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
Owner
Quinn Herden
Quinn Herden
Implementation of paper "Graph Condensation for Graph Neural Networks"

GCond A PyTorch implementation of paper "Graph Condensation for Graph Neural Networks" Code will be released soon. Stay tuned :) Abstract We propose a

Wei Jin 66 Dec 04, 2022
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

Note: the current releases of this toolbox are a beta release, to test working with Haskell's, Python's, and R's code repositories. Metrics provides i

Ben Hamner 1.6k Dec 26, 2022
Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and

Adam Goodge 25 Dec 28, 2022
Space Ship Simulator using python

FlyOver Basic space-ship simulator using python How to run? Just double click run.py What modules do i need? All modules that i currently using is bui

0 Oct 09, 2022
Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.

WECHSEL Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. arXiv: https://arx

Institute of Computational Perception 45 Dec 29, 2022
Official implementation of Self-supervised Graph Attention Networks (SuperGAT), ICLR 2021.

SuperGAT Official implementation of Self-supervised Graph Attention Networks (SuperGAT). This model is presented at How to Find Your Friendly Neighbor

Dongkwan Kim 127 Dec 28, 2022
This project is for a Twitter bot that monitors a bird feeder in my backyard. Any detected birds are identified and posted to Twitter.

Backyard Birdbot Introduction This is a silly hobby project to use existing ML models to: Detect any birds sighted by a webcam Identify whic

Chi Young Moon 71 Dec 25, 2022
Tracing Versus Freehand for Evaluating Computer-Generated Drawings (SIGGRAPH 2021)

Tracing Versus Freehand for Evaluating Computer-Generated Drawings (SIGGRAPH 2021) Zeyu Wang, Sherry Qiu, Nicole Feng, Holly Rushmeier, Leonard McMill

Zach Zeyu Wang 23 Dec 09, 2022
Working demo of the Multi-class and Anomaly classification model using the CLIP feature space

πŸ‘οΈ Hindsight AI: Crime Classification With Clip About For Educational Purposes Only This is a recursive neural net trained to classify specific crime

Miles Tweed 2 Jun 05, 2022
E-RAFT: Dense Optical Flow from Event Cameras

E-RAFT: Dense Optical Flow from Event Cameras This is the code for the paper E-RAFT: Dense Optical Flow from Event Cameras by Mathias Gehrig, Mario Mi

Robotics and Perception Group 71 Dec 12, 2022
MarcoPolo is a clustering-free approach to the exploration of bimodally expressed genes along with group information in single-cell RNA-seq data

MarcoPolo is a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering Overview MarcoPolo

Chanwoo Kim 13 Dec 18, 2022
Code and data (Incidents Dataset) for ECCV 2020 Paper "Detecting natural disasters, damage, and incidents in the wild".

Incidents Dataset See the following pages for more details: Project page: IncidentsDataset.csail.mit.edu. ECCV 2020 Paper "Detecting natural disasters

Ethan Weber 67 Dec 27, 2022
R-package accompanying the paper "Dynamic Factor Model for Functional Time Series: Identification, Estimation, and Prediction"

dffm The goal of dffm is to provide functionality to apply the methods developed in the paper β€œDynamic Factor Model for Functional Time Series: Identi

Sven Otto 3 Dec 09, 2022
Locationinfo - A script helps the user to show network information such as ip address

Description This script helps the user to show network information such as ip ad

Roxcoder 1 Dec 30, 2021
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

Multimodal Deep Learning πŸŽ† πŸŽ† πŸŽ† Announcing the multimodal deep learning repository that contains implementation of various deep learning-based model

Deep Cognition and Language Research (DeCLaRe) Lab 398 Dec 30, 2022
A working implementation of the Categorical DQN (Distributional RL).

Categorical DQN. Implementation of the Categorical DQN as described in A distributional Perspective on Reinforcement Learning. Thanks to @tudor-berari

Florin Gogianu 98 Sep 20, 2022
SOLOv2 on onnx & tensorRT

SOLOv2.tensorRT: NOTE: code based on WXinlong/SOLO add support to TensorRT inference onnxruntime tensorRT full_dims and dynamic shape postprocess with

47 Nov 26, 2022
[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.

MiVOS (CVPR 2021) - Mask Propagation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [arXiv] [Paper PDF] [Project Page] [Papers with Code] This repo impleme

Rex Cheng 106 Jan 03, 2023
Segcache: a memory-efficient and scalable in-memory key-value cache for small objects

Segcache: a memory-efficient and scalable in-memory key-value cache for small objects This repo contains the code of Segcache described in the followi

TheSys Group @ CMU CS 78 Jan 07, 2023
Discovering Dynamic Salient Regions with Spatio-Temporal Graph Neural Networks

Discovering Dynamic Salient Regions with Spatio-Temporal Graph Neural Networks This is the official code for DyReg model inroduced in Discovering Dyna

Bitdefender Machine Learning 11 Nov 08, 2022