s = linalg.svd(c, compute_uv=False)
assert_almost_equal(
s[..., 0] / s[..., -1], linalg.cond(a),
single_decimal=5, double_decimal=11) 示例10: do
# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import cond [as 別名]
def do(self, a, b):
c = asarray(a) # a might be a matrix
s = linalg.svd(c, compute_uv=False)
old_assert_almost_equal(s[0]/s[-1], linalg.cond(a), decimal=5)