patternly
- Info:
Paper draft link will be posted here
- Author:
Drew Vlasnik, Ishanu Chattopadhyay
- Laboratory:
The Laboratory for Zero Knowledge Discovery, The University of Chicago https://zed.uchicago.edu
- Description:
Discovery of emergent anomalies in data streams without explicit prior models of correct or aberrant behavior, based on the modeling of ergodic, quasi-stationary finite valued processes as probabilistic finite state automata (PFSA).
- Documentation:
Installation:
pip install patternly --user -UUsage:
See examples.
from patternly.detection import AnomalyDetection, StreamingDetection
Release files for patternly 0.0.33
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| patternly-0.0.33.tar.gz | 11.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| patternly-0.0.33-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 23.4 kB
Release files / patternly-0.0.33.tar.gz
| Download URL | patternly-0.0.33.tar.gz |
|---|---|
| Size | 11.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / patternly-0.0.33-py3-none-any.whl
| Download URL | patternly-0.0.33-py3-none-any.whl |
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| Size | 11.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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twine/3.2.0 pkginfo/1.5.0.1 requests/2.27.1 setuptools/49.1.3 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.9
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