v.1.1.13

Added passive learning of Stochastic Mealy Machines (SMMs)

Experimental setting which adapts Alergia for learning of SMMs. Active SMM learning is for the most part more sample-efficient than active MDP learning, but in the passive setting we cannot compare sample efficiency only the quality of the learned model. From initial experiments passive SMM learning is for the most part as precise as passive MDP learning, but in some cases it is even less precise. However, if the system that was used to generate data for passive learning has many input/output pairs originating from the same state, or can be efficiently encoded as SMM, passive SMM learning seems to be more precise. Note that this conclusions are made based on few experiments.

Other Changes

• minor usability tweaks
• Alergia implicit delete of data structures
• optimization of FPTA creation