def test_likelihoods_equal_priors(self):
"""likelihoods should equal Pr(D|H) if priors the same"""
equal = [0.25, 0.25, 0.25, 0.25]
unequal = [0.5, 0.25, 0.125, 0.125]
equal_answer = [1, 1, 1, 1]
unequal_answer = [2, 1, 0.5, 0.5]
for obs, exp in zip(likelihoods(equal, equal), equal_answer):
self.assertFloatEqual(obs, exp)
for obs, exp in zip(likelihoods(unequal, equal), unequal_answer):
self.assertFloatEqual(obs, exp)
def test_likelihoods_equal_evidence(self):
"""likelihoods should return vector of 1's if evidence equal for all"""
equal = [0.25, 0.25, 0.25, 0.25]
unequal = [0.5, 0.25, 0.125, 0.125]
equal_answer = [1, 1, 1, 1]
unequal_answer = [2, 1, 0.5, 0.5]
not_unity = [0.7, 0.7, 0.7, 0.7]
for obs, exp in zip(likelihoods(equal, unequal), equal_answer):
self.assertFloatEqual(obs, exp)
# should be the same if evidences don't sum to 1
for obs, exp in zip(likelihoods(not_unity, unequal), equal_answer):
self.assertFloatEqual(obs, exp)
def test_likelihoods_unequal_evidence(self):
"""likelihoods should update based on weighted sum if evidence unequal"""
not_unity = [1, 0.5, 0.25, 0.25]
unequal = [0.5, 0.25, 0.125, 0.125]
products = [1.4545455, 0.7272727, 0.3636364, 0.3636364]
# if priors and evidence both unequal, likelihoods should change
# (calculated using StarCalc)
for obs, exp in zip(likelihoods(not_unity, unequal), products):
self.assertFloatEqual(obs, exp)
