def test_setitem2():
d = Distribution(['00', '11'], [1, 0])
d.make_sparse()
d['11'] = 1/2
d.normalize()
assert_true('11' in d)
assert_almost_equal(d['11'], 1/3)
def test_setitem2():
d = Distribution(['00', '11'], [1, 0])
d.make_sparse()
d['11'] = 1 / 2
d.normalize()
assert '11' in d
assert d['11'] == pytest.approx(1 / 3)
def test_setitem2():
d = Distribution(['00', '11'], [1, 0])
d.make_sparse()
d['11'] = 1/2
d.normalize()
assert '11' in d
assert d['11'] == pytest.approx(1/3)
def test_setitem2():
d = Distribution(['00', '11'], [1, 0])
d.make_sparse()
d['11'] = 1 / 2
d.normalize()
assert_true('11' in d)
assert_almost_equal(d['11'], 1 / 3)
def test_zipped():
pmf = [0.125, 0.125, 0.125, 0.125, 0.25, 0, 0.25]
outcomes = ['000', '011', '101', '110', '222', '321', '333']
d = Distribution(outcomes, pmf)
outcomes_, pmf_ = list(zip(*d.zipped()))
d2 = Distribution(outcomes_, pmf_)
assert d.is_approx_equal(d2)
outcomes_, pmf_ = list(zip(*d.zipped(mode='atoms')))
d3 = Distribution(outcomes_, pmf_)
assert d.is_approx_equal(d3)
outcomes_, pmf_ = list(zip(*d.zipped(mode='patoms')))
d4 = Distribution(outcomes_, pmf_)
d.make_sparse()
assert np.allclose(d.pmf, d4.pmf)
def test_zipped():
pmf = [.125, .125, .125, .125, .25, 0, .25]
outcomes = ['000', '011', '101', '110', '222', '321', '333']
d = Distribution(outcomes, pmf)
outcomes_, pmf_ = list(zip(*d.zipped()))
d2 = Distribution(outcomes_, pmf_)
assert_true(d.is_approx_equal(d2))
outcomes_, pmf_ = list(zip(*d.zipped(mode='atoms')))
d3 = Distribution(outcomes_, pmf_)
assert_true(d.is_approx_equal(d3))
outcomes_, pmf_ = list(zip(*d.zipped(mode='patoms')))
d4 = Distribution(outcomes_, pmf_)
d.make_sparse()
np.testing.assert_allclose(d.pmf, d4.pmf)
def test_has_outcome1():
d = Distribution(['0', '1'], [1, 0])
d.make_sparse()
assert not d.has_outcome('1', null=False)
def test_has_outcome1():
d = Distribution(['0', '1'], [1, 0])
d.make_sparse()
assert_false(d.has_outcome('1', null=False))
