PyTorch-NEAT | PyTorch NEAT builds upon NEAT-Python | Machine Learning library

by uber-research Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | PyTorch-NEAT Summary

PyTorch-NEAT is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. PyTorch-NEAT has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

PyTorch NEAT builds upon NEAT-Python by providing some functions which can turn a NEAT-Python genome into either a recurrent PyTorch network or a PyTorch CPPN for use in HyperNEAT or Adaptive HyperNEAT. We also provide some environments in which to test NEAT and Adaptive HyperNEAT, and a more involved example using the CPPN infrastructure with Adaptive HyperNEAT on a T-maze.

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Quality

Security

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Support

PyTorch-NEAT has a low active ecosystem.

It has 504 star(s) with 101 fork(s). There are 27 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 2 have been closed. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of PyTorch-NEAT is current.

Quality

PyTorch-NEAT has 0 bugs and 0 code smells.

Security

PyTorch-NEAT has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

PyTorch-NEAT code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PyTorch-NEAT is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

PyTorch-NEAT releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

PyTorch-NEAT saves you 1163 person hours of effort in developing the same functionality from scratch.

It has 2625 lines of code, 172 functions and 25 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed PyTorch-NEAT and discovered the below as its top functions. This is intended to give you an instant insight into PyTorch-NEAT implemented functionality, and help decide if they suit your requirements.

Run a test
Evaluate a genome
Create a AdaptiveNet
Create a cppn
Create a AdaptiveLinearNet
Reset the graph
Get coordinate inputs
Resets the weights
Activate a network
Reset the session
Resets activations

Get all kandi verified functions for this library.

PyTorch-NEAT Key Features

No Key Features are available at this moment for PyTorch-NEAT.

PyTorch-NEAT Examples and Code Snippets

No Code Snippets are available at this moment for PyTorch-NEAT.

Community Discussions

Trending Discussions on PyTorch-NEAT

How to import Python package (Pytorch-neat) that is not installable from pip/conda repositories?

QUESTION

How to import Python package (Pytorch-neat) that is not installable from pip/conda repositories?

Asked 2019-Apr-07 at 23:20

I am trying to used Pytorch-neat package https://github.com/uber-research/PyTorch-NEAT but I don't understand the workflow of using it. I already installed python-neat package and I can import it using import neat in my Jupyter notebook. But what should I do with Pytroch-neat code? There is no pytorch-neat package in Conda or pip repositories, so, I guess that this Pytroch-neat code is not compiled and distributed as the Python package for Jupyter notebook. But what should I do with this code? E.g. sample script contains the code:

...

ANSWER

Answered 2019-Apr-07 at 23:20

To import from pytorch_neat you have to clone the repository and manually copy directory pytorch_neat into your site-packages (or any directory in sys.path).

Source https://stackoverflow.com/questions/55563322

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

Vulnerabilities

No vulnerabilities reported

Install PyTorch-NEAT

You can download it from GitHub.
You can use PyTorch-NEAT like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.