def ncycles(iterable, n):
"Returns the sequence elements n times"
- return chain.from_iterable(repeat(iterable, n))
+ return chain.from_iterable(repeat(tuple(iterable), n))
def dotproduct(vec1, vec2):
return sum(map(operator.mul, vec1, vec2))
def random_product(*args, repeat=1):
"Random selection from itertools.product(*args, **kwds)"
pools = [tuple(pool) for pool in args] * repeat
- return [random.choice(pool) for pool in pools]
+ return tuple(random.choice(pool) for pool in pools)
def random_permuation(iterable, r=None):
"Random selection from itertools.permutations(iterable, r)"
pool = tuple(iterable)
r = len(pool) if r is None else r
- return random.sample(pool, r)
+ return tuple(random.sample(pool, r))
def random_combination(iterable, r):
"Random selection from itertools.combinations(iterable, r)"
pool = tuple(iterable)
- return sorted(random.sample(pool, r), key=pool.index)
+ return tuple(sorted(random.sample(pool, r), key=pool.index))
def random_combination_with_replacement(iterable, r):
"Random selection from itertools.combinations_with_replacement(iterable, r)"
pool = tuple(iterable)
- return sorted(map(random.choice, repeat(pool, r)), key=pool.index)
+ return tuple(sorted(map(random.choice, repeat(pool, r)), key=pool.index))
Note, many of the above recipes can be optimized by replacing global lookups
with local variables defined as default values. For example, the