Clustering with nature inspired algorithms
Project description
Clustering with nature inspired algorithms
pyriad
offers clustering with a variety of nature inspired algorithms built with Python on top of the deep learning library PyTorch.
You can extend pyriad
according to your own needs. You can implement custom algorithms by extending simple abstract classes.
Pyriad is highly parallelizable and transferable to GPU.
Algorithms
As of today, the following algorithms have been implemented:
- Particle Swarm Optimization (PSO) [1]
- Cuckoo Search (CS) [2]
- Grey Wolf Optimization (GWO) [3]
- Flower Pollination Algorithm (FP) [4]
Installation
- Install PyTorch. You can find it here: PyTorch
pip install pyriad
Examples
You can find examples in examples/
directory
You can also run examples: python examples/pso_iris.py
You might want to export PYTHONPATH=/path/to/this/directory
Contribute
- Implement new algorithms
- Improve code design
- Improve comments and readme
- Tests
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file pyriad-0.1.2.tar.gz
.
File metadata
- Download URL: pyriad-0.1.2.tar.gz
- Upload date:
- Size: 5.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.33.0 CPython/3.6.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ed408289e4ac2e6d04f4b209969bb4efb51781f928f4f1e748733c3fc985ff36 |
|
MD5 | 0c400dc7ab7c6d7f2d8b192244d6b800 |
|
BLAKE2b-256 | a40cf397d4cd336b75e78fce5f0ef59c53cfa0ae0add99c424359fe3efda6c84 |
File details
Details for the file pyriad-0.1.2-py3-none-any.whl
.
File metadata
- Download URL: pyriad-0.1.2-py3-none-any.whl
- Upload date:
- Size: 9.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.33.0 CPython/3.6.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0cf5b3000f0625843a32044a90bdea4dc2b417cbdac811650c851101effddf11 |
|
MD5 | 46e11dbd65b69e46102cc573f74ec145 |
|
BLAKE2b-256 | dd89cd1114719fd7addad454ee6511ca31d99a37cd4121e60e5aeaf3b2bca90b |