Visual Automata is a Python 3 library built as a wrapper for Caleb Evans' Automata library to add more visualization features.

Overview

Latest Version Supported Python versions Downloads

Visual Automata

Copyright 2021 Lewi Lie Uberg
Released under the MIT license

Visual Automata is a Python 3 library built as a wrapper for Caleb Evans' Automata library to add more visualization features.

Contents

Prerequisites

pip install automata-lib
pip install pandas
pip install graphviz
pip install colormath
pip install jupyterlab

Installing

pip install visual-automata

VisualDFA

Importing

Import needed classes.

from automata.fa.dfa import DFA

from visual_automata.fa.dfa import VisualDFA

Instantiating DFAs

Define an automata-lib DFA that can accept any string ending with 00 or 11.

dfa = VisualDFA(
    states={"q0", "q1", "q2", "q3", "q4"},
    input_symbols={"0", "1"},
    transitions={
        "q0": {"0": "q3", "1": "q1"},
        "q1": {"0": "q3", "1": "q2"},
        "q2": {"0": "q3", "1": "q2"},
        "q3": {"0": "q4", "1": "q1"},
        "q4": {"0": "q4", "1": "q1"},
    },
    initial_state="q0",
    final_states={"q2", "q4"},
)

Converting

An automata-lib DFA can be converted to a VisualDFA.

Define an automata-lib DFA that can accept any string ending with 00 or 11.

dfa = DFA(
    states={"q0", "q1", "q2", "q3", "q4"},
    input_symbols={"0", "1"},
    transitions={
        "q0": {"0": "q3", "1": "q1"},
        "q1": {"0": "q3", "1": "q2"},
        "q2": {"0": "q3", "1": "q2"},
        "q3": {"0": "q4", "1": "q1"},
        "q4": {"0": "q4", "1": "q1"},
    },
    initial_state="q0",
    final_states={"q2", "q4"},
)

Convert automata-lib DFA to VisualDFA.

dfa = VisualDFA(dfa)

Minimal-DFA

Creates a minimal DFA which accepts the same inputs as the old one. Unreachable states are removed and equivalent states are merged. States are renamed by default.

new_dfa = VisualDFA(
    states={'q0', 'q1', 'q2'},
    input_symbols={'0', '1'},
    transitions={
        'q0': {'0': 'q0', '1': 'q1'},
        'q1': {'0': 'q0', '1': 'q2'},
        'q2': {'0': 'q2', '1': 'q1'}
    },
    initial_state='q0',
    final_states={'q1'}
)
new_dfa.table
      0    1
→q0  q0  *q1
*q1  q0   q2
q2   q2  *q1
new_dfa.show_diagram()

alt text

minimal_dfa = VisualDFA.minify(new_dfa)
minimal_dfa.show_diagram()

alt text

minimal_dfa.table
                0        1
→{q0,q2}  {q0,q2}      *q1
*q1       {q0,q2}  {q0,q2}

Transition Table

Outputs the transition table for the given DFA.

dfa.table
       0    1
→q0   q3   q1
q1    q3  *q2
*q2   q3  *q2
q3   *q4   q1
*q4  *q4   q1

Check input strings

1001 does not end with 00 or 11, and is therefore Rejected

dfa.input_check("1001")
          [Rejected]                         
Step: Current state: Input symbol: New state:
1                →q0             1         q1
2                 q1             0         q3
3                 q3             0        *q4
4                *q4             1         q1

10011 does end with 11, and is therefore Accepted

dfa.input_check("10011")
          [Accepted]                         
Step: Current state: Input symbol: New state:
1                →q0             1         q1
2                 q1             0         q3
3                 q3             0        *q4
4                *q4             1         q1
5                 q1             1        *q2

Show Diagram

For IPython dfa.show_diagram() may be used.
For a python script dfa.show_diagram(view=True) may be used to automatically view the graph as a PDF file.

dfa.show_diagram()

alt text

The show_diagram method also accepts input strings, and will return a graph with gradient red arrows for Rejected results, and gradient green arrows for Accepted results. It will also display a table with transitions states stepwise. The steps in this table will correspond with the [number] over each traversed arrow.

