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