def _sample_chain(args):
"""Sample a single chain for OptGPSampler.
center and n_samples are updated locally and forgotten afterwards.
"""
n, idx = args # has to be this way to work in Python 2.7
center = sampler.center
np.random.seed((sampler._seed + idx) % np.iinfo(np.int32).max)
pi = np.random.randint(sampler.n_warmup)
prev = sampler.warmup[pi, ]
prev = step(sampler, center, prev - center, 0.95)
n_samples = max(sampler.n_samples, 1)
samples = np.zeros((n, center.shape[0]))
for i in range(1, sampler.thinning * n + 1):
pi = np.random.randint(sampler.n_warmup)
delta = sampler.warmup[pi, ] - center
prev = step(sampler, prev, delta)
if sampler.problem.homogeneous and (n_samples * sampler.thinning %
sampler.nproj == 0):
prev = sampler._reproject(prev)
center = sampler._reproject(center)
if i % sampler.thinning == 0:
samples[i // sampler.thinning - 1, ] = prev
center = ((n_samples * center) / (n_samples + 1) + prev /
(n_samples + 1))
n_samples += 1
return (sampler.retries, samples)
def _sample_chain(args):
"""Sample a single chain for OptGPSampler.
center and n_samples are updated locally and forgotten afterwards.
"""
n, idx = args # has to be this way to work in Python 2.7
center = sampler.center
np.random.seed((sampler._seed + idx) % np.iinfo(np.int32).max)
pi = np.random.randint(sampler.n_warmup)
prev = sampler.warmup[pi, ]
prev = step(sampler, center, prev - center, 0.95)
n_samples = max(sampler.n_samples, 1)
samples = np.zeros((n, center.shape[0]))
for i in range(1, sampler.thinning * n + 1):
pi = np.random.randint(sampler.n_warmup)
delta = sampler.warmup[pi, ] - center
prev = step(sampler, prev, delta)
if sampler.problem.homogeneous and (
n_samples * sampler.thinning % sampler.nproj == 0):
prev = sampler._reproject(prev)
center = sampler._reproject(center)
if i % sampler.thinning == 0:
samples[i//sampler.thinning - 1, ] = prev
center = ((n_samples * center) / (n_samples + 1) +
prev / (n_samples + 1))
n_samples += 1
return (sampler.retries, samples)
def __single_iteration(self):
pi = np.random.randint(self.n_warmup)
# mix in the original warmup points to not get stuck
delta = self.warmup[pi, ] - self.center
self.prev = step(self, self.prev, delta)
if self.problem.homogeneous and (self.n_samples *
self.thinning % self.nproj == 0):
self.prev = self._reproject(self.prev)
self.center = self._reproject(self.center)
self.center = ((self.n_samples * self.center) / (self.n_samples + 1) +
self.prev / (self.n_samples + 1))
self.n_samples += 1