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[ML] Fix influencer count and influence calculation #150
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params, influencedValue[0]); | ||
if (computeInfluencedValue(value, count, i->second.first, i->second.second, | ||
params, influencedValue[0]) == false) { | ||
LOG_ERROR(<< "Failed to compute influencer value"); |
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@tveasey do you have a better idea for this error message?
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I think, at least for us, it is very useful to see the function arguments, i.e. value
, count
, i->second.first
and i->second.second
.
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LGTM. Left a couple of suggestions only.
} | ||
}; | ||
|
||
//! \brief Computes the value of the variance statistic on a set difference. | ||
class CVarianceDifference { | ||
public: | ||
//! Features. | ||
void operator()(const TDouble1Vec& v, |
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Maybe document the parameters to this function too? i.e. v
== overall variance and mean, n
== overall count, vi
== influencer variance and mean, etc.
docs/CHANGELOG.asciidoc
Outdated
@@ -59,6 +59,7 @@ Age seasonal components in proportion to the fraction of values with which they' | |||
Persist and restore was missing some of the trend model state ({pull}#99[#99]) | |||
Stop zero variance data generating a log error in the forecast confidence interval calculation ({pull}#107[#107]) | |||
Fix corner case failing to calculate lgamma values and the correspoinding log errors ({pull}#126[#126]) | |||
Influence count per bucket was wrong and lead to wrong influencer scoring ({pull}#150[#150]) |
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On second thoughts, I'd say the "Influence count per bucket for metric population analyses was wrong..."
Fix counting of influencer per bucket for metric population analyses, prior this fix the count has always been set to 1. Fixes elastic#24
Fix counting of influencer per bucket for metric population analyses, prior this fix the count has always been set to 1. Fixes #24
This fixes counting of influencer per bucket, prior this fix the count has always been set to 1.
Notes:
testVarp
) but test was forgotten to be added in the test suiteFixes #24
Release note: Fixes influence count per bucket for metric population analyses, which was
wrong and lead to incorrect influencer scoring