def test_tc7():
""" Test T = I with two variables """
outcomes = ['00', '01', '11']
pmf = [1 / 3] * 3
d = D(outcomes, pmf)
assert_almost_equal(T(d), I(d, [0], [1]))
d.set_rv_names("XY")
assert_almost_equal(T(d), I(d, ['X'], ['Y']))
def test_tc7():
""" Test T = I with two variables """
outcomes = ['00', '01', '11']
pmf = [1/3] * 3
d = D(outcomes, pmf)
assert T(d) == pytest.approx(I(d, [0], [1]))
d.set_rv_names("XY")
assert T(d) == pytest.approx(I(d, ['X'], ['Y']))
def total_correlation(Xs):
pmf = []
Xs = np.asarray(Xs)
for stock in range(len(Xs)):
s = pd.Series(Xs[stock])
b = (s.groupby(s).transform('count') / len(s)).values
pmf.append(b)
pmf = np.asarray(pmf)
pmf /= pmf.sum()
custm = stats.rv_discrete(name='custm', values=(Xs, pmf))
d = Distribution.from_ndarray(pmf)
t = T(d)
return t
def test_mis1(d):
"""
Test that all the mutual informations match for bivariate distributions.
"""
i = I(d)
t = T(d)
b = B(d)
j = J(d)
ii = II(d)
assert i == pytest.approx(t)
assert t == pytest.approx(b)
assert b == pytest.approx(j)
assert j == pytest.approx(ii)
def test_tc8():
""" Test that T fails on SDs """
d = SD([1/3]*3)
with pytest.raises(ditException):
T(d)
def test_tc6():
""" Test T with overlapping subvariables """
d = n_mod_m(4, 2)
assert T(d, [[0, 1, 2], [1, 2, 3]]) == pytest.approx(3.0)
d.set_rv_names("XYZW")
assert T(d, [['X', 'Y', 'Z'], ['Y', 'Z', 'W']]) == pytest.approx(3.0)
def test_tc5():
""" Test T with subvariables """
d = n_mod_m(4, 2)
assert T(d, [[0, 1], [2], [3]]) == pytest.approx(1.0)
d.set_rv_names("XYZW")
assert T(d, [['X', 'Y'], ['Z'], ['W']]) == pytest.approx(1.0)
def test_tc4():
""" Test conditional T """
d = n_mod_m(4, 2)
assert T(d, [[0], [1]], [2, 3]) == pytest.approx(1.0)
d.set_rv_names("XYZW")
assert T(d, [['X'], ['Y']], ['Z', 'W']) == pytest.approx(1.0)
def test_tc2():
""" Test T of subvariables, with names """
d = n_mod_m(4, 2)
assert T(d, [[0], [1], [2]]) == pytest.approx(0.0)
d.set_rv_names("XYZW")
assert T(d, [['X'], ['Y'], ['Z']]) == pytest.approx(0.0)
def test_tc1(n):
""" Test T of parity distributions """
d = n_mod_m(n, 2)
assert T(d) == pytest.approx(1.0)
def test_caekl_4(d):
"""
Test that CAEKL is always less than or equal to the normalized total
correlation.
"""
assert J(d) <= (T(d) / 3) + 1e-6
def test_tc6():
""" Test T with overlapping subvariables """
d = n_mod_m(4, 2)
assert_almost_equal(T(d, [[0, 1, 2], [1, 2, 3]]), 3.0)
d.set_rv_names("XYZW")
assert_almost_equal(T(d, [['X', 'Y', 'Z'], ['Y', 'Z', 'W']]), 3.0)
def test_tc5():
""" Test T with subvariables """
d = n_mod_m(4, 2)
assert_almost_equal(T(d, [[0, 1], [2], [3]]), 1.0)
d.set_rv_names("XYZW")
assert_almost_equal(T(d, [['X', 'Y'], ['Z'], ['W']]), 1.0)
def test_tc4():
""" Test conditional T """
d = n_mod_m(4, 2)
assert_almost_equal(T(d, [[0], [1]], [2, 3]), 1.0)
d.set_rv_names("XYZW")
assert_almost_equal(T(d, [['X'], ['Y']], ['Z', 'W']), 1.0)
def test_tc2():
""" Test T of subvariables, with names """
d = n_mod_m(4, 2)
assert_almost_equal(T(d, [[0], [1], [2]]), 0.0)
d.set_rv_names("XYZW")
assert_almost_equal(T(d, [['X'], ['Y'], ['Z']]), 0.0)
def test_tc1():
""" Test T of parity distributions """
for n in range(3, 7):
d = n_mod_m(n, 2)
yield assert_almost_equal, T(d), 1.0