def inv(A):
"""
Compute the inverse of a sparse matrix
.. versionadded:: 0.12.0
Parameters
----------
A : (M,M) ndarray or sparse matrix
square matrix to be inverted
Returns
-------
Ainv : (M,M) ndarray or sparse matrix
inverse of `A`
Notes
-----
This computes the sparse inverse of `A`. If the inverse of `A` is expected
to be non-sparse, it will likely be faster to convert `A` to dense and use
scipy.linalg.inv.
"""
I = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
Ainv = spsolve(A, I)
return Ainv
def inv(A):
"""
Compute the inverse of a sparse matrix
Parameters
----------
A : (M,M) ndarray or sparse matrix
square matrix to be inverted
Returns
-------
Ainv : (M,M) ndarray or sparse matrix
inverse of `A`
Notes
-----
This computes the sparse inverse of `A`. If the inverse of `A` is expected
to be non-sparse, it will likely be faster to convert `A` to dense and use
scipy.linalg.inv.
.. versionadded:: 0.12.0
"""
I = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
Ainv = spsolve(A, I)
return Ainv
def test_padecases_dtype_sparse_complex(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
dtype = np.complex128
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale) * eye(3, dtype=dtype)
assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
def test_padecases_dtype_sparse_complex(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
dtype = np.complex128
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale) * eye(3, dtype=dtype)
assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
def test_padecases_dtype_sparse(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
for dtype in [np.float64, np.complex128]:
# test double-precision cases
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale) * identity(3, dtype=dtype)
assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
def test_padecases_dtype_sparse(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
for dtype in [np.float64, np.complex128]:
# test double-precision cases
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale) * identity(3, dtype=dtype)
assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
def test_padecases_dtype_sparse_complex(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
dtype = np.complex128
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=SparseEfficiencyWarning)
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale) * eye(3, dtype=dtype)
assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
def test_padecases_dtype_sparse_complex(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
dtype = np.complex128
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=SparseEfficiencyWarning)
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale) * eye(3, dtype=dtype)
assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
def test_padecases_dtype_sparse_complex(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
dtype = np.complex128
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale) * eye(3, dtype=dtype)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a csc_matrix is expensive.")
assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
def test_padecases_dtype_sparse_complex(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
dtype = np.complex128
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale) * eye(3, dtype=dtype)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a csc_matrix is expensive.")
assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
def inv(A):
"""
Compute the inverse of a sparse matrix
Parameters
----------
A : (M,M) ndarray or sparse matrix
square matrix to be inverted
Returns
-------
Ainv : (M,M) ndarray or sparse matrix
inverse of `A`
Notes
-----
This computes the sparse inverse of `A`. If the inverse of `A` is expected
to be non-sparse, it will likely be faster to convert `A` to dense and use
scipy.linalg.inv.
Examples
--------
>>> from scipy.sparse import csc_matrix
>>> from scipy.sparse.linalg import inv
>>> A = csc_matrix([[1., 0.], [1., 2.]])
>>> Ainv = inv(A)
>>> Ainv
<2x2 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in Compressed Sparse Column format>
>>> A.dot(Ainv)
<2x2 sparse matrix of type '<class 'numpy.float64'>'
with 2 stored elements in Compressed Sparse Column format>
>>> A.dot(Ainv).todense()
matrix([[ 1., 0.],
[ 0., 1.]])
.. versionadded:: 0.12.0
"""
#check input
if not scipy.sparse.isspmatrix(A):
raise TypeError('Input must be a sparse matrix')
I = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
Ainv = spsolve(A, I)
return Ainv
def inv(A):
"""
Compute the inverse of a sparse matrix
Parameters
----------
A : (M,M) ndarray or sparse matrix
square matrix to be inverted
Returns
-------
Ainv : (M,M) ndarray or sparse matrix
inverse of `A`
Notes
-----
This computes the sparse inverse of `A`. If the inverse of `A` is expected
to be non-sparse, it will likely be faster to convert `A` to dense and use
scipy.linalg.inv.
Examples
--------
>>> from scipy.sparse import csc_matrix
>>> from scipy.sparse.linalg import inv
>>> A = csc_matrix([[1., 0.], [1., 2.]])
>>> Ainv = inv(A)
>>> Ainv
<2x2 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in Compressed Sparse Column format>
>>> A.dot(Ainv)
<2x2 sparse matrix of type '<class 'numpy.float64'>'
with 2 stored elements in Compressed Sparse Column format>
>>> A.dot(Ainv).todense()
matrix([[ 1., 0.],
[ 0., 1.]])
.. versionadded:: 0.12.0
"""
#check input
if not scipy.sparse.isspmatrix(A):
raise TypeError('Input must be a sparse matrix')
I = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
Ainv = spsolve(A, I)
return Ainv
def inv(A):
"""Compute the inverse of a sparse matrix
Parameters
----------
A : ndarray or sparse matrix
square matrix to be inverted
Returns
-------
Ainv : inverse of A
Notes
-----
This computes the sparse inverse of A. If the inverse of A is expected
to be non-sparse, it will likely be faster to convert A to dense and use
scipy.linalg.inv.
