019f8fd211
git-subtree-dir: users/wpcarro git-subtree-mainline:464bbcb15cgit-subtree-split:24f5a642afChange-Id: I6105b3762b79126b3488359c95978cadb3efa789
50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
import random
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# This class of problems is known as "resevoir sampling".
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def choose_a(m, xs):
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"""
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Randomly choose `m` elements from `xs`.
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This algorithm runs in linear time with respect to the size of `xs`.
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"""
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result = [None] * m
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for i in range(len(xs)):
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j = random.randint(0, i)
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if j < m:
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result[j] = xs[i]
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return result
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def choose_b(m, xs):
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"""
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This algorithm, which copies `xs`, which runs in linear time, and then
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shuffles the copies, which also runs in linear time, achieves the same
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result as `choose_a` and both run in linear time.
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`choose_a` is still preferable since it has a coefficient of one, while this
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version has a coefficient of two because it copies + shuffles.
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"""
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ys = xs[:]
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random.shuffle(ys)
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return ys[:m]
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def choose_c(m, xs):
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"""
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This is one, possibly inefficient, way to randomly sample `m` elements from
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`xs`.
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"""
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choices = set()
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while len(choices) < m:
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choices.add(random.randint(0, len(xs) - 1))
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return [xs[i] for i in choices]
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# ROYGBIV
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xs = [
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'red',
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'orange',
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'yellow',
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'green',
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'blue',
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'indigo',
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'violet',
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]
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print(choose_b(3, xs))
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print(choose_c(3, xs))
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