Implements reservoir sampler randomly sampling stream of features #33
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bkowshik
approved these changes
Jun 27, 2018
| i = random.randint(0, size - 1) | ||
| self.reservoir[i] = v | ||
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| self.pushed += 1 |
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First we designate a counter, which will be incremented for every data point seen.
| '''Randomly samples k items from a stream of unknown n items. | ||
| ''' | ||
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| def __init__(self, capacity): |
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The reservoir is generally a list or array of predefined size.
| self.reservoir = [] | ||
| self.pushed = 0 | ||
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| def push(self, v): |
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Now we can begin adding data.
| size = len(self.reservoir) | ||
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| if size < self.capacity: | ||
| self.reservoir.append(v) |
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Until we encounter size elements, elements are added directly to reservoir
| assert size == self.capacity | ||
| assert size <= self.pushed | ||
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| p = self.capacity / self.pushed |
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Once reservoir is full, incoming data points have a size / counter chance to replace an existing sample point
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For #7. Work in progress.
This changeset implements a a randomized online algorithm "reservoir sampling" for randomly sampling k items from a stream of unknown n items. We can use this to randomly sample e.g. k building features in the osmium handlers without having to store all features first or doing two passes.
Tasks:
Refs: