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License: Apache License 2.0
Implements the Karnin-Lang-Liberty (KLL) algorithm in python
License: Apache License 2.0
The important code files have a file header that says that they are Apache licensed, but the README says
this:
It should NOT be used for any commercial purposes.
Such a restriction is incompatible with the Apache license. If you want the code to be licensed as
Apache, it would help your readers if you would clarify this statement. Perhaps you meant that it
should be read as "this code should not be considered production ready"?
In addition, if you want the Apache license to apply and for this to be recognized by the github
automated scanners, it would help all around if you would insert a LICENSE containing the
full and literal text of the Apache.
My own stake in this is that I would like to use this code for testing and benchmarking of the t-digest,
but since I have a strict Apache license on the entire t-digest distro, I can't afford the ambiguity here.
I will produce a pull request with these changes that you can accept for deny according to your preference.
Dear Author Liberty,
Thank you so much for providing the KLL sketch codes. In the "Optimal Quantile Approximation in Streams" and "Streaming Quantiles Algorithms" they both talked about replace the log(n) stack of compactors of capacity 2 with a sampler. I have serveral questions regarding the sampler.
If without sampler, will KLL becomes a deterministic algorithm?
In this implementation, sampler are not included?
With Many Thanks!
Hi,
I am a little bit confused about this.
If I want to set the ε to 0.01, which means that | R̃(x) − R(x)| ≤ 0.01 * n. What value should k be set?
Is there some certain connection between k and ε? I am looking forward to your reply.
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