def random_sample(prob, seq, random_state=None):
""" Return elements from a sequence with probability of prob
Returns a lazy iterator of random items from seq.
``random_sample`` considers each item independently and without
replacement. See below how the first time it returned 13 items and the
next time it returned 6 items.
>>> seq = list(range(100))
>>> list(random_sample(0.1, seq)) # doctest: +SKIP
[6, 9, 19, 35, 45, 50, 58, 62, 68, 72, 78, 86, 95]
>>> list(random_sample(0.1, seq)) # doctest: +SKIP
[6, 44, 54, 61, 69, 94]
Providing an integer seed for ``random_state`` will result in
deterministic sampling. Given the same seed it will return the same sample
every time.
>>> list(random_sample(0.1, seq, random_state=2016))
[7, 9, 19, 25, 30, 32, 34, 48, 59, 60, 81, 98]
>>> list(random_sample(0.1, seq, random_state=2016))
[7, 9, 19, 25, 30, 32, 34, 48, 59, 60, 81, 98]
``random_state`` can also be any object with a method ``random`` that
returns floats between 0.0 and 1.0 (exclusive).
>>> from random import Random
>>> randobj = Random(2016)
>>> list(random_sample(0.1, seq, random_state=randobj))
[7, 9, 19, 25, 30, 32, 34, 48, 59, 60, 81, 98]
"""
if not hasattr(random_state, 'random'):
random_state = Random(random_state)
return filter(lambda _: random_state.random() < prob, seq)
def random_sample(prob, seq, random_state=None):
""" Return elements from a sequence with probability of prob
Returns a lazy iterator of random items from seq.
``random_sample`` considers each item independently and without
replacement. See below how the first time it returned 13 items and the
next time it returned 6 items.
>>> seq = list(range(100))
>>> list(random_sample(0.1, seq)) # doctest: +SKIP
[6, 9, 19, 35, 45, 50, 58, 62, 68, 72, 78, 86, 95]
>>> list(random_sample(0.1, seq)) # doctest: +SKIP
[6, 44, 54, 61, 69, 94]
Providing an integer seed for ``random_state`` will result in
deterministic sampling. Given the same seed it will return the same sample
every time.
>>> list(random_sample(0.1, seq, random_state=2016))
[7, 9, 19, 25, 30, 32, 34, 48, 59, 60, 81, 98]
>>> list(random_sample(0.1, seq, random_state=2016))
[7, 9, 19, 25, 30, 32, 34, 48, 59, 60, 81, 98]
``random_state`` can also be any object with a method ``random`` that
returns floats between 0.0 and 1.0 (exclusive).
>>> from random import Random
>>> randobj = Random(2016)
>>> list(random_sample(0.1, seq, random_state=randobj))
[7, 9, 19, 25, 30, 32, 34, 48, 59, 60, 81, 98]
"""
if not hasattr(random_state, 'random'):
random_state = Random(random_state)
return filter(lambda _: random_state.random() < prob, seq)
def remove(predicate, seq):
""" Return those items of collection for which predicate(item) is true.
>>> def iseven(x):
... return x % 2 == 0
>>> list(remove(iseven, [1, 2, 3, 4]))
[1, 3]
"""
return filter(lambda x: not predicate(x), seq)
def remove(predicate, coll):
""" Return those items of collection for which predicate(item) is true.
>>> def iseven(x):
... return x % 2 == 0
>>> list(remove(iseven, [1, 2, 3, 4]))
[1, 3]
"""
return filter(lambda x: not predicate(x), coll)
def test_remove():
r = remove(iseven, range(5))
assert type(r) is not list
assert list(r) == list(filter(isodd, range(5)))
def test_remove():
r = remove(iseven, range(5))
assert type(r) is not list
assert list(r) == list(filter(isodd, range(5)))
def test_map_filter_are_lazy():
def bad(x):
raise Exception()
map(bad, [1, 2, 3])
filter(bad, [1, 2, 3])
def test_map_filter_are_lazy():
def bad(x):
raise Exception()
map(bad, [1, 2, 3])
filter(bad, [1, 2, 3])
