x[i], x[j] = x[j], x[i]
def sample(self, population, k):
- """Chooses k unique random elements from a population sequence.
+ """Chooses k unique random elements from a population sequence or set.
Returns a new list containing elements from the population while
leaving the original population unchanged. The resulting list is
large population: sample(range(10000000), 60)
"""
- # XXX Although the documentation says `population` is "a sequence",
- # XXX attempts are made to cater to any iterable with a __len__
- # XXX method. This has had mixed success. Examples from both
- # XXX sides: sets work fine, and should become officially supported;
- # XXX dicts are much harder, and have failed in various subtle
- # XXX ways across attempts. Support for mapping types should probably
- # XXX be dropped (and users should pass mapping.keys() or .values()
- # XXX explicitly).
-
# Sampling without replacement entails tracking either potential
# selections (the pool) in a list or previous selections in a set.
# preferred since the list takes less space than the
# set and it doesn't suffer from frequent reselections.
+ if isinstance(population, (set, frozenset)):
+ population = tuple(population)
+ if not hasattr(population, '__getitem__') or hasattr(population, 'keys'):
+ raise TypeError("Population must be a sequence or set. For dicts, use dict.keys().")
+ random = self.random
n = len(population)
if not 0 <= k <= n:
- raise ValueError("sample larger than population")
- random = self.random
+ raise ValueError("Sample larger than population")
_int = int
result = [None] * k
setsize = 21 # size of a small set minus size of an empty list
if k > 5:
setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets
- if n <= setsize or hasattr(population, "keys"):
- # An n-length list is smaller than a k-length set, or this is a
- # mapping type so the other algorithm wouldn't work.
+ if n <= setsize:
+ # An n-length list is smaller than a k-length set
pool = list(population)
for i in range(k): # invariant: non-selected at [0,n-i)
j = _int(random() * (n-i))
result[i] = pool[j]
pool[j] = pool[n-i-1] # move non-selected item into vacancy
else:
- try:
- selected = set()
- selected_add = selected.add
- for i in range(k):
+ selected = set()
+ selected_add = selected.add
+ for i in range(k):
+ j = _int(random() * n)
+ while j in selected:
j = _int(random() * n)
- while j in selected:
- j = _int(random() * n)
- selected_add(j)
- result[i] = population[j]
- except (TypeError, KeyError): # handle (at least) sets
- if isinstance(population, list):
- raise
- return self.sample(tuple(population), k)
+ selected_add(j)
+ result[i] = population[j]
return result
## -------------------- real-valued distributions -------------------
self.gen.sample(tuple('abcdefghijklmnopqrst'), 2)
def test_sample_on_dicts(self):
- self.gen.sample(dict.fromkeys('abcdefghijklmnopqrst'), 2)
-
- # SF bug #1460340 -- random.sample can raise KeyError
- a = dict.fromkeys(list(range(10)) +
- list(range(10,100,2)) +
- list(range(100,110)))
- self.gen.sample(a, 3)
-
- # A followup to bug #1460340: sampling from a dict could return
- # a subset of its keys or of its values, depending on the size of
- # the subset requested.
- N = 30
- d = dict((i, complex(i, i)) for i in range(N))
- for k in range(N+1):
- samp = self.gen.sample(d, k)
- # Verify that we got ints back (keys); the values are complex.
- for x in samp:
- self.assert_(type(x) is int)
- samp.sort()
- self.assertEqual(samp, list(range(N)))
+ self.assertRaises(TypeError, self.gen.sample, dict.fromkeys('abcdef'), 2)
def test_gauss(self):
# Ensure that the seed() method initializes all the hidden state. In
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