Skip to main content

A package to learn linear operators

Project description

SVG Image

Install

To install this package as a dependency, run:

pip install linear-operator-learning

Development

To develop this project, please setup uv by running the following commands:

  1. curl -LsSf https://astral.sh/uv/install.sh | sh
  2. git clone git@github.com:CSML-IIT-UCL/linear_operator_learning.git & cd linear_operator_learning
  3. uv sync --dev
  4. uv run pre-commit install

Optional

Set up your IDE to automatically apply the ruff styling.

Contributing

Please adhere to the following principles while contributing to the project:

  1. Adopt a functional style of programming. Avoid abstractions (classes) like they were plague.
  2. To add a new feature, create a branch and when done open a Pull Request. It is not possible to approve your own PR.
  3. Write tests on the functional level and not on the integration level (which shouldn't matter anyway).
  4. The package contains both numpy and torch based algorithms. Let's keep them separated.
  5. The functions shouldn't change the dtype or device of the inputs (that is, keep a functional approach)
  6. Try to complement your contributions with simple examples to be added in the examples folder

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

linear_operator_learning-0.1.9.tar.gz (166.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

linear_operator_learning-0.1.9-py3-none-any.whl (17.7 kB view details)

Uploaded Python 3

File details

Details for the file linear_operator_learning-0.1.9.tar.gz.

File metadata

File hashes

Hashes for linear_operator_learning-0.1.9.tar.gz
Algorithm Hash digest
SHA256 c95d381e9d8108dab2e6867f334d1a970ab23fc26fa7f5c2074e2ed75f9474f3
MD5 a116cac344542388b4965c3d5a11ee8e
BLAKE2b-256 6dc498b54badd23b261d65d4a9d06d7fcd2ce0f763c4421a1e54bf393238a83a

See more details on using hashes here.

File details

Details for the file linear_operator_learning-0.1.9-py3-none-any.whl.

File metadata

File hashes

Hashes for linear_operator_learning-0.1.9-py3-none-any.whl
Algorithm Hash digest
SHA256 e2d5a3ca07b8d2e67daaa84b89b9fd6cd2d3b3ea0b7c2ca6b8a89e17296624ff
MD5 e387893ee43f0dce036534eb0d8e48a3
BLAKE2b-256 9f1a52a5c88fade9cae0a16d2a74cec66396be97c2c2d9dc1245cbd0732702e6

See more details on using hashes here.

Supported by

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