rohaquinlop / automathon Goto Github PK

Change the format to lowercase on variables names.

Improve the code quality

Find unused dependencies and remove them

Reduce the cognitive complexity of the actual functions

Add the following algorithms:

DFA:

• intersection
• difference
• symmetric_difference

NFA:

• intersection

Refactor DFA and NFA definition to make use of dataclasses, replace __init__ with dataclasses.

Refac this...

def __init__(self, name: str, unit_price: float, quantity_on_hand: int = 0):
    self.name = name
    self.unit_price = unit_price
    self.quantity_on_hand = quantity_on_hand

to this...

from dataclasses import dataclass

@dataclass
class InventoryItem:
    """Class for keeping track of an item in inventory."""
    name: str
    unit_price: float
    quantity_on_hand: int = 0

    def total_cost(self) -> float:
        return self.unit_price * self.quantity_on_hand

I've used the tool to generate a cartesian product of 3 finite automata, but the resulting graph is huge and when I try to view the picture, it becomes problematic to understand what is happening because the font is very small and the graph nodes themselves are too huge. It would be great if we had the option to select those graph parameters, or even better, introduce some python graphviz interface to be able to configure the graph plot settings to our preferences.

Similar as I'm doing in complexipy make use of GitHub actions to deploy directly to PyPI

Explore implementations and add the minimize function to the DFA class, this function already exists in the NFA class.
Consider reading the book Automata Theory: An Algorithmic Approach used by @tchaumeny in his article.

According to the formal definition of the NFA that is mentioned in the book Automata and Computability, the NFA contains multiple start states instead of one single initial state as is defined here in the library.

To maintain the formal equality, it is important to consider this as a refactor of the NFA definition.

Explore the different options to create a docs website with all the information about this library

Update documentation with the new methods

Hi,

Thanks for the library!

I generate an NFA with the following data and attempt to remove epsilon transitions.

The result is incorrect: in the produced DFA, it becomes possible to jump from A to D without passing through B and C.

Another issue I've found is that if there are two mirroring epsilon transitions, i.e. from 1 to 2 and from 2 to 1, attempting to remove epsilon transitions breaks with a max recursion error.

Any idea what's happening? If need be I could potentially help with a fix!

Thanks

Considering some of the changes that were made during the code refactoring, it is necessary to update the documentation

Checkout python library for testing called hypothesis

Thanks for your library that I have used to illustrate an article about DFA (see here)!

I actually wanted to use integers to define the states and I found that this is not possible (it fails when calling automata.view(...)), is there any specific reason for this restriction?

It seems that any hashable object could be a state and the graphviz representation could just use str().

Upgrade to the latest rules_python and organize the project structure