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mlpy package

mlpy 3.5.0+ds-3build1 source package in Ubuntu

Changelog

mlpy (3.5.0+ds-3build1) noble; urgency=medium

  * No-change rebuild with Python 3.12 only

 -- Graham Inggs <email address hidden>  Wed, 10 Apr 2024 18:41:36 +0000

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Uploaded by:
Graham Inggs
Uploaded to:
Noble
Original maintainer:
Debian Science Team
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Oracular release universe python
Noble release universe python

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File Size SHA-256 Checksum
mlpy_3.5.0+ds.orig.tar.xz 1.4 MiB bf7b960fdb80eb6baf49831c831d6cff8afd96c1e91000c04028d0c5eeef5272
mlpy_3.5.0+ds-3build1.debian.tar.xz 13.0 KiB 67fa2806fecec259aa98fe281fd8c54dd32b67ce0348d3baf153420c29148604
mlpy_3.5.0+ds-3build1.dsc 2.3 KiB 2402788fb7dddb721874e430659bd4ac965b0107b338aaf7f4a4337b0986d03d

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Binary packages built by this source

python-mlpy-doc: documentation and examples for mlpy

 mlpy provides high level procedures that support, with few lines of
 code, the design of rich Data Analysis Protocols (DAPs) for
 preprocessing, clustering, predictive classification and feature
 selection. Methods are available for feature weighting and ranking,
 data resampling, error evaluation and experiment landscaping.
 .
 This package provides user documentation for mlpy in various formats
 (HTML, PDF).

python3-mlpy: high-performance Python package for predictive modeling

 mlpy provides high level procedures that support, with few lines of
 code, the design of rich Data Analysis Protocols (DAPs) for
 preprocessing, clustering, predictive classification and feature
 selection. Methods are available for feature weighting and ranking,
 data resampling, error evaluation and experiment landscaping.
 .
 mlpy includes: SVM (Support Vector Machine), KNN (K Nearest
 Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression,
 Penalized, Diagonal Linear Discriminant Analysis) for classification
 and feature weighting, I-RELIEF, DWT and FSSun for feature weighting,
 RFE (Recursive Feature Elimination) and RFS (Recursive Forward
 Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated,
 Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time
 Warping), Hierarchical Clustering, k-medoids, Resampling Methods,
 Metric Functions, Canberra indicators.

python3-mlpy-lib: low-level implementations and bindings for mlpy

 mlpy provides high level procedures that support, with few lines of
 code, the design of rich Data Analysis Protocols (DAPs) for
 preprocessing, clustering, predictive classification and feature
 selection. Methods are available for feature weighting and ranking,
 data resampling, error evaluation and experiment landscaping.
 .
 This is an add-on package for the mlpy providing compiled core functionality.

python3-mlpy-lib-dbgsym: debug symbols for python3-mlpy-lib