IWT (Iterative Weighted Thresholding) feature selection classifier based on PyTorch and scikit-learn.
Project description
IWT Classifier
A PyTorch-based feature selection classifier implementing Iterative Weighted Thresholding (IWT).
Adapted from HIWT(https://github.com/jianglanfan/HIWT-GSC)
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
iwt_pytorch-0.1.4.tar.gz
(53.2 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file iwt_pytorch-0.1.4.tar.gz.
File metadata
- Download URL: iwt_pytorch-0.1.4.tar.gz
- Upload date:
- Size: 53.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.11.7 {"installer":{"name":"uv","version":"0.11.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":null,"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
9391534cb5ddb1235070d0ed34fba7109416bc3fcd2c234626b08b9487f5cf53
|
|
| MD5 |
7539b28bf320e2e151ddd742002620c2
|
|
| BLAKE2b-256 |
e664cd0043dd3a08efb969873b4616dbb45953897e5573bd285ef759238ef4c5
|
File details
Details for the file iwt_pytorch-0.1.4-py3-none-any.whl.
File metadata
- Download URL: iwt_pytorch-0.1.4-py3-none-any.whl
- Upload date:
- Size: 5.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.11.7 {"installer":{"name":"uv","version":"0.11.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":null,"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
37e16055d7c2830b25dcf4fef7b49dd99663ab8cd8f7a306c25636950898b6c5
|
|
| MD5 |
07fc961871d23c21e1369dae040c0113
|
|
| BLAKE2b-256 |
2750af5844c9ff9cfec066089bfe7a8e65c6f81076df8e29ff0d6ed2be49629f
|