def test_coin_biased_towards_falsehood():
p = 1.0 / 500
for i in range(255):
if i != 1:
assert not cu.biased_coin(ConjectureData.for_buffer([0, i, 0, 1]), p)
assert cu.biased_coin(ConjectureData.for_buffer([0, 1, 0, 0]), p)
def test_coin_biased_towards_truth():
p = 1 - 1.0 / 500
for i in range(1, 255):
assert cu.biased_coin(ConjectureData.for_buffer([0, i, 0, 0]), p)
assert not cu.biased_coin(ConjectureData.for_buffer([0, 0, 0, 1]), p)
def test_coin_biased_towards_falsehood():
p = 1.0 / 500
for i in range(255):
assert not cu.biased_coin(ConjectureData.for_buffer([i]), p)
second_order = [
cu.biased_coin(ConjectureData.for_buffer([255, i]), p) for i in range(255)
]
assert False in second_order
assert True in second_order
def test_coin_biased_towards_falsehood():
p = 1.0 / 500
for i in range(255):
assert not cu.biased_coin(ConjectureData.for_buffer([i]), p)
second_order = [
cu.biased_coin(ConjectureData.for_buffer([255, i]), p) for i in range(255)
]
assert False in second_order
assert True in second_order
def run(self, state_machine, print_steps=None):
if print_steps is None:
print_steps = current_verbosity() >= Verbosity.debug
self.data.hypothesis_runner = state_machine
stopping_value = 1 - 1.0 / (1 + self.n_steps * 0.5)
try:
steps = 0
while True:
if steps == self.n_steps:
stopping_value = 0
self.data.start_example()
if not cu.biased_coin(self.data, stopping_value):
self.data.stop_example()
break
value = self.data.draw(state_machine.steps())
steps += 1
if steps <= self.n_steps:
if print_steps:
state_machine.print_step(value)
state_machine.execute_step(value)
self.data.stop_example()
finally:
state_machine.teardown()
def do_draw(self, data):
seen = set()
result = []
if self.max_size == self.min_size:
while len(result) < self.max_size:
v = data.draw(self.element_strategy)
k = self.key(v)
if k not in seen:
result.append(v)
seen.add(k)
return result
stopping_value = 1 - 1.0 / (1 + self.average_size)
duplicates = 0
while len(result) < self.max_size:
data.start_example()
if len(result) >= self.min_size:
more = cu.biased_coin(data, stopping_value)
else:
more = True
if not more:
data.stop_example()
break
value = data.draw(self.element_strategy)
data.stop_example()
k = self.key(value)
if k in seen:
duplicates += 1
assume(duplicates <= len(result))
continue
seen.add(k)
result.append(value)
assume(len(result) >= self.min_size)
return result
def run(self, state_machine, print_steps=None):
if print_steps is None:
print_steps = current_verbosity() >= Verbosity.debug
self.data.hypothesis_runner = state_machine
stopping_value = 1 - 1.0 / (1 + self.n_steps * 0.5)
try:
state_machine.check_invariants()
steps = 0
while True:
if steps >= self.n_steps:
stopping_value = 0
self.data.start_example()
if not cu.biased_coin(self.data, stopping_value):
self.data.stop_example()
break
assert steps < self.n_steps
value = self.data.draw(state_machine.steps())
steps += 1
if print_steps:
state_machine.print_step(value)
state_machine.execute_step(value)
self.data.stop_example()
state_machine.check_invariants()
finally:
state_machine.teardown()
def is_enabled(self, name):
"""Tests whether the feature named ``name`` should be enabled on this
test run."""
if self.__data is None or self.__data.frozen:
# Feature set objects might hang around after data generation has
# finished. If this happens then we just report all new features as
# enabled, because that's our shrinking direction and they have no
# impact on data generation if they weren't used while it was
# running.
return not self.__is_disabled.get(name, False)
data = self.__data
data.start_example(label=FEATURE_LABEL)
