Metadata-Version: 1.1
Name: molml
Version: 0.3.0
Summary: An interface between molecules and machine learning
Home-page: https://github.com/crcollins/molml/
Author: Chris Collins
Author-email: chris@crcollins.com
License: MIT
Description: MolML
        =====
        [![Build Status](https://travis-ci.org/crcollins/molml.svg?branch=master)](https://travis-ci.org/crcollins/molml)
        [![Coverage Status](https://coveralls.io/repos/github/crcollins/molml/badge.svg?branch=master)](https://coveralls.io/github/crcollins/molml?branch=master)
        [![PyPI version](https://img.shields.io/pypi/v/MolML.svg?style=flat)](http://pypi.python.org/pypi/MolML)
        [![License](https://img.shields.io/pypi/l/MolML.svg?style=flat)](https://github.com/crcollins/molml/blob/master/LICENSE.txt)
        
        
        A library to interface molecules and machine learning. The goal of this library is to be a simple way to convert molecules into a vector representation for later use with libraries such as [scikit-learn](http://scikit-learn.org/). This is done using a similar API scheme.
        
        All of the coordinates are assumed to be in angstroms.
        
        
        Example Usage
        =============
        
        ```python
            >>> from molml.features import CoulombMatrix
            >>> feat = CoulombMatrix()
            >>> H2 = (
            ...         ['H', 'H'],
            ...         [
            ...             [0.0, 0.0, 0.0],
            ...             [1.0, 0.0, 0.0],
            ...         ]
            ... )
            >>> HCN = (
            ...         ['H', 'C', 'N'],
            ...         [
            ...             [-1.0, 0.0, 0.0],
            ...             [ 0.0, 0.0, 0.0],
            ...             [ 1.0, 0.0, 0.0],
            ...         ]
            ... )
            >>> feat.fit([H2, HCN])
            CoulombMatrix(input_type='list', n_jobs=1)
            >>> feat.transform([H2])
            array([[ 0.5,  1. ,  0. ,  1. ,  0.5,  0. ,  0. ,  0. ,  0. ]])
            >>> feat.transform([H2, HCN])
            array([[  0.5      ,   1.       ,   0.       ,   1.       ,   0.5      ,
                    0.       ,   0.       ,   0.       ,   0.       ],
                    [  0.5      ,   6.       ,   3.5      ,   6.       ,  36.8581052,
                    42.       ,   3.5      ,  42.       ,  53.3587074]])
        ```
        
        For more examples, look in the [examples](https://github.com/crcollins/molml/tree/master/examples). Note: To run some of the examples scikit-learn>=0.16.0 is required.
        
        
        Dependencies
        ============
        
        MolML requires python 2.7, numpy, scipy, and pathos. The specific versions that have been tested are numpy 1.9.1, scipy 0.15.1, and pathos 0.2.0, but newer versions should work.
        
        
        Install
        =======
        
        Once the dependeicies are installed, the package can be installed with pip.
        
            $ pip install molml
        
        Or for the bleeding edge version, you can use
        
            $ pip install git+git://github.com/crcollins/molml
        
        
        Development
        ===========
        
        To install a development version, just clone the git repo.
        
            $ git clone https://github.com/crcollins/molml
        
        Pull requests and bug reports are welcomed!
        
        
        Testing
        =======
        
        To run the tests, make sure that `nose` is installed and then run:
        
            $ nosetests
        
        To include coverage information, make sure that `coverage` is installed and then run:
        
            $ nosetests --with-coverage --cover-package=molml --cover-erase
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
