AC2 is an amino acid compressor that uses a neural network to do the mixing of experts.
AC2 depends on make and gcc.
cd src
make
NOTE: The default compilation options use micro-architecture dependent instructions, because AC2 greatly benefits from vector instructions. This means that due to the different floating point accuracies (e.g. fused multiply–add) the compressed file might not decompress with a binary compressed in a different computer or with a different compiler version or options. To ensure the file decompresses, use binaries (AC2 and AD2) that were compiled in the same environment.
Reference free:
# Compression of file BT, using level 7, learning rate 0.16 and 80 hidden nodes.
./AC2 -l 7 -lr 0.16 -hs 80 BT
# Decompression
./AD2 BT.co
A complete description of the parameters can be read by invoking:
./AC2 -h
./AD2 -h
The mixer interface and implementation is done in:
mix.h
mix.c
The neural network implementation used by the mixer is done in:
nn.h
nn.c
fastonebigheader.h contains fast math functions, and is used by the neural network and the mixer.
NOTE: #include "defs.h" should be removed from nn.h.
With these five files integrated into the project, the compressor now needs to #include "mix.h" in the file where the mixing will occur.
The mixer is initialized with: mix_state_t *mxs = mix_init(NUMBER_OF_MODELS, ALPHABET_SIZE, NUMBER_OF_HIDDEN_NODES);
Every time a new symbol is seen the mixing is done with const float* y = mix(mxs, probs);.
probs[MODEL_IDX][SYMBOL_IDX]is a matrix with the probabilities (]0,1[) for each model and for each symbol in the alphabet. The return value contains the non-normalized probabilities for each symbol. One way of normalizing these probabilities is to divide each by the total sum.
Training must be done after invoking the mixing with mix_update_state(mxs, probs, sym, lr);. Where sym is the actual symbol seen encoded as an int [0, ALPHABET_SIZE[ andlris the learning rate.
Finally, the mixer can be freed with mix_free(mxs);
If you use AC2, please cite:
- Milton Silva, Diogo Pratas, Armando J Pinho, "AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models", Entropy, Volume 23, Issue 5, 2021, https://doi.org/10.3390/e23050530