def test_mixture_list_of_poissons(self):
with Model() as model:
w = Dirichlet("w",
floatX(np.ones_like(self.pois_w)),
shape=self.pois_w.shape)
mu = Gamma("mu", 1.0, 1.0, shape=self.pois_w.size)
Mixture(
"x_obs",
w,
[Poisson.dist(mu[0]), Poisson.dist(mu[1])],
observed=self.pois_x)
step = Metropolis()
trace = sample(5000,
step,
random_seed=self.random_seed,
progressbar=False,
chains=1)
assert_allclose(np.sort(trace["w"].mean(axis=0)),
np.sort(self.pois_w),
rtol=0.1,
atol=0.1)
assert_allclose(np.sort(trace["mu"].mean(axis=0)),
np.sort(self.pois_mu),
rtol=0.1,
atol=0.1)
def test_plots_multidimensional():
# Test single trace
from .models import multidimensional_model
start, model, _ = multidimensional_model()
with model as model:
h = np.diag(find_hessian(start))
step = Metropolis(model.vars, h)
trace = sample(3000, step, start)
traceplot(trace)
def test_normal_mixture(self):
with Model() as model:
w = Dirichlet("w", floatX(np.ones_like(self.norm_w)), shape=self.norm_w.size)
mu = Normal("mu", 0.0, 10.0, shape=self.norm_w.size)
tau = Gamma("tau", 1.0, 1.0, shape=self.norm_w.size)
NormalMixture("x_obs", w, mu, tau=tau, observed=self.norm_x)
step = Metropolis()
trace = sample(5000, step, random_seed=self.random_seed, progressbar=False, chains=1)
assert_allclose(np.sort(trace["w"].mean(axis=0)), np.sort(self.norm_w), rtol=0.1, atol=0.1)
assert_allclose(
np.sort(trace["mu"].mean(axis=0)), np.sort(self.norm_mu), rtol=0.1, atol=0.1
)
def test_multichain_plots():
from pymc.examples import disaster_model as dm
with dm.model as model:
# Run sampler
step1 = Slice([dm.early_mean, dm.late_mean])
step2 = Metropolis([dm.switchpoint])
start = {'early_mean': 2., 'late_mean': 3., 'switchpoint': 50}
ptrace = psample(1000, [step1, step2], start, threads=2)
forestplot(ptrace, vars=['early_mean', 'late_mean'])
autocorrplot(ptrace, vars=['switchpoint'])
def test_plots():
# Test single trace
from pymc.examples import arbitrary_stochastic as asmod
with asmod.model as model:
start = model.test_point
h = find_hessian(start)
step = Metropolis(model.vars, h)
trace = sample(3000, step, start)
forestplot(trace)
autocorrplot(trace)
def simple_init():
start, model, moments = simple_model()
step = Metropolis(model.vars, np.diag([1.]), model=model)
return model, start, step, moments
def __init__(self, stochastic, cov=None, scale=1., proposal_sd=None, verbose=None, tally=True):
Metropolis.__init__(self, stochastic, scale=scale, proposal_sd=proposal_sd, proposal_distribution="Normal", verbose=verbose, tally=tally)
self.Cov = cov