def test_equal_to_dict(self):
d = EqualToDict({a: "a", b: "b"})
# Ensure that we call `EqualTo`.
self.assertFalse(Item.equal_to_called)
self.assertEqual(d[a], "a")
self.assertTrue(Item.equal_to_called)
self.assertEqual(d[b], "b")
self.assertTrue(a in d)
# Ensure hash collision does not occur.
self.assertEqual(hash(a.value), hash(a))
self.assertFalse(a.value in d)
# Obtaining the original representation (e.g. for `pybind11`):
# - Constructing using `dict` will not be what is desired; the keys at
# present are not directly convertible, thus would create an error.
# N.B. At present, this behavior may not be overridable via Python, as
# copying is done via `dict.update`, which has a special case for
# `dict`-inheriting types which does not have any hooks for key
# transformations.
raw_attempt = dict(d)
keys = list(raw_attempt.keys())
self.assertFalse(isinstance(keys[0], Item))
# - Calling `raw()` should provide the desired behavior.
raw = d.raw()
keys = list(raw.keys())
self.assertTrue(isinstance(keys[0], Item))
def test_equal_to_dict(self):
d = EqualToDict({a: "a", b: "b"})
# Ensure that we call `EqualTo`.
self.assertFalse(Item.equal_to_called)
self.assertEqual(d[a], "a")
self.assertTrue(Item.equal_to_called)
self.assertEqual(d[b], "b")
self.assertTrue(a in d)
# Ensure hash collision does not occur.
self.assertEqual(hash(a.value), hash(a))
self.assertFalse(a.value in d)
# Obtaining the original representation (e.g. for `pybind11`):
# - Constructing using `dict` will not be what is desired; the keys at
# present are not directly convertible, thus would create an error.
# N.B. At present, this behavior may not be overridable via Python, as
# copying is done via `dict.update`, which has a special case for
# `dict`-inheriting types which does not have any hooks for key
# transformations.
raw_attempt = dict(d)
keys = list(raw_attempt.keys())
self.assertFalse(isinstance(keys[0], Item))
# - Calling `raw()` should provide the desired behavior.
raw = d.raw()
keys = list(raw.keys())
self.assertTrue(isinstance(keys[0], Item))
def test_constructor_map(self):
powers_in = {x: 2, y: 3, z: 4}
m = sym.Monomial(powers_in)
powers_out = EqualToDict(m.get_powers())
self.assertEqual(powers_out[x], 2)
self.assertEqual(powers_out[y], 3)
self.assertEqual(powers_out[z], 4)
def test_dreal_satisfiability(self):
x = Variable("x")
f = logical_and(x > 1, x < 2)
interval_box = EqualToDict(
DrealSolver.CheckSatisfiability(f=f, delta=0.01))
self.assertEqual(len(interval_box), 1)
self.assertIsInstance(interval_box[x], DrealSolver.Interval)
def test_minimize(self):
x = Variable("x")
delta = 0.01
interval_box = EqualToDict(
DrealSolver.Minimize(
objective=x**2,
constraint=logical_and(x >= 1, x <= 10),
delta=delta,
local_optimization=DrealSolver.LocalOptimization.kUse))
self.assertEqual(len(interval_box), 1)
self.assertIsInstance(interval_box[x], DrealSolver.Interval)
print(interval_box[x].diam())
self.assertAlmostEqual(interval_box[x].mid(), 1.0, delta=delta)
def test_get_powers(self):
m = sym.Monomial(x, 2) * sym.Monomial(y) # m = x²y
powers = EqualToDict(m.get_powers())
self.assertEqual(powers[x], 2)
self.assertEqual(powers[y], 1)
for comp_k in range(len(logscores_by_comp)):
logscores_by_comp[
comp_k] -= multivariate_normal.logpdf(
val, self.g.means_[comp_k, k],
self.g.covariances_[comp_k, k])
return logsumexp(logscores_by_comp + log_weights)
elif self.model_type == "kde":
total_log_density = 0.
for k, val in enumerate(x[1:]):
if val != 0:
total_log_density += self.kdes[k].logpdf(val)
return total_log_density
uniform_variable_store = {}
uniform_variable_to_range = EqualToDict()
def random_uniform(name, low, high):
if name not in uniform_variable_store.keys():
var = sym.Variable(name, sym.Variable.Type.RANDOM_UNIFORM)
uniform_variable_store[name] = var
uniform_variable_to_range[var] = (low, high)
else:
var = uniform_variable_store[name]
return var * (high - low) + low
pattern_index_to_name = ["Random", "Large Spike", "Decreasing", "Small Spike"]
pattern_transition_matrix = np.array([[0.2, 0.3, 0.15, 0.35],
[0.5, 0.05, 0.2, 0.25],
def test_constructor_vars_exponents(self):
m = sym.Monomial([x, y], [1, 2])
powers_out = EqualToDict(m.get_powers())
self.assertEqual(powers_out[x], 1)
self.assertEqual(powers_out[y], 2)
