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Commit 10eb15c

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EmaasitEmaasit
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Merge pull request #31 from Emaasit/packaging
install in conda env
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‎README.rst

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@@ -6,7 +6,7 @@ pymc-learn: Practical Probabilistic Machine Learning in Python
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:alt: Pymc-Learn logo
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:align: center
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|Travis| |Coverage| |Docs| |License| |Pypi| |Binder|
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|status| |Travis| |Coverage| |Docs| |License| |Pypi| |Binder|
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**Contents:**
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Quick Install
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-----------------
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``pymc-learn`` requires a working Python interpreter (2.7 or 3.5+).
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It is recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.anaconda.com/download/>`_,
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which has one-click installers available on all major platforms.
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Assuming a standard Python environment is installed on your machine
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(including pip), ``pymc-learn`` itself can be installed in one line using pip:
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You can install ``pymc-learn`` from PyPi using pip as follows:
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.. code-block:: bash
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.. CAUTION::
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``pymc-learn`` is under heavy development.
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It is recommended installing ``pymc-learn`` in a Conda environment because it
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provides `Math Kernel Library <https://anaconda.org/anaconda/mkl-service>`_ (MKL)
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routines to accelerate math functions. If you are having trouble, try using
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a distribution of Python that includes these packages like
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`Anaconda <https://www.anaconda.com/download/>`_.
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Dependencies
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................
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changelog.rst
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cite.rst
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.. |Binder| image:: https://mybinder.org/badge.svg
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.. |Binder| image:: https://img.shields.io/badge/try-online-579ACA.svg?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAFkAAABZCAMAAABi1XidAAAB8lBMVEX///9XmsrmZYH1olJXmsr1olJXmsrmZYH1olJXmsr1olJXmsrmZYH1olL1olJXmsr1olJXmsrmZYH1olL1olJXmsrmZYH1olJXmsr1olL1olJXmsrmZYH1olL1olJXmsrmZYH1olL1olL0nFf1olJXmsrmZYH1olJXmsq8dZb1olJXmsrmZYH1olJXmspXmspXmsr1olL1olJXmsrmZYH1olJXmsr1olL1olJXmsrmZYH1olL1olLeaIVXmsrmZYH1olL1olL1olJXmsrmZYH1olLna31Xmsr1olJXmsr1olJXmsrmZYH1olLqoVr1olJXmsr1olJXmsrmZYH1olL1olKkfaPobXvviGabgadXmsqThKuofKHmZ4Dobnr1olJXmsr1olJXmspXmsr1olJXmsrfZ4TuhWn1olL1olJXmsqBi7X1olJXmspZmslbmMhbmsdemsVfl8ZgmsNim8Jpk8F0m7R4m7F5nLB6jbh7jbiDirOEibOGnKaMhq+PnaCVg6qWg6qegKaff6WhnpKofKGtnomxeZy3noG6dZi+n3vCcpPDcpPGn3bLb4/Mb47UbIrVa4rYoGjdaIbeaIXhoWHmZYHobXvpcHjqdHXreHLroVrsfG/uhGnuh2bwj2Hxk17yl1vzmljzm1j0nlX1olL3AJXWAAAAbXRSTlMAEBAQHx8gICAuLjAwMDw9PUBAQEpQUFBXV1hgYGBkcHBwcXl8gICAgoiIkJCQlJicnJ2goKCmqK+wsLC4usDAwMjP0NDQ1NbW3Nzg4ODi5+3v8PDw8/T09PX29vb39/f5+fr7+/z8/Pz9/v7+zczCxgAABC5JREFUeAHN1ul3k0UUBvCb1CTVpmpaitAGSLSpSuKCLWpbTKNJFGlcSMAFF63iUmRccNG6gLbuxkXU66JAUef/9LSpmXnyLr3T5AO/rzl5zj137p136BISy44fKJXuGN/d19PUfYeO67Znqtf2KH33Id1psXoFdW30sPZ1sMvs2D060AHqws4FHeJojLZqnw53cmfvg+XR8mC0OEjuxrXEkX5ydeVJLVIlV0e10PXk5k7dYeHu7Cj1j+49uKg7uLU61tGLw1lq27ugQYlclHC4bgv7VQ+TAyj5Zc/UjsPvs1sd5cWryWObtvWT2EPa4rtnWW3JkpjggEpbOsPr7F7EyNewtpBIslA7p43HCsnwooXTEc3UmPmCNn5lrqTJxy6nRmcavGZVt/3Da2pD5NHvsOHJCrdc1G2r3DITpU7yic7w/7Rxnjc0kt5GC4djiv2Sz3Fb2iEZg41/ddsFDoyuYrIkmFehz0HR2thPgQqMyQYb2OtB0WxsZ3BeG3+wpRb1vzl2UYBog8FfGhttFKjtAclnZYrRo9ryG9uG/FZQU4AEg8ZE9LjGMzTmqKXPLnlWVnIlQQTvxJf8ip7VgjZjyVPrjw1te5otM7RmP7xm+sK2Gv9I8Gi++BRbEkR9EBw8zRUcKxwp73xkaLiqQb+kGduJTNHG72zcW9LoJgqQxpP3/Tj//c3yB0tqzaml05/+orHLksVO+95kX7/7qgJvnjlrfr2Ggsyx0eoy9uPzN5SPd86aXggOsEKW2Prz7du3VID3/tzs/sSRs2w7ovVHKtjrX2pd7ZMlTxAYfBAL9jiDwfLkq55Tm7ifhMlTGPyCAs7RFRhn47JnlcB9RM5T97ASuZXIcVNuUDIndpDbdsfrqsOppeXl5Y+XVKdjFCTh+zGaVuj0d9zy05PPK3QzBamxdwtTCrzyg/2Rvf2EstUjordGwa/kx9mSJLr8mLLtCW8HHGJc2R5hS219IiF6PnTusOqcMl57gm0Z8kanKMAQg0qSyuZfn7zItsbGyO9QlnxY0eCuD1XL2ys/MsrQhltE7Ug0uFOzufJFE2PxBo/YAx8XPPdDwWN0MrDRYIZF0mSMKCNHgaIVFoBbNoLJ7tEQDKxGF0kcLQimojCZopv0OkNOyWCCg9XMVAi7ARJzQdM2QUh0gmBozjc3Skg6dSBRqDGYSUOu66Zg+I2fNZs/M3/f/Grl/XnyF1Gw3VKCez0PN5IUfFLqvgUN4C0qNqYs5YhPL+aVZYDE4IpUk57oSFnJm4FyCqqOE0jhY2SMyLFoo56zyo6becOS5UVDdj7Vih0zp+tcMhwRpBeLyqtIjlJKAIZSbI8SGSF3k0pA3mR5tHuwPFoa7N7reoq2bqCsAk1HqCu5uvI1n6JuRXI+S1Mco54YmYTwcn6Aeic+kssXi8XpXC4V3t7/ADuTNKaQJdScAAAAAElFTkSuQmCC
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:target: https://mybinder.org/v2/gh/pymc-learn/pymc-learn/master?filepath=%2Fdocs%2Fnotebooks?urlpath=lab
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.. |Travis| image:: https://travis-ci.com/pymc-learn/pymc-learn.svg?branch=master
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.. |Pypi| image:: https://badge.fury.io/py/pymc-learn.svg
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:target: https://badge.fury.io/py/pymc-learn
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.. |status| image:: https://img.shields.io/badge/Status-Beta-blue.svg

