def process_observations(self, obs):
y = obs['observations']
n = y.size
# # XXX
# which = np.array(range(y.size)) < 100
# y[which] = (y * y)[which]
dt = obs['dt'].item()
z = y == 0
y[z] = 0.5
yy = outer(y, y)
dt = 1
logy = np.log(y)
# TMP
logy = y * y
# logy[:int(n / 4)] = y[:int(n / 4)]
ylogy = outer(logy, y)
self.yy.update(yy, dt)
self.ylogy.update(ylogy, dt)
invy = 1.0 / y
self.einvy.update(invy, dt)
self.ey.update(y, dt)
self.elogy.update(logy, dt)
self.covy.update(y, dt)
self.covfy.update(logy, dt)
def update(self, value, dt=1.0):
self.num_samples += dt
n = value.size
if self.maximum is None:
self.maximum = value.copy()
self.minimum = value.copy()
self.P_t = np.zeros(shape=(n, n), dtype=value.dtype)
else:
# TODO: check dimensions
if not (value.shape == self.maximum.shape):
raise ValueError('Value shape changed: %s -> %s' %
(self.maximum.shape, value.shape))
self.maximum = np.maximum(value, self.maximum)
self.minimum = np.minimum(value, self.minimum)
self.mean_accum.update(value, dt)
mean = self.mean_accum.get_value()
value_norm = value - mean
P = outer(value_norm, value_norm)
self.covariance_accum.update(P, dt)
self.last_value = value
