Automata Tactics Mac OS

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Tools to build automata from your custom rule

Project description

Tools to build automata from your custom rule.

This package provides a set of handy tools to programmatically build automata, so you can build NFA、DFA、MinimizedDFA、WFA from any custom rules.

Usage

Mac

Install

Import

See example in examples/NFAfromCustomRule.py

Mac Os Versions

BuildAutomata

characterStruct

Build simple (0)-[a]->(1) automata

unionStruct

Build automata that is an 'or' of two sub-automata (1)<-[a]-(0)-[b]->(1)

concatenationStruct

Build automata that is an 'and' of two sub-automata (0)-[a]->(1)-[b]->(2)

starStruct

Automata Tactics Mac Os Catalina

Build automata that looks like the 'Kleene closure'

skipStruct

Build automata that looks like the 'Kleene closure' but without the loop back (1)<-[ε]-(2), so it only match the token once at most.

repeatRangeStruct

Build automata that will match the same token for several times (0)-[a]->(1)-[a]->(2)-[a]->(3)

repeatStruct

Build automata that will match the same token for n to m times

(0)-[a]->(1)-[a]->(4), (0)-[a]->(2)-[a]->(3)-[a]->(4)

Automata

See example in features/steps/customRule.py

where executor is a function like the one in examples/NFAfromCustomRule.py:

setExecuter

Set an executor to the automata that can freely use state and transition of the automata, and return a boolean value.

setTokenizer

Set an tokenizer to the automata that can transform string to list of string token, which will be used by the executer.

NFAtoDFA

Make automata state transitions not so ambiguous

DFAtoMinimizedDFA

Allow you minify Automata state

Weighted Finite Automata

WFA, it can execute automata use matrix multiplication, so it can be very fast compare to brute force execution, especially when state space is large.

Automata Tactics Mac Os Download

get_word_to_index

Given [['token', 'another'], ['token_in_rule']], return something like

Automata Tactics Mac OS

Automata Tactics Mac Os Download

So we can translate automata state to a matrix.

WFA

Given an automata, a word index like {'token': 0, 'another': 1, ...}, and a function that transform automata to tensor (see example at customRuleDFAToTensor), return a WFA instance.

Development

Environment

Create environment from the text file:

Save env file: conda list --explicit > automataTools-env.txt

Python Path

Create a .env file with content PYTHONPATH=automataTools

Publish

To pypi

To Conda

Resources

Probably the original reference source

Release historyRelease notifications RSS feed

2.0.1

2.0.0

1.1.1

1.1.0

1.0.9

1.0.8

1.0.7

1.0.6

1.0.5

1.0.4

1.0.3

1.0.2

1.0.1

1.0.0

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