Implementation of the AuToMATo clustering algorithm introduced in AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm.
Example of running AuToMATo
>>> from automato import Automato
>>> from sklearn.datasets import make_blobs
>>> X, y = make_blobs(centers=2, random_state=42)
>>> aut = Automato(random_state=42).fit(X)
>>> aut.n_clusters_
2
>>> (aut.labels_ == y).all()
True
Installation and requirements
AuToMATo can be installed via pip by running pip install -U automato.
Required Python dependencies are specified in pyproject.toml. Provided that uv is installed, these dependencies can be installed by running uv pip install -r pyproject.toml. The environment specified in uv.lock can be recreated by running uv sync.
Installing AuToMATo from PyPI for uv users
$ uv init
$ uv add automato
$ uv run python
>>> from automato import Automato
>>> ...
Metadata
Release files for automato 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| automato-0.1.1.tar.gz | 26.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| automato-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 54.4 kB
Release files / automato-0.1.1.tar.gz
| Download URL | automato-0.1.1.tar.gz |
|---|---|
| Size | 26.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/6.1.0 CPython/3.10.12
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Release files / automato-0.1.1-py3-none-any.whl
| Download URL | automato-0.1.1-py3-none-any.whl |
|---|---|
| Size | 28.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.10.12
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