Fix bug in cdf method of piecewise linear distributions #564
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Codecov Report
@@ Coverage Diff @@
## master #564 +/- ##
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- Coverage 83.27% 83.27% -0.01%
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Files 178 178
Lines 9958 9955 -3
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- Hits 8293 8290 -3
Misses 1665 1665
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lostella
commented
Jan 17, 2020
lostella
commented
Jan 20, 2020
Comment on lines
-339
to
-347
| thresh = F.zeros_like(slopes) + 20.0 | ||
| I_grt_thresh = F.broadcast_greater_equal(slopes, thresh) | ||
| I_sml_thresh = F.broadcast_lesser(slopes, thresh) | ||
| slopes_proj = ( | ||
| I_sml_thresh | ||
| * F.Activation(data=(I_sml_thresh * slopes), act_type="softrelu") | ||
| + I_grt_thresh * slopes | ||
| ) | ||
| # slopes = F.Activation(slopes, 'softrelu') |
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I'm not sure what all this logic was needed for: I checked and softrelu seems to behave fine with inputs much larger than 20 (when it's essentially the identity function). Maybe this was added in a tentative of fixing all other issues?
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Yes, this is outdated. There used to be an mxnet bug but the fix should already be in 1.4 (https://github.com/apache/incubator-mxnet/pull/6834/files).
vafl
approved these changes
Jan 20, 2020
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Issue #, if available: partially addressing #563
Description of changes: This fixes a bug in the cdf method of PiecewiseLinear, which was returning values larger than 1.0: this addresses the simpler example in #563. The more complex example there has to do with numerical problems, which make the cdf evaluation fail there, and crps as well as a consequence I believe. That problem will need more care.
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