Welcome to the Probabilistic Model's package.
This package contains an interface for any kind of probabilistic models. The aim of this package is to provide a clean, unifying, well documented API to probabilistic models. Just like sklearn does for classical machine learning models.
Read the docs here https://cram2.github.io/cognitive_robot_abstract_machine/probabilistic_model/.
Development
Testing
The project uses unittest for testing. To run the tests locally:
pip install -e ".[dev]"
python -m unittest discover test
Continuous Integration
This project uses GitHub Actions for continuous integration. The CI pipeline runs automatically on push to main/master branches and on pull requests to these branches. It sets up a Python environment, installs dependencies, and runs the tests.
You can see the status of the CI pipeline in the GitHub Actions tab of the repository.
Release files for probabilistic-model 26.7.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| probabilistic_model-26.7.0.tar.gz | 104.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| probabilistic_model-26.7.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 235.6 kB
Release files / probabilistic_model-26.7.0.tar.gz
| Download URL | probabilistic_model-26.7.0.tar.gz |
|---|---|
| Size | 104.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d48576dcdd15ef9e73cd2da2b1d6160fb099b50478cac9ee559f103cc938415f
|
|
BLAKE2b-256 checksum How to use checksums |
3ea7b9a070655907b10cc2c349b7042228e0e4aadb2767503f96d6897a9c0d43
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 26, 2026.
Transparency logRelease files / probabilistic_model-26.7.0-py3-none-any.whl
| Download URL | probabilistic_model-26.7.0-py3-none-any.whl |
|---|---|
| Size | 130.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a461ad10d0fe4dcb95d268f72e41d59a1262dbb8c344ca5bd2544b0aff96ab60
|
|
BLAKE2b-256 checksum How to use checksums |
ab12af14df4cde1c7ce387c478a9560cbc45b8dcd030abd45836adcb255d2b37
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 26, 2026.
Transparency log
