Skip to main content

No project description provided

Project description

About MultiNEAT

This is a fork of the original multineat library.

MultiNEAT is a portable software library for performing neuroevolution, a form of machine learning that trains neural networks with a genetic algorithm. It is based on NEAT, an advanced method for evolving neural networks through complexification. The neural networks in NEAT begin evolution with very simple genomes which grow over successive generations. The individuals in the evolving population are grouped by similarity into species, and each of them can compete only with the individuals in the same species.

The combined effect of speciation, starting from the simplest initial structure and the correct matching of the genomes through marking genes with historical markings yields an algorithm which is proven to be very effective in many domains and benchmarks against other methods.

NEAT was developed around 2002 by Kenneth Stanley in the University of Texas at Austin.

License

GNU Lesser General Public License v3.0

Documentation

http://multineat.com/docs.html

Building and installation instructions

To install as a python library

pip install .

To install as a cpp library

mkdir build && cd build
cmake ..
make -j4
(sudo) make install

Installing options:

  • if you want to install the release version without debugging symbols, add this option to the cmake command:

    cmake .. -DCMAKE_BUILD_TYPE=Release
    
  • if you want to install the multineat in a different folder, add this option to the cmake command:

    cmake .. -CMAKE_INSTALL_PREFIX=/path/to/install/folder/
    

These options may be combined togheter

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

multineat-0.12-cp312-cp312-win_amd64.whl (455.8 kB view hashes)

Uploaded CPython 3.12 Windows x86-64

multineat-0.12-cp312-cp312-musllinux_1_1_x86_64.whl (1.1 MB view hashes)

Uploaded CPython 3.12 musllinux: musl 1.1+ x86-64

multineat-0.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (610.9 kB view hashes)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

multineat-0.12-cp312-cp312-macosx_11_0_arm64.whl (577.8 kB view hashes)

Uploaded CPython 3.12 macOS 11.0+ ARM64

multineat-0.12-cp312-cp312-macosx_10_14_x86_64.whl (644.5 kB view hashes)

Uploaded CPython 3.12 macOS 10.14+ x86-64

multineat-0.12-cp311-cp311-win_amd64.whl (452.4 kB view hashes)

Uploaded CPython 3.11 Windows x86-64

multineat-0.12-cp311-cp311-musllinux_1_1_x86_64.whl (1.1 MB view hashes)

Uploaded CPython 3.11 musllinux: musl 1.1+ x86-64

multineat-0.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (608.7 kB view hashes)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

multineat-0.12-cp311-cp311-macosx_11_0_arm64.whl (577.2 kB view hashes)

Uploaded CPython 3.11 macOS 11.0+ ARM64

multineat-0.12-cp311-cp311-macosx_10_14_x86_64.whl (629.7 kB view hashes)

Uploaded CPython 3.11 macOS 10.14+ x86-64

multineat-0.12-cp310-cp310-win_amd64.whl (451.4 kB view hashes)

Uploaded CPython 3.10 Windows x86-64

multineat-0.12-cp310-cp310-musllinux_1_1_x86_64.whl (1.1 MB view hashes)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

multineat-0.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (607.5 kB view hashes)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

multineat-0.12-cp310-cp310-macosx_11_0_arm64.whl (575.8 kB view hashes)

Uploaded CPython 3.10 macOS 11.0+ ARM64

multineat-0.12-cp310-cp310-macosx_10_14_x86_64.whl (628.4 kB view hashes)

Uploaded CPython 3.10 macOS 10.14+ x86-64

multineat-0.12-cp39-cp39-win_amd64.whl (428.6 kB view hashes)

Uploaded CPython 3.9 Windows x86-64

multineat-0.12-cp39-cp39-musllinux_1_1_x86_64.whl (1.1 MB view hashes)

Uploaded CPython 3.9 musllinux: musl 1.1+ x86-64

multineat-0.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (608.0 kB view hashes)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

multineat-0.12-cp39-cp39-macosx_11_0_arm64.whl (575.9 kB view hashes)

Uploaded CPython 3.9 macOS 11.0+ ARM64

multineat-0.12-cp39-cp39-macosx_10_14_x86_64.whl (628.4 kB view hashes)

Uploaded CPython 3.9 macOS 10.14+ x86-64

multineat-0.12-cp38-cp38-win_amd64.whl (451.3 kB view hashes)

Uploaded CPython 3.8 Windows x86-64

multineat-0.12-cp38-cp38-musllinux_1_1_x86_64.whl (1.1 MB view hashes)

Uploaded CPython 3.8 musllinux: musl 1.1+ x86-64

multineat-0.12-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (607.3 kB view hashes)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

multineat-0.12-cp38-cp38-macosx_11_0_arm64.whl (576.0 kB view hashes)

Uploaded CPython 3.8 macOS 11.0+ ARM64

multineat-0.12-cp38-cp38-macosx_10_14_x86_64.whl (628.3 kB view hashes)

Uploaded CPython 3.8 macOS 10.14+ x86-64

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page