# If we've already decided on this feature then we don't actually
# need to draw anything, but we do write the same decision to the
# input stream. This allows us to lazily decide whether a feature
# is enabled, because it means that if we happen to delete the part
# of the test case where we originally decided, the next point at
# which we make this decision just makes the decision it previously
# made.
is_disabled = cu.biased_coin(
self.__data, self.__p_disabled, forced=self.__is_disabled.get(name)
)
self.__is_disabled[name] = is_disabled
data.stop_example()
return not is_disabled
def do_draw(self, data):
seen = set()
result = []
if self.max_size == self.min_size:
while len(result) < self.max_size:
v = data.draw(self.element_strategy)
k = self.key(v)
if k not in seen:
result.append(v)
seen.add(k)
return result
stopping_value = 1 - 1.0 / (1 + self.average_size)
duplicates = 0
while len(result) < self.max_size:
data.start_example()
if len(result) >= self.min_size:
more = cu.biased_coin(data, stopping_value)
else:
more = True
if not more:
data.stop_example()
break
value = data.draw(self.element_strategy)
data.stop_example()
k = self.key(value)
if k in seen:
duplicates += 1
assume(duplicates <= len(result))
continue
seen.add(k)
result.append(value)
assume(len(result) >= self.min_size)
return result
def test_unbiased_coin_has_no_second_order():
flips = [
cu.biased_coin(ConjectureData.for_buffer([i]), 0.5)
for i in range(256)
]
counts = Counter(flips)
assert counts[True] == counts[False] == 128
def test_drawing_an_exact_fraction_coin():
count = 0
total = 0
p = Fraction(3, 8)
for i in range(4):
for j in range(4):
total += 1
if cu.biased_coin(ConjectureData.for_buffer([i, j, 0]), p):
count += 1
assert p == Fraction(count, total)
def test_unbiased_coin_has_no_second_order():
counts = Counter()
for i in range(256):
buf = hbytes([i])
data = ConjectureData.for_buffer(buf)
result = cu.biased_coin(data, 0.5)
if data.buffer == buf:
counts[result] += 1
assert counts[False] == counts[True] > 0
def test_unbiased_coin_has_no_second_order():
counts = Counter()
for i in range(256):
buf = hbytes([i])
data = ConjectureData.for_buffer(buf)
result = cu.biased_coin(data, 0.5)
if data.buffer == buf:
counts[result] += 1
assert counts[False] == counts[True] > 0
def do_draw(self, data):
if len(self.intervals) > 256:
if biased_coin(data, 0.2):
i = integer_range(data, 256, len(self.intervals) - 1)
else:
i = integer_range(data, 0, 255)
else:
i = integer_range(data, 0, len(self.intervals) - 1)
i = self.rewrite_integer(i)
return chr(self.intervals[i])
def do_draw(self, data):
if cu.biased_coin(data, self.__p):
return data.draw(self) + data.draw(self)
else:
# We draw n as two separate calls so that it doesn't show up as a
# single block. If it did, the heuristics that allow us to move
# blocks around would fire and it would move right, which would
# then allow us to shrink it more easily.
n = (data.draw_bits(16) << 16) | data.draw_bits(16)
if n == MAX_INT:
return (POISON,)
else:
return (None,)
def do_draw(self, data):
if cu.biased_coin(data, self.__p):
return data.draw(self) + data.draw(self)
else:
# We draw n as two separate calls so that it doesn't show up as a
# single block. If it did, the heuristics that allow us to move
# blocks around would fire and it would move right, which would
# then allow us to shrink it more easily.
n = (data.draw_bits(16) << 16) | data.draw_bits(16)
if n == MAX_INT:
return (POISON, )
else:
return (None, )
def do_draw(self, data):
if self.max_size == self.min_size:
return [
data.draw(self.element_strategy) for _ in range(self.min_size)
]
stopping_value = 1 - 1.0 / (1 + self.average_length)
result = []
while True:
data.start_example()
more = cu.biased_coin(data, stopping_value)
value = data.draw(self.element_strategy)
data.stop_example()
if not more:
if len(result) < self.min_size:
continue
else:
break
result.append(value)
if self.max_size < float('inf'):
result = result[:self.max_size]
return result
def do_draw(self, data):
if self.max_size == self.min_size:
return [
data.draw(self.element_strategy) for _ in range(self.min_size)
]
stopping_value = 1 - 1.0 / (1 + self.average_length)
result = []
while len(result) < self.max_size:
data.start_example()
more = cu.biased_coin(data, stopping_value)
if not more:
data.stop_example()
if len(result) < self.min_size:
continue
else:
break
value = data.draw(self.element_strategy)
data.stop_example()
result.append(value)
else:
cu.write(data, TERMINATOR)
return result
def do_draw(self, data):
if self.max_size == self.min_size:
return [
data.draw(self.element_strategy)
for _ in range(self.min_size)
]
stopping_value = 1 - 1.0 / (1 + self.average_length)
result = []
while True:
data.start_example()
more = cu.biased_coin(data, stopping_value)
value = data.draw(self.element_strategy)
data.stop_example()
if not more:
if len(result) < self.min_size:
continue
else:
break
result.append(value)
if self.max_size < float('inf'):
result = result[:self.max_size]
return result
def run(self, state_machine, print_steps=None):
if print_steps is None:
print_steps = current_verbosity() >= Verbosity.debug
stopping_value = 1 - 1.0 / (1 + self.n_steps * 0.5)
try:
steps = 0
while True:
if steps == self.n_steps:
stopping_value = 0
self.data.start_example()
if not cu.biased_coin(self.data, stopping_value):
self.data.stop_example()
break
value = self.data.draw(state_machine.steps())
steps += 1
if steps <= self.n_steps:
if print_steps:
state_machine.print_step(value)
state_machine.execute_step(value)
self.data.stop_example()
finally:
state_machine.teardown()
def test_drawing_an_exact_fraction_coin():
count = 0
for i in hrange(8):
if cu.biased_coin(ConjectureData.for_buffer([i]), Fraction(3, 8)):
count += 1
assert count == 3
def test_drawing_impossible_coin_still_writes():
data = ConjectureData.for_buffer([1, 0])
assert not data.buffer
assert not cu.biased_coin(data, 0)
assert data.buffer
def test_drawing_certain_coin_still_writes():
data = ConjectureData.for_buffer([0, 1])
assert not data.buffer
assert cu.biased_coin(data, 1)
assert data.buffer
def _draw_loop_dimensions(self, data, use=None):
# All shapes are handled in column-major order; i.e. they are reversed
base_shape = self.base_shape[::-1]
result_shape = list(base_shape)
shapes = [[] for _ in range(self.num_shapes)]
if use is None:
use = [True for _ in range(self.num_shapes)]
else:
assert len(use) == self.num_shapes
assert all(isinstance(x, bool) for x in use)
for dim_count in range(1, self.max_dims + 1):
