Turing_Machine | Turing Machine - an abstract performer

by tivole Python Version: Current License: MIT

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kandi X-RAY | Turing_Machine Summary

Turing_Machine is a Python library. Turing_Machine has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Turing_Machine build file is not available. You can download it from GitHub.

Turing Machine (TM) - an abstract performer (abstract computing machine). It was proposed by Alan Turing in 1936 to formalize the concept of an algorithm. The Turing Machine is an extension of the finite state machine and, according to the Church-Turing thesis, is able to simulate all performers (by setting transition rules) that somehow implement the process of stepwise calculation in which each step of the calculation is quite elementary. That is, any intuitive algorithm can be implemented using some Turing machine.

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Turing_Machine has a low active ecosystem.

It has 7 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Turing_Machine has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Turing_Machine is current.

Quality

Turing_Machine has 0 bugs and 0 code smells.

Security

Turing_Machine has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Turing_Machine code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Turing_Machine is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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Turing_Machine releases are not available. You will need to build from source code and install.

Turing_Machine has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

It has 254 lines of code, 7 functions and 2 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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Turing_Machine Key Features

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Turing_Machine Examples and Code Snippets

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Community Discussions

Trending Discussions on Turing_Machine

What is the most efficient way to represent the infinite tape of a Turing machine in Python?

QUESTION

What is the most efficient way to represent the infinite tape of a Turing machine in Python?

Asked 2017-Mar-30 at 07:36

A Turing machine manipulates symbols on an infinite strip of tape according to a table of rules.

In my case entries on the tape can be either 0's and 1's (binary).

The tape starts off 'empty' with an infinity of 0's in both positive and negative directions. This tape gets written on based on the rules that the machine follows, potentially adding 0's or 1's to the unwritten edges.

Since the tape values are binary is there a most efficient way to represent them in memory as the machine writes to the tape, adding new values to the left or right? Obviously I don't need to represent the infinite zeros (unwritten tape), but I do need to expand the data structure as new values are added to the ends of the tape.

A python list could do the trick, shown here inserting a 1 on the left:

...

ANSWER

Answered 2017-Mar-30 at 07:36

Given that the tape is infinite at both ends, you will have to consider the (-inf, -1] range of indices too. The best representation, IMO, would be a pair of lists - one for the non-negative range and the other for the negative range (with indexing reversed). As you move beyond the allocated range of the tape, you will simply have to append to the respective list (which, unlike prepending to a list, is a fast operation).

A draft implementation (using an enhanced version of GrowingList from this answer):

Source https://stackoverflow.com/questions/43110398

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Turing_Machine

Now you can use Turing_Machine.
Run git clone https://github.com/tivole/Turing_Machine.git to clone repository.
Run cd Turing_Machine/src to change directory.
Run pip install -r requirements.txt to install required libraries.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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