Metadata-Version: 1.1
Name: hep-ml
Version: 0.4.0
Summary: Machine Learning for High Energy Physics
Home-page: https://github.com/arogozhnikov/hep_ml
Author: Alex Rogozhnikov
Author-email: axelr@yandex-team.ru
License: Apache 2.0
Description: hep\_ml
        =======
        
        **hep\_ml** provides specific machine learning tools for purposes of
        high energy physics (written in python).
        
        Main points
        -----------
        
        -  uniform classifiers - the classifiers with low correlation of
           predictions and mass (or some other variable(s))
        -  **uBoost** optimized implementation inside
        -  **UGradientBoosting** (with different losses, specially
           **FlatnessLoss** is very interesting)
        -  measures of uniformity (see **hep\_ml.metrics**)
        -  advanced losses for classification, regression and ranking for
           **UGradientBoosting** (see **hep\_ml.losses**).
        -  **hep\_ml.nnet** - theano-based flexible neural networks
        -  **hep\_ml.reweight** - reweighting multidimensional distributions
           (*multi* here means 2, 3, 5 and more dimensions - see GBReweighter!)
        -  **sklearn**-compatibility of estimators.
        
        Installation
        ------------
        
        .. code:: bash
        
            pip install hep_ml
        
        To use latest version, clone it and install with ``pip``:
        
        .. code:: bash
        
            git clone https://github.com/arogozhnikov/hep_ml.git
            cd hep_ml
            sudo pip install .
        
        Links
        -----
        
        -  `documentation <https://arogozhnikov.github.io/hep_ml/>`__
        -  `notebook
           examples <https://github.com/arogozhnikov/hep_ml/tree/master/notebooks>`__
        -  `repository <https://github.com/arogozhnikov/hep_ml>`__
        -  `issue tracker <https://github.com/arogozhnikov/hep_ml/issues>`__
        -  `old repository <https://github.com/anaderi/lhcb_trigger_ml>`__
        
        Related projects
        ----------------
        
        Libraries you'll require to make your life easier.
        
        -  `IPython Notebook <http://ipython.org/notebook.html>`__ — web-shell
           for python
        -  `scikit-learn <http://scikit-learn.org/>`__ — general-purpose library
           for machine learning in python
        -  `REP <https://github.com/yandex/REP>`__ — python wrappers around
           different machine learning libraries (including TMVA) + goodies,
           required to plot learning curves and reports after classification.
           Required to execute *howto*\ s from this repository
        -  `numpy <http://www.numpy.org/>`__ — 'MATLAB in python', vector
           operation in python. Use it you need to perform any number crunching.
        -  `theano <http://deeplearning.net/software/theano/>`__ — optimized
           vector analytical math engine in python
        -  `ROOT <https://root.cern.ch/>`__ — main data format in high energy
           physics
        -  `root\_numpy <http://rootpy.github.io/root_numpy/>`__ — python
           library to deal with ROOT files (without pain)
        
        License
        -------
        
        Apache 2.0, library is open-source.
        
Keywords: machine learning,supervised learning,uncorrelated methods of machine learning,high energy physics,particle physics
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2.7 
Classifier: Programming Language :: Python :: 3.4 
