56 lines
2.2 KiB
Markdown
56 lines
2.2 KiB
Markdown
## 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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│ └── stripped.gz
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Where stripped.gz can be found at <https://oeis.org/wiki/JSON_Format,_Compressed_Files>
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## To do
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- [ ] 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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- [x] Wrangle the return types to something semi-elegant
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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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- [ ] Infrabayesianism x2: Deterministic game of producing a fixed deterministic prediction, and then the adversary picking whatever minimizes your loss
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- [ ] Write the actor
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---
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An implementation of Infrabayesianism over OEIS sequences.
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<https://oeis.org/wiki/JSON_Format,_Compressed_Files>
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Or "Just-in-Time bayesianism", where getting a new hypothesis = getting a new sequence from OEIS which has the numbers you've seen so far.
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Implementing Infrabayesianism as a game over OEIS sequences. Two parts:
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1. Prediction over interleaved sequences. I choose two OEIS sequences, and interleave them: a1, b1, a2, b2.
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- Now, you don't have hypothesis over the whole set, but two hypothesis over the
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- I could also have a chemistry like iteration:
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a1
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a2 b1
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a3 b2 c1
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a4 b3 c2 d1
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a5 b4 c3 d2 e1
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.................
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- And then it would just be computationally absurd to have hypotheses over the whole
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2. Game where: You provide a deterministic procedure for estimating the probability of each OEIS sequence giving a list of trailing examples.
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