Bug Fixes in MLE fits #73
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fhagemann
reviewed
Sep 25, 2024
fhagemann
reviewed
Sep 25, 2024
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| # get fit function with background center | ||
| fit_function = get_th228_fit_functions(; background_center = background_center)[fit_func] | ||
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| # create loglikehood function: f_loglike(v) that can be evaluated for any set of v (fit parameter) | ||
| f_loglike = let f_fit = fit_function, h = h | ||
| v -> hist_loglike(Base.Fix2(f_fit, v), h) | ||
| v -> hist_loglike(x -> x in Interval(extrema(h.edges[1])...) ? f_fit(x, v) : 0, h) |
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Then, also f_loglike_array might need an update..
… can be determined
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## dev #73 +/- ##
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- Coverage 15.46% 15.01% -0.45%
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Files 32 33 +1
Lines 2489 2670 +181
==========================================
+ Hits 385 401 +16
- Misses 2104 2269 +165 ☔ View full report in Codecov by Sentry. |
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All tests are finally running again. Fixed all fits and improved with more BGMLE. |
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This PR prevents the MLE fits from crashing. Changes made inlcude:
advanced_time_and_memory_controlPossibility to set the maximal used memory and time for a single MLE fit by enabling a specific callback after each step of the iterationsLBFGSalgorithm fromOptim.jlwith theMoreThuenteline search algo from theLineSearches.jlpackage. theNalderZhangseems to perform in general more unstable sometimes doesn't even find a starting point. There are tunable parameters, but theMoreThuenteshowed in the current tests on Legend data better results