def another_simple_model():
_model = models.simple_model()[1]
with _model:
pm.Potential('pot', tt.ones((10, 10)))
return _model
def test_dlogp():
start, model, (mu, sig) = simple_model()
dlogp = model.fastdlogp()
close_to(dlogp(start), -(start["x"] - mu) / sig ** 2, 1.0 / sig ** 2 / 100.0)
pm.Bernoulli('trials', p, observed=trials)
trace = pm.sample(1000, step=pm.Metropolis())
approx = Empirical(trace)
approx.randidx(None).eval()
approx.randidx(1).eval()
approx.random_fn(no_rand=True)
approx.random_fn(no_rand=False)
approx.histogram_logp.eval()
def test_init_from_noize(self):
with models.multidimensional_model()[1]:
approx = Empirical.from_noise(100)
assert approx.histogram.eval().shape == (100, 6)
_model = models.simple_model()[1]
with _model:
pm.Potential('pot', tt.ones((10, 10)))
_advi = ADVI()
_fullrank_advi = FullRankADVI()
_svgd = SVGD()
@pytest.mark.parametrize(
['method', 'kwargs', 'error'],
[('undefined', dict(), KeyError), (1, dict(), TypeError),
(_advi, dict(start={}), None), (_fullrank_advi, dict(), None),
(_svgd, dict(), None), ('advi', dict(), None),
('advi->fullrank_advi', dict(frac=.1), None),
('advi->fullrank_advi', dict(frac=1), ValueError),
('fullrank_advi', dict(), None), ('svgd', dict(), None),
def test_logp():
start, model, (mu, sig) = simple_model()
lp = model.fastlogp
lp(start)
close_to(lp(start), sp.norm.logpdf(start["x"], mu, sig).sum(), tol)
def another_simple_model():
_model = models.simple_model()[1]
with _model:
pm.Potential('pot', tt.ones((10, 10)))
return _model
def setup_method(self):
_, self.model, _ = simple_model()
p = pm.Uniform('p')
pm.Bernoulli('trials', p, observed=trials)
trace = pm.sample(1000, step=pm.Metropolis())
histogram = Histogram(trace)
histogram.randidx(None).eval()
histogram.randidx(1).eval()
histogram.random_fn(no_rand=True)
histogram.random_fn(no_rand=False)
histogram.histogram_logp.eval()
def test_init_from_noize(self):
with models.multidimensional_model()[1]:
histogram = Histogram.from_noise(100)
assert histogram.histogram.eval().shape == (100, 6)
_model = models.simple_model()[1]
with _model:
pm.Potential('pot', tt.ones((10, 10)))
_advi = ADVI()
_fullrank_advi = FullRankADVI()
_svgd = SVGD()
@pytest.mark.parametrize(
['method', 'kwargs', 'error'],
[
('undefined', dict(), KeyError),
(1, dict(), TypeError),
(_advi, dict(), None),
(_fullrank_advi, dict(), None),
(_svgd, dict(), None),
def test_accuracy_normal():
_, model, (mu, _) = simple_model()
with model:
newstart = find_MAP(Point(x=[-10.5, 100.5]))
close_to(newstart["x"], [mu, mu],
select_by_precision(float64=1e-5, float32=1e-4))
