Commit 2f7065b1 authored by Thomas Schiex's avatar Thomas Schiex
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parent 61cc3d2f
rrn-17.csv.xz is a compressed csv file with the hardest 17 hints RRN test set problems.
train-sufficient-statistics/ contains precomputed sufficient statistics built from 8,000 samples from the RRN training set. These can be computed in linear time in the sample size. A is for exact data, B is for LeNet decoded images.
lambdas/ contains precomputed values of lambda (using a dedicated grid search in lambda log-space).
PEMRF.py is the implementation of PE_MRF/ADMM with L1, L2 and L1/L2 norms.
The main programs are Sudoku-train-validate.py and Visual-Sudoku-train-validate.py. They will train the CFN parameters from the sufficient statistics and the value of lambda, produce a CFN and solve test set instances using it (either from exact hints or from Visual hints).
Learned-CFN-13000-3.5624615290574217.cfn.gz contains a CFN learned with 13,000 samples. We will have a look into it.
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Main program is MNIST_sudoku_CFN.py and it takes as argument a grid number.
Same game: replace the xxx? with suitable statements.
data/ contains MNIST images.
MNIST_test_marginals contains the 'pickled' output of LeNet on every test digit in MNIST test set (organized as a per digit list of outputs per instance of the digit). An output itself is a numpy array of negated logits, that can directly be used as scores to minimize).
MNIST_test_indices is just a mapping to find the image index that corresponds to evey output.
These 2 files have been produced using MNIST_train.py (PyTorch).
The program itself is in MNIST_sudoku_CP.py. It takes one argument: a grid number in SAT-Net test set (0 to 999).
The satnet.csv.xz file contains hint/solution pairs as used in the SAT-Net paper.
Just replace the missing xxx? in Sudoku.py by suitable toulbar2 API calls to solve the 1st Sudoku in the SAT-Net test set.
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