This tool converts a Nondeterministic Finite Automata (NFA) into a Deterministic Finite Automata (DFA)

Overview

nfa-to-dfa

This tool converts a Nondeterministic Finite Automata (NFA) into a Deterministic Finite Automata (DFA)

Using convert.py

You may provide an input NFA in one of two ways.

  1. Define the NFA via a CLI

    • When invoking the program, provide a name for the output file
      • > python convert.py
    • After walking you through the NFA definition, it will create a DFA in the form of .fa
  2. Define the NFA via an input file

    • When invoking the program, provide an input file of type .nfa
      • > python convert.py .nfa
    • This will create a DFA in the form of .fa

Formatting a .nfa file

The input .nfa should be formatted as follows:

  1. The first line holds a set of states the NFA could initially be on, enclosed in curly braces, delimted by commas
  2. The second line holds a set of states that the NFA will accept, enclosed in curly braces, delimted by commas
  3. The following lines will define the state transitions, as a tuple, for each state you wish to define.
    • The first symbol is the current state.
    • The second symbol will be the transition symbol.
    • The following symbol (or symbols, delimted by space) will define the set of all states the automata will transition into.
example.nfa
{1}
{1, 3}

1 a 2 3
1 b 1

2 a 3
2 b 1

3 a 3
3 b 3
You might also like...
Finite difference solution of 2D Poisson equation. Can handle Dirichlet, Neumann and mixed boundary conditions.
Finite difference solution of 2D Poisson equation. Can handle Dirichlet, Neumann and mixed boundary conditions.

Poisson-solver-2D Finite difference solution of 2D Poisson equation Current version can handle Dirichlet, Neumann, and mixed (combination of Dirichlet

Finite Element Analysis
Finite Element Analysis

FElupe - Finite Element Analysis FElupe is a Python 3.6+ finite element analysis package focussing on the formulation and numerical solution of nonlin

Using NumPy to solve the equations of fluid mechanics together with Finite Differences, explicit time stepping and Chorin's Projection methods
Using NumPy to solve the equations of fluid mechanics together with Finite Differences, explicit time stepping and Chorin's Projection methods

Computational Fluid Dynamics in Python Using NumPy to solve the equations of fluid mechanics ๐ŸŒŠ ๐ŸŒŠ ๐ŸŒŠ together with Finite Differences, explicit time

Converts geometry node attributes to built-in attributes

Attribute Converter Simplifies converting attributes created by geometry nodes to built-in attributes like UVs or vertex colors, as a single click ope

Fuzzing tool (TFuzz): a fuzzing tool based on program transformation

T-Fuzz T-Fuzz consists of 2 components: Fuzzing tool (TFuzz): a fuzzing tool based on program transformation Crash Analyzer (CrashAnalyzer): a tool th

Raptor-Multi-Tool - Raptor Multi Tool With Python
Raptor-Multi-Tool - Raptor Multi Tool With Python

Promises ๐Ÿ”ฅ 20 Stars and I'll fix every error that there is 50 Stars and we will

Torchlight2 lan game server tool - A message forwarding tool for Torchlight 2 lan game

Torchlight 2 Lan Game Server Tool A message forwarding tool for Torchlight 2 lan

Simple tool to combine(merge) onnx models.  Simple Network Combine Tool for ONNX.
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

snc4onnx Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools 1.

Owner
Quinn Herden
Quinn Herden
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

null 187 Dec 26, 2022
A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

Multi-Agent-Deep-Deterministic-Policy-Gradients A Pytorch implementation of the multi agent deep deterministic policy gradients(MADDPG) algorithm This

Phil Tabor 159 Dec 28, 2022
Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty

Deep Deterministic Uncertainty This repository contains the code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic

Jishnu Mukhoti 69 Nov 28, 2022
Official pytorch implementation for Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion (CVPR 2022)

Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion This repository contains a pytorch implementation of "Learning to Listen: Modeling

null 50 Dec 17, 2022
An Active Automata Learning Library Written in Python

AALpy An Active Automata Learning Library AALpy is a light-weight active automata learning library written in pure Python. You can start learning auto

TU Graz - SAL Dependable Embedded Systems Lab (DES Lab) 78 Dec 30, 2022
Grow Function: Generate 3D Stacked Bifurcating Double Deep Cellular Automata based organisms which differentiate using a Genetic Algorithm...

Grow Function: A 3D Stacked Bifurcating Double Deep Cellular Automata which differentiates using a Genetic Algorithm... TLDR;High Def Trees that you can mint as NFTs on Solana

Nathaniel Gibson 4 Oct 8, 2022
Applications using the GTN library and code to reproduce experiments in "Differentiable Weighted Finite-State Transducers"

gtn_applications An applications library using GTN. Current examples include: Offline handwriting recognition Automatic speech recognition Installing

Facebook Research 68 Dec 29, 2022
Automatic differentiation with weighted finite-state transducers.

GTN: Automatic Differentiation with WFSTs Quickstart | Installation | Documentation What is GTN? GTN is a framework for automatic differentiation with

null 100 Dec 29, 2022
Converts given image (png, jpg, etc) to amogus gif.

Image to Amogus Converter Converts given image (.png, .jpg, etc) to an amogus gif! Usage Place image in the /target/ folder (or anywhere realistically

Hank Magan 1 Nov 24, 2021
Finite-temperature variational Monte Carlo calculation of uniform electron gas using neural canonical transformation.

CoulombGas This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX,

FermiFlow 9 Mar 3, 2022