Please note that for visual purposes additional arrows are added if a transition is traversed more than once.

dfa.show_diagram("1001")
          [Rejected]                         
Step: Current state: Input symbol: New state:
1                →q0             1         q1
2                 q1             0         q3
3                 q3             0        *q4
4                *q4             1         q1

alt text

dfa.show_diagram("10011")
          [Accepted]                         
Step: Current state: Input symbol: New state:
1                →q0             1         q1
2                 q1             0         q3
3                 q3             0        *q4
4                *q4             1         q1
5                 q1             1        *q2

alt text

Authors

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

You might also like...
An open-source NLP research library, built on PyTorch.
An open-source NLP research library, built on PyTorch.

An Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks. Quic

An open-source NLP research library, built on PyTorch.
An open-source NLP research library, built on PyTorch.

An Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks. Quic

A deep learning-based translation library built on Huggingface transformers

DL Translate A deep learning-based translation library built on Huggingface transformers and Facebook's mBART-Large 💻 GitHub Repository 📚 Documentat

Natural Language Processing library built with AllenNLP 🌲🌱
Natural Language Processing library built with AllenNLP 🌲🌱

Custom Natural Language Processing with big and small models 🌲🌱

A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python.

Multilingual Latent Dirichlet Allocation (LDA) Pipeline This project is for text clustering using the Latent Dirichlet Allocation (LDA) algorithm. It

This repository contains Python scripts for extracting linguistic features from Filipino texts.

Filipino Text Linguistic Feature Extractors This repository contains scripts for extracting linguistic features from Filipino texts. The scripts were

A pytorch implementation of the ACL2019 paper
A pytorch implementation of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".

RE2 This is a pytorch implementation of the ACL 2019 paper "Simple and Effective Text Matching with Richer Alignment Features". The original Tensorflo

Code for paper "Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features"

Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features Train python main.py --dataset brazil-flights C

An easy to use, user-friendly and efficient code for extracting OpenAI CLIP (Global/Grid) features from image and text respectively.

Extracting OpenAI CLIP (Global/Grid) Features from Image and Text This repo aims at providing an easy to use and efficient code for extracting image &

Comments
  • FrozenNFA constructor attempts to call deepcopy on frozendicts

    FrozenNFA constructor attempts to call deepcopy on frozendicts

    The VisualNFA constructor attempts to create a deep copy of the passed nfa, especially the transitions dictionary: https://github.com/lewiuberg/visual-automata/blob/3ea0cdc4de9d3919250919b70fbc036d75120a85/visual_automata/fa/nfa.py#L469

    The deepcopy method is monkeypatched onto dict via curse: https://github.com/lewiuberg/visual-automata/blob/3ea0cdc4de9d3919250919b70fbc036d75120a85/visual_automata/fa/nfa.py#L32

    However, automata-lib 7.0.1 returns a frozendict from the frozendict package instead, so the method call fails. It is not clear if copying the frozendict is at all necessary; deepcopy returns the object as-is.

    MRE

    Using most recent versions:

    • automata-lib 7.0.1
    • visual_automata 1.1.1
    from automata.fa.nfa import NFA
    from visual_automata.fa.nfa import VisualNFA
    
    nfa = NFA(states={"q0"}, input_symbols={"i0"}, transitions={"q0": {"i0": {"q0"}}}, initial_state="q0",
              final_states={"q0"})
    VisualNFA(nfa).show_diagram(view=True)
    

    Expected Behavior

    The automaton is shown.

