本文整理汇总了Python中scipy.linalg.lu方法的典型用法代码示例。如果您正苦于以下问题：Python linalg.lu方法的具体用法？Python linalg.lu怎么用？Python linalg.lu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.linalg的用法示例。
在下文中一共展示了linalg.lu方法的5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 import linalg [as 别名]
# 或者: from scipy.linalg import lu [as 别名]
def __init__(self,in_channel):
super(InvConv,self).__init__()
weight=np.random.randn(in_channel,in_channel)
q,_=linalg.qr(weight)
w_p,w_l,w_u=linalg.lu(q.astype(np.float32))
w_s=np.diag(w_u)
w_u=np.triu(w_u,1)
u_mask=np.triu(np.ones_like(w_u),1)
l_mask=u_mask.T
self.register_buffer('w_p',torch.from_numpy(w_p))
self.register_buffer('u_mask',torch.from_numpy(u_mask))
self.register_buffer('l_mask',torch.from_numpy(l_mask))
self.register_buffer('l_eye',torch.eye(l_mask.shape[0]))
self.register_buffer('s_sign',torch.sign(torch.from_numpy(w_s)))
self.w_l=torch.nn.Parameter(torch.from_numpy(w_l))
self.w_s=torch.nn.Parameter(torch.log(1e-7+torch.abs(torch.from_numpy(w_s))))
self.w_u=torch.nn.Parameter(torch.from_numpy(w_u))
self.weight=None
self.invweight=None
return 示例2: __init__
# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import lu [as 别名]
def __init__(self, in_channel):
super().__init__()
weight = np.random.randn(in_channel, in_channel)
q, _ = la.qr(weight)
w_p, w_l, w_u = la.lu(q.astype(np.float32))
w_s = np.diag(w_u)
w_u = np.triu(w_u, 1)
u_mask = np.triu(np.ones_like(w_u), 1)
l_mask = u_mask.T
w_p = torch.from_numpy(w_p)
w_l = torch.from_numpy(w_l)
w_s = torch.from_numpy(w_s)
w_u = torch.from_numpy(w_u)
self.register_buffer('w_p', w_p)
self.register_buffer('u_mask', torch.from_numpy(u_mask))
self.register_buffer('l_mask', torch.from_numpy(l_mask))
self.register_buffer('s_sign', torch.sign(w_s))
self.register_buffer('l_eye', torch.eye(l_mask.shape[0]))
self.w_l = nn.Parameter(w_l)
self.w_s = nn.Parameter(logabs(w_s))
self.w_u = nn.Parameter(w_u) 示例3: _traverse_grid_
# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import lu [as 别名]
def _traverse_grid_(self):
""" Solve using linear systems of equations """
P, L, U = linalg.lu(self.coeffs)
aux = np.zeros(self.M-1)
for j in reversed(range(self.N)):
aux[0] = np.dot(-self.a[1], self.grid[0, j])
x1 = linalg.solve(L, self.grid[1:self.M, j+1]+aux)
x2 = linalg.solve(U, x1)
self.grid[1:self.M, j] = x2 示例4: _traverse_grid_
# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import lu [as 别名]
def _traverse_grid_(self):
""" Solve using linear systems of equations """
P, L, U = linalg.lu(self.M1)
for j in reversed(range(self.N)):
x1 = linalg.solve(L,
np.dot(self.M2,
self.grid[1:self.M, j+1]))
x2 = linalg.solve(U, x1)
self.grid[1:self.M, j] = x2 示例5: randomized_range_finder
# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import lu [as 别名]
def randomized_range_finder(A, size, n_iter):
"""
Computes an orthonormal matrix whose range
approximates the range of A.
Parameters
----------
A: 2D array
The input data matrix
size: integer
Size of the return array
n_iter: integer
Number of power iterations used to stabilize the result
Returns
-------
Q: 2D array
A (size x size) projection matrix, the range of which
approximates well the range of the input matrix A.
Notes
-----
scikit-learn implementation
"""
# Generating normal random vectors with shape: (A.shape[1], size)
Q = np.random.normal(size=(A.shape[1], size))
# Perform power iterations with Q to further 'imprint' the top
# singular vectors of A in Q
for i in range(n_iter):
Q, _ = linalg.lu(np.dot(A, Q), permute_l=True)
Q, _ = linalg.lu(np.dot(A.T, Q), permute_l=True)
# Sample the range of A using by linear projection of Q
# Extract an orthonormal basis
Q, _ = linalg.qr(np.dot(A, Q), mode='economic')
return Q