本文整理汇总了Python中scipy.sparse.linalg.LinearOperator.__init__方法的典型用法代码示例。如果您正苦于以下问题：Python LinearOperator.__init__方法的具体用法？Python LinearOperator.__init__怎么用？Python LinearOperator.__init__使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy.sparse.linalg.LinearOperator的用法示例。
在下文中一共展示了LinearOperator.__init__方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from scipy.sparse.linalg import LinearOperator [as 别名]
# 或者: from scipy.sparse.linalg.LinearOperator import __init__ [as 别名]
def __init__(self, n, c):
"""n is dimension, c is a constant to be multiplied by the identity matrix."""
self.c = c
if LINOP_SUBCLASSING:
LinearOperator.__init__(self, dtype = float, shape = (n, n))
else:
LinearOperator.__init__(self, dtype = float, shape = (n, n), matvec = lambda x : type(self)._matvec(self, x))示例2: __init__
# 需要导入模块: from scipy.sparse.linalg import LinearOperator [as 别名]
# 或者: from scipy.sparse.linalg.LinearOperator import __init__ [as 别名]
def __init__(self, F, Delta, u):
n = F.shape[0]
assert (F.shape[1] == n) and (u.shape == (n,))
self.F, self.Delta, self.u = F, Delta, u
self.u_prime = self.Delta - self.u
self.ones = np.ones(n, dtype=float)
LinearOperator.__init__(self, dtype=float, shape=self.F.shape)示例3: __init__
# 需要导入模块: from scipy.sparse.linalg import LinearOperator [as 别名]
# 或者: from scipy.sparse.linalg.LinearOperator import __init__ [as 别名]
def __init__(self,
A,
coarsening=pyamgcl_ext.coarsening.smoothed_aggregation,
relaxation=pyamgcl_ext.relaxation.spai0,
prm={}
):
"""
Class constructor.
Creates algebraic multigrid hierarchy.
Parameters
----------
A : the system matrix in scipy.sparse format
coarsening : {ruge_stuben, aggregation, *smoothed_aggregation*, smoothed_aggr_emin}
The coarsening type to use for construction of the multigrid
hierarchy.
relaxation : {damped_jacobi, gauss_seidel, chebyshev, *spai0*, ilu0}
The relaxation scheme to use for multigrid cycles.
prm : dictionary with amgcl parameters
"""
Acsr = A.tocsr()
self.P = pyamgcl_ext.make_preconditioner(
coarsening, relaxation, prm,
Acsr.indptr.astype(numpy.int32),
Acsr.indices.astype(numpy.int32),
Acsr.data.astype(numpy.float64)
)
LinearOperator.__init__(self, A.shape, self.P)示例4: __init__
# 需要导入模块: from scipy.sparse.linalg import LinearOperator [as 别名]
# 或者: from scipy.sparse.linalg.LinearOperator import __init__ [as 别名]
def __init__(self, shape, matvec):
self._shape = shape
self._action = matvec
LinearOperator.__init__(self,
shape=self._shape,
matvec=self.parallelDot,
dtype=np.complex128)示例5: __init__
# 需要导入模块: from scipy.sparse.linalg import LinearOperator [as 别名]
# 或者: from scipy.sparse.linalg.LinearOperator import __init__ [as 别名]
def __init__(self, K, quad, **kwargs):
assert len(K.shape) == 1
shape = (K.shape[0]-4, K.shape[0]-4)
N = shape[0]
LinearOperator.__init__(self, shape, None, **kwargs)
ck = ones(K.shape)
if quad == "GC": ck[N-1] = 2
self.dd = -4*pi*(K[:N]+1)/(K[:N]+3)*(K[:N]+2)**2
self.ud = [2*pi*(K[:N-2]+1)*(K[:N-2]+2)]
self.ld = [2*pi*(K[2:N]-1)*(K[2:N]+2)]示例6: __init__
# 需要导入模块: from scipy.sparse.linalg import LinearOperator [as 别名]
# 或者: from scipy.sparse.linalg.LinearOperator import __init__ [as 别名]
def __init__(self, mat, r=10, leaf_side=16):
LinearOperator.__init__(self, dtype=mat.dtype, shape=mat.shape,
matvec=self._matvec,
rmatvec=self._rmatvec)
self.mat = BlackBox(mat)
self.r = r
self.leaf_side = leaf_side
self.leaf_size = leaf_side**2
N = int(np.sqrt(self.mat.shape[0]))
self.pattern = distance_matrix(N, format='coo')
perm = hilbert_traverse(N)
conjugate_sparse(self.pattern, perm)
self.pattern = self.pattern.tocsr()
self.mat.permutate(perm)
self.root = hmat_node(self, tuple(zip((0, 0), self.mat.shape)))
return示例7: __init__
# 需要导入模块: from scipy.sparse.linalg import LinearOperator [as 别名]
# 或者: from scipy.sparse.linalg.LinearOperator import __init__ [as 别名]
def __init__(self, A, pmask, prm={}):
"""
Class constructor.
Parameters
----------
A : the system matrix in scipy.sparse format
prm : dictionary with amgcl parameters
"""
Acsr = A.tocsr()
self.P = pyamgcl_ext.make_simple(
prm,
Acsr.indptr.astype(numpy.int32),
Acsr.indices.astype(numpy.int32),
Acsr.data.astype(numpy.float64),
pmask.astype(numpy.int32)
)
if [int(v) for v in scipy.__version__.split('.')] < [0, 16, 0]:
LinearOperator.__init__(self, A.shape, self.P)
else:
LinearOperator.__init__(self, dtype=numpy.float64, shape=A.shape)