def test_uniform1():
""" Test uniform distribution """
for n in range(2, 10):
d = uniform(n)
assert_equal(d.outcomes, tuple(range(n)))
assert_almost_equal(d[0], 1/n)
assert_almost_equal(entropy(d), np.log2(n))
def test_uniform3():
_as = [1, 2, 3, 4, 5]
_bs = [5, 7, 9, 11, 13]
for a, b in zip(_as, _bs):
d = uniform(a, b)
assert_equal(len(d.outcomes), b-a)
assert_almost_equal(d[a], 1/(b-a))
def test_renyi_entropy_1(alpha):
"""
Test that the Renyi entropy of a uniform distirbution is indipendent of the
order.
"""
d = uniform(8)
assert renyi_entropy(d, alpha) == pytest.approx(3)
def test_disequilibrium3():
"""
Test that uniform ScalarDistributions have zero disequilibrium.
"""
for n in range(2, 11):
d = uniform(n)
yield assert_almost_equal, disequilibrium(d), 0
def test_renyi_entropy_1(alpha):
"""
Test that the Renyi entropy of a uniform distirbution is indipendent of the
order.
"""
d = uniform(8)
assert renyi_entropy(d, alpha) == pytest.approx(3)
def test_renyi_entropy_3(alpha):
"""
Test that negative orders raise ValueErrors.
"""
d = uniform(8)
with pytest.raises(ValueError):
renyi_entropy(d, alpha)
def test_gcre_4():
"""
Test that equal-length uniform distributions all have the same GCRE.
"""
gcres = [GCRE(uniform(i, i + 5)) for i in range(-5, 1)]
for gcre in gcres:
yield assert_almost_equal, gcre, gcres[0]
def test_uniform1():
""" Test uniform distribution """
for n in range(2, 10):
d = uniform(n)
assert_equal(d.outcomes, tuple(range(n)))
assert_almost_equal(d[0], 1 / n)
assert_almost_equal(entropy(d), np.log2(n))
def test_gcre_4():
"""
Test that equal-length uniform distributions all have the same GCRE.
"""
gcres = [ GCRE(uniform(i, i+5)) for i in range(-5, 1) ]
for gcre in gcres:
yield assert_almost_equal, gcre, gcres[0]
def test_disequilibrium4():
"""
Test that uniform Distributions have zero disequilibrium.
"""
for n in range(2, 11):
d = Distribution.from_distribution(uniform(n))
yield assert_almost_equal, disequilibrium(d), 0
def test_LMPR_complexity2():
"""
Test that uniform ScalarDistirbutions have zero complexity.
"""
for n in range(2, 11):
d = uniform(n)
yield assert_almost_equal, LMPR_complexity(d), 0
def test_uniform1():
""" Test uniform distribution """
for n in range(2, 10):
d = uniform(n)
assert d.outcomes == tuple(range(n))
assert d[0] == pytest.approx(1 / n)
assert entropy(d) == pytest.approx(np.log2(n))
def test_LMPR_complexity3():
"""
Test that uniform Distirbutions have zero complexity.
"""
for n in range(2, 11):
d = Distribution.from_distribution(uniform(n))
yield assert_almost_equal, LMPR_complexity(d), 0
def test_uniform1():
""" Test uniform distribution """
for n in range(2, 10):
d = uniform(n)
assert d.outcomes == tuple(range(n))
assert d[0] == pytest.approx(1/n)
assert entropy(d) == pytest.approx(np.log2(n))
def test_renyi_entropy_3():
"""
Test that negative orders raise ValueErrors.
"""
d = uniform(8)
for alpha in [-np.inf, -5, -1, -1/2, -0.0000001]:
yield assert_raises, ValueError, renyi_entropy, d, alpha
def test_renyi_entropy_3(alpha):
"""
Test that negative orders raise ValueErrors.
"""
d = uniform(8)
with pytest.raises(ValueError):
renyi_entropy(d, alpha)
def test_renyi_entropy_1():
"""
Test that the Renyi entropy of a uniform distirbution is indipendent of the
order.
"""
d = uniform(8)
for alpha in [0, 1/2, 1, 2, 5, np.inf]:
yield assert_almost_equal, renyi_entropy(d, alpha), 3
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_cgcre_1():
"""
Test the CGCRE against known values.
"""
d = conditional_uniform2()
cgcre = CGCRE(d, 1, [0])
uniforms = [ GCRE(uniform(i)) for i in range(1, 6) ]
assert np.allclose(cgcre.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_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 np.allclose(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_array_almost_equal(ccre.outcomes, uniforms)
def test_cgcre_1():
"""
Test the CGCRE against known values.
"""
d = conditional_uniform2()
cgcre = CGCRE(d, 1, [0])
uniforms = [GCRE(uniform(i)) for i in range(1, 6)]
assert_array_almost_equal(cgcre.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_uniform3():
""" Test uniform distribution construction """
_as = [1, 2, 3, 4, 5]
_bs = [5, 7, 9, 11, 13]
for a, b in zip(_as, _bs):
d = uniform(a, b)
assert_equal(len(d.outcomes), b - a)
assert_almost_equal(d[a], 1 / (b - a))
def test_uniform3():
""" Test uniform distribution construction """
_as = [1, 2, 3, 4, 5]
_bs = [5, 7, 9, 11, 13]
for a, b in zip(_as, _bs):
d = uniform(a, b)
assert_equal(len(d.outcomes), b-a)
assert_almost_equal(d[a], 1/(b-a))
def test_gcre_1():
"""
Test the GCRE against known values for the uniform distribution.
