Metadata-Version: 1.1
Name: pycasso
Version: 0.1.0
Summary: Picasso Python Package
Home-page: https://hmjianggatech.github.io/picasso/
Author: Haoming Jiang
Author-email: jianghm.ustc@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: Picasso Python Package
        ======================
        PICASSO: Penalized Generalized Linear Model Solver - Unleash the Power of Non-convex Penalty
        
        Unleash the power of nonconvex penalty
        --------------------------------------
        L1 penalized regression (LASSO) is great for feature selection. However when you use LASSO in
        very noisy setting, especially when some columns in your data have strong colinearity, LASSO
        tends to give biased estimator due to the penalty term. As demonstrated in the example below,
        the lowest estimation error among all the lambdas computed is as high as **16.41%**.
        
        Requirements
        ------------
        
        - Linux or MacOS
        
        It may take lots of effort to build on Windows. One way to do it is using CMAKE and MSVC.
        Be careful of issues like the system bits.
        
        
        Installation
        ------------
        
        In the following process, you may need to be root (``sudo``).
        
        Install from source file (Github):
        
        - Clone ``picasso.git`` via ``git clone https://github.com/jasonge27/picasso.git``
        - Make sure you have `setuptools <https://pypi.python.org/pypi/setuptools>`__
        
          Using **Makefile**
        - Run ``sudo make Pyinstall`` command.
        
          Using **CMAKE**
        - Build the source file first via the ``cmake`` with ``CMakeLists.txt`` in the root directory.
          (You will see a ``.so`` or ``.lib`` file under ``(root)/lib/`` )
        - Run ``cd python-package; sudo python setup.py install`` command.
        
        
        Install from PyPI:
        
        - ``pip install pycasso``
        - **Note**: Owing to the setting on different OS, our distribution might not be working in your environment (especially in **Windows**). Thus please build from source.
        
        You can test if the package has been successfully installed by:
        
        .. code-block:: python
        
                import pycasso
                pycasso.test()
        
        ..
        
        Usage
        -----
        
        .. code-block:: python
        
                from pycasso import *
                x = [[1,2,3,4,5,0],[3,4,1,7,0,1],[5,6,2,1,4,0]]
                y = [3.1,6.9,11.3]
                s = core.Solver(x,y)
                s.train()
                s.predict()
        
        ..
        
        For Developer
        -------------
        Please follow the `sphinx syntax style
        <https://thomas-cokelaer.info/tutorials/sphinx/docstring_python.html>`__
        
        To update the document: ``cd doc; make html``
        
        Copy Right
        ----------
        
        :Author: Jason Ge, Haoming Jiang
        :Maintainer: Haoming Jiang <jianghm@gatech.edu>
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: License :: OSI Approved :: MIT License
