Metadata-Version: 2.1
Name: padasip
Version: 1.2.1
Summary: Python Adaptive Signal Processing
Home-page: http://matousc89.github.io/padasip/
Download-URL: https://github.com/matousc89/padasip/
Author: Matous Cejnek
Author-email: matousc@gmail.com
Maintainer: Matous Cejnek
License: MIT
Keywords: signal-processing,adaptive filters
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Adaptive Technologies
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python
License-File: LICENSE.txt

This library is designed to simplify adaptive signal
processing tasks within python
(filtering, prediction, reconstruction).
For code optimisation, this library uses numpy for array operations.

Also in this library is presented some new methods for adaptive signal processing.
The library is designed to be used with datasets and also with
real-time measuring (sample-after-sample feeding).

============================
Tutorials and Documentation
============================

Everything is on github:

http://matousc89.github.io/padasip/

================
Current Features
================

********************
Data Preprocessing
********************

- Principal Component Analysis (PCA)

- Linear Discriminant Analysis (LDA)

******************
Adaptive Filters
******************

The library features multiple adaptive filters. Input vectors for filters can be
constructed manually or with the assistance of included functions.
So far it is possible to use following filters:

- LMS (least-mean-squares) adaptive filter

- NLMS (normalized least-mean-squares) adaptive filter

- LMF (least-mean-fourth) adaptive filter

- NLMF (normalized least-mean-fourth) adaptive filter

- SSLMS (sign-sign least-mean-squares) adaptive filter

- NSSLMS (normalized sign-sign least-mean-squares) adaptive filter

- RLS (recursive-least-squares) adaptive filter

- GNGD (generalized normalized gradient descent) adaptive filter

- AP (affine projection) adaptive filter

- GMCC (generalized maximum correntropy criterion) adaptive filter

- OCNLMS (online centered normalized least-mean-squares) adaptive filter

- Llncosh (least lncosh) adaptive filter

******************
Detection Tools
******************

The library features two novelty/outlier detection tools

- Error and Learning Based Novelty Detection (ELBND)

- Learning Entropy (LE)

- Extreme Seeking Entropy (ESE)


