def test_disequilibrium6(n): """ Test that peaked Distributions have non-zero disequilibrium. """ d = ScalarDistribution([1] + [0] * (n - 1)) d.make_dense() d = Distribution.from_distribution(d) assert disequilibrium(d) >= 0
def test_disequilibrium6(n): """ Test that peaked Distributions have non-zero disequilibrium. """ d = ScalarDistribution([1] + [0]*(n-1)) d.make_dense() d = Distribution.from_distribution(d) assert disequilibrium(d) >= 0
def test_LMPR_complexity5(n): """ Test that peaked Distributions have zero complexity. """ d = ScalarDistribution([1] + [0] * (n - 1)) d.make_dense() d = Distribution.from_distribution(d) assert LMPR_complexity(d) == pytest.approx(0)
def test_LMPR_complexity5(n): """ Test that peaked Distributions have zero complexity. """ d = ScalarDistribution([1] + [0]*(n-1)) d.make_dense() d = Distribution.from_distribution(d) assert LMPR_complexity(d) == pytest.approx(0)
def test_disequilibrium6(): """ Test that peaked Distributions have non-zero disequilibrium. """ for n in range(2, 11): d = ScalarDistribution([1] + [0]*(n-1)) d.make_dense() d = Distribution.from_distribution(d) yield assert_greater, disequilibrium(d), 0
def test_LMPR_complexity4(): """ Test that peaked Distributions have zero complexity. """ for n in range(2, 11): d = ScalarDistribution([1] + [0]*(n-1)) d.make_dense() d = Distribution.from_distribution(d) yield assert_almost_equal, LMPR_complexity(d), 0
def test_is_approx_equal2(): d1 = ScalarDistribution([1 / 2, 1 / 2, 0]) d1.make_dense() d2 = ScalarDistribution([1 / 2, 0, 1 / 2]) d2.make_dense() assert not d1.is_approx_equal(d2)
def test_del2(): d = ScalarDistribution([1 / 2, 1 / 2]) d.make_dense() del d[1] d.normalize() assert d[0] == pytest.approx(1)
def test_is_approx_equal2(): d1 = ScalarDistribution([1 / 2, 1 / 2, 0]) d1.make_dense() d2 = ScalarDistribution([1 / 2, 0, 1 / 2]) d2.make_dense() assert_false(d1.is_approx_equal(d2))
def test_del2(): d = ScalarDistribution([1 / 2, 1 / 2]) d.make_dense() del d[1] d.normalize() assert_almost_equal(d[0], 1)
def test_is_approx_equal1(): d1 = ScalarDistribution([1 / 2, 1 / 2, 0]) d1.make_dense() d2 = ScalarDistribution([1 / 2, 1 / 2, 0]) d2.make_dense() assert_true(d1.is_approx_equal(d2))