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_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_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_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_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_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