class BDSEEstimatorRobust(BDSEEstimator):
def __init__(self, **other):
BDSEEstimator.__init__(self, **other)
self.T = ExpectationWeighted()
self.U = ExpectationWeighted()
self.y_mean = ExpectationWeighted() # XXX: not necessary, y_stats.get_mean()
self.y_stats = MeanCovariance() # TODO: make robust
self.u_stats = MeanCovariance()
self.once = False
def merge(self, other):
assert isinstance(other, BDSEEstimatorRobust)
self.T.merge(other.T)
self.U.merge(other.U)
self.y_stats.merge(other.y_stats)
self.y_mean.merge(other.y_mean)
self.u_stats.merge(other.u_stats)
@contract(u='array[K],K>0,finite',
y='array[N],N>0,finite',
y_dot='array[N],finite',
w='array[N]')
def update(self, y, u, y_dot, w):
self.once = True
self.nsamples += 1
check_all_finite(y)
check_all_finite(u)
check_all_finite(y_dot)
check_all_finite(w)
self.n = y.size
self.k = u.size
self.y_stats.update(y) # TODO: make robust
self.u_stats.update(u)
# remove mean
u_n = u - self.u_stats.get_mean()
self.y_mean.update(y, w) # TODO: make robust
y_n = y - self.y_mean.get_value(fill_value=0.5)
# weights
y_n_w = w
y_dot_w = w
u_n_w = np.ones(u.shape)
T_k = outer(outer(y_n, y_dot), u_n)
T_k_w = outer(outer(y_n_w, y_dot_w), u_n_w)
U_k = outer(y_dot, u_n)
U_k_w = outer(y_dot_w, u_n_w)
assert T_k.shape == (self.n, self.n, self.k)
assert U_k.shape == (self.n, self.k)
# update tensor
self.T.update(T_k, T_k_w)
self.U.update(U_k, U_k_w)
