tweak: experiment with nimble file, add deps to makefile.
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compute_constrained_bayes.nimble
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compute_constrained_bayes.nimble
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# Package
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version = "0.1.0"
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author = "NunoSempere"
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description = "A nimble package which showcases a few toy versions of bayesian updating under constrained compute"
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license = "MIT"
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srcDir = "src"
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bin = @["compute_constrained_bayes"]
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# Dependencies
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requires "nim >= 1.6.12"
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requires "https://github.com/treeform/print"
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index.md
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index.md
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## Dependencies
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nimble install https://github.com/nim-lang/bigints
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https://nimdocs.com/nim-lang/bigints/bigints.html
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nimble install print
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https://github.com/treeform/print
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## Dependencies
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The data folder is not included, but its contents are:
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.
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├── data
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│ ├── stripped
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@ -16,9 +13,13 @@ The data folder is not included, but its contents are:
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Where stripped.gz can be found at <https://oeis.org/wiki/JSON_Format,_Compressed_Files>
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## Gotchas
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Nimble doesn't play well with relative directories. Ignore and use make instead?
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## To do
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- [ ] Exploration of OEIS data
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- [x] Exploration of OEIS data
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- [ ] Subdivide subsequent tasks into steps
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- [x] Simple prediction of the next integer
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- [x] Simple predictions v1
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@ -26,11 +27,15 @@ Where stripped.gz can be found at <https://oeis.org/wiki/JSON_Format,_Compressed
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- [-] Maybe add some caching, e.g., write continuations to file, and read them next time.
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- [ ] JIT Bayesianism:
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- [x] Function to predict with a variable number of hypotheses
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- [ ] Function to start predicting with a small number of hypotheses, and get more if the initial ones aren't enough.
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- [ ] Add the loop of: start with some small number of sequences, and if these aren't enough, read more.
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- [ ] ...
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- [ ] Infrabayesianism x1: Predicting interleaved sequences
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- [x] Function to start predicting with a small number of hypotheses, and get more if the initial ones aren't enough.
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- [x] Add the loop of: start with some small number of sequences, and if these aren't enough, read more.
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- [x] Clean-up
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- [ ] Infrabayesianism
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- [ ] Infrabayesianism x1: Predicting interleaved sequences.
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- Yeah, actually, I think this just captures an implicit assumption of Bayesianism as actually practiced.
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- [ ] Infrabayesianism x2: Deterministic game of producing a fixed deterministic prediction, and then the adversary picking whatever minimizes your loss
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- I am actually not sure of what the procedure is exactly for computing that loss. Do you minimize over subsequent rounds of the game, or only for the first round? Look this up.
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- Also maybe ask for help from e.g., Alex Mennen.
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- [ ] Write the actor
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---
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@ -4,6 +4,9 @@ VERBOSE=--verbosity:0
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build: compute_constrained_bayes.nim
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nim c $(VERBOSE) compute_constrained_bayes.nim
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deps:
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nimble install print@1.0.2
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run: compute_constrained_bayes
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./compute_constrained_bayes $(VERBOSE)
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