def make_node(self, C, b):
C = as_tensor_variable(C)
b = as_tensor_variable(b)
assert C.ndim == 2
assert b.ndim in (1, 2)
# infer dtype by solving the most simple
# case with (1, 1) matrices
o_dtype = scipy.linalg.solve(
np.eye(1).astype(C.dtype),
np.eye(1).astype(b.dtype)).dtype
x = tensor(shape=b.broadcastable, dtype=o_dtype)
return Apply(self, [C, b], [x])
def make_node(self, A, b):
assert imported_scipy, "Scipy not available. Scipy is needed for the Solve op"
A = as_tensor_variable(A)
b = as_tensor_variable(b)
assert A.ndim == 2
assert b.ndim in [1, 2]
# infer dtype by solving the most simple
# case with (1, 1) matrices
o_dtype = scipy.linalg.solve(
np.eye(1).astype(A.dtype),
np.eye(1).astype(b.dtype)).dtype
x = tensor(broadcastable=b.broadcastable, dtype=o_dtype)
return Apply(self, [A, b], [x])
def make_node(self, A, b):
A = as_tensor_variable(A)
b = as_tensor_variable(b)
if A.ndim != 2:
raise ValueError(f"`A` must be a matrix; got {A.type} instead.")
if b.ndim not in (1, 2):
raise ValueError(f"`b` must be a matrix or a vector; got {b.type} instead.")
# Infer dtype by solving the most simple case with 1x1 matrices
o_dtype = scipy.linalg.solve(
np.eye(1).astype(A.dtype), np.eye(1).astype(b.dtype)
).dtype
x = tensor(shape=b.broadcastable, dtype=o_dtype)
return Apply(self, [A, b], [x])