def test_mixture(self, p1, p2):
mix1 = Prob.mixture([1, 0], [p1, p2])
mix2 = Prob.mixture([0, 1], [p1, p2])
mix3 = Prob.mixture([1, 1], [p1, p2])
assert mix1 == p1
assert mix2 == p2
assert mix3 == Prob({'a': 0.75, 'b': 0.25})
def test_mixture_with_bad_coeffs(self, p1, p2):
with pytest.raises(ValueError):
Prob.mixture([1, 1, 1], [p1, p2])
def p1(self):
return Prob({'a': 0.5, 'b': 0.5})
def test_encode(self, p3):
assert list(p3.encode('abc')) == [0.75, 0.25, 0.0]
assert list(p3.encode()) == [0.75, 0.25]
assert list(Prob([3, 1]).encode()) == [0.75, 0.25]
def test_mode(self):
prob = Prob({'a': 0.2, 'b': 0.3, 'c': 0.5})
assert prob.max() == 0.5
assert prob.mode() == 'c'
def test_kl_divergence(self, p1, p2):
assert p1.kl_divergence(p1) == 0
assert p1.kl_divergence(p2) == float('inf')
assert p2.kl_divergence(p2) == 0
assert abs(p2.kl_divergence(p1) - 0.69) < 5e-2
assert p1.kl_divergence(Prob([1])) == float('inf')
def test_probability_with_support(self, p1):
p = Prob(p1, support='abc')
assert p['c'] == 0.0
def test_probability_can_be_non_normalized(self):
p = Prob([1, 1], normalize=False)
assert sum(p.values()) == 2
def test_prob_from_list(self):
p = Prob([0.5, 0.25, 0.25])
assert p[0] == 0.5
assert p[1] == 0.25
assert p[2] == 0.25
def p3(self):
return Prob({'a': 0.75, 'b': 0.25})
def p2(self):
return Prob({'a': 1, 'b': 0})