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),
('svgd', dict(start={}), None),
('svgd', dict(local_rv={_model.free_RVs[0]: (0, 1)}), ValueError)])
def test_fit(method, kwargs, error):
with _model:
if error is not None:
def test_from_empirical(another_simple_model):
with another_simple_model:
emp = Empirical.from_noise(1000)
svgd = SVGD.from_empirical(emp)
svgd.fit(20)
def test_from_empirical(another_simple_model):
with another_simple_model:
emp = Empirical.from_noise(1000)
svgd = SVGD.from_empirical(emp)
svgd.fit(20)