Python库 | dfa_identify-3.6.0.tar.gz

# dfa-identify Python library for identifying (learning) minimal DFAs from labeled examples by reduction to SAT. [](https://cloud.drone.io/mvcisback/dfa-identify) [](https://badge.fury.io/py/dfa-identify) [](https://opensource.org/licenses/MIT) **Table of Contents** - [Installation](#installation) - [Usage](#usage) - [Encoding](#encoding) - [Goals and related libraries](#goals-and-related-libraries) # Installation If you just need to use `dfa`, you can just run: `$ pip install dfa` For developers, note that this project uses the [poetry](https://poetry.eustace.io/) python package/dependency management tool. Please familarize yourself with it and then run: `$ poetry install` # Usage `dfa_identify` is centered around the `find_dfa` and `find_dfas` function. Both take in sequences of accepting and rejecting "words", where are word is a sequence of arbitrary python objects. 1. `find_dfas` returns all minimally sized (no `DFA`s exist of size smaller) consistent with the given labeled data. 2. `find_dfa` returns an arbitrary (first) minimally sized `DFA`. The returned `DFA` object is from the [dfa](https://github.com/mvcisback/dfa) library. ```python from dfa_identify import find_dfa accepting = ['a', 'abaa', 'bb'] rejecting = ['abb', 'b'] my_dfa = find_dfa(accepting=accepting, rejecting=rejecting) assert all(my_dfa.label(x) for x in accepting) assert all(not my_dfa.label(x) for x in rejecting) ``` Because words are sequences of arbitrary python objects, the identification problem, with `a` ↦ 0 and `b` ↦ 1, is given below: ```python accepting = [[0], [0, 'z', 0, 0], ['z', 'z']] rejecting = [[0, 'z', 'z'], ['z']] my_dfa = find_dfa(accepting=accepting, rejecting=rejecting) ``` # Minimality There are two forms of "minimality" supported by `dfa-identify`. 1. By default, dfa-identify returns DFAs that have the minimum number of states required to seperate the accepting and rejecting set. 2. If the `order_by_stutter` flag is set to `True`, then the `find_dfas` (lazily) orders the DFAs so that the number of self loops (stuttering transitions) appearing the DFAs decreases. `find_dfa` thus returns a DFA with the most number of self loops given the minimal number of states. # Encoding This library currently uses the encodings outlined in [Heule, Marijn JH, and Sicco Verwer. "Exact DFA identification using SAT solvers." International Colloquium on Grammatical Inference. Springer, Berlin, Heidelberg, 2010.](https://link.springer.com/chapter/10.1007/978-3-642-15488-1_7) and [Ulyantsev, Vladimir, Ilya Zakirzyanov, and Anatoly Shalyto. "Symmetry Breaking Predicates for SAT-based DFA Identification."](https://arxiv.org/abs/1602.05028). The key difference is in the use of the symmetry breaking clauses. Two kinds are exposed. 1. clique (Heule 2010): Partially breaks symmetries by analyzing conflict graph. 2. bfs (Ulyantsev 2016): Breaks all symmetries so that each model corresponds to a unique DFA. # Goals and related libraries There are many other python libraries that perform DFA and other automata inference. 1. [DFA-Inductor-py](https://github.com/ctlab/DFA-Inductor-py) - State of the art passive inference via reduction to SAT (as of 2019). 2. [z3gi](https://gitlab.science.ru.nl/rick/z3gi): Uses SMT backed passive learning algorithm. 3. [lstar](https://pypi.org/project/lstar/): Active learning algorithm based L* derivative. The primary goal of this library is to loosely track the state of the art in passive SAT based inference while providing a simple implementation and API.