Skip to main content

A package to learn linear operators

Project description

Linear Operator Learning

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-0.1.8.tar.gz (61.4 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-0.1.8-py3-none-any.whl (14.0 kB view details)

Uploaded Python 3

File details

Details for the file linear_operator_learning-0.1.8.tar.gz.

File metadata

File hashes

Hashes for linear_operator_learning-0.1.8.tar.gz
Algorithm Hash digest
SHA256 fec20162086c3814ddb82267661f6b78a55011b6d3180adb6ea716617c1c7e69
MD5 15f9ca4a05ec17b00f2a5f3a0e42b1d3
BLAKE2b-256 0cb8c69b89dda1c80058bff4ad2e6d6482bbbf03fa7d132d04b4931bf30594f2

See more details on using hashes here.

File details

Details for the file linear_operator_learning-0.1.8-py3-none-any.whl.

File metadata

File hashes

Hashes for linear_operator_learning-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 6eda2f6975db1350c30ecd00467d87032aa67f55b3f31603a5149a9b54bfd91f
MD5 d4a50aa8eb16a64adca5db4719ad940c
BLAKE2b-256 c4f8d408f24a0d86ed4fa009894533c7a2b67c24730104e77994715c80366542

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