def test_composite_parameter_name_clashes():
with pytest.raises(ValueError) as e:
params.CompositeParameter(
params.BiasedCoin(0.5),
arg0=params.BiasedCoin(0.5),
)
assert 'duplicate' in e.value.args[0].lower()
with pytest.raises(ValueError) as e:
params.CompositeParameter(__init__=params.BiasedCoin(0.5), )
assert 'invalid' in e.value.args[0].lower()
class FixedBoundedFloatStrategy(SearchStrategy):
"""A strategy for floats distributed between two endpoints.
The conditional distribution tries to produce values clustered
closer to one of the ends.
"""
descriptor = float
parameter = params.CompositeParameter(
cut=params.UniformFloatParameter(0, 1),
leftwards=params.BiasedCoin(0.5),
)
def __init__(self, lower_bound, upper_bound):
SearchStrategy.__init__(self)
self.lower_bound = float(lower_bound)
self.upper_bound = float(upper_bound)
def produce(self, random, pv):
if pv.leftwards:
left = self.lower_bound
right = pv.cut
else:
left = pv.cut
right = self.upper_bound
return left + random.random() * (right - left)
def simplify(self, value):
yield self.lower_bound
yield self.upper_bound
yield (self.lower_bound + self.upper_bound) * 0.5
class ExponentialFloatStrategy(FloatStrategy):
"""
A float strategy such that every conditional distribution is of the form
aX + b where a = +/- 1 and X is an exponentially distributed random
variable.
"""
parameter = params.CompositeParameter(
lambd=params.GammaParameter(2, 50),
zero_point=params.NormalParameter(0, 1),
negative=params.BiasedCoin(0.5),
)
def produce(self, random, pv):
value = random.expovariate(pv.lambd)
if pv.negative:
value = -value
return pv.zero_point + value
def test_biased_coin_only_accepts_proper_probabilities():
for bad in [-1.0, 0, 1.0, 10e6]:
with pytest.raises(ValueError):
params.BiasedCoin(bad)