def test_empirical_distrib(self):
st = CategoricalTableBuilder("var1")
st.add_row("val1", 0.6)
st.add_row("val2", 0.4)
builder = ConditionalTableBuilder("var2")
builder.add_row(Assignment("var1", "val1"), "val1", 0.9)
builder.add_row(Assignment("var1", "val1"), "val2", 0.1)
builder.add_row(Assignment("var1", "val2"), "val1", 0.2)
builder.add_row(Assignment("var1", "val2"), "val2", 0.8)
bn = BNetwork()
var1 = ChanceNode("var1", st.build())
bn.add_node(var1)
var2 = ChanceNode("var2", builder.build())
var2.add_input_node(var1)
bn.add_node(var2)
sampling = SamplingAlgorithm(2000, 500)
distrib = sampling.query_prob(bn, "var2", Assignment("var1", "val1"))
assert distrib.get_prob("val1") == pytest.approx(0.9, abs=0.05)
assert distrib.get_prob("val2") == pytest.approx(0.1, abs=0.05)
distrib2 = sampling.query_prob(bn, "var2")
assert distrib2.get_prob("val1") == pytest.approx(0.62, abs=0.05)
assert distrib2.get_prob("val2") == pytest.approx(0.38, abs=0.05)
def test_empirical_distrib_continuous(self):
continuous = ContinuousDistribution("var1",
UniformDensityFunction(-1.0, 3.0))
bn = BNetwork()
var1 = ChanceNode("var1", continuous)
bn.add_node(var1)
sampling = SamplingAlgorithm(2000, 200)
distrib2 = sampling.query_prob(bn, "var1")
assert len(distrib2.get_posterior(
Assignment()).get_values()) == pytest.approx(
Settings.discretization_buckets, abs=2)
assert distrib2.to_continuous().get_cumulative_prob(
-1.1) == pytest.approx(0, abs=0.001)
assert distrib2.to_continuous().get_cumulative_prob(
1.0) == pytest.approx(0.5, abs=0.06)
assert distrib2.to_continuous().get_cumulative_prob(
3.1) == pytest.approx(1.0, abs=0.00)
assert continuous.get_prob_density(-2.0) == pytest.approx(
distrib2.to_continuous().get_prob_density(-2.0), abs=0.1)
assert continuous.get_prob_density(-0.5) == pytest.approx(
distrib2.to_continuous().get_prob_density(-0.5), abs=0.1)
assert continuous.get_prob_density(1.8) == pytest.approx(
distrib2.to_continuous().get_prob_density(1.8), abs=0.1)
assert continuous.get_prob_density(3.2) == pytest.approx(
distrib2.to_continuous().get_prob_density(3.2), abs=0.1)
def test_network3bis(self):
iz = SamplingAlgorithm(5000, 300)
bn = NetworkExamples.construct_basic_network2()
query = iz.query_prob(bn, ["Burglary"],
Assignment(["JohnCalls", "MaryCalls"]))
assert query.get_prob(Assignment("Burglary",
False)) == pytest.approx(0.362607,
abs=0.06)
assert query.get_prob(Assignment("Burglary",
True)) == pytest.approx(0.637392,
abs=0.06)
query2 = iz.query_prob(bn, ["Alarm", "Burglary"],
Assignment(["Alarm", "MaryCalls"]))
assert query2.get_prob(Assignment(["Alarm", "!Burglary"
])) == pytest.approx(0.35970,
abs=0.05)