    Actual Behavior

    Traceback (most recent call last):
      File "/path/to/scratch_1.py", line 6, in <module>
        VisualNFA(nfa).show_diagram(view=True)
      File "/path/to/site-packages/visual_automata/fa/nfa.py", line 619, in show_diagram
        all_transitions_pairs = self._transitions_pairs(self.nfa.transitions)
      File "/path/to/site-packages/visual_automata/fa/nfa.py", line 469, in _transitions_pairs
        all_transitions = all_transitions.deepcopy()
    AttributeError: 'frozendict.frozendict' object has no attribute 'deepcopy'
    
    opened by no-preserve-root 3
  • VisualDFA constructor implicitly checks wrapped automaton cardinality

    VisualDFA constructor implicitly checks wrapped automaton cardinality

    The VisualDFA constructor checks the dfa parameter using https://github.com/lewiuberg/visual-automata/blob/3ea0cdc4de9d3919250919b70fbc036d75120a85/visual_automata/fa/dfa.py#L34

    This checks if dfa is truthy. Since the DFA class defines a __len__ method (and no __bool__), is is truthy iff len(dfa) != 0. Unfortunately, the length checks the dfa's cardinality, i.e., the size if the input language. For infinite-language DFAs, an exception is then raised. As a result, infinite DFAs cannot be visualized.

    This could be fixed by testing if dfa is None. VisualNFA is not affected since NFA does not define a __len__ method at the moment, but would fail if a similar method would be added to NFA.

    MRE

    Using most recent versions:

    • automata-lib 7.0.1
    • visual_automata 1.1.1
    from automata.fa.dfa import DFA
    from visual_automata.fa.dfa import VisualDFA
    
    dfa = DFA(states={"q0"}, input_symbols={"i0"}, transitions={"q0": {"i0": "q0"}}, initial_state="q0",
              final_states={"q0"})
    VisualDFA(dfa).show_diagram(view=True)
    

    Expected Behavior

    The automaton is shown.

    Actual Behavior

    Traceback (most recent call last):
      File "/path/to/scratch_1.py", line 6, in <module>
        VisualDFA(dfa).show_diagram(view=True)
      File "/path/to/site-packages/visual_automata/fa/dfa.py", line 34, in __init__
        if dfa:
      File "/path/to/site-packages/automata/fa/dfa.py", line 160, in __len__
        return self.cardinality()
      File "/path/to/site-packages/automata/fa/dfa.py", line 792, in cardinality
        raise exceptions.InfiniteLanguageException("The language represented by the DFA is infinite.")
    automata.base.exceptions.InfiniteLanguageException: The language represented by the DFA is infinite.
    

    Workaround

    Manually copying the automaton works:

    VisualDFA(states=dfa.states, input_symbols=dfa.input_symbols, transitions=dfa.transitions,
              initial_state=dfa.initial_state, final_states=dfa.final_states).show_diagram(view=True)
    
    opened by no-preserve-root 1
Releases(1093bea)
Owner
Lewi Uberg
Lewi Uberg
The Sudachi synonym dictionary in Solar format.

solr-sudachi-synonyms The Sudachi synonym dictionary in Solar format. Summary Run a script that checks for updates to the Sudachi dictionary every hou

Karibash 3 Aug 19, 2022
تولید اسم های رندوم فینگیلیش

karafs کرفس تولید اسم های رندوم فینگیلیش installation ➜ pip install karafs usage دو زبانه ➜ karafs -n 10 توت فرنگی بی ناموس toot farangi-ye bi_namoos

Vaheed NÆINI (9E) 36 Nov 24, 2022
MRC approach for Aspect-based Sentiment Analysis (ABSA)

B-MRC MRC approach for Aspect-based Sentiment Analysis (ABSA) Paper: Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extracti

Phuc Phan 1 Apr 05, 2022
Club chatbot

Chatbot Club chatbot Instructions to get the Chatterbot working Step 1. First make sure you are using a version of Python 3 or newer. To check your ve

5 Mar 07, 2022
ConvBERT: Improving BERT with Span-based Dynamic Convolution

ConvBERT Introduction In this repo, we introduce a new architecture ConvBERT for pre-training based language model. The code is tested on a V100 GPU.

YITUTech 237 Dec 10, 2022
Ukrainian TTS (text-to-speech) using Coqui TTS

title emoji colorFrom colorTo sdk app_file pinned Ukrainian TTS 🐸 green green gradio app.py false Ukrainian TTS 📢 🤖 Ukrainian TTS (text-to-speech)

Yurii Paniv 85 Dec 26, 2022
aMLP Transformer Model for Japanese

aMLP-japanese Japanese aMLP Pretrained Model aMLPとは、Liu, Daiらが提案する、Transformerモデルです。 ざっくりというと、BERTの代わりに使えて、より性能の良いモデルです。 詳しい解説は、こちらの記事などを参考にしてください。 この

tanreinama 13 Aug 11, 2022
🤕 spelling exceptions builder for lazy people

🤕 spelling exceptions builder for lazy people

Vlad Bokov 3 May 12, 2022
숭실대학교 컴퓨터학부 전공종합설계프로젝트

✨ 시각장애인을 위한 버스도착 알림 장치 ✨ 👀 개요 현대 사회에서 대중교통 위치 정보를 이용하여 사람들이 간단하게 이용할 대중교통의 정보를 얻고 쉽게 대중교통을 이용할 수 있다. 해당 정보는 각종 어플리케이션과 대중교통 이용시설에서 위치 정보를 제공하고 있지만 시각

taegyun 3 Jan 25, 2022
Beyond Paragraphs: NLP for Long Sequences

Beyond Paragraphs: NLP for Long Sequences

AI2 338 Dec 02, 2022
Codes for processing meeting summarization datasets AMI and ICSI.

Meeting Summarization Dataset Meeting plays an essential part in our daily life, which allows us to share information and collaborate with others. Wit

xcfeng 39 Dec 14, 2022
A single model that parses Universal Dependencies across 75 languages.

A single model that parses Universal Dependencies across 75 languages. Given a sentence, jointly predicts part-of-speech tags, morphology tags, lemmas, and dependency trees.

Dan Kondratyuk 189 Nov 29, 2022
A repo for materials relating to the tutorial of CS-332 NLP

CS-332-NLP A repo for materials relating to the tutorial of CS-332 NLP Contents Tutorial 1: Introduction Corpus Regular expression Tokenization Tutori

Alok singh 9 Feb 15, 2022
customer care chatbot made with Rasa Open Source.

Customer Care Bot Customer care bot for ecomm company which can solve faq and chitchat with users, can contact directly to team. 🛠 Features Basic E-c

Dishant Gandhi 23 Oct 27, 2022
This repo contains simple to use, pretrained/training-less models for speaker diarization.

PyDiar This repo contains simple to use, pretrained/training-less models for speaker diarization. Supported Models Binary Key Speaker Modeling Based o

12 Jan 20, 2022
Machine translation models released by the Gourmet project

Gourmet Models Overview The Gourmet project has released several machine translation models to translate low-resource languages. This repository conta

Edinburgh NLP 5 Dec 08, 2021
Mycroft Core, the Mycroft Artificial Intelligence platform.

Mycroft Mycroft is a hackable open source voice assistant. Table of Contents Getting Started Running Mycroft Using Mycroft Home Device and Account Man

Mycroft 6.1k Jan 09, 2023
Two-stage text summarization with BERT and BART

Two-Stage Text Summarization Description We experiment with a 2-stage summarization model on CNN/DailyMail dataset that combines the ability to filter

Yukai Yang (Alexis) 6 Oct 22, 2022
Use PaddlePaddle to reproduce the paper:mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

MT5_paddle Use PaddlePaddle to reproduce the paper:mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer English | 简体中文 mT5: A Massively

2 Oct 17, 2021
This project converts your human voice input to its text transcript and to an automated voice too.

Human Voice to Automated Voice & Text Introduction: In this project, whenever you'll speak, it will turn your voice into a robot voice and furthermore

Hassan Shahzad 3 Oct 15, 2021