def test_shapes(backend, moves, nwalkers=32, ndim=3, nsteps=10, seed=1234):
# Set up the random number generator.
np.random.seed(seed)
with backend() as be:
# Initialize the ensemble, moves and sampler.
coords = np.random.randn(nwalkers, ndim)
sampler = EnsembleSampler(nwalkers, ndim, normal_log_prob,
moves=moves, backend=be)
# Run the sampler.
sampler.run_mcmc(coords, nsteps)
chain = sampler.get_chain()
assert len(chain) == nsteps, "wrong number of steps"
tau = sampler.get_autocorr_time(quiet=True)
assert tau.shape == (ndim,)
# Check the shapes.
assert sampler.chain.shape == (nwalkers, nsteps, ndim), \
"incorrect coordinate dimensions"
assert sampler.get_chain().shape == (nsteps, nwalkers, ndim), \
"incorrect coordinate dimensions"
assert sampler.lnprobability.shape == (nsteps, nwalkers), \
"incorrect probability dimensions"
assert sampler.acceptance_fraction.shape == (nwalkers,), \
"incorrect acceptance fraction dimensions"
# Check the shape of the flattened coords.
assert sampler.get_chain(flat=True).shape == \
(nsteps * nwalkers, ndim), "incorrect coordinate dimensions"
assert sampler.get_log_prob(flat=True).shape == \
(nsteps*nwalkers,), "incorrect probability dimensions"
def test_shapes(backend, moves, nwalkers=32, ndim=3, nsteps=10, seed=1234):
# Set up the random number generator.
np.random.seed(seed)
with backend() as be:
# Initialize the ensemble, moves and sampler.
coords = np.random.randn(nwalkers, ndim)
sampler = EnsembleSampler(nwalkers,
ndim,
normal_log_prob,
moves=moves,
backend=be)
# Run the sampler.
sampler.run_mcmc(coords, nsteps)
chain = sampler.get_chain()
assert len(chain) == nsteps, "wrong number of steps"
tau = sampler.get_autocorr_time(quiet=True)
assert tau.shape == (ndim, )
# Check the shapes.
with pytest.warns(DeprecationWarning):
assert sampler.chain.shape == (
nwalkers,
nsteps,
ndim,
), "incorrect coordinate dimensions"
with pytest.warns(DeprecationWarning):
assert sampler.lnprobability.shape == (
nwalkers,
nsteps,
), "incorrect probability dimensions"
assert sampler.get_chain().shape == (
nsteps,
nwalkers,
ndim,
), "incorrect coordinate dimensions"
assert sampler.get_log_prob().shape == (
nsteps,
nwalkers,
), "incorrect probability dimensions"
assert sampler.acceptance_fraction.shape == (
nwalkers, ), "incorrect acceptance fraction dimensions"
# Check the shape of the flattened coords.
assert sampler.get_chain(flat=True).shape == (
nsteps * nwalkers,
ndim,
), "incorrect coordinate dimensions"
assert sampler.get_log_prob(flat=True).shape == (
nsteps * nwalkers, ), "incorrect probability dimensions"
index = 0
autocorr = np.empty(max_n)
# This will be useful to testing convergence
old_tau = np.inf
# Now we'll sample for up to max_n steps
for sample in s.sample(params0, iterations=max_n, progress=True):
# Only check convergence every 10000 steps
if s.iteration % 10000:
continue
# Compute the autocorrelation time so far
# Using tol=0 means that we'll always get an estimate even
# if it isn't trustworthy
tau = s.get_autocorr_time(tol=0)
autocorr[index] = np.mean(tau)
index += 1
# Check convergence
converged = np.all(tau * 100 < s.iteration)
converged &= np.all(np.abs(old_tau - tau) / tau < 0.01)
if converged:
break
old_tau = tau
#
# graphical convergence check
#
n = 10000 * np.arange(1, index + 1)
y = autocorr[:index]
pyplot.plot(n, n / 100.0, "--k")
pool.wait()
sys.exit(0)
sampler = EnsembleSampler(nwalkers, ndim, lnprob4D, pool=pool)
pos, lnprob, rand_state = sampler.run_mcmc(p0, nburn)
sampler.reset(
) # Reset the chain to remove the burn-in samples; keep walker positions.
pos, lnprob, rand_stateR = sampler.run_mcmc(
pos, nsteps, rstate0=rand_state) # rstate0=rstate
if pool.is_master():
meanacceptance = np.mean(sampler.acceptance_fraction)
autocorrelationtimes = sampler.get_autocorr_time()
# Print out the mean acceptance fraction. In general, acceptance_fraction has an entry for each walker so, in this case, it is a
# 50-dimensional vector.
print "Mean acceptance fraction:" + str(meanacceptance)
# Estimate the integrated autocorrelation time for the time series in each parameter.
print "Autocorrelation time:" + str(autocorrelationtimes)
samples = sampler.flatchain
lnprob = sampler.flatlnprobability
length = len(samples[:, 0])
lnpriors = []
