Pre-release
This release is a pre-release and may not be stable for production use.
aakr (Auto Associative Kernel Regression)
aakr is a Python implementation of the Auto-Associative Kernel Regression (AAKR). The algorithm is suitable for signal reconstruction, which can further be used for condition monitoring, anomaly detection etc.
Documentation is available at https://aakr.readthedocs.io.
Installation
pip install aakr
Quickstart
Given historical normal condition X_nc examples and new observations X_obs of size n_samples x n_features, what values we expect to see in normal conditions for the new observations?
from aakr import AAKR
# Create AAKR model
aakr = AAKR()
# Fit the model with normal condition examples
aakr.fit(X_nc)
# Ask for values expected to be seen in normal conditions
X_obs_nc = aakr.transform(X_obs)
References
Jesse Myrberg (jesse.myrberg@gmail.com)
1 maintainer
Metadata
1 maintainer
Release files for aakr 0.0.1a0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| aakr-0.0.1a0.tar.gz | 5.5 kB | Details |
Release files / aakr-0.0.1a0.tar.gz
| Download URL | aakr-0.0.1a0.tar.gz |
|---|---|
| Size | 5.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c089825df88e7b775c250ffebe5beb8f0995117f1466e0170253ac7ab0f5b6ca
|
|
BLAKE2b-256 checksum How to use checksums |
4ed043b8f3add6e503cc7a5606b7968c721b57f61f36aa7f77c65535ffe6458d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.0 CPython/3.8.6
|
