def test_maxent_2():
"""
Text a distribution with differing alphabets.
"""
d1 = uniform(['00', '10', '21', '31'])
d2 = uniform(['00', '01', '10', '11', '20', '21', '30', '31'])
d1_maxent = maxent_dist(d1, [[0], [1]])
assert d2.is_approx_equal(d1_maxent, rtol=1e-3, atol=1e-3)
def test_maxent_1(vars):
"""
Test xor only fixing individual marginals.
"""
d1 = uniform(['000', '011', '101', '110'])
d2 = uniform(['000', '001', '010', '011', '100', '101', '110', '111'])
d1_maxent = maxent_dist(d1, vars)
assert d2.is_approx_equal(d1_maxent, rtol=1e-3, atol=1e-3)
def test_maxent_2():
"""
Text a distribution with differing alphabets.
"""
d1 = uniform(['00', '10', '21', '31'])
d2 = uniform(['00', '01', '10', '11', '20', '21', '30', '31'])
d1_maxent = maxent_dist(d1, [[0], [1]])
assert d2.is_approx_equal(d1_maxent, rtol=1e-3, atol=1e-3)
def test_maxent_1(vars):
"""
Test xor only fixing individual marginals.
"""
d1 = uniform(['000', '011', '101', '110'])
d2 = uniform(['000', '001', '010', '011', '100', '101', '110', '111'])
d1_maxent = maxent_dist(d1, vars)
assert d2.is_approx_equal(d1_maxent, rtol=1e-3, atol=1e-3)
def test_maxent_3():
"""
Test the RdnUnqXor distribution.
"""
X00, X01, X02, Y01, Y02 = 'rR', 'aA', [0, 1], 'bB', [0, 1]
inputs = product(X00, X01, X02, Y01, Y02)
events = [(x00+x01+str(x02), x00+y01+str(y02), x00+x01+y01+str(x02^y02)) for x00, x01, x02, y01, y02 in inputs]
RdnUnqXor = uniform(events)
d = maxent_dist(RdnUnqXor, [[0, 1], [0, 2], [1, 2]])
assert H(d) == pytest.approx(6)
def test_maxent_3():
"""
Test the RdnUnqXor distribution.
"""
X00, X01, X02, Y01, Y02 = 'rR', 'aA', [0, 1], 'bB', [0, 1]
inputs = product(X00, X01, X02, Y01, Y02)
events = [(x00 + x01 + str(x02), x00 + y01 + str(y02),
x00 + x01 + y01 + str(x02 ^ y02))
for x00, x01, x02, y01, y02 in inputs]
RdnUnqXor = uniform(events)
d = maxent_dist(RdnUnqXor, [[0, 1], [0, 2], [1, 2]])
assert H(d) == pytest.approx(6)
def _measure(d, sources, target):
"""
I_ME*, the maximum entropy distribution satisfying the * assumption of
BROJA.
Parameters
----------
d : Distribution
The distribution to compute i_mes for.
sources : iterable of iterables
The source variables.
target : iterable
The target variable.
Returns
-------
i_mes : float
The value of I_ME*.
"""
dp = maxent_dist(d, [source + target for source in sources])
i_mes = I(dp)
return i_mes
