No project description provided
Project description
sgGWR
Installation
We recommend installing JAX package for efficient computation. To install sgGWR with JAX, please execute the following on your terminal.
pip install sgGWR[jax]
If you cannot install JAX (e.g., Windows users), you can omit [jax] option.
pip install sgGWR
Reference
Please cite the following article:
- Nishi, H., & Asami, Y. (2024). Stochastic gradient geographical weighted regression (sgGWR): Scalable bandwidth optimization for geographically weighted regression. International Journal of Geographical Information Science, 38(2), 354–380. https://doi.org/10.1080/13658816.2023.2285471
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 Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
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
sggwr-0.1.5-py3-none-any.whl
(26.6 kB
view details)
File details
Details for the file sggwr-0.1.5-py3-none-any.whl.
File metadata
- Download URL: sggwr-0.1.5-py3-none-any.whl
- Upload date:
- Size: 26.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.11.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4742ee33032aeb65f117ee09346cdcf4394cf4bd6df687e85621681876d5cba6
|
|
| MD5 |
4a38e0300d0cf4f6a34d8259d59ebe25
|
|
| BLAKE2b-256 |
a309726d0fab8a08e607c2f156b7b54cf88d4ddf2f48c28cb0594cde6e0d7157
|
