def test_binomial_model_update():
model = BinomialModel(m=10, alpha=1, beta=1)
data = np.array([7, 4, 2, 5, 5, 4, 6, 7, 2, 4])
model.update(data)
assert model.n_samples_ == 10
def test_binomial_model_stats():
model = BinomialModel(m=10, alpha=4, beta=6)
assert model.pppdf(0) == approx(0.0325077399)
assert model.pppdf(10) == approx(0.0030959752)
assert model.pppdf(20) == approx(0)
assert model.ppmean() == approx(4.0)
assert model.ppvar() == approx(4.36363636364)
def test_binomial_mv_check_model_input():
modelA = BinomialModel(m=10, alpha=1, beta=1)
modelB = BinomialModel(m=10, alpha=1, beta=1)
with raises(TypeError):
BinomialMVTest(models=[modelA, modelB])
def test_binomial_ab_check_models():
modelA = BinomialModel(m=10, alpha=1, beta=1)
modelB = GeometricModel(alpha=1, beta=1)
with raises(TypeError):
BinomialABTest(modelA=modelA, modelB=modelB)
def test_binomial_model_pppdf_x():
model = BinomialModel(m=10, alpha=4, beta=6)
assert model.pppdf([-1, 0, 10, 20]) == approx(
[0, 0.0325077399, 0.0030959752, 0])
def test_binomial_model_m():
with raises(ValueError):
BinomialModel(m=-1, alpha=1, beta=1)
def test_negative_binomial_ab_check_model():
modelA = NegativeBinomialModel(r=10, alpha=1, beta=1)
modelB = BinomialModel(m=10, alpha=1, beta=1)
with raises(TypeError):
NegativeBinomialABTest(modelA=modelA, modelB=modelB)