def __init__(self,
mu=array([0, 0]),
Sigma=eye(2),
is_cholesky=False,
ell=None):
Distribution.__init__(self, len(Sigma))
assert (len(shape(mu)) == 1)
assert (max(shape(Sigma)) == len(mu))
self.mu = mu
self.ell = ell
if is_cholesky:
self.L = Sigma
if ell == None:
assert (shape(Sigma)[0] == shape(Sigma)[1])
else:
assert (shape(Sigma)[1] == ell)
else:
assert (shape(Sigma)[0] == shape(Sigma)[1])
if ell is not None:
self.L, _, _ = MatrixTools.low_rank_approx(Sigma, ell)
self.L = self.L.T
assert (shape(self.L)[1] == ell)
else:
try:
self.L = cholesky(Sigma)
except LinAlgError:
# some really crude check for PSD (which only corrects for orunding errors
self.L = cholesky(Sigma + eye(len(Sigma)) * 1e-5)
def __init__(self, mu=array([0, 0]), Sigma=eye(2), is_cholesky=False, ell=None):
Distribution.__init__(self, len(Sigma))
assert(len(shape(mu)) == 1)
assert(max(shape(Sigma)) == len(mu))
self.mu = mu
self.ell = ell
if is_cholesky:
self.L = Sigma
if ell == None:
assert(shape(Sigma)[0] == shape(Sigma)[1])
else:
assert(shape(Sigma)[1] == ell)
else:
assert(shape(Sigma)[0] == shape(Sigma)[1])
if ell is not None:
self.L, _, _ = MatrixTools.low_rank_approx(Sigma, ell)
self.L = self.L.T
assert(shape(self.L)[1] == ell)
else:
try:
self.L = cholesky(Sigma)
except LinAlgError:
# some really crude check for PSD (which only corrects for orunding errors
self.L = cholesky(Sigma+eye(len(Sigma))*1e-5)
