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 __init__(self):
self.Q = ExpectationWeighted() # XXX bug
self.P = ExpectationWeighted()
self.G = ExpectationWeighted()
self.B = ExpectationWeighted()
self.C = None
self.C_needs_update = True
self.R = None
self.R_needs_update = True
self.H = None
self.H_needs_update = True
self.once = False
class BGDSEstimator1DRobust(BGDSEstimator):
'''
Dimensions of G:
- for 1D signals: (K x 1 x N )
Dimensions of P (covariance of gradient):
(1 x 1 x H x W )
Dimensions of Q:
(K x K)
Dimensions of C:
- for 1D signals: (K x N )
'''
def __init__(self):
self.Q = ExpectationWeighted() # XXX bug
self.P = ExpectationWeighted()
self.G = ExpectationWeighted()
self.B = ExpectationWeighted()
self.C = None
self.C_needs_update = True
self.R = None
self.R_needs_update = True
self.H = None
self.H_needs_update = True
self.once = False
@contract(y='(array[M]|array[MxN]),shape(x)',
y_dot='shape(x)', u='array[K]', w='array[M]')
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
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)
