Use mutual information and accelerated gradient method to filter out and optimize nonconvex sparse learning problems on large genetic data based on bed/bim/fam. Multiprocessing is now available.
Project description
MI_AG
Use mutual information and accelerated gradient method to filter out and optimize nonconvex sparse learning problems on large genetic data based on bed/bim/fam. The corresponding paper is coming soon...
Release history Release notifications | RSS feed
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
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 MI_AG-0.6.6.tar.gz.
File metadata
- Download URL: MI_AG-0.6.6.tar.gz
- Upload date:
- Size: 46.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a08ac507004772b66e0e94ad834c2a606356697924c3f0e48a8e8953658e9604
|
|
| MD5 |
1d9805b8977e818aee6c36631fa93f6d
|
|
| BLAKE2b-256 |
b4cfe71acfa8f58699f55077319e5e1d9db8c637cc66b15aadbbd9bfbbf35f8d
|
File details
Details for the file MI_AG-0.6.6-py3-none-any.whl.
File metadata
- Download URL: MI_AG-0.6.6-py3-none-any.whl
- Upload date:
- Size: 34.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
722d65062194c7b4ac64a47bc5be70e55d0d6bd92dd58da69540f7ef9c33947c
|
|
| MD5 |
83229bace3e46d9dc251480c39d24467
|
|
| BLAKE2b-256 |
a40c1372e115bc8ee1df876235c19dbb84148e436d031bd922e43e2c4ee5a56b
|
