dmmilThe library's functioning parameters are described by the parameters contained in the ini files.
The settings_common.ini file describes the general parameters of the system functioning:
[MainParams] processing_type (Novelty filter or Predict) – choose system processing type mode continuous_mode (bool) – if True, then if GUI mode will perform automatically. Otherwise, it’s will need to manually press the "Nxt" button to perform each tact draw_layers (bool) – if True, then in GUI RNN1 and RNN2 layers will be drawn
[Forecasting] (use if processing_type == Predict) predictstepsnum (int) – forecasting horizon
[NoveltyFiltering] (use if processing_type == Novelty filter) Inithistoryperiod (int) – the number of the first elements of the processed sample during which the novelty is not determined (initialization period) novfiltweightsgain (float) – the gain factor of the weights responsible for the novelty data in RNN2 novfiltdetectborder (float) – novelty detection border
[RnnGeometry] – layers and logical fields sizes in RNN1 and RNN2 l (int) m (int) d (int) q (int)
[RnnDataStreaming] sspsubmitinterval (int) – tacts interval for next ssp submitting
[NeuronParams] refractinterval (int) – the number of tacts of neuronal refractoriness after excitation.
The settings_rnn1.ini file file describes RNN1 parameters:
[OutputFields] (struct [int, int, int]) – the number of the layer, the number horizontally and the number vertically of the logical field from which the output data is taken
[IOParams] dictionary_filename – the path to the file with the list of words (word links) processed in the neural network input_data_filename – the path to the file with the processed data encoded in SSP format
[ControlParams] flag_learning (bool) – if True, then the weights of synapses change during the passage of data through the neural network
The settings_rnn2.ini file file describes RNN2 parameters:
[NeuronParams] border_type (Const or Concurrent) – neurons excitation border type border_const_value (float) – border value for border_type == Const border_concurrent_winners (int) – number of winner neurons in one logical field for border_type == Concurrent
[OutputFields] – similarly settings_rnn1.ini