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

v.1.1.9

• Bug fixes
• New features
• Optimizations

v1.1

Alergia is implemented and added to AALpy

• Efficient passive learning of Markov Chains and Markov Decision Processes
• Simple to use, just pass the data to the run_Alergia
• Active version of Alergia is also included

v.1.0.5

• Add new eq. oracle (Combinatorial Test Set Coverage)
• Fix minor bugs
• Make stochastic learning more extensible by introducing custom Comparability Check class

v.1.0.3

Fix minor bugs

• invoke post() after finding a counterexample

v1.0.0

Time to start learning automata.

New releases with minor changes and functionality additions will come out frequently.
Next major release, containing new learning algorithms and many other features will come out later this year.

Wiki is still work in progress, but for average user current state of Wiki and Examples.py should be enough to get conformable with AALpy.