def sample(self, model, evidence):
z = evidence['z']
T = evidence['T']
g = evidence['g']
h = evidence['h']
transition_var_g = evidence['transition_var_g']
shot_id = evidence['shot_id']
observation_var_g = model.known_params['observation_var_g']
observation_var_h = model.known_params['observation_var_h']
prior_mu_g = model.hyper_params['g']['mu']
prior_cov_g = model.hyper_params['g']['cov']
N = len(z)
n = len(g)
# Make g, h, and z vector valued to avoid ambiguity
g = g.copy().reshape((n, 1))
h = h.copy().reshape((n, 1))
z_g = ma.asarray(nan + zeros((n, 1)))
obs_cov = ma.asarray(inf + zeros((n, 1, 1)))
for i in xrange(n):
z_i = z[shot_id == i]
T_i = T[shot_id == i]
if 1 in T_i and 2 in T_i:
# Sample mean and variance for multiple observations
n_obs_g, n_obs_h = sum(T_i == 1), sum(T_i == 2)
obs_cov_g, obs_cov_h = observation_var_g / n_obs_g, observation_var_h / n_obs_h
z_g[i] = (mean(z_i[T_i == 1]) / obs_cov_g +
mean(z_i[T_i == 2] - h[i]) / obs_cov_h) / (
1 / obs_cov_g + 1 / obs_cov_h)
obs_cov[i] = 1 / (1 / obs_cov_g + 1 / obs_cov_h)
elif 1 in T_i:
n_obs_g = sum(T_i == 1)
z_g[i] = mean(z_i[T_i == 1])
obs_cov[i] = observation_var_g / n_obs_g
elif 2 in T_i:
n_obs_h = sum(T_i == 2)
z_g[i] = mean(z_i[T_i == 2] - h[i])
obs_cov[i] = observation_var_h / n_obs_h
z_g[isnan(z_g)] = ma.masked
obs_cov[isinf(obs_cov)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = array([
prior_mu_g[0],
])
kalman.initial_state_covariance = array([
prior_cov_g[0],
])
kalman.transition_matrices = eye(1)
kalman.transition_covariance = array([
transition_var_g,
])
kalman.observation_matrices = eye(1)
kalman.observation_covariance = obs_cov
sampled_g = forward_filter_backward_sample(kalman, z_g, prior_mu_g,
prior_cov_g)
return sampled_g.reshape((n, ))
def sample(self, model, evidence):
z = evidence['z']
T = evidence['T']
g = evidence['g']
h = evidence['h']
transition_var_g = evidence['transition_var_g']
shot_id = evidence['shot_id']
observation_var_g = model.known_params['observation_var_g']
observation_var_h = model.known_params['observation_var_h']
prior_mu_g = model.hyper_params['g']['mu']
prior_cov_g = model.hyper_params['g']['cov']
N = len(z)
n = len(g)
## Make g, h, and z vector valued to avoid ambiguity
#g = g.copy().reshape((n, 1))
#h = h.copy().reshape((n, 1))
#
pdb.set_trace()
z_g = ma.asarray(nan + zeros(n))
obs_cov = ma.asarray(inf + zeros(n))
if 1 in T:
z_g[T==1] = z[T==1]
obs_cov[T==1] = observation_var_g
if 2 in T:
z_g[T==2] = z[T==2] - h[T==2]
obs_cov[T==2] = observation_var_h
#for i in xrange(n):
