def test_ccre_1(crvs):
"""
Test that independent RVs have CCRE = CRE.
"""
d = miwin()
rv = (set([0, 1, 2]) - set(crvs)).pop()
ccre1 = CCRE(d, rv, crvs)
ccre2 = CCRE(d, rv)
assert CRE(d)[rv] == pytest.approx(mean(ccre1))
assert CRE(d)[rv] == pytest.approx(mean(ccre2))
assert standard_deviation(ccre1) == pytest.approx(0)
def test_ccre_1():
"""
Test that independent RVs have CCRE = CRE.
"""
d = miwin()
for crvs in combinations([0, 1, 2], 2):
rv = (set([0, 1, 2]) - set(crvs)).pop()
ccre1 = CCRE(d, rv, crvs)
ccre2 = CCRE(d, rv)
yield assert_almost_equal, CRE(d)[rv], mean(ccre1)
yield assert_almost_equal, CRE(d)[rv], mean(ccre2)
yield assert_almost_equal, standard_deviation(ccre1), 0
def test_cre_3():
"""
Test that the CRE fails when the events are not numbers.
"""
dist = Xor()
with pytest.raises(TypeError):
CRE(dist)
def test_ccre_3():
"""
Test a correlated distribution.
"""
d = conditional_uniform2()
ccre = CCRE(d, 1, [0])
uniforms = sorted([CRE(uniform(i - 2, 3)) for i in range(5)])
assert_array_almost_equal(ccre.outcomes, uniforms)
def test_ccre_2():
"""
Test a correlated distribution.
"""
d = conditional_uniform1()
ccre = CCRE(d, 1, [0])
uniforms = [CRE(uniform(i)) for i in range(1, 6)]
assert_array_almost_equal(ccre.outcomes, uniforms)
def test_ccre_3():
"""
Test a correlated distribution.
"""
d = conditional_uniform2()
ccre = CCRE(d, 1, [0])
uniforms = sorted(CRE(uniform(i - 2, 3)) for i in range(5))
assert np.allclose(ccre.outcomes, uniforms)
def test_cre_1():
"""
Test the CRE against known values for several uniform distributions.
"""
dists = [uniform(-n // 2, n // 2) for n in range(2, 23, 2)]
results = [
0.5, 0.81127812, 1.15002242, 1.49799845, 1.85028649, 2.20496373,
2.56111354, 2.91823997, 3.27604979, 3.6343579, 3.99304129
]
for d, r in zip(dists, results):
yield assert_almost_equal, r, CRE(d)
def test_cre_2():
"""
Test the CRE of a multivariate distribution (CRE is of each marginal).
"""
d = miwin()
assert_array_almost_equal(CRE(d), [3.34415526, 3.27909534, 2.56831826])
def test_cre_2():
"""
Test the CRE of a multivariate distribution (CRE is of each marginal).
"""
d = miwin()
assert np.allclose(CRE(d), [3.34415526, 3.27909534, 2.56831826])
def test_cre_1(n, val):
"""
Test the CRE against known values for several uniform distributions.
"""
dist = uniform(-n // 2, n // 2)
assert CRE(dist) == pytest.approx(val)
