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gaurav-arya / StochasticAD.jl

Research package for automatic differentiation of programs containing discrete randomness.
MIT License
195 stars 15 forks source link

Add random scattering example #93

Open Moelf opened 1 year ago

Moelf commented 1 year ago

This should work as it is, but we might want to wait until more automatic solution is added, and clean up implementation before finally merging

codecov-commenter commented 8 months ago

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Comparison is base (3638b3b) 88.04% compared to head (6d5e786) 88.04%.

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Additional details and impacted files ```diff @@ Coverage Diff @@ ## main #93 +/- ## ======================================= Coverage 88.04% 88.04% ======================================= Files 15 15 Lines 753 753 ======================================= Hits 663 663 Misses 90 90 ``` | [Files](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya) | Coverage Δ | | |---|---|---| | [src/backends/abstract\_wrapper.jl](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya#diff-c3JjL2JhY2tlbmRzL2Fic3RyYWN0X3dyYXBwZXIuamw=) | `75.00% <ø> (ø)` | | | [src/backends/dict.jl](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya#diff-c3JjL2JhY2tlbmRzL2RpY3Quamw=) | `92.42% <ø> (ø)` | | | [src/backends/pruned\_aggressive.jl](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya#diff-c3JjL2JhY2tlbmRzL3BydW5lZF9hZ2dyZXNzaXZlLmps) | `91.37% <ø> (ø)` | | | [src/backends/strategy\_wrapper.jl](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya#diff-c3JjL2JhY2tlbmRzL3N0cmF0ZWd5X3dyYXBwZXIuamw=) | `100.00% <ø> (ø)` | | | [src/discrete\_randomness.jl](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya#diff-c3JjL2Rpc2NyZXRlX3JhbmRvbW5lc3Muamw=) | `87.58% <ø> (ø)` | | | [src/general\_rules.jl](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya#diff-c3JjL2dlbmVyYWxfcnVsZXMuamw=) | `95.07% <ø> (ø)` | | | [src/propagate.jl](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya#diff-c3JjL3Byb3BhZ2F0ZS5qbA==) | `87.80% <ø> (ø)` | | | [src/smoothing.jl](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya#diff-c3JjL3Ntb290aGluZy5qbA==) | `90.47% <ø> (ø)` | | | [src/stochastic\_triple.jl](https://app.codecov.io/gh/gaurav-arya/StochasticAD.jl/pull/93?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=Gaurav+Arya#diff-c3JjL3N0b2NoYXN0aWNfdHJpcGxlLmps) | `69.13% <ø> (ø)` | |

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