# z_i = z[shot_id == i]
# T_i = T[shot_id == i]
# if 1 in T_i and 2 in T_i:
# # Sample mean and variance for multiple observations
# n_obs_g, n_obs_h = sum(T_i == 1), sum(T_i == 2)
# obs_cov_g, obs_cov_h = observation_var_g/n_obs_g, observation_var_h/n_obs_h
# z_g[i] = (mean(z_i[T_i == 1])/obs_cov_g + mean(z_i[T_i == 2] - h[i])/obs_cov_h)/(1/obs_cov_g + 1/obs_cov_h)
# obs_cov[i] = 1/(1/obs_cov_g + 1/obs_cov_h)
# elif 1 in T_i:
# n_obs_g = sum(T_i == 1)
# z_g[i] = mean(z_i[T_i == 1])
# obs_cov[i] = observation_var_g/n_obs_g
# elif 2 in T_i:
# n_obs_h = sum(T_i == 2)
# z_g[i] = mean(z_i[T_i == 2] - h[i])
# obs_cov[i] = observation_var_h/n_obs_h
z_g[isnan(z_g)] = ma.masked
obs_cov[isinf(obs_cov)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = array([prior_mu_g[0],])
kalman.initial_state_covariance = array([prior_cov_g[0],])
kalman.transition_matrices = eye(1)
kalman.transition_covariance = array([transition_var_g,])
kalman.observation_matrices = eye(1)
kalman.observation_covariance = obs_cov
sampled_g = forward_filter_backward_sample(kalman, z_g, prior_mu_g, prior_cov_g)
return sampled_g.reshape((n,))
def sample(self, model, evidence):
z = evidence['z']
g = evidence['g']
h = evidence['h']
T = evidence['T']
phi = evidence['phi']
transition_var_h = evidence['transition_var_h']
shot_id = evidence['shot_id']
observation_var_h = model.known_params['observation_var_h']
mu_h = model.known_params['mu_h']
prior_mu_h = model.hyper_params['h']['mu']
prior_cov_h = model.hyper_params['h']['cov']
n = len(h)
N = len(z)
# Making g, h, and z vector valued to avoid ambiguity
g = g.copy().reshape((n, 1))
h = h.copy().reshape((n, 1))
z_h = ma.asarray(nan + zeros((n, 1)))
obs_cov = ma.asarray(inf + zeros((n, 1, 1)))
for i in xrange(n):
z_i = z[shot_id == i]
T_i = T[shot_id == i]
if 2 in T_i:
# Sample mean and variance for multiple observations
n_obs = sum(T_i == 2)
z_h[i] = mean(z_i[T_i == 2])
obs_cov[i] = observation_var_h / n_obs
z_h[isnan(z_h)] = ma.masked
obs_cov[isinf(obs_cov)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = array([
prior_mu_h[0],
])
kalman.initial_state_covariance = array([
prior_cov_h[0],
])
kalman.transition_matrices = array([
phi,
])
kalman.transition_covariance = array([
transition_var_h,
])
kalman.transition_offsets = mu_h * (1 - phi) * ones((n, 1))
kalman.observation_matrices = eye(1)
kalman.observation_offsets = g
kalman.observation_covariance = obs_cov
sampled_h = forward_filter_backward_sample(kalman, z_h, prior_mu_h,
prior_cov_h)
return sampled_h.reshape((n, ))
def visualize_gibbs(sampler, evidence):
pdb.set_trace()
z, T, C, d, g, h, transition_var_g, transition_var_h, canopy_cover = \
[evidence[var] for var in ['z', 'T', 'C', 'd', 'g', 'h', 'transition_var_g', 'transition_var_h', 'canopy_cover']]
g = g.reshape((len(g), ))
h = h.reshape((len(h), ))
dists = sorted(list(set(d)))
print "transition_var_g: %s" % transition_var_g
print "transition_var_h: %s" % transition_var_h
print "T counts: %s" % [sum(T==i) for i in range(3)]
from matplotlib import pyplot as plt
fig = plt.figure()
plt.plot(d[T==0], z[T==0], 'r.')
plt.plot(d[T==1], z[T==1], 'k.')
plt.plot(d[T==2], z[T==2], 'g.')
