def test_dfd():
"""
Test distribution_from_data.
"""
data = [0,0,0,1,1,1]
d1 = Distribution([(0,), (1,)], [1/2, 1/2])
d2 = Distribution([(0,0), (0,1), (1,1)], [2/5, 1/5, 2/5])
d1_ = distribution_from_data(data, 1, base='linear')
d2_ = distribution_from_data(data, 2, base='linear')
assert d1.is_approx_equal(d1_)
assert d2.is_approx_equal(d2_)
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_pr_1():
"""
Test
"""
d1 = Distribution(list(product([0, 1], repeat=4)), [1 / 16] * 16)
d2 = pr_box(0.0)
assert d1.is_approx_equal(d2)
def test_pr_1():
"""
Test
"""
d1 = Distribution(list(product([0, 1], repeat=4)), [1/16]*16)
d2 = pr_box(0.0)
assert d1.is_approx_equal(d2)
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_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_pr_2():
"""
Test
"""
d1 = Distribution([(0, 0, 0, 0), (0, 0, 1, 1), (0, 1, 0, 0), (0, 1, 1, 1),
(1, 0, 0, 0), (1, 0, 1, 1), (1, 1, 0, 1), (1, 1, 1, 0)],
[1 / 8] * 8)
d2 = pr_box(1.0, name=True)
assert d1.is_approx_equal(d2)
def test_pr_2():
"""
Test
"""
d1 = Distribution([(0, 0, 0, 0),
(0, 0, 1, 1),
(0, 1, 0, 0),
(0, 1, 1, 1),
(1, 0, 0, 0),
(1, 0, 1, 1),
(1, 1, 0, 1),
(1, 1, 1, 0)], [1/8]*8)
d2 = pr_box(1.0, name=True)
assert d1.is_approx_equal(d2)