def test_init11():
outcomes = ['0', '1']
pmf = [1 / 2, 1 / 2]
d = Distribution(outcomes, pmf)
sd = ScalarDistribution.from_distribution(d)
# Different sample space representations
assert not d.is_approx_equal(sd)
def test_init11():
outcomes = ["0", "1"]
pmf = [1 / 2, 1 / 2]
d = Distribution(outcomes, pmf)
sd = ScalarDistribution.from_distribution(d)
# Different sample space representations
assert_false(d.is_approx_equal(sd))
def test_init9():
outcomes = [0, 1, 2]
pmf = [1 / 3] * 3
d1 = ScalarDistribution(outcomes, pmf)
d2 = ScalarDistribution.from_distribution(d1, base=10)
d1.set_base(10)
assert d1.is_approx_equal(d2)
def test_init9():
outcomes = [0, 1, 2]
pmf = [1 / 3] * 3
d1 = ScalarDistribution(outcomes, pmf)
d2 = ScalarDistribution.from_distribution(d1, base=10)
d1.set_base(10)
assert_true(d1.is_approx_equal(d2))
def test_fanos_inequality(dist):
"""
H(X|Y) <= hb(P_e) + P_e log(|X| - 1)
"""
dist1 = SD.from_distribution(dist.marginal([0]))
dist2 = SD.from_distribution(dist.marginal([1]))
ce = H(dist, [0], [1])
X = len(set().union(dist1.outcomes, dist2.outcomes))
eq_dist = dist1 == dist2
P_e = eq_dist[False] if False in eq_dist else 0
hb = H(SD([P_e, 1 - P_e]))
assert ce <= hb + P_e * np.log2(X - 1) + epsilon
def test_init12():
outcomes = ['0', '1']
pmf = [1/2, 1/2]
d = Distribution(outcomes, pmf)
sd = ScalarDistribution.from_distribution(d, base=10)
d.set_base(10)
# Different sample space representations
assert_false(d.is_approx_equal(sd))
def test_init12():
outcomes = ['0', '1']
pmf = [1 / 2, 1 / 2]
d = Distribution(outcomes, pmf)
sd = ScalarDistribution.from_distribution(d, base=10)
d.set_base(10)
# Different sample space representations
assert_false(d.is_approx_equal(sd))
def test_fanos_inequality(dist):
"""
H(X|Y) <= hb(P_e) + P_e log(|X| - 1)
"""
dist1 = SD.from_distribution(dist.marginal([0]))
dist2 = SD.from_distribution(dist.marginal([1]))
ce = H(dist, [0], [1])
X = len(set().union(dist1.outcomes, dist2.outcomes))
eq_dist = dist1 == dist2
P_e = eq_dist[False] if False in eq_dist else 0
hb = H(SD([P_e, 1-P_e]))
assert ce <= hb + P_e * np.log2(X - 1) + epsilon
def test_init8():
outcomes = [0, 1, 2]
pmf = [1 / 3] * 3
d1 = ScalarDistribution(outcomes, pmf)
d2 = ScalarDistribution.from_distribution(d1)
assert d1.is_approx_equal(d2)
def test_init8():
outcomes = [0, 1, 2]
pmf = [1 / 3] * 3
d1 = ScalarDistribution(outcomes, pmf)
d2 = ScalarDistribution.from_distribution(d1)
assert_true(d1.is_approx_equal(d2))
