def test_get_value():
"""
Expect to get a single number representing the best fit for the PMF.
"""
a = PMF((0, 0.5, 0.75, 1))
assert a.get_value() == 0.5625
def test_get_value():
"""
Expect to get a single number representing the best fit for the PMF.
"""
a = PMF((0, 0.5, 0.75, 1))
assert a.get_value() == 0.5625
def test_normalize():
"""
Expect all the probabilities of the PMF to add up to 1.
"""
a = PMF()
a.hypotheses = {0: 1, 1: 1}
a.normalize()
assert a.hypotheses[0] == 0.5
def test_normalize():
"""
Expect all the probabilities of the PMF to add up to 1.
"""
a = PMF()
a.hypotheses = {0: 1, 1: 1}
a.normalize()
assert a.hypotheses[0] == 0.5
def test_create():
"""
Expect to create a new PMF with given hypotheses.
"""
test = {0: 0.25, 0.5: 0.25, 0.75: 0.25, 1: 0.25}
a = PMF(tuple(test.keys()))
assert a.hypotheses == test
b = PMF(test)
assert b.hypotheses == test
c = PMF()
assert c.hypotheses == {}
