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Description:
padasip 1.2.2
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
Variable step-size least-mean-square (VSLMS) with Ang’s adaptation.
Variable step-size least-mean-square (VSLMS) with Benveniste’s adaptation
Variable step-size least-mean-square (VSLMS) with Mathews’s adaptation
Detection Tools
The library features two novelty/outlier detection tools
Error and Learning Based Novelty Detection (ELBND)
Learning Entropy (LE)
Extreme Seeking Entropy (ESE)
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
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