plt.plot(dists, g, 'k-', linewidth=3, alpha=.5)
for i in xrange(len(canopy_cover)):
canopy = ma.asarray(g+h)
canopy[C!=i] = ma.masked
plt.fill_between(dists, g, canopy, color='g', alpha=canopy_cover[i]*.7)
def moveon(event):
plt.close()
fig.canvas.mpl_connect('key_press_event', moveon)
plt.show()
def sample(self, model, evidence):
z = evidence['z']
T, surfaces, sigma_g, sigma_h = [evidence[var] for var in ['T', 'surfaces', 'sigma_g', 'sigma_h']]
mu_h, phi, sigma_z_g, sigma_z_h = [model.known_params[var] for var in ['mu_h', 'phi', 'sigma_z_g', 'sigma_z_h']]
prior_mu_g, prior_cov_g = [model.hyper_params[var] for var in ['prior_mu_g', 'prior_cov_g']]
prior_mu_h, prior_cov_h = [model.hyper_params[var] for var in ['prior_mu_h', 'prior_cov_h']]
n = len(g)
y = ma.asarray(ones((n, 2))*nan)
if sum(T==1) > 0:
y[T==1, 0] = z[T==1]
if sum(T==2) > 0:
y[T==2, 1] = z[T==2]
y[isnan(y)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean=[prior_mu_g[0], prior_mu_h[0]]
kalman.initial_state_covariance=diag([prior_cov_g[0,0], prior_cov_h[0,0]])
kalman.transition_matrices=[[1, 0], [0, phi]]
kalman.transition_offsets =ones((n, 2))*[0, mu_h*(1-phi)]
kalman.transition_covariance=[[sigma_g**2, 0], [0, sigma_h**2]]
kalman.observation_matrices=[[1, 0], [1, 1]]
kalman.observation_covariance=[[sigma_z_g**2, 0], [0, sigma_z_h**2]]
sampled_surfaces = forward_filter_backward_sample(kalman, y)
return sampled_surfaces
def sample(self, model, evidence):
z = evidence['z']
T, g, sigma_g = [evidence[var] for var in ['T', 'g', 'sigma_g']]
sigma_z_g = model.known_params['sigma_z_g']
prior_mu_g, prior_cov_g = [
model.hyper_params[var] for var in ['prior_mu_g', 'prior_cov_g']
]
n = len(g)
z_g = z.copy()
z_g[T == 0] = nan
z_g = ma.asarray(z_g)
z_g[isnan(z_g)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = prior_mu_g[0]
kalman.initial_state_covariance = prior_cov_g[0, 0]
kalman.transition_matrices = 1
kalman.transition_covariance = sigma_g**2
kalman.observation_matrices = 1
kalman.observation_covariance = sigma_z_g**2
pdb.set_trace()
sampled_g = forward_filter_backward_sample(kalman, z_g)
return sampled_g
def sample(self, model, evidence):
z = evidence['z']
T, g, h, sigma_g = [evidence[var] for var in ['T', 'g', 'h', 'sigma_g']]
sigma_z_g = model.known_params['sigma_z_g']
sigma_z_h = model.known_params['sigma_z_h']
prior_mu_g, prior_cov_g = [model.hyper_params[var] for var in ['prior_mu_g', 'prior_cov_g']]
n = len(g)
# Must be a more concise way to deal with scalar vs vector
g = g.copy().reshape((n,1))
h = h.copy().reshape((n,1))
z_g = ma.asarray(z.copy().reshape((n,1)))
obs_cov = sigma_z_g**2*ones((n,1,1))
if sum(T == 0) > 0:
z_g[T == 0] = nan
if sum(T == 2) > 0:
z_g[T == 2] -= h[T == 2]
obs_cov[T == 2] = sigma_z_h**2
z_g[isnan(z_g)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = array([prior_mu_g[0],])
kalman.initial_state_covariance = array([prior_cov_g[0,0],])
kalman.transition_matrices = eye(1)
kalman.transition_covariance = array([sigma_g**2,])
kalman.observation_matrices = eye(1)
kalman.observation_covariance = obs_cov
sampled_g = forward_filter_backward_sample(kalman, z_g)
return sampled_g.reshape((n,))
def sample(self, model, evidence):
z, T, g, h, sigma_h, phi = [evidence[var] for var in ['z', 'T', 'g', 'h', 'sigma_h', 'phi']]
sigma_z_h = model.known_params['sigma_z_h']
mu_h = model.known_params['mu_h']
prior_mu_h = model.hyper_params['prior_mu_h']
prior_cov_h = model.hyper_params['prior_cov_h']
n = len(h)
g = g.copy().reshape((n,1))
h = h.copy().reshape((n,1))
z_h = ma.asarray(z.copy().reshape((n,1)))
if sum(T == 0) > 0:
z_h[T == 0] = nan
if sum(T == 1) > 0:
z_h[T == 1] = nan
if sum(T == 2) > 0:
z_h[T == 2] -= g[T == 2]
z_h[isnan(z_h)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = array([prior_mu_h[0],])
kalman.initial_state_covariance = array([prior_cov_h[0,0],])
kalman.transition_matrices = array([phi,])
kalman.transition_covariance = array([sigma_h**2,])
kalman.transition_offsets = mu_h*(1-phi)*ones((n, 1))
kalman.observation_matrices = eye(1)
kalman.observation_covariance = array([sigma_z_h**2,])
sampled_h = forward_filter_backward_sample(kalman, z_h)
return sampled_h.reshape((n,))
def visualize_gibbs(sampler, evidence):
pdb.set_trace()
z, T, C, d, g, h, transition_var_g, transition_var_h, canopy_cover = \
[evidence[var] for var in ['z', 'T', 'C', 'd', 'g', 'h', 'transition_var_g', 'transition_var_h', 'canopy_cover']]
g = g.reshape((len(g), ))
h = h.reshape((len(h), ))
dists = sorted(list(set(d)))
print "transition_var_g: %s" % transition_var_g
print "transition_var_h: %s" % transition_var_h
print "T counts: %s" % [sum(T == i) for i in range(3)]
from matplotlib import pyplot as plt
fig = plt.figure()
plt.plot(d[T == 0], z[T == 0], 'r.')
