A mixer based on stacked generalization, using neural networks. Used in GeCo3 and in AC2 with small modifications.
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.
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);