def sample_nu(self, use_prior=False):
'''
Update self._nu with one slice sampling draw
To ensure stationarity, the upper bound for sampling nu(x,y) is set to be 1/tau(y).
Return the draw of self._nu
'''
def sample_func_nu(nu):
self.set_nu(nu, x, y)
if use_prior:
return self.log_likelihood() + self.log_prior()
else:
return self.log_likelihood()
for x in xrange(self._A):
for y in xrange(self._A):
if self._non_diagonal:
if x==y:
assert self._nu[x,y]==0
continue
upper_bound_nu = 1.0 / self._tau[y] - (sum(self._nu[:,y]) - self._nu[x,y])
slice_sample(self._nu[x,y], sample_func_nu, window_size=self._nu_window_size, L_bound=0, R_bound=upper_bound_nu, step_out_limit=4)
return self.get_nu()
def sample_tau_and_nu_revised(self, use_prior=False):
'''
Update self._tau and self._nu with one slice sampling round
The ordering is specified as
for x in range(A):
sample tau[x]
for y in range(A):
sample nu[y, x]
Return one draw of self._tau, self._nu
'''
## closure
def sample_func_tau(tau):
self.set_tau(tau, x)
if use_prior:
return self.log_likelihood() + self.log_prior()
else:
return self.log_likelihood()
def sample_func_nu(nu):
## !! [y,x] not [x,y] !!
self.set_nu(nu, y, x)
if use_prior:
return self.log_likelihood() + self.log_prior()
else:
return self.log_likelihood()
for x in range(self._A):
upper_bound_tau = 1.0 / sum(self._nu[:, x])
# TODO: remove the assert
assert self._tau[
x] <= upper_bound_tau, "tau = %f, upper_bound_tau = %f" % (
self._tau[x], upper_bound_tau)
# slice sampling with inf stepping out limit
slice_sample(self._tau[x],
sample_func_tau,
window_size=1.0,
L_bound=0,
R_bound=upper_bound_tau,
step_out_limit=4)
for y in range(self._A):
# TODO: remove the assert
upper_bound_nu = 1.0 / self._tau[x] - (sum(self._nu[:, x]) -
self._nu[y, x])
## TODO: remove
upper_bound_nu = min(upper_bound_nu, 10)
assert self._nu[
y, x] <= upper_bound_nu, "nu = %f, upper_bound_nu = %f" % (
self._nu[y, x], upper_bound_nu)
# slice sampling with inf stepping out limit
slice_sample(self._nu[y, x],
sample_func_nu,
window_size=5.0,
L_bound=0,
R_bound=upper_bound_nu,
step_out_limit=4)
return self.get_tau(), self.get_nu()
def sample_tau(self, use_prior=False):
'''
Update self._tau of shape (self._A) with one slice sampling draw.
To ensure stationarity, which suffices to be ensured by tau(x) * nu(y,x) <1 for every (x,y).
The upper bound for sampling tau(x) is set to be min(1/nu(y,x)) among y.
Return the draw of self._tau
'''
## closure
def sample_func_tau(tau):
self.set_tau(tau, x)
if use_prior:
return self.log_likelihood() + self.log_prior()
else:
return self.log_likelihood()
for x in range(self._A):
## constraining spectral radius by the max col sum
upper_bound_tau = 1.0 / sum(self._nu[:, x])
slice_sample(self._tau[x],
sample_func_tau,
window_size=self._tau_window_size,
L_bound=0.1,
R_bound=upper_bound_tau,
step_out_limit=4)
return self.get_tau()
def sample_word_concentration(self, window_size=200, step_out_limit=0):
def sample_func(word_concentration):
self.set_word_concentration(word_concentration, x)
return self.log_prob()
for x in range(self._A):
slice_sample(self._word_concentration[x],
sample_func,
window_size=window_size,
L_bound=1e-4,
R_bound=self._word_concentration_upper_bound,
step_out_limit=step_out_limit)
return self._word_concentration.copy()
def sample_time_decay(self, window_size=100, step_out_limit=0):
def sample_func(time_decay):
self.set_time_decay(time_decay, x)
return self.log_prob()
for x in range(self._A):
slice_sample(self._time_decay[x],
sample_func,
window_size=window_size,
L_bound=0,
R_bound=self._time_decay_upper_bound,
step_out_limit=step_out_limit)
return self._time_decay.copy()
def sample_word_pseudocounts(self, window_size=10, step_out_limit=0):
def sample_func(word_pseudocount):
self.set_word_pseudocounts(word_pseudocount, x, v)
return self.log_prob()
for x in range(self._A):
for v in range(self._V):
slice_sample(self._word_pseudocounts[x, v],
sample_func,
window_size=window_size,
L_bound=0,
R_bound=self._word_pseudocount_upper_bound,
step_out_limit=step_out_limit)
return self._word_pseudocounts.copy()
def sample_tau_and_nu_revised(self, use_prior=False):
'''
Update self._tau and self._nu with one slice sampling round
The ordering is specified as
for x in xrange(A):
sample tau[x]
for y in xrange(A):
sample nu[y, x]
Return one draw of self._tau, self._nu
'''
## closure
def sample_func_tau(tau):
self.set_tau(tau, x)
if use_prior:
return self.log_likelihood() + self.log_prior()
else:
return self.log_likelihood()
def sample_func_nu(nu):
