def sample(self, y, x_prev, n):
"""
draw a sample from the proposal conditioned on current y and
previous x
"""
SN = 1
x_next = np.zeros(SN, dtype=model.DTYPE_LATENT_STATE).view(np.recarray)
x_det = x_prev['x'] + x_prev['xdot'] * self.DELTA_T
y_det = x_prev['y'] + x_prev['ydot'] * self.DELTA_T
norm = np.random.normal
x_next['x'] = x_det + norm(0, self.POS_NOISE_STD, size=SN)
x_next['y'] = y_det + norm(0, self.POS_NOISE_STD, size=SN)
x_next['xdot'] = norm(x_prev['xdot'], self.VELOCITY_NOISE_STD, size=SN)
x_next['ydot'] = norm(x_prev['ydot'], self.VELOCITY_NOISE_STD, size=SN)
x_next['phi'] = util.vonmises_rv(x_prev['phi'], self.PHI_NOISE_STD**2)
val, failures = drift_reject.rej_sample(
x_prev['theta'] - self.THETA_OFFSET, self.THETA_DRIFT_SIZE,
self.THETA_ENVELOPE_SIZE)
x_next['theta'] = self.THETA_OFFSET + val
return x_next[0]
def sample(self, y, x_prev, n):
"""
draw a sample from the proposal conditioned on current y and
previous x
"""
est_mu, est_var = self.cached_mean_var(y, n)
SN = 1
x_next = np.zeros(SN, dtype=model.DTYPE_LATENT_STATE).view(np.recarray)
x_det = x_prev['x'] + x_prev['xdot'] * self.DELTA_T
y_det = x_prev['y'] + x_prev['ydot'] * self.DELTA_T
norm = np.random.normal
if est_mu != None and (est_var > 0.001).all():
x_next['x'] = norm(est_mu[0], np.sqrt(est_var[0]))
x_next['y'] = norm(est_mu[1], np.sqrt(est_var[1]))
else:
x_next['x'] = x_det + norm(0, self.POS_NOISE_STD, size=SN)
x_next['y'] = y_det + norm(0, self.POS_NOISE_STD, size=SN)
x_next['xdot'] = norm(x_prev['xdot'], self.VELOCITY_NOISE_STD, size=SN)
x_next['ydot'] = norm(x_prev['ydot'], self.VELOCITY_NOISE_STD, size=SN)
x_next['phi'] = norm(x_prev['phi'], self.PHI_NOISE_STD, size=SN)
val, failures = drift_reject.rej_sample(
x_prev['theta'] - self.THETA_OFFSET, self.THETA_DRIFT_SIZE,
self.THETA_ENVELOPE_SIZE)
x_next['theta'] = self.THETA_OFFSET + val
x_next['meta'] = 0
return x_next[0]
def sample_next_latent(self, xn, n):
"""
Return X_{n+1} | x_n
"""
# right now this is totally linear, gaussian with
# an identity covariance matrix
SN = 1
x_next = np.zeros(SN, dtype=DTYPE_LATENT_STATE).view(np.recarray)
x_det = xn["x"] + xn["xdot"] * self.DELTA_T
y_det = xn["y"] + xn["ydot"] * self.DELTA_T
x_next["x"] = x_det + np.random.normal(0, self.POS_NOISE_STD, size=SN)
x_next["y"] = y_det + np.random.normal(0, self.POS_NOISE_STD, size=SN)
x_next["xdot"] = np.random.normal(xn["xdot"], self.VELOCITY_NOISE_STD, size=SN)
x_next["ydot"] = np.random.normal(xn["ydot"], self.VELOCITY_NOISE_STD, size=SN)
x_next["phi"] = util.vonmises_rv(xn["phi"], self.PHI_NOISE_STD ** 2)
val, failures = drift_reject.rej_sample(
xn["theta"] - self.THETA_OFFSET, self.THETA_DRIFT_SIZE, self.THETA_ENVELOPE_SIZE
)
x_next["theta"] = self.THETA_OFFSET + val
