def sample(self, model, data, nsamples=None, walker_initial_pos=None):
if nsamples is not None:
# deprecated as of 3.3
from holopy.fitting import fit_warning
fit_warning('EmceeStrategy(nsamples=X)',
'passing nsamples to EmceeStrategy.sample')
self.nsamples = nsamples
if walker_initial_pos is not None:
# deprecated as of 3.3
from holopy.fitting import fit_warning
fit_warning('EmceeStrategy(walker_initial_pos=X)',
'passing walker_initial_pos to EmceeStrategy.sample')
self.walker_initial_pos = walker_initial_pos
time_start = time.time()
if self.npixels is not None:
data = make_subset_data(data, pixels=self.npixels, seed=self.seed)
if self.walker_initial_pos is None:
self.walker_initial_pos = model.generate_guess(self.nwalkers,
seed=self.seed)
sampler = sample_emcee(model=model, data=data, nwalkers=self.nwalkers,
walker_initial_pos=self.walker_initial_pos,
nsamples=self.nsamples, parallel=self.parallel,
seed=self.seed)
samples = emcee_samples_DataArray(sampler, model._parameters)
lnprobs = emcee_lnprobs_DataArray(sampler)
d_time = time.time() - time_start
kwargs = {'lnprobs': lnprobs, 'samples': samples}
return SamplingResult(data, model, self, d_time, kwargs)
def sample(self, model, data, nsamples=1000, walker_initial_pos=None):
if self.pixels is not None:
data = make_subset_data(data, pixels=self.pixels)
if walker_initial_pos is None:
walker_initial_pos = self.make_guess(model.parameters)
sampler = sample_emcee(model=model,
data=data,
nwalkers=self.nwalkers,
walker_initial_pos=walker_initial_pos,
nsamples=nsamples,
threads=self.threads,
cleanup_threads=self.cleanup_threads,
seed=self.seed)
try:
acor = sampler.acor
except emcee.autocorr.AutocorrError:
acor = None
samples = emcee_samples_DataArray(sampler, model.parameters)
lnprobs = emcee_lnprobs_DataArray(sampler)
return SamplingResult(xr.Dataset({
'samples': samples,
'lnprobs': lnprobs,
'data': data
}),
model=model,
strategy=self)
def generate_sampling_result():
samples = np.array([[[1, 2], [11, 12], [3, 3]], [[0, 3], [1, 3], [5, 6]]])
samples = xr.DataArray(samples,
dims=['walker', 'chain', 'parameter'],
coords={'parameter': ['p1', 'p2']})
lnprobs = xr.DataArray([[10, 9, 8], [7, 6, 5]], dims=['walker', 'chain'])
result = SamplingResult(DATA,
MODEL,
EmceeStrategy(),
10,
kwargs={
'samples': samples,
'lnprobs': lnprobs
})
return result
def sample(self, data, strategy=None):
strategy = self.validate_strategy(strategy, 'sample')
kwargs = {'samples': None, 'lnprobs': None, 'intervals': None}
return SamplingResult(data, self, strategy, 0, kwargs)