| This repository contains the software QSynt accompanying the paper "Program Synthesis for the OEIS". The preprint is available [here](https://arxiv.org/abs/2202.11908). |
|
|
| Solutions found during a training run can be inspected in the file |
| `result/full_prog`. |
|
|
| ### Try the Web interface |
| http://grid01.ciirc.cvut.cz/~thibault/synt.html |
|
|
| ### Install on the Ubuntu OS a modified HOL (required) |
| In your /home/your_username directory: |
| |
| ``` |
| sudo apt install rlwrap |
| sudo apt install polyml |
| git clone https://github.com/HOL-Theorem-Prover/HOL |
| cd HOL |
| git checkout 0782c4413311d5debebda3f2e6cac9560911cb64 |
| poly < "tools/smart-configure.sml" |
| cat tools/sequences/kernel tools/sequences/core-theories > shortseq |
| bin/build --seq=shortseq |
| ``` |
| |
| Edit your .bashrc (or .bash_aliases) by adding the following line: |
| PATH=/home/your_username/HOL/bin:$PATH |
| |
| ### Install oeis-synthesis: |
| In this directory, edit the file `dir.sml` by replacing the value of |
| `val selfdir = "/home/thibault/oeis-synthesis"` by |
| `val selfdir = "the_directory_where_this_file_is_located"`. |
| |
| Save the file and run in this directory: |
| ``` |
| Holmake |
| ``` |
| |
| ### Test oeis-synthesis (requires 10GB of ram to run with a timeout of 600.0 seconds): |
| In this directory: |
| ``` |
| rlwrap hol |
| load "synt"; open synt; |
| val _ = synt 60.0 16 [1,2,4,8,16]; |
| val _ = synt 30.0 16 [2,4,8,16,32]; |
| val _ = synt 20.0 16 [1,2,3,4]; |
| ``` |
| |
| Choose the sequence you desire to look for instead of |
| [1,2,4,8,16] and you may set the timeout to another value than 60.0 seconds. |
| The second argument (16) precises the number of generated numbers (predictions). |
| |
| You can set the following flag to prevent polynomial normalization of the program: |
| ``` |
| kernel.polynorm_flag := false; |
| ``` |
| |
| ### Train oeis-syntheis (requires 200GB of ram and 20 cores): |
| In this directory: |
| ``` |
| rlwrap hol |
| load "mcts"; open mcts; |
| expname := "your_experiment_name"; |
| time_opt := SOME 600.0; |
| (* use_mkl := true; if you have installed mkl *) |
| rl_search "" 0; |
| ``` |
| |
| ### Install MKL libary (optional for faster training) |
| Downloading/Installing MKL: |
| ``` |
| Ubuntu 20.04: sudo apt install intel-mkl |
| Ubuntu 18.04: https://github.com/eddelbuettel/mkl4deb |
| ``` |
| Initializing bash variables: |
| ``` |
| export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:$LD_LIBRARY_PATH |
| export LD_LIBRARY_PATH=/opt/intel/lib/intel64:$LD_LIBRARY_PATH |
| sh /opt/intel/mkl/bin/mklvars.sh intel64 |
| ``` |
| |
| In the tnn_in_c directory and run: |
| ``` |
| gcc -o tree tree.c -DMKL_ILP64 -m64 -I/opt/intel/mkl/include -L/opt/intel/lib/intel64 -L/opt/intel/mkl/lib/intel64 -Wl,--no-as-needed -lmkl_intel_ilp64 -lmkl_intel_thread -lmkl_core -liomp5 -lpthread -lm -ldl |
| ``` |
| |