def __call__(self, A, b, inference=False):
if inference is True:
solve = slinalg.Solve()
x = solve(A, b)
else:
x = nlinalg.matrix_inverse(A).dot(b)
return x
def energy(self, x):
L1x = slinalg.Solve(lower=True)(self.L, x)
return .5 * L1x.T.dot(L1x)
def velocity(self, x):
solve = slinalg.Solve(lower=True)
y = solve(self.L, x)
return solve(self.L.T, y)
def __getattr__(cls, attr):
if config.lazy:
return getattr(tt, attr)
else:
return getattr(np, attr)
class math(metaclass=MathType):
"""Alias for ``numpy`` or ``theano.tensor``, depending on `config.lazy`."""
pass
# Cholesky solve
_solve_lower = sla.Solve(A_structure="lower_triangular", lower=True)
_solve_upper = sla.Solve(A_structure="upper_triangular", lower=False)
def _cho_solve(cho_A, b):
return _solve_upper(tt.transpose(cho_A), _solve_lower(cho_A, b))
class LinAlgType(type):
"""Linear algebra operations."""
@autocompile
def cho_solve(self, cho_A, b):
return _cho_solve(cho_A, b)
@autocompile
def solve(self, X, flux, cho_C, mu, LInv):
