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RPM: python3-module-mdp
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A versão atual: 3.6.0.15.g64f14eee-alt1
Data da compilação: 30 julho 2023, 14:38 ( 38.6 weeks ago )
Tamanho:: 986.66 Kb
Home page: https://pypi.org/project/MDP/
Licença: BSD-3-Clause
Sumário: Modular toolkit for Data Processing
Descrição:
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Data da compilação: 30 julho 2023, 14:38 ( 38.6 weeks ago )
Tamanho:: 986.66 Kb
Home page: https://pypi.org/project/MDP/
Licença: BSD-3-Clause
Sumário: Modular toolkit for Data Processing
Descrição:
Modular toolkit for Data Processing (MDP) is a Python data processing
framework.
From the user's perspective, MDP is a collection of supervised and
unsupervised learning algorithms and other data processing units that
can be combined into data processing sequences and more complex
feed-forward network architectures.
From the scientific developer's perspective, MDP is a modular framework,
which can easily be expanded. The implementation of new algorithms is
easy and intuitive. The new implemented units are then automatically
integrated with the rest of the library.
The base of available algorithms is steadily increasing and includes, to
name but the most common, Principal Component Analysis (PCA and NIPALS),
several Independent Component Analysis algorithms (CuBICA, FastICA,
TDSEP, JADE, and XSFA), Slow Feature Analysis, Gaussian Classifiers,
Restricted Boltzmann Machine, and Locally Linear Embedding.
Mantenedor currente: Daniel Zagaynov framework.
From the user's perspective, MDP is a collection of supervised and
unsupervised learning algorithms and other data processing units that
can be combined into data processing sequences and more complex
feed-forward network architectures.
From the scientific developer's perspective, MDP is a modular framework,
which can easily be expanded. The implementation of new algorithms is
easy and intuitive. The new implemented units are then automatically
integrated with the rest of the library.
The base of available algorithms is steadily increasing and includes, to
name but the most common, Principal Component Analysis (PCA and NIPALS),
several Independent Component Analysis algorithms (CuBICA, FastICA,
TDSEP, JADE, and XSFA), Slow Feature Analysis, Gaussian Classifiers,
Restricted Boltzmann Machine, and Locally Linear Embedding.
Lista dos contribuidores
- python3-module-mdp
- python3-module-mdp-doc
- python3-module-mdp-tests