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[SPARK-21057][ML] Do not use a PascalDistribution in countApprox - #18276
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srowen wants to merge 2 commits into
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[SPARK-21057][ML] Do not use a PascalDistribution in countApprox#18276srowen wants to merge 2 commits into
srowen wants to merge 2 commits into
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SparkQA
commented
Jun 12, 2017
Test build #77931 has finished for PR 18276 at commit
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dongjoon-hyun
commented
Jun 12, 2017
Member
Retest this please. |
SparkQA
commented
Jun 12, 2017
Test build #77938 has finished for PR 18276 at commit
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SparkQA
commented
Jun 13, 2017
Test build #77987 has finished for PR 18276 at commit
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srowen
commented
Jun 14, 2017
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Merged to master |
lovasoa
reviewed
Jun 14, 2017
| // p of the data. This suggests data is counted at a rate of sum / p across the whole data | ||
| // set. The total expected count from the rest is distributed as | ||
| // (1-p) Poisson(sum / p) = Poisson(sum*(1-p)/p) | ||
| val dist = new PoissonDistribution(sum * (1 - p) / p) |
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@srowen I know it is a little late for a review, but now that we have a single distribution, it would make the code clearer to estimate directly the total count with the poisson distribution. That is removing the 1-p here and the sum + in the final BoundedDouble.
dataknocker pushed a commit
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Jun 16, 2017
## What changes were proposed in this pull request? Use Poisson analysis for approx count in all cases. ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closesapache#18276 from srowen/SPARK-21057.
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What changes were proposed in this pull request?
Use Poisson analysis for approx count in all cases.
How was this patch tested?
Existing tests.