def test_neg_binomial(self):
d = Distribution("NEGATIVE_BINOMIAL", 0, 1, 0.5)
assert list(d.simulate(1000))
assert d.get_sup_bound == -1
assert d.get_inf_bound == 0
assert d.get_probability == 0.5
assert d.get_parameter == 1
assert d.get_ident() == 3
d = NegativeBinomial(0, 1, 0.5)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_neg_binomial(self):
d = Distribution("NEGATIVE_BINOMIAL", 0, 1, 0.5)
assert list(d.simulate(1000))
assert d.get_sup_bound == -1
assert d.get_inf_bound == 0
assert d.get_probability == 0.5
assert d.get_parameter == 1
assert d.get_ident() == 3
d = NegativeBinomial(0, 1, 0.5)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_poisson(self):
d = Distribution("POISSON", 0, 2)
assert list(d.simulate(1000))
assert d.get_sup_bound == -1
assert d.get_inf_bound == 0
assert d.get_probability == -1
assert d.get_parameter == 2
assert d.get_ident() == 2
d = Poisson(0, 2)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_uniform(self):
d = Distribution("UNIFORM", 0, 10)
assert list(d.simulate(1000))
assert d.get_sup_bound == 10
assert d.get_inf_bound == 0
assert d.get_probability == -1
assert d.get_parameter == -1
assert d.get_ident() == 4
d = Uniform(0, 10)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_poisson(self):
d = Distribution("POISSON", 0, 2)
assert list(d.simulate(1000))
assert d.get_sup_bound == -1
assert d.get_inf_bound == 0
assert d.get_probability == -1
assert d.get_parameter == 2
assert d.get_ident() == 2
d = Poisson(0, 2)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_uniform(self):
d = Distribution("UNIFORM", 0, 10)
assert list(d.simulate(1000))
assert d.get_sup_bound == 10
assert d.get_inf_bound == 0
assert d.get_probability == -1
assert d.get_parameter == -1
assert d.get_ident() == 4
d = Uniform(0, 10)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_to_histogram(self):
d = Distribution("NEGATIVE_BINOMIAL", 0, 1, 0.5)
h = d.simulate(1000)
d2 = ToDistribution(h)
assert h and d2
h2 = ToHistogram(d2)
assert h2
assert h == h2
def test_to_histogram(self):
d = Distribution("NEGATIVE_BINOMIAL", 0, 1, 0.5)
h = d.simulate(1000)
d2 = ToDistribution(h)
assert h and d2
h2 = ToHistogram(d2)
assert h2
assert h == h2