plt.plot(d[T == 1], z[T == 1], 'k.')
plt.plot(d[T == 2], z[T == 2], 'g.')
plt.plot(dists, g, 'k-', linewidth=3, alpha=.5)
for i in xrange(len(canopy_cover)):
canopy = ma.asarray(g + h)
canopy[C != i] = ma.masked
plt.fill_between(dists,
g,
canopy,
color='g',
alpha=canopy_cover[i] * .7)
def moveon(event):
plt.close()
fig.canvas.mpl_connect('key_press_event', moveon)
plt.show()
def sample(self, model, evidence):
z = evidence['z']
g = evidence['g']
h = evidence['h']
T = evidence['T']
phi = evidence['phi']
transition_var_h = evidence['transition_var_h']
shot_id = evidence['shot_id']
observation_var_h = model.known_params['observation_var_h']
mu_h = model.known_params['mu_h']
prior_mu_h = model.hyper_params['h']['mu']
prior_cov_h = model.hyper_params['h']['cov']
n = len(h)
N = len(z)
# Making g, h, and z vector valued to avoid ambiguity
z_h = ma.asarray(nan + zeros(n))
obs_cov = ma.asarray(inf + zeros(n))
if 2 in T:
z_h[T==2] = z[T==2]
obs_cov[T==2] = observation_var_h
pdb.set_trace()
#for i in xrange(n):
# z_i = z[shot_id == i]
# T_i = T[shot_id == i]
# if 2 in T_i:
# # Sample mean and variance for multiple observations
# n_obs = sum(T_i == 2)
# z_h[i] = mean(z_i[T_i == 2])
# obs_cov[i] = observation_var_h/n_obs
z_h[isnan(z_h)] = ma.masked
obs_cov[isinf(obs_cov)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = array([prior_mu_h[0],])
kalman.initial_state_covariance = array([prior_cov_h[0],])
kalman.transition_matrices = array([phi,])
kalman.transition_covariance = array([transition_var_h,])
kalman.transition_offsets = mu_h*(1-phi)*ones((n, 1))
kalman.observation_matrices = eye(1)
kalman.observation_offsets = g
kalman.observation_covariance = obs_cov
sampled_h = forward_filter_backward_sample(kalman, z_h, prior_mu_h, prior_cov_h)
return sampled_h.reshape((n,))
def sample(self, model, evidence):
z, T, g, h, sigma_h, phi = [
evidence[var] for var in ['z', 'T', 'g', 'h', 'sigma_h', 'phi']
]
sigma_z_h = model.known_params['sigma_z_h']
mu_h = model.known_params['mu_h']
prior_mu_h = model.hyper_params['prior_mu_h']
prior_cov_h = model.hyper_params['prior_cov_h']
n = len(h)
g = g.copy().reshape((n, 1))
h = h.copy().reshape((n, 1))
z_h = ma.asarray(z.copy().reshape((n, 1)))
if sum(T == 0) > 0:
z_h[T == 0] = nan
if sum(T == 1) > 0:
z_h[T == 1] = nan
if sum(T == 2) > 0:
z_h[T == 2] -= g[T == 2]
z_h[isnan(z_h)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = array([
prior_mu_h[0],
])
kalman.initial_state_covariance = array([
prior_cov_h[0, 0],
])
kalman.transition_matrices = array([
phi,
])
kalman.transition_covariance = array([
sigma_h**2,
])
kalman.transition_offsets = mu_h * (1 - phi) * ones((n, 1))
kalman.observation_matrices = eye(1)
kalman.observation_covariance = array([
sigma_z_h**2,
])
sampled_h = forward_filter_backward_sample(kalman, z_h)
return sampled_h.reshape((n, ))
def sample(self, model, evidence):
