def jump_plot(distribution, nsamples=100, **kwargs):
"""
Plots samples drawn from distribution with dwelling time on the x-axis
and the sample value on the y-axis
1D only
"""
distr = distribution(ndims=1, nbatch=1, **kwargs)
# sampler = MarkovJumpHMC(np.array([0]).reshape(1,1),
sampler = MarkovJumpHMC(distr.Xinit, distr.E, distr.dEdX, epsilon=0.3, beta=0.2, num_leapfrog_steps=5)
x_t = []
d_t = []
transitions = []
last_L_count, last_F_count, last_R_count = 0, 0, 0
for idx in xrange(nsamples):
sampler.sampling_iteration()
x_t.append(sampler.state.X[0, 0])
d_t.append(sampler.dwelling_times[0])
if sampler.L_count - last_L_count == 1:
transitions.append("L")
last_L_count += 1
elif sampler.F_count - last_F_count == 1:
transitions.append("F")
last_F_count += 1
elif sampler.R_count - last_R_count == 1:
transitions.append("R")
last_R_count += 1
t = np.cumsum(d_t)
plt.scatter(t, x_t)
t = np.array(t).reshape(len(t), 1)
x_t = np.array(x_t).reshape(len(x_t), 1)
transitions = np.array(transitions).reshape(len(transitions), 1)
data = np.concatenate((x_t, t, transitions), axis=1)
return pd.DataFrame(data, columns=["x", "t", "transitions"])
def generate_initialization(distribution):
""" Run mjhmc for BURN_IN_STEPS on distribution, generating a fair set of initial states
:param distribution: Distribution object. Must have nbatch == MAX_N_PARTICLES
:returns: a set of fair initial states and an estimate of the variance for emc and true both
:rtype: tuple: (array of shape (distribution.ndims, MAX_N_PARTICLES), float, float)
"""
print('Generating fair initialization for {} by burning in {} steps'.format(
type(distribution).__name__, BURN_IN_STEPS))
assert BURN_IN_STEPS > VAR_STEPS
assert distribution.nbatch == MAX_N_PARTICLES
mjhmc = MarkovJumpHMC(distribution=distribution, resample=False)
for _ in xrange(BURN_IN_STEPS - VAR_STEPS):
mjhmc.sampling_iteration()
assert mjhmc.resample == False
emc_var_estimate, mjhmc = online_variance(mjhmc, distribution)
# we discard v since p(x,v) = p(x)p(v)
fair_x = mjhmc.state.copy().X
# otherwise will go into recursive loop
distribution.mjhmc = False
control = ControlHMC(distribution=distribution.reset())
for _ in xrange(BURN_IN_STEPS - VAR_STEPS):
control.sampling_iteration()
true_var_estimate, control = online_variance(control, distribution)
return (fair_x, emc_var_estimate, true_var_estimate)
def generate_initialization(distribution):
""" Run mjhmc for BURN_IN_STEPS on distribution, generating a fair set of initial states
:param distribution: Distribution object. Must have nbatch == MAX_N_PARTICLES
:returns: a set of fair initial states and an estimate of the variance for emc and true both
:rtype: tuple: (array of shape (distribution.ndims, MAX_N_PARTICLES), float, float)
"""
print(
'Generating fair initialization for {} by burning in {} steps'.format(
type(distribution).__name__, BURN_IN_STEPS))
assert BURN_IN_STEPS > VAR_STEPS
# must rebuild graph to nbatch=MAX_N_PARTICLES
if distribution.backend == 'tensorflow':
distribution.build_graph()
mjhmc = MarkovJumpHMC(distribution=distribution, resample=False)
for _ in xrange(BURN_IN_STEPS - VAR_STEPS):
mjhmc.sampling_iteration()
assert mjhmc.resample == False
emc_var_estimate, mjhmc = online_variance(mjhmc, distribution)
# we discard v since p(x,v) = p(x)p(v)
mjhmc_endpt = mjhmc.state.copy().X
# otherwise will go into recursive loop
distribution.mjhmc = False
try:
distribution.gen_init_X()
except NotImplementedError:
print("No explicit init method found, using mjhmc endpoint")
distribution.E_count = 0
distribution.dEdX_count = 0
control = ControlHMC(distribution=distribution)
for _ in xrange(BURN_IN_STEPS - VAR_STEPS):
control.sampling_iteration()
true_var_estimate, control = online_variance(control, distribution)
control_endpt = control.state.copy().X
return mjhmc_endpt, emc_var_estimate, true_var_estimate, control_endpt
def generate_initialization(distribution):
""" Run mjhmc for BURN_IN_STEPS on distribution, generating a fair set of initial states
:param distribution: Distribution object. Must have nbatch == MAX_N_PARTICLES
:returns: a set of fair initial states and an estimate of the variance for emc and true both
:rtype: tuple: (array of shape (distribution.ndims, MAX_N_PARTICLES), float, float)
"""
print('Generating fair initialization for {} by burning in {} steps'.format(
type(distribution).__name__, BURN_IN_STEPS))
assert BURN_IN_STEPS > VAR_STEPS
# must rebuild graph to nbatch=MAX_N_PARTICLES
if distribution.backend == 'tensorflow':
distribution.build_graph()
mjhmc = MarkovJumpHMC(distribution=distribution, resample=False)
for _ in xrange(BURN_IN_STEPS - VAR_STEPS):
mjhmc.sampling_iteration()
assert mjhmc.resample == False
emc_var_estimate, mjhmc = online_variance(mjhmc, distribution)
# we discard v since p(x,v) = p(x)p(v)
mjhmc_endpt = mjhmc.state.copy().X
# otherwise will go into recursive loop
distribution.mjhmc = False
try:
distribution.gen_init_X()
except NotImplementedError:
print("No explicit init method found, using mjhmc endpoint")
distribution.E_count = 0
distribution.dEdX_count = 0
control = ControlHMC(distribution=distribution)
for _ in xrange(BURN_IN_STEPS - VAR_STEPS):
control.sampling_iteration()
true_var_estimate, control = online_variance(control, distribution)
control_endpt = control.state.copy().X
return mjhmc_endpt, emc_var_estimate, true_var_estimate, control_endpt
def jump_plot(distribution, nsamples=100, **kwargs):
"""
Plots samples drawn from distribution with dwelling time on the x-axis
and the sample value on the y-axis
1D only
"""
distr = distribution(ndims=1, nbatch=1, **kwargs)
# sampler = MarkovJumpHMC(np.array([0]).reshape(1,1),
sampler = MarkovJumpHMC(distr.Xinit,
distr.E,
distr.dEdX,
epsilon=.3,
beta=.2,
num_leapfrog_steps=5)
x_t = []
d_t = []
transitions = []
last_L_count, last_F_count, last_R_count = 0, 0, 0
for idx in xrange(nsamples):
sampler.sampling_iteration()
x_t.append(sampler.state.X[0, 0])
d_t.append(sampler.dwelling_times[0])
if sampler.L_count - last_L_count == 1:
transitions.append("L")
last_L_count += 1
elif sampler.F_count - last_F_count == 1:
transitions.append("F")
last_F_count += 1
elif sampler.R_count - last_R_count == 1:
transitions.append("R")
last_R_count += 1
t = np.cumsum(d_t)
plt.scatter(t, x_t)
t = np.array(t).reshape(len(t), 1)
x_t = np.array(x_t).reshape(len(x_t), 1)
transitions = np.array(transitions).reshape(len(transitions), 1)
data = np.concatenate((x_t, t, transitions), axis=1)
return pd.DataFrame(data, columns=['x', 't', 'transitions'])
