def _sample_noisy(self, comp, vals):
def logprob(hypers):
mean = hypers[0]
amp2 = hypers[1]
noise = hypers[2]
# This is pretty hacky, but keeps things sane.
if mean > np.max(vals) or mean < np.min(vals):
return -np.inf
if amp2 < 0 or noise < 0:
return -np.inf
cov = amp2 * (self.cov_func(self.ls, comp, None) + 1e-6 * np.eye(
comp.shape[0])) + noise * np.eye(comp.shape[0])
chol = spla.cholesky(cov, lower=True)
solve = spla.cho_solve((chol, True), vals - mean)
lp = -np.sum(np.log(np.diag(chol))) - 0.5 * np.dot(
vals - mean, solve)
# Roll in noise horseshoe prior.
lp += np.log(np.log(1 + (self.noise_scale / noise)**2))
# Roll in amplitude lognormal prior
lp -= 0.5 * (np.log(amp2) / self.amp2_scale)**2
return lp
hypers = util.slice_sample(np.array([self.mean, self.amp2,
self.noise]),
logprob,
compwise=False)
self.mean = hypers[0]
self.amp2 = hypers[1]
self.noise = hypers[2]
def _sample_noiseless(self, comp, vals):
def logprob(hypers):
mean = hypers[0]
amp2 = hypers[1]
noise = 1e-3
if amp2 < 0:
return -np.inf
cov = amp2 * (self.cov_func(self.ls, comp, None) + 1e-6 * np.eye(
comp.shape[0])) + noise * np.eye(comp.shape[0])
chol = spla.cholesky(cov, lower=True)
solve = spla.cho_solve((chol, True), vals - mean)
lp = -np.sum(np.log(np.diag(chol))) - 0.5 * np.dot(
vals - mean, solve)
# Roll in amplitude lognormal prior
lp -= 0.5 * (np.log(amp2) / self.amp2_scale)**2
return lp
hypers = util.slice_sample(np.array([self.mean, self.amp2,
self.noise]),
logprob,
compwise=False)
self.mean = hypers[0]
self.amp2 = hypers[1]
self.noise = 1e-3
def handle_slice_sampler_exception(exception, starting_point, proposal_measure, opt_compwise=False):
'''
Handles slice sampler exceptions. If the slice sampler shrank to zero the slice sampler will be restarted
a few times. If this fails or if the exception was another this method will raise the given exception.
Args:
exception: the exception that occured
starting_point: the starting point that was used
proposal_measure: the used proposal measure
opt_compwise: how to set the compwise option
Returns:
the output of the slice sampler
Raises:
Exception: the first argument
'''
if exception.message == "Slice sampler shrank to zero!":
log("Slice sampler shrank to zero! Action: trying to restart " + str(NUMBER_OF_RESTARTS)
+ " times with same starting point")
restarts_left = NUMBER_OF_RESTARTS
while restarts_left > 0:
try:
return slice_sample(starting_point, proposal_measure, compwise=opt_compwise)
except Exception as e:
log("Restart failed. " + str(restarts_left) + " restarts left. Exception was: " + e.message)
restarts_left = restarts_left - 1
# if we leave the while loop we will raise the exception we got
import traceback
print traceback.format_exc()
raise exception
def _sample_noiseless(self, comp, vals):
def logprob(hypers):
mean = hypers[0]
amp2 = hypers[1]
