def test_extract(self):
"""run and test the extract methods"""
m = self.data
assert m.extract_convolution() == ExtractDistribution(m, "Convolution")
assert m.extract(1) == Binomial(0, 10, 0.5)
assert m.extract(2) == NegativeBinomial(0, 1, 0.1)
assert ExtractDistribution(m, "Elementary", 1) == Binomial(0, 10, 0.5)
assert ExtractDistribution(m, "Elementary", 2) == \
NegativeBinomial(0, 1, 0.1)
def test_extract(self):
"""run and test the extract methods"""
m = self.data
assert m.extract_compound() == ExtractDistribution(m, "Compound")
assert m.extract_sum() == Binomial(2, 5, 0.5)
assert m.extract_sum() == ExtractDistribution(m, "Sum")
assert m.extract_elementary() == NegativeBinomial(0, 2, 0.5)
assert m.extract_elementary() == ExtractDistribution(m, "Elementary")
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_constructor_from_dists_and_threshold(self):
compound1 = self.data
compound2 = Compound(Binomial(2, 5, 0.5),
NegativeBinomial(0, 2, 0.5),
Threshold=0.99999)
assert str(compound1) == str(compound2)
def build_data(self):
d1 = Binomial(2, 5, 0.5)
d2 = NegativeBinomial(0, 2, 0.5)
comp = Compound(d1, d2)
return comp
def build_data(self):
d1 = Binomial(0, 10, 0.5)
d2 = NegativeBinomial(0, 1, 0.1)
conv = Convolution(d1, d2, d1, d2)
conv = Convolution(d1, d2)
return conv
def conv(self):
d1 = Binomial(0, 10, 0.5)
d2 = NegativeBinomial(0, 1, 0.1)
m = Convolution(d1, d2)
return m
def comp(self):
d1 = Binomial(2, 5, 0.5)
d2 = NegativeBinomial(0, 2, 0.5)
c = Compound(d1, d2)
return c
def mixt_data(self):
d1 = Binomial(0, 10, 0.5)
d2 = NegativeBinomial(0, 1, 0.1)
mixt = Mixture(0.1, d1, 0.4, d2)
mixt_data = mixt.simulate(self.N)
return mixt_data
def conv_data(self):
d1 = Binomial(0, 10, 0.5)
d2 = NegativeBinomial(0, 1, 0.1)
conv = Convolution(d1, d2)
conv_data = conv.simulate(self.N)
return conv_data
def comp_data(self):
d1 = Binomial(0, 10, 0.5)
d2 = NegativeBinomial(0, 1, 0.1)
comp = Compound(d1, d2)
comp_data = comp.simulate(self.N)
return comp_data