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_binomial_ll():
"""
Test log-probability computation in the binomial distribution.
"""
me = ModelEngine(Binomial())
assert_almost_equal(me.ll((0.25, 2), 1), -0.98082925)
assert_almost_equal_deep(
me.ll(
[[(0.25, 2), (0.25, 5)], [(0.75, 4), (0.75, 8)]],
[[1, 4], [3, 8]],
),
[[-0.98082925, -4.22342160], [-0.86304622, -2.30145658]],
)
def test_mixed_binomial_ll():
"""
Test log-probability computation in the mixed binomial distribution.
"""
me = ModelEngine(MixedBinomial())
assert_almost_equal(me.ll(0.25, (1, 2)), -0.98082925)
assert_almost_equal_deep(
me.ll(
[[0.25], [0.75]],
[[(1, 2), (4, 5)], [(3, 4), (8, 8)]],
),
[[-0.98082925, -4.22342160], [-0.86304622, -2.30145658]],
)
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_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_delta_ll():
"""
Test likelihood computation under the trivial delta distribution.
"""
engine = ModelEngine(Delta(float))
assert_equal(engine.ll(42.0, 42.1), numpy.finfo(float).min)
assert_equal(engine.ll(42.0, 42.0), 0.0)
def test_mixed_binomial_ll():
"""
Test log-probability computation in the mixed binomial distribution.
"""
me = ModelEngine(MixedBinomial())
assert_almost_equal(me.ll(0.25, (1, 2)), -0.98082925)
assert_almost_equal_deep(
me.ll(
[[0.25],
[0.75]],
[[(1, 2), (4, 5)],
[(3, 4), (8, 8)]],
),
[[-0.98082925, -4.22342160],
[-0.86304622, -2.30145658]],
)
def test_binomial_ll():
"""
Test log-probability computation in the binomial distribution.
"""
me = ModelEngine(Binomial())
assert_almost_equal(me.ll((0.25, 2), 1), -0.98082925)
assert_almost_equal_deep(
me.ll(
[[(0.25, 2), (0.25, 5)],
[(0.75, 4), (0.75, 8)]],
[[1, 4],
[3, 8]],
),
[[-0.98082925, -4.22342160],
[-0.86304622, -2.30145658]],
)
def test_delta_ll():
"""
Test likelihood computation under the trivial delta distribution.
"""
engine = ModelEngine(Delta(float))
assert_equal(engine.ll(42.0, 42.1), numpy.finfo(float).min)
assert_equal(engine.ll(42.0, 42.0), 0.0)
def test_tuple_ll():
"""
Test log-likelihood computation under the tuple distribution.
"""
model = Tuple([(Binomial(), 2), (Binomial(), 1)])
engine = ModelEngine(model)
assert_almost_equal(
engine.ll(
([(0.25, 1), (0.75, 1)], [(0.5, 1)]),
([1, 1], [0]),
),
numpy.log(0.25 * 0.75 * 0.5),
)
assert_almost_equal(
engine.ll(
([(0.25, 1), (0.75, 1)], [(0.5, 1)]),
([0, 1], [0]),
),
numpy.log(0.75 * 0.75 * 0.5),
)
def test_tuple_ll():
"""
Test log-likelihood computation under the tuple distribution.
"""
model = Tuple([(Binomial(), 2), (Binomial(), 1)])
engine = ModelEngine(model)
assert_almost_equal(
engine.ll(
([(0.25, 1), (0.75, 1)], [(0.5, 1)]),
([1, 1], [0]),
),
numpy.log(0.25 * 0.75 * 0.5),
)
assert_almost_equal(
engine.ll(
([(0.25, 1), (0.75, 1)], [(0.5, 1)]),
([0, 1], [0]),
),
numpy.log(0.75 * 0.75 * 0.5),
)
