Ubuntu
mlpy package

mlpy 3.5.0+ds-1.3build1 source package in Ubuntu

Changelog

mlpy (3.5.0+ds-1.3build1) jammy; urgency=medium

  * No-change rebuild against libgsl27

 -- Steve Langasek <email address hidden>  Tue, 07 Dec 2021 17:38:30 +0000

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Uploaded by:
Steve Langasek
Uploaded to:
Jammy
Original maintainer:
Ubuntu Developers
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

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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-1.3build1.debian.tar.xz 4.6 KiB 59a923ce1f9a871c94dbab18ef998407f9fa4372a7df5ed1c40c9a2b94a693b7
mlpy_3.5.0+ds-1.3build1.dsc 2.4 KiB ea89496518370ee67a4ed68917a9c224999243b88a0050f60fd4efed44acff94

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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