return x_next[0]
def sample_next_latent_model(self, xn, x):
# right now this is totally linear, gaussian with
# an identity covariance matrix
SN = 1
x_next = np.zeros(SN, dtype=DTYPE_LATENT_STATE).view(np.recarray)
x_det = xn['x'] + xn['xdot'] * self.DELTA_T
y_det = xn['y'] + xn['ydot'] * self.DELTA_T
x_next['x'] = x_det + np.random.normal(0, self.POS_NOISE_STD, size=SN)
x_next['y'] = y_det + np.random.normal(0, self.POS_NOISE_STD, size=SN)
x_next['xdot'] = np.random.normal(xn['xdot'],
self.VELOCITY_NOISE_STD,
size=SN)
x_next['ydot'] = np.random.normal(xn['ydot'],
self.VELOCITY_NOISE_STD,
size=SN)
x_next['phi'] = np.random.normal(xn['phi'],
self.PHI_NOISE_STD,
size=SN)
val, failures = drift_reject.rej_sample(
xn['theta'] - self.THETA_OFFSET, self.THETA_DRIFT_SIZE,
self.THETA_ENVELOPE_SIZE)
x_next['theta'] = self.THETA_OFFSET + val
return x_next[0]
def sample(self, y, x_prev, n):
"""
draw a sample from the proposal conditioned on current y and
previous x
"""
est_mu, est_var = self.cached_mean_var(y, n)
SN = 1
x_next = np.zeros(SN, dtype=model.DTYPE_LATENT_STATE).view(np.recarray)
x_det = x_prev['x'] + x_prev['xdot'] * self.DELTA_T
y_det = x_prev['y'] + x_prev['ydot'] * self.DELTA_T
norm = np.random.normal
if est_mu != None and (est_var > 0.001).all():
x_next['x']= norm(est_mu[0], np.sqrt(est_var[0]))
x_next['y'] = norm(est_mu[1], np.sqrt(est_var[1]))
else:
x_next['x']= x_det + norm(0,
self.POS_NOISE_STD, size=SN)
x_next['y'] = y_det + norm(0,
self.POS_NOISE_STD, size=SN)
x_next['xdot'] = norm(x_prev['xdot'],
self.VELOCITY_NOISE_STD, size=SN)
x_next['ydot'] = norm(x_prev['ydot'],
self.VELOCITY_NOISE_STD, size=SN)
x_next['phi'] = norm(x_prev['phi'],
self.PHI_NOISE_STD, size=SN)
val, failures = drift_reject.rej_sample(x_prev['theta'] - self.THETA_OFFSET,
self.THETA_DRIFT_SIZE,
self.THETA_ENVELOPE_SIZE)
x_next['theta'] = self.THETA_OFFSET + val
x_next['meta'] = 0
return x_next[0]
def sample(self, y, x_prev, n):
"""
draw a sample from the proposal conditioned on current y and
previous x
"""
SN = 1
x_next = np.zeros(SN, dtype=model.DTYPE_LATENT_STATE).view(np.recarray)
x_det = x_prev['x'] + x_prev['xdot'] * self.DELTA_T
y_det = x_prev['y'] + x_prev['ydot'] * self.DELTA_T
norm = np.random.normal
x_next['x']= x_det + norm(0,
self.POS_NOISE_STD, size=SN)
x_next['y'] = y_det + norm(0,
self.POS_NOISE_STD, size=SN)
x_next['xdot'] = norm(x_prev['xdot'],
self.VELOCITY_NOISE_STD, size=SN)
x_next['ydot'] = norm(x_prev['ydot'],
self.VELOCITY_NOISE_STD, size=SN)
x_next['phi'] = util.vonmises_rv(x_prev['phi'],
self.PHI_NOISE_STD**2)
val, failures = drift_reject.rej_sample(x_prev['theta'] - self.THETA_OFFSET,
self.THETA_DRIFT_SIZE,
self.THETA_ENVELOPE_SIZE)
x_next['theta'] = self.THETA_OFFSET + val
return x_next[0]