"""
I = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
Ainv = spsolve(A, I)
return Ainv
def inv(A):
"""Compute the inverse of a sparse matrix
Parameters
----------
A : ndarray or sparse matrix
square matrix to be inverted
Returns
-------
Ainv : inverse of A
Notes
-----
This computes the sparse inverse of A. If the inverse of A is expected
to be non-sparse, it will likely be faster to convert A to dense and use
scipy.linalg.inv.
"""
I = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
Ainv = spsolve(A, I)
return Ainv
def expm(A):
"""
Compute the matrix exponential using Pade approximation.
.. versionadded:: 0.12.0
Parameters
----------
A : (M,M) array or sparse matrix
2D Array or Matrix (sparse or dense) to be exponentiated
Returns
-------
expA : (M,M) ndarray
Matrix exponential of `A`
References
----------
N. J. Higham,
"The Scaling and Squaring Method for the Matrix Exponential Revisited",
SIAM. J. Matrix Anal. & Appl. 26, 1179 (2005).
"""
n_squarings = 0
Aissparse = isspmatrix(A)
if Aissparse:
A_L1 = max(abs(A).sum(axis=0).flat)
ident = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
else:
A = asarray(A)
A_L1 = norm(A, 1)
ident = eye(A.shape[0], A.shape[1], dtype=A.dtype)
if A.dtype == 'float64' or A.dtype == 'complex128':
if A_L1 < 1.495585217958292e-002:
U, V = _pade3(A, ident)
elif A_L1 < 2.539398330063230e-001:
U, V = _pade5(A, ident)
elif A_L1 < 9.504178996162932e-001:
U, V = _pade7(A, ident)
elif A_L1 < 2.097847961257068e+000:
U, V = _pade9(A, ident)
else:
maxnorm = 5.371920351148152
n_squarings = max(0, int(ceil(log2(A_L1 / maxnorm))))
A = A / 2**n_squarings
U, V = _pade13(A, ident)
elif A.dtype == 'float32' or A.dtype == 'complex64':
if A_L1 < 4.258730016922831e-001:
U, V = _pade3(A, ident)
elif A_L1 < 1.880152677804762e+000:
U, V = _pade5(A, ident)
else:
maxnorm = 3.925724783138660
n_squarings = max(0, int(ceil(log2(A_L1 / maxnorm))))
A = A / 2**n_squarings
U, V = _pade7(A, ident)
else:
raise ValueError("invalid type: " + str(A.dtype))
P = U + V # p_m(A) : numerator
Q = -U + V # q_m(A) : denominator
if Aissparse:
from scipy.sparse.linalg import spsolve
R = spsolve(Q, P)
else:
R = solve(Q, P)
# squaring step to undo scaling
for i in range(n_squarings):
R = R.dot(R)
return R
def expm(A):
"""Compute the matrix exponential using Pade approximation.
.. versionadded:: 0.12.0
Parameters
----------
A : array or sparse matrix, shape(M,M)
2D Array or Matrix (sparse or dense) to be exponentiated
Returns
-------
expA : array, shape(M,M)
Matrix exponential of A
References
----------
N. J. Higham,
"The Scaling and Squaring Method for the Matrix Exponential Revisited",
SIAM. J. Matrix Anal. & Appl. 26, 1179 (2005).
"""
n_squarings = 0
Aissparse = isspmatrix(A)
if Aissparse:
A_L1 = max(abs(A).sum(axis=0).flat)
ident = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
else:
A = asarray(A)
A_L1 = norm(A,1)
ident = eye(A.shape[0], A.shape[1], dtype=A.dtype)
if A.dtype == 'float64' or A.dtype == 'complex128':
if A_L1 < 1.495585217958292e-002:
U,V = _pade3(A, ident)
elif A_L1 < 2.539398330063230e-001:
U,V = _pade5(A, ident)
elif A_L1 < 9.504178996162932e-001:
U,V = _pade7(A, ident)
elif A_L1 < 2.097847961257068e+000:
U,V = _pade9(A, ident)
else:
maxnorm = 5.371920351148152
n_squarings = max(0, int(ceil(log2(A_L1 / maxnorm))))
A = A / 2**n_squarings
U,V = _pade13(A, ident)
elif A.dtype == 'float32' or A.dtype == 'complex64':
if A_L1 < 4.258730016922831e-001:
U,V = _pade3(A, ident)
elif A_L1 < 1.880152677804762e+000:
U,V = _pade5(A, ident)
else:
maxnorm = 3.925724783138660
n_squarings = max(0, int(ceil(log2(A_L1 / maxnorm))))
A = A / 2**n_squarings
U,V = _pade7(A, ident)
else:
raise ValueError("invalid type: "+str(A.dtype))
P = U + V # p_m(A) : numerator
Q = -U + V # q_m(A) : denominator
if Aissparse:
from scipy.sparse.linalg import spsolve
R = spsolve(Q, P)
else:
R = solve(Q,P)
# squaring step to undo scaling
for i in range(n_squarings):
R = R.dot(R)
return R
