def uniform_sample(self, n, remove):
assert n >= 0
ixs = list(sample_n_k(self.length, n))
vals = []
if n > 0:
for ix in ixs:
val = self._write(ix, 0.0)
vals.append(val)
if not remove:
for ix, val in zip(ixs, vals):
self._write(ix, val)
return ixs, vals
def uniform_sample(self, n: int,
remove: bool) -> Tuple[List[int], List[float]]:
assert n >= 0
ixs = list(sample_n_k(self.length, n))
vals: List[float] = []
if n > 0:
for ix in ixs:
val = self._write(ix, 0.0)
assert val is not None
vals.append(val)
if not remove:
for ix, val in zip(ixs, vals):
self._write(ix, val)
return ixs, vals
def _uniform_sample_indices_and_probabilities(
self, n: int) -> Tuple[List[int], List[float]]:
indices = list(sample_n_k(len(self.data), n))
probabilities = [1 / len(self)] * len(indices)
return indices, probabilities
def sample(self, k):
return [self[i] for i in sample_n_k(len(self), k)]
def _uniform_sample_indices_and_probabilities(self, n):
indices = list(sample_n_k(len(self.data), n))
probabilities = [1 / len(self)] * len(indices)
return indices, probabilities
def test_slow(self):
self.samples = [sample_n_k(self.n, self.k) for _ in range(10000)]
self.subtest_total_counts()
self.subtest_order_counts()
def test_fast(self):
self.samples = [sample_n_k(self.n, self.k) for _ in range(200)]
self.subtest_constraints()