"""
dists = [ uniform(-n//2, n//2) for n in range(2, 23, 2) ]
results = [0.5, 1.31127812, 2.06831826, 2.80927657, 3.54316518, 4.27328199,
5.00113503, 5.72751654, 6.45288453, 7.17752308, 7.90161817]
for d, r in zip(dists, results):
yield assert_almost_equal, r, GCRE(d)
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_gcre_1():
"""
Test the GCRE against known values for the uniform distribution.
"""
dists = [uniform(-n // 2, n // 2) for n in range(2, 23, 2)]
results = [
0.5, 1.31127812, 2.06831826, 2.80927657, 3.54316518, 4.27328199,
5.00113503, 5.72751654, 6.45288453, 7.17752308, 7.90161817
]
for d, r in zip(dists, results):
yield assert_almost_equal, r, GCRE(d)
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_LMPR_complexity3(n):
"""
Test that uniform Distirbutions have zero complexity.
"""
d = Distribution.from_distribution(uniform(n))
assert LMPR_complexity(d) == pytest.approx(0)
def test_LMPR_complexity2(n):
"""
Test that uniform ScalarDistirbutions have zero complexity.
"""
d = uniform(n)
assert LMPR_complexity(d) == pytest.approx(0)
def test_disequilibrium4(n):
"""
Test that uniform Distributions have zero disequilibrium.
"""
d = Distribution.from_distribution(uniform(n))
assert disequilibrium(d) == pytest.approx(0)
def test_disequilibrium3(n):
"""
Test that uniform ScalarDistributions have zero disequilibrium.
"""
d = uniform(n)
assert disequilibrium(d) == pytest.approx(0)
def test_LMPR_complexity3(n):
"""
Test that uniform Distirbutions have zero complexity.
"""
d = Distribution.from_distribution(uniform(n))
assert LMPR_complexity(d) == pytest.approx(0)
def test_H6():
""" Test H for uniform distributions using ScalarDistributions """
for i in range(2, 10):
d = uniform(i)
yield assert_almost_equal, H(d), np.log2(i)
def test_gcre_1(n, val):
"""
Test the GCRE against known values for the uniform distribution.
"""
dist = uniform(-n // 2, n // 2)
assert GCRE(dist) == pytest.approx(val)
def test_H7(i):
""" Test H for uniform distributions using SDs in various bases """
d = uniform(i)
d.set_base(i)
assert H(d) == pytest.approx(1)
def test_H4(i):
""" Test H for uniform distributions """
d = D.from_distribution(uniform(i))
assert H(d) == pytest.approx(np.log2(i))
def test_H6(i):
""" Test H for uniform distributions using ScalarDistributions """
d = uniform(i)
assert H(d) == pytest.approx(np.log2(i))
def test_H5(i):
""" Test H for uniform distributions in various bases """
d = D.from_distribution(uniform(i))
d.set_base(i)
assert H(d) == pytest.approx(1)
def test_H4(i):
""" Test H for uniform distributions """
d = D.from_distribution(uniform(i))
assert H(d) == pytest.approx(np.log2(i))
def test_uniform4(a, b):
""" Test uniform distribution failures """
with pytest.raises(ValueError):
uniform(a, b)
def test_H4():
""" Test H for uniform distributions """
for i in range(2, 10):
d = D.from_distribution(uniform(i))
yield assert_almost_equal, H(d), np.log2(i)
def test_uniform3(a, b):
""" Test uniform distribution construction """
d = uniform(a, b)
assert len(d.outcomes) == b - a
assert d[a] == pytest.approx(1 / (b - a))
def test_uniform3(a, b):
""" Test uniform distribution construction """
d = uniform(a, b)
assert len(d.outcomes) == b-a
assert d[a] == pytest.approx(1/(b-a))
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)
def test_uniform4(a, b):
""" Test uniform distribution failures """
with pytest.raises(ValueError):
uniform(a, b)
def test_H4():
""" Test H for uniform distributions """
for i in range(2, 10):
d = D.from_distribution(uniform(i))
yield assert_almost_equal, H(d), np.log2(i)
def test_H6(i):
""" Test H for uniform distributions using ScalarDistributions """
d = uniform(i)
assert H(d) == pytest.approx(np.log2(i))
def test_H5():
""" Test H for uniform distributions in various bases """
for i in range(2, 10):
d = D.from_distribution(uniform(i))
d.set_base(i)
yield assert_almost_equal, H(d), 1
def test_H5():
""" Test H for uniform distributions in various bases """
for i in range(2, 10):
d = D.from_distribution(uniform(i))
d.set_base(i)
yield assert_almost_equal, H(d), 1
def test_H7():
""" Test H for uniform distributions using SDs in various bases """
for i in range(2, 10):
d = uniform(i)
d.set_base(i)
yield assert_almost_equal, H(d), 1
def test_H6():
""" Test H for uniform distributions using ScalarDistributions """
for i in range(2, 10):
d = uniform(i)
yield assert_almost_equal, H(d), np.log2(i)
def test_H5(i):
""" Test H for uniform distributions in various bases """
d = D.from_distribution(uniform(i))
d.set_base(i)
assert H(d) == pytest.approx(1)
def test_H7():
""" Test H for uniform distributions using SDs in various bases """
for i in range(2, 10):
d = uniform(i)
d.set_base(i)
yield assert_almost_equal, H(d), 1
def test_H7(i):
""" Test H for uniform distributions using SDs in various bases """
d = uniform(i)
d.set_base(i)
assert H(d) == pytest.approx(1)
def test_LMPR_complexity2(n):
"""
Test that uniform ScalarDistirbutions have zero complexity.
"""
d = uniform(n)
assert LMPR_complexity(d) == pytest.approx(0)
