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Add Hamming, Jensen-Shannon, KL-Divergence, Russell rao and Correlation distance metrics support #306
Add Hamming, Jensen-Shannon, KL-Divergence, Russell rao and Correlation distance metrics support #306
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…dy a multiplying factor so inferring 0s to avoid log is already in place
…eeded as we are multiplying with a 0 in case any of the inputs to log() is 0 so there is no resultant inf. this improves perf further now 3.7x compared to naive kernel version
@cjnolet Mahesh found that in some metrics there's a huge perf gain if we used the fast-math instrinsics. Thus, we were discussing about introducing "fast" versions of some of these metrics (eg: |
…ich only requires log (x), and post processing revert the log(x) back to x
@cjnolet ping for help with review. |
…'t match with sklearn results otherwise
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LGTM
I notice this is marked w/ the |
@cjnolet yes that is the proposed plan to have additional distance metrics for jensen-shannon & kl-divergence as |
Can one of the admins verify this patch? |
add to allowlist |
@teju85 can you rerun the tests? seems like the some CI error causing failure. |
rerun tests |
Ah I missed your question, @cjnolet . This was an oversight from me. This is not a breaking change. I've rectified it. |
@gpucibot merge |
…o distance metrics (#4155) -- This PR depends on RAFT PR - rapidsai/raft#306 -- Adds cpp & python interfaces for these distance metrics with pytest support for each of them. -- also remove redundant commented code in canberra distance metric Authors: - Mahesh Doijade (https://github.com/mdoijade) Approvers: - Corey J. Nolet (https://github.com/cjnolet) URL: #4155
…o distance metrics (rapidsai#4155) -- This PR depends on RAFT PR - rapidsai/raft#306 -- Adds cpp & python interfaces for these distance metrics with pytest support for each of them. -- also remove redundant commented code in canberra distance metric Authors: - Mahesh Doijade (https://github.com/mdoijade) Approvers: - Corey J. Nolet (https://github.com/cjnolet) URL: rapidsai#4155
This PR introduces the following distances:
with unit tests for each of them.