def test_negative_binomial_model_update():
model = NegativeBinomialModel(r=10, alpha=1, beta=1)
data = np.array([26, 31, 36, 35, 24, 44, 25, 40, 55, 24])
model.update(data)
assert model.n_samples_ == 10
def test_negative_binomial_mv_check_model_input():
modelA = NegativeBinomialModel(r=10, alpha=1, beta=1)
modelB = NegativeBinomialModel(r=10, alpha=1, beta=1)
with raises(TypeError):
NegativeBinomialMVTest(models=[modelA, modelB])
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)
def test_negative_binomial_model_mean_var():
model = NegativeBinomialModel(r=10, alpha=1, beta=6)
assert np.isnan(model.ppmean())
assert np.isnan(model.ppvar())
def test_negative_binomial_model_stats():
model = NegativeBinomialModel(r=10, alpha=4, beta=6)
assert model.ppmean() == approx(20)
assert model.ppvar() == approx(390)
def test_negative_binomial_model_pppdf_x():
model = NegativeBinomialModel(r=10, alpha=4, beta=6)
assert model.pppdf([-1, 0, 10, 20]) == approx(
[0, 0.0030959752, 0.0428785607, 0.0239013606])
def test_negative_binomial_r():
with raises(ValueError):
NegativeBinomialModel(r=0, alpha=1, beta=1)