def test_engine_ll_complex_model():
"""
Test log-likelihood computation under a complex model.
"""
from cargo.log import get_logger
get_logger("cargo.llvm.constructs", level="DEBUG")
# test the model
from cargo.statistics import (
Tuple,
ModelEngine,
MixedBinomial,
FiniteMixture,
)
model = FiniteMixture(Tuple([(MixedBinomial(), 128)]), 8)
engine = ModelEngine(model)
# generate some fake parameters
sample = numpy.empty((), model.sample_dtype)
parameter = numpy.empty((), model.parameter_dtype)
sample["d0"]["n"] = 4
sample["d0"]["k"] = 1
parameter["p"] = 1.0 / 8.0
parameter["c"]["d0"] = 0.5
assert_almost_equal(engine.ll(parameter, sample), -177.445678223)
def test_finite_mixture_ml():
"""
Test EM estimation of finite mixture distributions.
"""
me = ModelEngine(FiniteMixture(MixedBinomial(), 2))
assert_finite_mixture_ml_ok(me)
def test_finite_mixture_ll():
"""
Test finite-mixture log-likelihood computation.
"""
engine = ModelEngine(FiniteMixture(Delta(float), 2))
p = [[(0.25, 1.0), (0.75, 2.0)]]
assert_almost_equal(engine.ll(p, 1.0 ), numpy.log(0.25))
assert_almost_equal(engine.ll(p, [ 2.0]), numpy.log(0.75))
assert_almost_equal(engine.ll(p, [42.0]), numpy.finfo(float).min)
def test_finite_mixture_marginal():
"""
Test finite-mixture marginal computation.
"""
from cargo.statistics.mixture import marginalize_mixture
model = FiniteMixture(MixedBinomial(), 2)
out = marginalize_mixture(model, [[(0.25, 0.25), (0.75, 0.75)]])
assert_equal(out.tolist(), [0.625])
def test_finite_mixture_given_enveloped():
"""
Test mixture posterior computation with enveloped arrays.
"""
model = FiniteMixture(Delta(float), 2)
engine = ModelEngine(model)
out = engine.given([(0.25, 1.0), (0.75, 2.0)], [[2.0]] * 3)
for i in xrange(3):
assert_almost_equal(out["p"][i, 0], 0.0)
assert_almost_equal(out["p"][i, 1], 1.0)
def test_finite_mixture_given_binomials():
"""
Test finite-mixture posterior-parameter computation with binomials.
"""
model = FiniteMixture(MixedBinomial(), 2)
engine = ModelEngine(model)
out = engine.given([(0.25, 0.25), (0.75, 0.75)], [(1, 1), (2, 3)])
assert_equal(out["c"].tolist(), [0.25, 0.75])
assert_almost_equal(out["p"][0], 0.03571429)
assert_almost_equal(out["p"][1], 0.9642857)
def test_finite_mixture_given():
"""
Test finite-mixture posterior-parameter computation.
"""
model = FiniteMixture(Delta(float), 2)
engine = ModelEngine(model)
out = engine.given([(0.25, 1.0), (0.75, 2.0)], [2.0])
assert_equal(out["c"].tolist(), [1.0, 2.0])
assert_almost_equal(out["p"][0], 0.0)
assert_almost_equal(out["p"][1], 1.0)
def test_finite_mixture_map():
"""
Test EM estimation of MAP finite mixture parameters.
"""
engine = ModelEngine(FiniteMixture(MixedBinomial(), 2))
(e,) = \
engine.map(
[[(1, 1)] * 2],
[[(7, 8)] * 100 + [(1, 8)] * 200],
ones((1, 300)),
)
assert_almost_equal_deep(
e[numpy.argsort(e["p"])].tolist(),
[(1.0 / 3.0, 7.0 / 8.0),
(2.0 / 3.0, 1.0 / 8.0)],
places = 4,
)