pyAutoSpec is a simple library for demonstrating the spectral learning algorithm detailed in the 2014 paper Spectral learning of weighted automata by Borja Balle, Xavier Carreras, Franco M. Luque and Ariadna Quattoni.
You can find an introduction on my blog.
This is a toy implementation of spectral learning:
- it is not optimized
- it is not thoroughly tested
- it is as simple as possible
DO NOT USE IN PRODUCTION
If you need spectral learning in your application use scikit-splearn instead.
To build the library just run
pip install -e .
Or use the provided docker file:
docker build -t pyautospec .
docker run -p 8888:8888 -it pyautospec
and access the Jupyter notebook.
Suppose you have a function f that takes a string and returns a number, you
want to learn a weighted finite automaton that computes f, you do this
from pyautospec import SpectralLearning
alphabet = [ord(c) for c in range(ord('a'), ord('z')+1)]
learner = SpectralLearning(alphabet, 3)
automaton = learner.learn(f)see the Automata Learning example.
The same thing can be done for a function f that takes a number in an interval
and returns a number, in this case you can do this
from math import sin, pi
from pyautospec import FunctionWfa
# learn the sin function in the [0,2π] interval
sin_aut = FunctionWfa().fit(sin, x0=0.0, x1=2*pi, learn_resolution=2)see the Trigonometric Functions example.