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t-digest
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LiveStats reviews and mentions
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How percentile approximation works (and why it's more useful than averages)
Awhile ago I wrote a Python library called LiveStats[1] that computed any percentile for any amount of data using a fixed amount of memory per percentile. It uses an algorithm I found in an old paper[2] called P^2. It uses a polynomial to find good approximations.
The reason I made this was an old Amazon interview question. The question was basically, "Find the median of a huge data set without sorting it," and the "correct" answer was to have a fixed size sorted buffer and randomly evict items from it and then use the median of the buffer. However, a candidate I was interviewing had a really brilliant insight: if we estimate the median and move it a small amount for each new data point, it would be pretty close. I ended up doing some research on this and found P^2, which is a more sophisticated version of that insight.
[1]: https://github.com/cxxr/LiveStats
[2]: https://www.cs.wustl.edu/~jain/papers/ftp/psqr.pdf
Stats
cxxr/LiveStats is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of LiveStats is Python.
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