def update(self, y, u, y_dot, w=1.0):
self.once = True
self.nsamples += 1
self.n = y.size
self.k = u.size # XXX: check
self.y_stats.update(y, w)
self.u_stats.update(u, w)
# remove mean
u_n = u - self.u_stats.get_mean()
y_n = y - self.y_stats.get_mean()
# make products
T_k = outer(outer(y_n, y_dot), u_n)
assert T_k.shape == (self.n, self.n, self.k)
U_k = outer(y_dot, u_n)
assert U_k.shape == (self.n, self.k)
# update tensor
self.T.update(T_k, w)
self.U.update(U_k, w)
def update(self, y, y_dot, u, w):
self.once = True
M = y.shape[0]
check_all_finite(y)
check_all_finite(y_dot)
check_all_finite(u)
# TODO: check shape is conserved
self.is1D = y.ndim == 1
self.is2D = y.ndim == 2
gy = generalized_gradient(y)
y_dot_w = w
u_w = np.ones(u.shape)
gy_w = w.reshape((1, M))
assert gy.shape == gy_w.shape
Qi = outer(u, u)
Qi_w = outer(u_w, u_w)
self.Q.update(Qi, Qi_w)
Pi = outer_first_dim(gy)
Pi_w = outer_first_dim(gy_w)
self.P.update(Pi, Pi_w)
self.R_needs_update = True
Gi = outer(u, gy * y_dot)
Gi_w = outer(u_w, gy_w * y_dot_w)
self.G.update(Gi, Gi_w)
self.H_needs_update = True
Bk = outer(u, y_dot)
Bk_w = outer(u_w, y_dot_w)
self.B.update(Bk, Bk_w)
self.C_needs_update = True
self.last_y = y
self.last_gy = gy
self.last_y_dot = y_dot
self.last_u = u
self.last_w = w
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)