noise = 1e-3
# This is pretty hacky, but keeps things sane.
if mean > np.max(vals) or mean < np.min(vals):
return -np.inf
if amp2 < 0:
return -np.inf
cov = (amp2 * (self.cov_func(self.ls, comp, None) +
1e-6*np.eye(comp.shape[0])) + noise*np.eye(comp.shape[0]))
chol = spla.cholesky(cov, lower=True)
solve = spla.cho_solve((chol, True), vals - mean)
lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-mean, solve)
# Roll in amplitude lognormal prior
lp -= 0.5*(np.log(np.sqrt(amp2))/self.amp2_scale)**2
return lp
hypers = util.slice_sample(np.array(
[self.mean, self.amp2, self.noise]), logprob, compwise=False)
self.mean = hypers[0]
self.amp2 = hypers[1]
self.noise = 1e-3
def _sample_time_ls(self, comp, vals):
def logprob(ls):
if np.any(ls < 0) or np.any(ls > self.time_max_ls):
return -np.inf
cov = self.time_amp2 * (self.cov_func(ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + self.time_noise*np.eye(comp.shape[0])
chol = spla.cholesky(cov, lower=True)
solve = spla.cho_solve((chol, True), vals - self.time_mean)
lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-self.time_mean, solve)
return lp
self.time_ls = util.slice_sample(self.time_ls, logprob, compwise=True)
def _sample_ls(self, comp, vals):
def logprob(ls):
if np.any(ls < 0) or np.any(ls > self.max_ls):
return -np.inf
cov = self.amp2 * (self.cov_func(ls, comp, None) + 1e-6*np.eye(comp.shape[0])) + self.noise*np.eye(comp.shape[0])
chol = spla.cholesky(cov, lower=True)
solve = spla.cho_solve((chol, True), vals - self.mean)
lp = -np.sum(np.log(np.diag(chol)))-0.5*np.dot(vals-self.mean, solve)
return lp
self.ls = util.slice_sample(self.ls, logprob, compwise=True)
def _sample_ls(comp, vals, cov_func, start_point, mean, amp2, noise):
def logprob(ls):
if np.any(ls < 0) or np.any(ls > MAX_LS):
return -np.inf
cov = (amp2 * (cov_func(ls, comp, None) +
1e-6 * np.eye(comp.shape[0])) + noise * np.eye(comp.shape[0]))
chol = spla.cholesky(cov, lower=True)
solve = spla.cho_solve((chol, True), vals - mean)
lp = (-np.sum(np.log(np.diag(chol))) - 0.5 * np.dot(vals - mean, solve))
return lp
try:
return slice_sample(start_point, logprob, compwise=True)
except Exception as e:
return handle_slice_sampler_exception(e, start_point, logprob, True)
def sample_from_proposal_measure(starting_point, log_proposal_measure, number_of_points):
'''
Samples representer points for discretization of Pmin.
Args:
starting_point: The point where to start the sampling.
log_proposal_measure: A function that measures in log-scale how suitable a point is to represent Pmin.
number_of_points: The number of samples to draw.
Returns:
a numpy array containing the desired number of samples
'''
representer_points = np.zeros([number_of_points, starting_point.shape[0]])
chain_length = 3 * starting_point.shape[0]
#TODO: burnin?
for i in range(0, number_of_points):
#this for loop ensures better mixing
for c in range(0, chain_length):
try:
starting_point = slice_sample(starting_point, log_proposal_measure)
except Exception as e:
starting_point = handle_slice_sampler_exception(e, starting_point, log_proposal_measure)
representer_points[i] = starting_point
return representer_points
def _sample_mean_amp_noise(comp, vals, cov_func, start_point, ls):
default_noise = 1e-3
#if we get a start point that consists only of two variables that means we don't care for the noise
noiseless = (start_point.shape[0] == 2)
def logprob(hypers):
mean = hypers[0]
amp2 = hypers[1]
if not noiseless:
noise = hypers[2]
else:
noise = default_noise
# This is pretty hacky, but keeps things sane.
if mean > np.max(vals) or mean < np.min(vals):
return -np.inf
if amp2 < 0 or noise < 0:
return -np.inf
cov = (amp2 * (cov_func(ls, comp, None) +
1e-6 * np.eye(comp.shape[0])) + noise * np.eye(comp.shape[0]))
chol = spla.cholesky(cov, lower=True)
solve = spla.cho_solve((chol, True), vals - mean)
lp = -np.sum(np.log(np.diag(chol))) - 0.5 * np.dot(vals - mean, solve)
if not noiseless:
# Roll in noise horseshoe prior.
lp += np.log(np.log(1 + (NOISE_SCALE / noise) ** 2))
# Roll in amplitude lognormal prior
lp -= 0.5 * (np.log(amp2) / AMP2_SCALE) ** 2
#print "LP: " + str(lp)
return lp
try:
return slice_sample(start_point, logprob, compwise=False)
except Exception as e:
return handle_slice_sampler_exception(e, start_point, logprob, False)