z = evidence['z']
T, g, h, sigma_g = [
evidence[var] for var in ['T', 'g', 'h', 'sigma_g']
]
sigma_z_g = model.known_params['sigma_z_g']
sigma_z_h = model.known_params['sigma_z_h']
prior_mu_g, prior_cov_g = [
model.hyper_params[var] for var in ['prior_mu_g', 'prior_cov_g']
]
n = len(g)
# Must be a more concise way to deal with scalar vs vector
g = g.copy().reshape((n, 1))
h = h.copy().reshape((n, 1))
z_g = ma.asarray(z.copy().reshape((n, 1)))
obs_cov = sigma_z_g**2 * ones((n, 1, 1))
if sum(T == 0) > 0:
z_g[T == 0] = nan
if sum(T == 2) > 0:
z_g[T == 2] -= h[T == 2]
obs_cov[T == 2] = sigma_z_h**2
z_g[isnan(z_g)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = array([
prior_mu_g[0],
])
kalman.initial_state_covariance = array([
prior_cov_g[0, 0],
])
kalman.transition_matrices = eye(1)
kalman.transition_covariance = array([
sigma_g**2,
])
kalman.observation_matrices = eye(1)
kalman.observation_covariance = obs_cov
sampled_g = forward_filter_backward_sample(kalman, z_g)
return sampled_g.reshape((n, ))
def sample(self, model, evidence):
z = evidence['z']
T, g, sigma_g = [evidence[var] for var in ['T', 'g', 'sigma_g']]
sigma_z_g = model.known_params['sigma_z_g']
prior_mu_g, prior_cov_g = [model.hyper_params[var] for var in ['prior_mu_g', 'prior_cov_g']]
n = len(g)
z_g = z.copy()
z_g[T == 0] = nan
z_g = ma.asarray(z_g)
z_g[isnan(z_g)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean=prior_mu_g[0]
kalman.initial_state_covariance=prior_cov_g[0,0]
kalman.transition_matrices=1
kalman.transition_covariance=sigma_g**2
kalman.observation_matrices=1
kalman.observation_covariance=sigma_z_g**2
pdb.set_trace()
sampled_g = forward_filter_backward_sample(kalman, z_g)
return sampled_g
def sample(self, model, evidence):
z = evidence['z']
T, surfaces, sigma_g, sigma_h = [
evidence[var] for var in ['T', 'surfaces', 'sigma_g', 'sigma_h']
]
mu_h, phi, sigma_z_g, sigma_z_h = [
model.known_params[var]
for var in ['mu_h', 'phi', 'sigma_z_g', 'sigma_z_h']
]
prior_mu_g, prior_cov_g = [
model.hyper_params[var] for var in ['prior_mu_g', 'prior_cov_g']
]
prior_mu_h, prior_cov_h = [
model.hyper_params[var] for var in ['prior_mu_h', 'prior_cov_h']
]
n = len(g)
y = ma.asarray(ones((n, 2)) * nan)
if sum(T == 1) > 0:
y[T == 1, 0] = z[T == 1]
if sum(T == 2) > 0:
y[T == 2, 1] = z[T == 2]
y[isnan(y)] = ma.masked
kalman = self._kalman
kalman.initial_state_mean = [prior_mu_g[0], prior_mu_h[0]]
kalman.initial_state_covariance = diag(
[prior_cov_g[0, 0], prior_cov_h[0, 0]])
kalman.transition_matrices = [[1, 0], [0, phi]]
kalman.transition_offsets = ones((n, 2)) * [0, mu_h * (1 - phi)]
kalman.transition_covariance = [[sigma_g**2, 0], [0, sigma_h**2]]
kalman.observation_matrices = [[1, 0], [1, 1]]
kalman.observation_covariance = [[sigma_z_g**2, 0], [0, sigma_z_h**2]]
sampled_surfaces = forward_filter_backward_sample(kalman, y)
return sampled_surfaces
