mlpy 3.5.0+ds-1.3build3 source package in Ubuntu
Changelog
mlpy (3.5.0+ds-1.3build3) lunar; urgency=medium * No-change rebuild with Python 3.11 as supported -- Graham Inggs <email address hidden> Thu, 03 Nov 2022 15:59:22 +0000
Upload details
- Uploaded by:
- Graham Inggs
- Uploaded to:
- Lunar
- Original maintainer:
- Ubuntu Developers
- Architectures:
- any all
- Section:
- python
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| mlpy_3.5.0+ds.orig.tar.xz | 1.4 MiB | bf7b960fdb80eb6baf49831c831d6cff8afd96c1e91000c04028d0c5eeef5272 |
| mlpy_3.5.0+ds-1.3build3.debian.tar.xz | 4.7 KiB | 0c9a310205549df4d397b67b0930711a1e9b1ae2c9021be8cc0f7f1d0413e602 |
| mlpy_3.5.0+ds-1.3build3.dsc | 2.3 KiB | d6a3811520305ffd306a3cece0205a5c70ecddaa63cf901f7d49e3ce9c557c0e |
Available diffs
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
