Training Algorithm¶
The training code evaluates each input using :func:’lapart.train.lapart_train’
The supervised training algorithm used known inputs and outputs, and free parameters to create templates TA, TB, and L.
-
train.
lapArt_train
(xA, xB, rhoA=0.9, rhoB=0.9, beta=1e-06, alpha=1.0, nep=1, memory_folder='', update_templates=True, normalize_data=True)[source]¶ Train LAPART Algorithm
Parameters: - xA – A-Side Input Matrix (float)
- xB – B-Side Input Matrix (float)
- rhoA – A-Side free parameter (float)
- rhoB – B-Side free parameter (float)
- beta – Learning rate free parameter (float)
- alpha – Choice Parameter (float)
- nep – Number of epochs (integer)
- memory_folder – Folder to store memory (string)
- update_templates – Command to update or create new templates (boolean)
Return TA: A-Side template matrix (float)
Return TB: B-Side template matrix (float)
Return L: Associator matrix (float)
Return elapsed_time: Seconds to complete training (float)