def test_monty():
guest = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
# The actual prize is independent of the other distributions
prize = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
# Monty is dependent on both the guest and the prize.
monty = ConditionalProbabilityTable(
[['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5],
['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0],
['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0],
['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0],
['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5],
['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0],
['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0],
['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0],
['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]],
[guest, prize])
assert_equal(monty.log_probability(('A', 'B', 'C')), 0.)
assert_equal(monty.log_probability(('C', 'B', 'A')), 0.)
assert_equal(monty.log_probability(('C', 'C', 'C')), float("-inf"))
assert_equal(monty.log_probability(('A', 'A', 'A')), float("-inf"))
assert_equal(monty.log_probability(('B', 'A', 'C')), 0.)
assert_equal(monty.log_probability(('C', 'A', 'B')), 0.)
data = [['A', 'A', 'C'], ['A', 'A', 'C'], ['A', 'A', 'B'], ['A', 'A', 'A'],
['A', 'A', 'C'], ['B', 'B', 'B'], ['B', 'B', 'C'], ['C', 'C', 'A'],
['C', 'C', 'C'], ['C', 'C', 'C'], ['C', 'C', 'C'], ['C', 'B', 'A']]
monty.fit(data, weights=[1, 1, 3, 3, 1, 1, 3, 7, 1, 1, 1, 1])
assert_equal(monty.log_probability(('A', 'A', 'A')),
monty.log_probability(('A', 'A', 'C')))
assert_equal(monty.log_probability(('A', 'A', 'A')),
monty.log_probability(('A', 'A', 'B')))
assert_equal(monty.log_probability(('B', 'A', 'A')),
monty.log_probability(('B', 'A', 'C')))
assert_equal(monty.log_probability(('B', 'B', 'A')), float("-inf"))
assert_equal(monty.log_probability(('C', 'C', 'B')), float("-inf"))
def test_monty():
guest = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
# The actual prize is independent of the other distributions
prize = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
# Monty is dependent on both the guest and the prize.
monty = ConditionalProbabilityTable(
[['A', 'A', 'A', 0.0],
['A', 'A', 'B', 0.5],
['A', 'A', 'C', 0.5],
['A', 'B', 'A', 0.0],
['A', 'B', 'B', 0.0],
['A', 'B', 'C', 1.0],
['A', 'C', 'A', 0.0],
['A', 'C', 'B', 1.0],
['A', 'C', 'C', 0.0],
['B', 'A', 'A', 0.0],
['B', 'A', 'B', 0.0],
['B', 'A', 'C', 1.0],
['B', 'B', 'A', 0.5],
['B', 'B', 'B', 0.0],
['B', 'B', 'C', 0.5],
['B', 'C', 'A', 1.0],
['B', 'C', 'B', 0.0],
['B', 'C', 'C', 0.0],
['C', 'A', 'A', 0.0],
['C', 'A', 'B', 1.0],
['C', 'A', 'C', 0.0],
['C', 'B', 'A', 1.0],
['C', 'B', 'B', 0.0],
['C', 'B', 'C', 0.0],
['C', 'C', 'A', 0.5],
['C', 'C', 'B', 0.5],
['C', 'C', 'C', 0.0]], [guest, prize])
assert_equal(monty.log_probability(('A', 'B', 'C')), 0.)
assert_equal(monty.log_probability(('C', 'B', 'A')), 0.)
assert_equal(monty.log_probability(('C', 'C', 'C')), float("-inf"))
assert_equal(monty.log_probability(('A', 'A', 'A')), float("-inf"))
assert_equal(monty.log_probability(('B', 'A', 'C')), 0.)
assert_equal(monty.log_probability(('C', 'A', 'B')), 0.)
data = [['A', 'A', 'C'],
['A', 'A', 'C'],
['A', 'A', 'B'],
['A', 'A', 'A'],
['A', 'A', 'C'],
['B', 'B', 'B'],
['B', 'B', 'C'],
['C', 'C', 'A'],
['C', 'C', 'C'],
['C', 'C', 'C'],
['C', 'C', 'C'],
['C', 'B', 'A']]
monty.fit(data, weights=[1, 1, 3, 3, 1, 1, 3, 7, 1, 1, 1, 1])
assert_equal(monty.log_probability(('A', 'A', 'A')),
monty.log_probability(('A', 'A', 'C')))
assert_equal(monty.log_probability(('A', 'A', 'A')),
monty.log_probability(('A', 'A', 'B')))
assert_equal(monty.log_probability(('B', 'A', 'A')),
monty.log_probability(('B', 'A', 'C')))
assert_equal(monty.log_probability(('B', 'B', 'A')), float("-inf"))
assert_equal(monty.log_probability(('C', 'C', 'B')), float("-inf"))
