Ubuntu
mlpy package

mlpy 3.5.0+ds-1.3 source package in Ubuntu

Changelog

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

  * Non-maintainer upload.
  * Add Build-Dependency on tex-gyre, for docs (Closes: #997448)

 -- Stefano Rivera <email address hidden>  Sun, 21 Nov 2021 10:46:25 -0400

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

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mlpy_3.5.0+ds-1.3.dsc 1.7 KiB 53fce19bda09c10d53c6507b29e4112cb68cf9e175735347a315fd595117d4b9
mlpy_3.5.0+ds.orig.tar.xz 1.4 MiB bf7b960fdb80eb6baf49831c831d6cff8afd96c1e91000c04028d0c5eeef5272
mlpy_3.5.0+ds-1.3.debian.tar.xz 4.5 KiB 1224d3efaf2ab02261b078fa112404937d9c65e90c4801a0526f0ec5fb8483b6

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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