def test_sample_iter():
X = Normal('X', 0, 1)
Y = DiscreteUniform('Y', [1, 2, 7])
Z = Poisson('Z', 2)
scipy = import_module('scipy')
if not scipy:
skip('Scipy is not installed. Abort tests')
expr = X**2 + 3
iterator = sample_iter(expr)
expr2 = Y**2 + 5 * Y + 4
iterator2 = sample_iter(expr2)
expr3 = Z**3 + 4
iterator3 = sample_iter(expr3)
def is_iterator(obj):
if (hasattr(obj, '__iter__')
and (hasattr(obj, 'next') or hasattr(obj, '__next__'))
and callable(obj.__iter__) and obj.__iter__() is obj):
return True
else:
return False
assert is_iterator(iterator)
assert is_iterator(iterator2)
assert is_iterator(iterator3)
def test_sample_iter():
X = Normal("X", 0, 1)
Y = DiscreteUniform("Y", [1, 2, 7])
Z = Poisson("Z", 2)
expr = X ** 2 + 3
iterator = sample_iter(expr)
expr2 = Y ** 2 + 5 * Y + 4
iterator2 = sample_iter(expr2)
expr3 = Z ** 3 + 4
iterator3 = sample_iter(expr3)
def is_iterator(obj):
if (
hasattr(obj, "__iter__")
and (hasattr(obj, "next") or hasattr(obj, "__next__"))
and callable(obj.__iter__)
and obj.__iter__() is obj
):
return True
else:
return False
assert is_iterator(iterator)
assert is_iterator(iterator2)
assert is_iterator(iterator3)
def test_sample_iter():
X = Normal('X', 0, 1)
Y = DiscreteUniform('Y', [1, 2, 7])
Z = Poisson('Z', 2)
expr = X**2 + 3
iterator = sample_iter(expr)
expr2 = Y**2 + 5 * Y + 4
iterator2 = sample_iter(expr2)
expr3 = Z**3 + 4
iterator3 = sample_iter(expr3)
def is_iterator(obj):
if (hasattr(obj, '__iter__')
and (hasattr(obj, 'next') or hasattr(obj, '__next__'))
and callable(obj.__iter__) and obj.__iter__() is obj):
return True
else:
return False
assert is_iterator(iterator)
assert is_iterator(iterator2)
assert is_iterator(iterator3)
def sample(self):
"""
Returns
=======
sample: iterator object
iterator object containing the sample
"""
if not isinstance(self.transition_probabilities, (Matrix, ImmutableMatrix)):
raise ValueError("Transition Matrix must be provided for sampling")
Tlist = self.transition_probabilities.tolist()
samps = [random.choice(list(self.state_space))]
yield samps[0]
time = 1
densities = {}
for state in self.state_space:
states = list(self.state_space)
densities[state] = {states[i]: Tlist[state][i]
for i in range(len(states))}
while time < S.Infinity:
samps.append((next(sample_iter(FiniteRV("_", densities[samps[time - 1]])))))
yield samps[time]
time += 1