‎docs/install.rst

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Install pymc-learn
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===================
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``pymc-learn`` requires a working Python interpreter (2.7 or 3.3+).
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It is recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.continuum.io/downloads>`_,
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``pymc-learn`` requires a working Python interpreter (2.7 or 3.5+).
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It is recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.anaconda.com/download/>`_,
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which has one-click installers available on all major platforms.
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Assuming a standard Python environment is installed on your machine
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.. CAUTION::
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``pymc-learn`` is under heavy development.
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It is recommended installing ``pymc-learn`` in a Conda environment because it
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provides `Math Kernel Library <https://anaconda.org/anaconda/mkl-service>`_ (MKL)
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routines to accelerate math functions. If you are having trouble, try using
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a distribution of Python that includes these packages like
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`Anaconda <https://www.anaconda.com/download/>`_.
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This also installs required dependencies including Theano.
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For alternative Theano installations (e.g., gpu), please see the

‎docs/modules/neural_networks.rst

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.. currentmodule:: pmlearn.neural_network
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.. warning::
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.. NOTE::
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This implementation is not intended for large-scale applications. In particular,
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scikit-learn offers no GPU support. For much faster, GPU-based implementations,
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as well as frameworks offering much more flexibility to build deep learning
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architectures, see :ref:`related_projects`.
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Unlike scikit-learn, this implementation of neural networks in pymc-learn is
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intended for large-scale applications. Pymc-learn relies on Theano for GPU
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support.
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scikit-learn offers no GPU support.
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.. _multilayer_perceptron:
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‎setup.cfg

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[pydocstyle]
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add-ignore = D100,D104
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convention = numpy
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convention = numpy
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[bdist_wheel]
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universal=1

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