def bc_dist(expr):
""" Turn a*[X, Y] into [a*X, a*Y] """
factor, mat = expr.as_coeff_mmul()
if factor != 1 and unpack(mat).is_BlockMatrix:
B = unpack(mat).blocks
return BlockMatrix([[factor * B[i, j] for j in range(B.cols)]
for i in range(B.rows)])
return expr
def bc_dist(expr):
""" Turn a*[X, Y] into [a*X, a*Y] """
factor, mat = expr.as_coeff_mmul()
if factor != 1 and isinstance(unpack(mat), BlockMatrix):
B = unpack(mat).blocks
return BlockMatrix([[factor * B[i, j] for j in range(B.cols)]
for i in range(B.rows)])
return expr
from trace import Trace
return MatAdd(*[Trace(arg) for arg in self.args])
def canonicalize(self):
return canonicalize(self)
def validate(*args):
if not all(arg.is_Matrix for arg in args):
raise TypeError("Mix of Matrix and Scalar symbols")
A = args[0]
for B in args[1:]:
if A.shape != B.shape:
raise ShapeError("Matrices %s and %s are not aligned"%(A, B))
factor_of = lambda arg: arg.as_coeff_mmul()[0]
matrix_of = lambda arg: unpack(arg.as_coeff_mmul()[1])
def combine(cnt, mat):
from matmul import MatMul
if cnt == 1:
return mat
else:
return cnt * mat
rules = (rm_id(lambda x: x == 0 or isinstance(x, ZeroMatrix)),
unpack,
flatten,
glom(matrix_of, factor_of, combine),
sort(str))
canonicalize = exhaust(condition(lambda x: isinstance(x, MatAdd),
do_one(*rules)))
def canonicalize(self):
return canonicalize(self)
def validate(*args):
if not all(arg.is_Matrix for arg in args):
raise TypeError("Mix of Matrix and Scalar symbols")
A = args[0]
for B in args[1:]:
if A.shape != B.shape:
raise ShapeError("Matrices %s and %s are not aligned" % (A, B))
factor_of = lambda arg: arg.as_coeff_mmul()[0]
matrix_of = lambda arg: unpack(arg.as_coeff_mmul()[1])
def combine(cnt, mat):
from matmul import MatMul
if cnt == 1:
return mat
else:
return cnt * mat
rules = (rm_id(lambda x: x == 0 or isinstance(x, ZeroMatrix)), unpack, flatten,
glom(matrix_of, factor_of, combine), sort(str))
canonicalize = exhaust(
condition(lambda x: isinstance(x, MatAdd), do_one(*rules)))
