Ubuntu
mlpy package

mlpy 2.2.0~dfsg1-3build2 source package in Ubuntu

Changelog

mlpy (2.2.0~dfsg1-3build2) xenial; urgency=medium

  * Rebuild against new gsl SONAME change.

 -- Michael Terry <email address hidden>  Tue, 08 Dec 2015 10:06:18 -0500

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Uploaded by:
Michael Terry
Uploaded to:
Xenial
Original maintainer:
NeuroDebian Team
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Xenial release universe python

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File Size SHA-256 Checksum
mlpy_2.2.0~dfsg1.orig.tar.gz 170.0 KiB 3f003d3b42bf1fa78a3b88aa724821be680242bdb1305832f555ecc9417425b7
mlpy_2.2.0~dfsg1-3build2.debian.tar.xz 4.1 KiB f2e1962bb232c3bcdda3e7a9e37c0a820074ad132e32c95f55cdaedb2dba73d2
mlpy_2.2.0~dfsg1-3build2.dsc 2.1 KiB d0cfcc8e9d07102dba8e4c6347e737025cfb1e9bb006184578a811ada9d99af1

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Binary packages built by this source

python-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.

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).

python-mlpy-lib: No summary available for python-mlpy-lib in ubuntu yakkety.

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