def test_cpd_sampling():
d1 = DiscreteDistribution({"A": 0.1, "B": 0.9})
d2 = ConditionalProbabilityTable(
[["A", "A", 0.1], ["A", "B", 0.9], ["B", "A", 0.7], ["B", "B", 0.3]],
[d1])
# P(A) = 0.1*0.1 + 0.9*0.7 = 0.64
# P(B) = 0.1*0.9 + 0.9*0.3 = 0.36
true = [0.64, 0.36]
est = numpy.bincount([0 if d2.sample() == "A" else 1 for i in range(1000)]) / 1000.0
assert_almost_equal(est[0], true[0], 1)
assert_almost_equal(est[1], true[1], 1)
# when A is observed, it reduces to [0.1, 0.9]
true1 = [0.1, 0.9]
par_val = {}
par_val[d1] = "A"
est = numpy.bincount(
[0 if d2.sample(parent_values=par_val) == "A" else 1 for i in range(1000)]
) / 1000.0
assert_almost_equal(est[0], true1[0], 1)
assert_almost_equal(est[1], true1[1], 1)
true2= [0.7, 0.3]
par_val = {}
par_val[d1] = "B"
est = numpy.bincount(
[0 if d2.sample(parent_values=par_val) == "A" else 1 for i in range(1000)]
) / 1000.0
assert_almost_equal(est[0], true2[0], 1)
assert_almost_equal(est[1], true2[1], 1)
def test_cpd_sampling():
d1 = DiscreteDistribution({"A": 0.1, "B": 0.9})
d2 = ConditionalProbabilityTable(
[["A", "A", 0.1], ["A", "B", 0.9], ["B", "A", 0.7], ["B", "B", 0.3]],
[d1])
# P(A) = 0.1*0.1 + 0.9*0.7 = 0.64
# P(B) = 0.1*0.9 + 0.9*0.3 = 0.36
true = [0.64, 0.36]
est = numpy.bincount([0 if d2.sample() == "A" else 1
for i in range(1000)]) / 1000.0
assert_almost_equal(est[0], true[0], 1)
assert_almost_equal(est[1], true[1], 1)
# when A is observed, it reduces to [0.1, 0.9]
true1 = [0.1, 0.9]
par_val = {}
par_val[d1] = "A"
est = numpy.bincount([
0 if d2.sample(parent_values=par_val) == "A" else 1
for i in range(1000)
]) / 1000.0
assert_almost_equal(est[0], true1[0], 1)
assert_almost_equal(est[1], true1[1], 1)
true2 = [0.7, 0.3]
par_val = {}
par_val[d1] = "B"
est = numpy.bincount([
0 if d2.sample(parent_values=par_val) == "A" else 1
for i in range(1000)
]) / 1000.0
assert_almost_equal(est[0], true2[0], 1)
assert_almost_equal(est[1], true2[1], 1)
def test_distributions_cpt_random_sample():
d1 = DiscreteDistribution({"A": 0.1, "B": 0.9})
d = ConditionalProbabilityTable(
[["A", "A", 0.1], ["A", "B", 0.9], ["B", "A", 0.7], ["B", "B", 0.3]],
[d1])
x = numpy.array(['B', 'A', 'B', 'B', 'A', 'B', 'A', 'A', 'B', 'A', 'A',
'B', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A'])
assert_array_equal(d.sample(n=20, random_state=5), x)
assert_raises(AssertionError, assert_array_equal, d.sample(n=10), x)
