## Dependencies nimble install https://github.com/nim-lang/bigints https://nimdocs.com/nim-lang/bigints/bigints.html nimble install print https://github.com/treeform/print ## Dependencies The data folder is not included, but its contents are: . ├── data │   ├── stripped │   └── stripped.gz Where stripped.gz can be found at ## To do - [ ] Exploration of OEIS data - [ ] Subdivide subsequent tasks into steps - [x] Simple prediction of the next integer - [x] Simple predictions v1 - [x] Wrangle the return types to something semi-elegant - [-] Maybe add some caching, e.g., write continuations to file, and read them next time. - [ ] JIT Bayesianism: - [x] Function to predict with a variable number of hypotheses - [ ] Function to start predicting with a small number of hypotheses, and get more if the initial ones aren't enough. - [ ] Add the loop of: start with some small number of sequences, and if these aren't enough, read more. - [ ] ... - [ ] Infrabayesianism x1: Predicting interleaved sequences - [ ] Infrabayesianism x2: Deterministic game of producing a fixed deterministic prediction, and then the adversary picking whatever minimizes your loss - [ ] Write the actor --- An implementation of Infrabayesianism over OEIS sequences. 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. Implementing Infrabayesianism as a game over OEIS sequences. Two parts: 1. Prediction over interleaved sequences. I choose two OEIS sequences, and interleave them: a1, b1, a2, b2. - Now, you don't have hypothesis over the whole set, but two hypothesis over the - I could also have a chemistry like iteration: a1 a2 b1 a3 b2 c1 a4 b3 c2 d1 a5 b4 c3 d2 e1 ................. - And then it would just be computationally absurd to have hypotheses over the whole 2. Game where: You provide a deterministic procedure for estimating the probability of each OEIS sequence giving a list of trailing examples.