[ML] Improve bad input handling in distribution models #1114
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droberts195
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Apr 2, 2020
| @@ -577,10 +577,11 @@ class CLogSampleSquareDeviation : core::CNonCopyable { | |||
| result = 0.0; | |||
| for (std::size_t i = 0u; i < m_Samples.size(); ++i) { | |||
| double residual = m_Samples[i]; | |||
| if (residual <= 0.0) { | |||
| double n = maths_t::countForUpdate(m_Weights[i]); | |||
| if (residual <= x || !CMathsFuncs::isFinite(residual) || | |||
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Was it intentional to change <= 0.0 to <= x?
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Yes. This is actually was actually a bug I spotted while working on this. It causes us to discard a small number of sample we shouldn't be.
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Although, related tests have failed, so let me double check this.
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I got the new condition wrong as well :(. Corrected.
tveasey
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In particular, it more reliably stops non-finite inputs polluting the model.