Пример #1
0
            return obj

    @property
    def shape(self):
        return self.args[0].shape

    def _entry(self, i, j):
        return Mul(*[arg._entry(i, j) for arg in self.args])

    def _eval_transpose(self):
        from transpose import Transpose
        return HadamardProduct(*[Transpose(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))

rules = (unpack,
         flatten,
         sort(str))

canonicalize = exhaust(condition(lambda x: isinstance(x, HadamardProduct),
                                 do_one(*rules)))
Пример #2
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    """ Remove Identities from a MatMul

    This is a modified version of sympy.rules.rm_id.
    This is necesssary because MatMul may contain both MatrixExprs and Exprs
    as args.

    See Also
    --------
        sympy.rules.rm_id
    """
    # Separate Exprs from MatrixExprs in args
    factor, mmul = mul.as_coeff_mmul()
    # Apply standard rm_id for MatMuls
    result = rm_id(lambda x: x.is_Identity is True)(mmul)
    if result != mmul:
        return newmul(factor, *result.args)  # Recombine and return
    else:
        return mul

def factor_in_front(mul):
    factor, matrices = mul.as_coeff_matrices()
    if factor != 1:
        return newmul(factor, *matrices)
    return mul

rules = (any_zeros, remove_ids, xxinv, unpack, rm_id(lambda x: x == 1),
         factor_in_front, flatten)

canonicalize = exhaust(condition(lambda x: isinstance(x, MatMul),
                                 do_one(*rules)))
Пример #3
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    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)))