751b5327a9
Tonight I learned that random sample where each element in the sampling corpus has an equal likelihood of being chosen is a brand of algorithms known as "reservoir sampling". - Implement random.shuffle(..) - Implement random.choice(..) Surprisingly, candidates are expected to encounter problems like this during interviews.
39 lines
960 B
Python
39 lines
960 B
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 = xs[:m]
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for i in range(m, 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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# 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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