Comments (3)
how about separate packages for java and cpp implementations? apache datasketch is an example of using java and cpp+python binding
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I think that's another good option, and I think it's the option that would conform to most people's expectations about the scope of a single repository. The argument against this approach is that we want to convey that these two implementations are both attempting to implement the same "vision" for RCFs, and we will try to keep the respective core libraries feature compatible. I'll add "separate repos" as another option for discussion.
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In offline discussion, we have agreed to follow proposal 1 for now. We can choose to split the C library into its own repository in the future if it begins to significantly diverge from the Java implementation.
In order to implement this change, we need a new top-level README file which explains the RCF algorithm at a high level and describes the two implementations. @sudiptoguha is working on this, and once we have a draft we'll create a new PR to close this issue.
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Related Issues (20)
- RCF 4.0
- C, Ruby, and PHP libraries + standalone CLI HOT 1
- Enable multicentroid clustering in Rust HOT 2
- Thresholded Random Cut Forest not detecting some anomalies with small gap HOT 2
- rust summarize_list error HOT 1
- rust summarize_list error HOT 3
- Clarification regarding Shingle size, number of samples per tree and threshold HOT 7
- Error when updating tree HOT 7
- How can I serialize the object RandomCutForest to array bytes? HOT 2
- Sample Size & Rust HOT 1
- Rust serialization HOT 2
- Performance regression in 3.5.1 when restoring state HOT 3
- Remove restrictions from outputAfter setting HOT 1
- Make pastValues independent of forecasts
- Reduce noise from streaming normalization HOT 2
- Incorrect foreast cast result HOT 2
- is there any plans to support more language such as Python? HOT 2
- Rust panic HOT 8
- Revisit calibration in RCFCaster to improve forecasts near boundaries (and handle physical infeasibility, such as -ve values, etc.) HOT 1
- Addressing hyper-sensitivity for RCFs with homogenous observations HOT 8
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