## !! [y,x] not [x,y] !!
self.set_nu(nu, y, x)
if use_prior:
return self.log_likelihood() + self.log_prior()
else:
return self.log_likelihood()
for x in xrange(self._A):
upper_bound_tau = 1.0 / sum(self._nu[:,x])
# TODO: remove the assert
assert self._tau[x] <= upper_bound_tau, "tau = %f, upper_bound_tau = %f" % (self._tau[x], upper_bound_tau)
# slice sampling with inf stepping out limit
slice_sample(self._tau[x], sample_func_tau, window_size=1.0, L_bound=0, R_bound=upper_bound_tau, step_out_limit=4)
for y in xrange(self._A):
# TODO: remove the assert
upper_bound_nu = 1.0 / self._tau[x] - (sum(self._nu[:,x]) - self._nu[y,x])
## TODO: remove
upper_bound_nu = min(upper_bound_nu, 10)
assert self._nu[y,x] <= upper_bound_nu, "nu = %f, upper_bound_nu = %f" % (self._nu[y,x], upper_bound_nu)
# slice sampling with inf stepping out limit
slice_sample(self._nu[y,x], sample_func_nu, window_size=5.0, L_bound=0, R_bound=upper_bound_nu, step_out_limit=4)
return self.get_tau(), self.get_nu()
def sample_influence(self, window_size=10, step_out_limit=2):
def sample_func(influence):
self.set_influence(influence, x, y)
return self.log_prob()
for x in range(self._A):
for y in range(self._A):
if self._non_diagonal:
if x == y:
continue
slice_sample(self._influence[x, y],
sample_func,
window_size=window_size,
L_bound=0,
R_bound=self._influence_upper_bound,
step_out_limit=step_out_limit)
return self._influence.copy()
def sample_baseline_rate(self, use_prior=False):
'''
Update self.baseline_rate with one slice sampling draw
Return the draw of self._baseline_rate
'''
def sample_func_baseline_rate(baseline_rate):
self.set_baseline_rate(baseline_rate, x)
if use_prior:
return self.log_likelihood() + self.log_prior()
else:
return self.log_likelihood()
for x in xrange(self._A):
# slice sampling with inf stepping out limit
slice_sample(self._baseline_rate[x], sample_func_baseline_rate, window_size=self._baseline_rate_window_size, L_bound=0, step_out_limit=4)
return self.get_baseline_rate()
def sample_tau(self, use_prior=False):
'''
Update self._tau of shape (self._A) with one slice sampling draw.
To ensure stationarity, which suffices to be ensured by tau(x) * nu(y,x) <1 for every (x,y).
The upper bound for sampling tau(x) is set to be min(1/nu(y,x)) among y.
Return the draw of self._tau
'''
## closure
def sample_func_tau(tau):
self.set_tau(tau, x)
if use_prior:
return self.log_likelihood() + self.log_prior()
else:
return self.log_likelihood()
for x in xrange(self._A):
## constraining spectral radius by the max col sum
upper_bound_tau = 1.0 / sum(self._nu[:,x])
slice_sample(self._tau[x], sample_func_tau, window_size=self._tau_window_size, L_bound=0.1, R_bound=upper_bound_tau, step_out_limit=4)
return self.get_tau()
import scipy.stats
import matplotlib as plt
from pylab import *
import slice_sampler
import mh_sampler
def plot_hist(x_l, file_path):
figure()
n, bins, patches = hist(x_l, 20, histtype='bar')
plt.savefig(file_path)
def plot_trace(x_l, n_l, file_path):
figure()
plot(n_l,x_l)
plt.savefig(file_path)
data = np.array([3.7, 3.4, 5.5, 5.0, 5.4, 6.6, 4.8, 4.4, 5.1, 5.4])
# MH Sampling
x_chain, n_chain, acc = mh_sampler.mh_sample(10000,data,6.0,2.0)
print acc
plot_hist(x_chain[:acc+1], '../Figures/Prob4/mu_hist.pdf')
plot_trace(x_chain[:acc+1], n_chain[:acc+1], '../Figures/Prob4/mu_trace.pdf')
# Slice Sampling
n = 10000
x_chain = slice_sampler.slice_sample(n,data,10.0)
n_chain = np.arange(1,n+2,1)
plot_hist(x_chain, '../Figures/Prob5/mu_hist.pdf')
plot_trace(x_chain, n_chain, '../Figures/Prob5/mu_trace.pdf')
import slice_sampler
import mh_sampler
def plot_hist(x_l, file_path):
figure()
n, bins, patches = hist(x_l, 20, histtype='bar')
plt.savefig(file_path)
def plot_trace(x_l, n_l, file_path):
figure()
plot(n_l, x_l)
plt.savefig(file_path)
data = np.array([3.7, 3.4, 5.5, 5.0, 5.4, 6.6, 4.8, 4.4, 5.1, 5.4])
# MH Sampling
x_chain, n_chain, acc = mh_sampler.mh_sample(10000, data, 6.0, 2.0)
print acc
plot_hist(x_chain[:acc + 1], '../Figures/Prob4/mu_hist.pdf')
plot_trace(x_chain[:acc + 1], n_chain[:acc + 1],
'../Figures/Prob4/mu_trace.pdf')
# Slice Sampling
n = 10000
x_chain = slice_sampler.slice_sample(n, data, 10.0)
n_chain = np.arange(1, n + 2, 1)
plot_hist(x_chain, '../Figures/Prob5/mu_hist.pdf')
plot_trace(x_chain, n_chain, '../Figures/Prob5/mu_trace.pdf')
