Showing 233 open source projects for "library python"

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  • 1

    LWPR

    Locally Weighted Projection Regression (LWPR)

    Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite: [1] Sethu...
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  • 2
    This project develops a simple, fast and easy to use Python graph library using NumPy, Scipy and PySparse.
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  • 3
    OpenANN

    OpenANN

    An open source library for artificial neural networks.

    OpenANN is a C++ library that provides implementations for modular artificial neural networks with only a few dependencies and additional Python bindings.
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  • 4
    The intention of this project is to give all serious users of the SNNS a place where they find a bugfix and patch management and where they get useful information about the SNNS.
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  • 5
    The Python Computer Vision Framework is an opened project deisgned for all those interested in computer vision. It aims at making computer vision more easy and structured and matlab-free. It may also be used for other artistic and scientific areas.
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  • 6
    Yann
    Yann is Yet Another Neural Network. Yann is a library to create fast neural networks. It is also a GUI to easily create, edit, train, execute and investigate networks. Multiple topologies, runtime properties and ensemble learning are supported.
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  • 7
    A Python function library to extract EEG feature from EEG time series in standard Python and numpy data structure. Features include classical spectral analysis, entropies, fractal dimensions, DFA, inter-channel synchrony and order, etc.
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  • 8
    Python Machine learning library with multi-core support. Wraps existing ML libraries in order to be able to run and analyse experiments with one front-end API. Currently supports MLP, GA, GP, ESN and RBF algorithms.
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