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))
def test_del2():
d = ScalarDistribution([1 / 2, 1 / 2])
d.make_dense()
del d[1]
d.normalize()
assert_almost_equal(d[0], 1)
