def test_pid_dep3():
"""
Test that idep and ibroja differ on reduced or.
"""
d = bivariates['reduced or']
pid1 = PID_BROJA(d)
pid2 = PID_dep(d)
assert pid1 != pid2
def test_pid_dep4():
"""
Test that anddup is complete.
"""
d = trivariates['anddup']
pid = PID_dep(d)
assert pid.complete
assert pid.nonnegative
assert pid.consistent
def test_pid_dep1():
"""
Test idep on a generic distribution.
"""
d = bivariates['reduced or']
pid = PID_dep(d, ((0, ), (1, )), (2, ))
assert pid[((0, ), (1, ))] == pytest.approx(0.073761301440421256, abs=1e-4)
assert pid[((0, ), )] == pytest.approx(0.23751682301871169, abs=1e-4)
assert pid[((1, ), )] == pytest.approx(0.23751682301871169, abs=1e-4)
assert pid[((0, 1), )] == pytest.approx(0.45120505252215537, abs=1e-4)
def test_pid_dep2():
"""
Test idep on another generic distribution.
"""
d = trivariates['anddup']
pid = PID_dep(d, [[0], [1], [2]], [3])
for atom in pid._lattice:
if atom == ((0, ), (1, ), (2, )):
assert pid[atom] == pytest.approx(0.081704409646414788, abs=1e-4)
elif atom == ((0, 1), (1, 2)):
assert pid[atom] == pytest.approx(0.27042624480113808, abs=1e-4)
elif atom in [((0, ), (2, )), ((1, ), )]:
assert pid[atom] == pytest.approx(0.22957374150893717, abs=1e-4)
else:
assert pid[atom] == pytest.approx(0.0, abs=1e-4)
print(" ")
print("{0}{1} is Shd, {0} is UnqB, {1} is UnqA, {0:1} is Syn")
print(" ")
rcyBROJA = PID_BROJA(dist1, [[0], [1]], [2])
print(" ")
print(" The I_broja PID is plotted in Fig. 6 ")
print(rcyBROJA)
print(" ")
rcyCCS = PID_CCS(dist1, [[0], [1]], [2])
print(rcyCCS)
rcyDEP = PID_dep(dist1, [[0], [1]], [2])
print(rcyDEP)
rcyWB = PID_WB(dist1, [[0], [1]], [2])
print(rcyWB)
rcyPROJ = PID_Proj(dist1, [[0], [1]], [2])
print(rcyPROJ)
print(" ")
# Computation of the entropies
H_OBA = entropy(dist1)
p = PID_Proj(dist1, [[0], [1]], [2])
output[i, 8:12] = [
p.get_partial(n)
for n in sorted(p._lattice, key=dit.pid.lattice.sort_key(p._lattice))
]
p = PID_CCS(dist1, [[0], [1]], [2])
output[i, 12:16] = [
p.get_partial(n)
for n in sorted(p._lattice, key=dit.pid.lattice.sort_key(p._lattice))
]
p = PID_dep(dist1, [[0], [1]], [2])
output[i, 16:20] = [
p.get_partial(n)
for n in sorted(p._lattice, key=dit.pid.lattice.sort_key(p._lattice))
]
# Computation of the entropies
H_OBA = entropy(dist1)
H_B = entropy(dist1, [0])
H_A = entropy(dist1, [1])
H_O = entropy(dist1, [2])
H_OB = entropy(dist1, [[0], [2]])
H_OA = entropy(dist1, [[1], [2]])