def test_marginal_constraints():
d = dit.uniform_distribution(3, 2)
d.make_dense()
A, b = marginal_constraints(d, 2)
A_ = np.array([
[ 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 1., 1., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 1., 1., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 1., 1.],
[ 1., 0., 1., 0., 0., 0., 0., 0.],
[ 0., 1., 0., 1., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 1., 0., 1., 0.],
[ 0., 0., 0., 0., 0., 1., 0., 1.],
[ 1., 0., 0., 0., 1., 0., 0., 0.],
[ 0., 1., 0., 0., 0., 1., 0., 0.],
[ 0., 0., 1., 0., 0., 0., 1., 0.],
[ 0., 0., 0., 1., 0., 0., 0., 1.]
])
b_ = np.array([1] + [0.25] * 12)
assert np.allclose(A, A_)
assert np.allclose(b, b_)
def Or(k=2):
"""
[0] or [1] = [2]
"""
d = dit.uniform_distribution(k, ['01'])
d = dit.distconst.modify_outcomes(d, lambda x: ''.join(x))
d = dit.insert_rvf(d, lambda x: '1' if any(map(bool, map(int, x))) else '0')
return d
def test_rvfunctions_ints():
d = dit.uniform_distribution(2, 2)
rvf = dit.RVFunctions(d)
partition = [(d.outcomes[i],) for i in range(len(d))]
mapping = rvf.from_partition(partition)
d2 = dit.insert_rvf(d, mapping)
outcomes = ((0,0,0), (0,1,1), (1,0,2), (1,1,3))
assert_equal(d2.outcomes, outcomes)
def test_rvfunctions_ints():
d = dit.uniform_distribution(2, 2)
rvf = dit.RVFunctions(d)
partition = [(d.outcomes[i], ) for i in range(len(d))]
mapping = rvf.from_partition(partition)
d2 = dit.insert_rvf(d, mapping)
outcomes = ((0, 0, 0), (0, 1, 1), (1, 0, 2), (1, 1, 3))
assert_equal(d2.outcomes, outcomes)
def Or(k=2):
"""
[0] or [1] = [2]
"""
d = dit.uniform_distribution(k, ['01'])
d = dit.distconst.modify_outcomes(d, lambda x: ''.join(x))
d = dit.insert_rvf(d, lambda x: '1' if any(map(bool, map(int, x))) else '0')
return d
def test_product_with_rvs2():
"""
Test product_distribution() with an rvs specification.
"""
d = dit.example_dists.Xor()
d_iid = dit.product_distribution(d, [[0, 1]])
d_truth = dit.uniform_distribution(2, ['01'])
d_truth = dit.modify_outcomes(d_truth, lambda x: ''.join(x))
assert d_truth.is_approx_equal(d_iid)
def test_product():
"""
Smoke test for product_distribution().
"""
d = dit.example_dists.Xor()
d_iid = dit.product_distribution(d)
d_truth = dit.uniform_distribution(3, ['01'])
d_truth = dit.modify_outcomes(d_truth, lambda x: ''.join(x))
assert d_truth.is_approx_equal(d_iid)
def test_product():
"""
Smoke test for product_distribution().
"""
d = dit.example_dists.Xor()
d_iid = dit.product_distribution(d)
d_truth = dit.uniform_distribution(3, ['01'])
d_truth = dit.modify_outcomes(d_truth, lambda x: ''.join(x))
assert_true(d_truth.is_approx_equal(d_iid))
def test_product_with_rvs2():
"""
Test product_distribution() with an rvs specification.
"""
d = dit.example_dists.Xor()
d_iid = dit.product_distribution(d, [[0,1]])
d_truth = dit.uniform_distribution(2, ['01'])
d_truth = dit.modify_outcomes(d_truth, lambda x: ''.join(x))
assert_true(d_truth.is_approx_equal(d_iid))
def test_insert_rvf2():
# Test multiple insertion.
d = dit.uniform_distribution(2, 2)
d = dit.modify_outcomes(d, lambda x: ''.join(map(str, x)))
def xor(outcome):
o = str(int(outcome[0] != outcome[1]))
# Here we are returning 2 random variables
return o*2
# We are also inserting two times simultaneously.
d2 = dit.insert_rvf(d, [xor, xor])
outcomes = ('000000', '011111', '101111', '110000')
assert_equal(d2.outcomes, outcomes)
def test_insert_rvf2():
# Test multiple insertion.
d = dit.uniform_distribution(2, 2)
d = dit.modify_outcomes(d, lambda x: ''.join(map(str, x)))
def xor(outcome):
o = str(int(outcome[0] != outcome[1]))
# Here we are returning 2 random variables
return o*2
# We are also inserting two times simultaneously.
d2 = dit.insert_rvf(d, [xor, xor])
outcomes = ('000000', '011111', '101111', '110000')
assert d2.outcomes == outcomes
def test_insert_rvf1():
# Test multiple insertion.
d = dit.uniform_distribution(2, 2)
def xor(outcome):
o = int(outcome[0] != outcome[1])
# Here we are returning 2 random variables
return (o, o)
# We are also inserting two times simultaneously.
d2 = dit.insert_rvf(d, [xor, xor])
outcomes = ((0, 0, 0, 0, 0, 0), (0, 1, 1, 1, 1, 1), (1, 0, 1, 1, 1, 1),
(1, 1, 0, 0, 0, 0))
assert_equal(d2.outcomes, outcomes)
def test_insert_rvf1():
# Test multiple insertion.
d = dit.uniform_distribution(2, 2)
def xor(outcome):
o = int(outcome[0] != outcome[1])
# Here we are returning 2 random variables
return (o,o)
# We are also inserting two times simultaneously.
d2 = dit.insert_rvf(d, [xor, xor])
outcomes = (
(0, 0, 0, 0, 0, 0),
(0, 1, 1, 1, 1, 1),
(1, 0, 1, 1, 1, 1),
(1, 1, 0, 0, 0, 0)
)
assert_equal(d2.outcomes, outcomes)
def test_marginal_constraints():
d = dit.uniform_distribution(3, 2)
d.make_dense()
A, b = marginal_constraints(d, 2)
A_ = np.array([[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1], [1, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 1]])
b_ = np.array([1] + [0.25] * 12)
assert np.allclose(A, A_)
assert np.allclose(b, b_)
def test_uniform_distribution():
pmf = np.array([1 / 4] * 4)
dit.math.prng.seed(1)
d = dit.uniform_distribution(2, 2)
assert d.outcomes == ((0, 0), (0, 1), (1, 0), (1, 1))
assert np.allclose(d.pmf, pmf)
def test_uniform_distribution():
pmf = np.array([1 / 4] * 4)
dit.math.prng.seed(1)
d = dit.uniform_distribution(2, 2)
assert_equal(d.outcomes, ((0, 0), (0, 1), (1, 0), (1, 1)))
np.testing.assert_allclose(d.pmf, pmf)
def test_uniform_distribution():
pmf = np.array([1/4] * 4)
dit.math.prng.seed(1)
d = dit.uniform_distribution(2, 2)
assert d.outcomes == ((0, 0), (0, 1), (1, 0), (1, 1))
assert np.allclose(d.pmf, pmf)
def test_uniform_distribution():
pmf = np.array([1/4] * 4)
dit.math.prng.seed(1)
d = dit.uniform_distribution(2, 2)
assert_equal(d.outcomes, ((0, 0), (0, 1), (1, 0), (1, 1)))
np.testing.assert_allclose(d.pmf, pmf)
