Ubuntu
mlpy package

mlpy 3.5.0+ds-3 source package in Ubuntu

Changelog

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

  * Team upload.

  [Yogeswaran Umasankar]
  * Patch for build issue with Python 3.12.
    Closes: #1055693, #1054748

  [ Bas Couwenberg ]
  * Switch to cython3-legacy.
    Closes: #1056819

  [ Andreas Tille ]
  * Build-Depends: s/dh-python/dh-sequence-python3/ (routine-update)

 -- Andreas Tille <email address hidden>  Mon, 22 Jan 2024 08:12:16 +0100

Upload details

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

See full publishing history Publishing

Series Pocket Published Component Section

Downloads

File Size SHA-256 Checksum
mlpy_3.5.0+ds-3.dsc 2.3 KiB 4260614a7d24cf45585ab19e4a09267d886322c21cce49a61eef17005d3b7ff4
mlpy_3.5.0+ds.orig.tar.xz 1.4 MiB bf7b960fdb80eb6baf49831c831d6cff8afd96c1e91000c04028d0c5eeef5272
mlpy_3.5.0+ds-3.debian.tar.xz 12.9 KiB 4e355fed07d236e2163be1cd7bb2d7dd4e7ac5b5297ecbb0a5721c7f50127310

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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