def __init__(self):
matrix_pairs = {}
rng = np.random.RandomState(SEED)
for sz in SIZES:
scalar = rng.normal()
matrix_pairs[sz] = (matrices.ScaledIdentityMatrix(scalar, None),
scalar * np.identity(sz))
super().__init__(matrix_pairs, rng)
def __init__(self):
super().__init__()
for sz in SIZES:
scalar = self.rng.normal()
self.matrices[sz] = matrices.ScaledIdentityMatrix(scalar, sz)
self.np_matrices[sz] = scalar * np.identity(sz)
if AUTOGRAD_AVAILABLE:
self.grad_log_abs_det_sqrts[sz] = grad(
lambda s: 0.5 * anp.linalg.slogdet(s * anp.eye(sz))[1])(
scalar)
self.grad_quadratic_form_invs[sz] = partial(
grad(lambda s, v: (v / s) @ v), scalar)
def __init__(self):
super().__init__()
for sz in SIZES:
scalar = self.rng.normal()
self.matrices[sz] = matrices.ScaledIdentityMatrix(scalar, None)
self.np_matrices[sz] = scalar * np.identity(sz)
def matrix_pair(self, rng, size):
scalar = rng.normal()
return (matrices.ScaledIdentityMatrix(scalar, None), scalar * np.identity(size))