def test_tuple_ml():
"""
Test ML parameter estimation under the tuple distribution.
"""
from cargo.numpy import tolist_deeply
model = Tuple([(Binomial(estimation_n=1), 2),
(Binomial(estimation_n=1), 1)])
engine = ModelEngine(model)
assert_almost_equal_deep(
tolist_deeply(
engine.ml(
[([0, 1], [0])] * 2500 + [([1, 0], [1])] * 7500,
numpy.ones(10000),
), ),
([(0.75, 1), (0.25, 1)], [(0.75, 1)]),
)
assert_almost_equal_deep(
tolist_deeply(
engine.ml(
[([0, 1], [0])] * 1000 + [([1, 0], [1])] * 1000,
[1.00] * 1000 + [3.00] * 1000,
), ),
([(0.75, 1), (0.25, 1)], [(0.75, 1)]),
)
def test_tuple_ml():
"""
Test ML parameter estimation under the tuple distribution.
"""
from cargo.numpy import tolist_deeply
model = Tuple([(Binomial(estimation_n = 1), 2), (Binomial(estimation_n = 1), 1)])
engine = ModelEngine(model)
assert_almost_equal_deep(
tolist_deeply(
engine.ml(
[([0, 1], [0])] * 2500 + [([1, 0], [1])] * 7500,
numpy.ones(10000),
),
),
([(0.75, 1), (0.25, 1)], [(0.75, 1)]),
)
assert_almost_equal_deep(
tolist_deeply(
engine.ml(
[([0, 1], [0])] * 1000 + [([1, 0], [1])] * 1000,
[1.00] * 1000 + [3.00] * 1000,
),
),
([(0.75, 1), (0.25, 1)], [(0.75, 1)]),
)
def test_mixed_binomial_ml():
"""
Test max-likelihood estimation under the mixed binomial distribution.
"""
me = ModelEngine(MixedBinomial())
assert_almost_equal_deep(
me.ml(
[[(1, 2), (4, 5)], [(3, 4), (8, 8)]],
numpy.ones((2, 2)),
),
[5.0 / 7.0, 11.0 / 12.0],
)
def test_mixed_binomial_ml():
"""
Test max-likelihood estimation under the mixed binomial distribution.
"""
me = ModelEngine(MixedBinomial())
assert_almost_equal_deep(
me.ml(
[[(1, 2), (4, 5)],
[(3, 4), (8, 8)]],
numpy.ones((2, 2)),
),
[5.0 / 7.0, 11.0 / 12.0],
)
def test_binomial_ml():
"""
Test max-likelihood estimation under the binomial distribution.
"""
me = ModelEngine(Binomial(estimation_n=2))
assert_almost_equal_deep(
me.ml(
[[1, 2],
[2, 0]],
numpy.ones((2, 2)),
) \
.tolist(),
[(0.75, 2), (0.50, 2)],
)
def test_binomial_ml():
"""
Test max-likelihood estimation under the binomial distribution.
"""
me = ModelEngine(Binomial(estimation_n = 2))
assert_almost_equal_deep(
me.ml(
[[1, 2],
[2, 0]],
numpy.ones((2, 2)),
) \
.tolist(),
[(0.75, 2), (0.50, 2)],
)
