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Project for the EPFL cs422 course
The current loader doesn't assume anything about the order of the ngrams and ntokens files, and preloads everything into a large dictionary before creating the required Huffman trees.
We could exploit the fact that the ngrams are sorted to load this more efficiently.
Markov codec is currently optimized for maximum encoding/decoding speed. Memory usage could be reduced by computing some datastructures on the fly instead of storing them at load time.
The current datafile format is very simple and suitable for easy generation from a simple MapReduce job but is highly redundant.
Either support decompression on the fly, so that we can leave them in compressed format, or devise a more efficient storage format. One possibility for that would be to pickle objects.
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