Metadata-Version: 1.1
Name: hpelm
Version: 1.0.9
Summary: High-Performance implementation of an Extreme Learning Machine
Home-page: https://www.researchgate.net/profile/Anton_Akusok
Author: Anton Akusok
Author-email: akusok.a@gmail.com
License: BSD (3-clause)
Description: High Performance toolbox for Extreme Learning Machines.
        --------
        
        Extreme learning machines (ELM) are a particular kind of Artificial Neural Networks,
        which solve classification and regression problems. Their performance is comparable
        to a classical Multilayer Perceptron trained with Error Back-Propagation algorithm,
        but the training time is up to 6 orders of magnitude smaller. (yes, a million times!)
        
        ELMs are suitable for processing huge datasets and dealing with Big Data,
        and this toolbox is created as their fastest and most scalable implementation.
        
        Documentation is available here: http://hpelm.readthedocs.org, 
        it uses Numpydocs.
        
        NEW: Parallel HP-ELM tutorial! See the documentation: http://hpelm.readthedocs.org
        
        Highlights:
            - Efficient matrix math implementation without bottlenecks
            - Efficient data storage (HDF5 file format)
            - Data size not limited by the available memory
            - GPU accelerated computations (if you have one)
            - Regularization and model selection (for in-memory models)
        
        Main classes:
            - hpelm.ELM for in-memory computations (dataset fits into RAM)
            - hpelm.HPELM for out-of-memory computations (dataset on disk in HDF5 format)
        
        Example usage::
            >>> from hpelm import ELM
            >>> elm = ELM(X.shape[1], T.shape[1])
            >>> elm.add_neurons(20, "sigm")
            >>> elm.add_neurons(10, "rbf_l2")
            >>> elm.train(X, T, "LOO")
            >>> Y = elm.predict(X)
        
        If you use the toolbox, cite our open access paper "High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications" in IEEE Access.
        http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7140733&newsearch=true&queryText=High%20Performance%20Extreme%20Learning%20Machines
        
        @ARTICLE{7140733,
        author={Akusok, A. and Bj\"{o}rk, K.-M. and Miche, Y. and Lendasse, A.},
        journal={Access, IEEE},
        title={High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications},
        year={2015},
        volume={3},
        pages={1011-1025},
        doi={10.1109/ACCESS.2015.2450498},
        ISSN={2169-3536},
        month={},}
Keywords: ELM HPC regression classification ANN
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Information Analysis