dim = dim_count - 1
# We begin by drawing a valid dimension-size for the given
# dimension. This restricts the variability across the shapes
# at this dimension such that they can only choose between
# this size and a singleton dimension.
if len(base_shape) < dim_count or base_shape[dim] == 1:
# dim is unrestricted by the base-shape: shrink to min_side
dim_side = data.draw(self.side_strat)
elif base_shape[dim] <= self.max_side:
# dim is aligned with non-singleton base-dim
dim_side = base_shape[dim]
else:
# only a singleton is valid in alignment with the base-dim
dim_side = 1
for shape_id, shape in enumerate(shapes):
# Populating this dimension-size for each shape, either
# the drawn size is used or, if permitted, a singleton
# dimension.
if dim_count <= len(base_shape) and self.size_one_allowed:
# aligned: shrink towards size 1
side = data.draw(st.sampled_from([1, dim_side]))
else:
side = dim_side
# Use a trick where where a biased coin is queried to see
# if the given shape-tuple will continue to be grown. All
# of the relevant draws will still be made for the given
# shape-tuple even if it is no longer being added to.
# This helps to ensure more stable shrinking behavior.
if self.min_dims < dim_count:
use[shape_id] &= cu.biased_coin(
data, 1 - 1 / (1 + self.max_dims - dim)
)
if use[shape_id]:
shape.append(side)
if len(result_shape) < len(shape):
result_shape.append(shape[-1])
elif shape[-1] != 1 and result_shape[dim] == 1:
result_shape[dim] = shape[-1]
if not any(use):
break
result_shape = result_shape[: max(map(len, [self.base_shape] + shapes))]
assert len(shapes) == self.num_shapes
assert all(self.min_dims <= len(s) <= self.max_dims for s in shapes)
assert all(self.min_side <= s <= self.max_side for side in shapes for s in side)
return BroadcastableShapes(
input_shapes=tuple(tuple(reversed(shape)) for shape in shapes),
result_shape=tuple(reversed(result_shape)),
)
def test_drawing_an_exact_fraction_coin():
count = 0
for i in hrange(8):
if cu.biased_coin(ConjectureData.for_buffer([i]), Fraction(3, 8)):
count += 1
assert count == 3
def test_drawing_certain_coin_still_writes():
data = ConjectureData.for_buffer([0, 1])
assert not data.buffer
assert cu.biased_coin(data, 1)
assert data.buffer
def test_drawing_impossible_coin_still_writes():
data = ConjectureData.for_buffer([1, 0])
assert not data.buffer
assert not cu.biased_coin(data, 0)
assert data.buffer
def test_can_draw_arbitrary_fractions(p, b):
try:
cu.biased_coin(ConjectureData.for_buffer(b), p)
except StopTest:
reject()
def test_assert_biased_coin_always_treats_one_as_true():
assert cu.biased_coin(ConjectureData.for_buffer([0, 1]), p=1.0 / 257)
def test_biased_coin_can_be_forced():
assert cu.biased_coin(ConjectureData.for_buffer([0]), p=0.5, forced=True)
assert not cu.biased_coin(
ConjectureData.for_buffer([1]), p=0.5, forced=False)
def test_too_small_to_be_useful_coin():
assert not cu.biased_coin(ConjectureData.for_buffer([1]), 0.5**65)
def do_draw(self, data):
if cu.biased_coin(data, self.__poison_chance):
return POISON
else:
return data.draw(self.__ints)
def do_draw(self, data):
if cu.biased_coin(data, self.__poison_chance):
return POISON
else:
return data.draw(self.__ints)
