def aryule(c, k):
"""Solve Yule-Walker equation.
Args:
c (numpy array): Coefficients (i.e. autocorrelation)
k (int): Assuming the AR(k) model
Returns:
numpy array: k model parameters
Some formulations solve: C a = -c,
but we actually solve C a = c.
"""
a = np.zeros(k)
# ignore a singular matrix
C = toeplitz(c[:k])
if not np.all(C == 0.0) and np.isfinite(ln.cond(C)):
a = np.dot(ln.inv(C), c[1:])
return a
python类cond()的实例源码
def aryule(c, k):
"""Solve Yule-Walker equation.
Args:
c (numpy array): Coefficients (i.e. autocorrelation)
k (int): Assuming the AR(k) model
Returns:
numpy array: k model parameters
Some formulations solve: C a = -c,
but we actually solve C a = c.
"""
a = np.zeros(k)
# ignore a singular matrix
C = toeplitz(c[:k])
if not np.all(C == 0.0) and np.isfinite(ln.cond(C)):
a = np.dot(ln.inv(C), c[1:])
return a
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)
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])
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, 2), decimal=5)
def test_stacked_arrays_explicitly(self):
A = np.array([[1., 2., 1.], [0, -2., 0], [6., 2., 3.]])
assert_equal(linalg.cond(A, 2), linalg.cond(A[None, ...], 2)[0])
def test(self):
A = array([[1., 0, 0], [0, -2., 0], [0, 0, 3.]])
assert_almost_equal(linalg.cond(A, inf), 3.)
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)
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])
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, 2), decimal=5)
def test_stacked_arrays_explicitly(self):
A = np.array([[1., 2., 1.], [0, -2., 0], [6., 2., 3.]])
assert_equal(linalg.cond(A, 2), linalg.cond(A[None, ...], 2)[0])
def test(self):
A = array([[1., 0, 0], [0, -2., 0], [0, 0, 3.]])
assert_almost_equal(linalg.cond(A, inf), 3.)
test_linalg.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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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)
test_linalg.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
阅读 23
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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])
test_linalg.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
阅读 21
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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, 2), decimal=5)
test_linalg.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
阅读 20
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def test_stacked_arrays_explicitly(self):
A = np.array([[1., 2., 1.], [0, -2., 0], [6., 2., 3.]])
assert_equal(linalg.cond(A, 2), linalg.cond(A[None, ...], 2)[0])
test_linalg.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
阅读 25
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def test(self):
A = array([[1., 0, 0], [0, -2., 0], [0, 0, 3.]])
assert_almost_equal(linalg.cond(A, inf), 3.)
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)
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])
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, 2), decimal=5)
def test_stacked_arrays_explicitly(self):
A = np.array([[1., 2., 1.], [0, -2., 0], [6., 2., 3.]])
assert_equal(linalg.cond(A, 2), linalg.cond(A[None, ...], 2)[0])
def test(self):
A = array([[1., 0, 0], [0, -2., 0], [0, 0, 3.]])
assert_almost_equal(linalg.cond(A, inf), 3.)
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)
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])
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, 2), decimal=5)
def test_stacked_arrays_explicitly(self):
A = np.array([[1., 2., 1.], [0, -2., 0], [6., 2., 3.]])
assert_equal(linalg.cond(A, 2), linalg.cond(A[None, ...], 2)[0])
def test(self):
A = array([[1., 0, 0], [0, -2., 0], [0, 0, 3.]])
assert_almost_equal(linalg.cond(A, inf), 3.)
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)
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])
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, 2), decimal=5)
