Metadata-Version: 1.1
Name: liknorm-py
Version: 1.0.6
Summary: Liknorm Python wrapper.
Home-page: https://github.com/limix/liknorm-py
Author: Danilo Horta
Author-email: horta@ebi.ac.uk
License: MIT
Description: # liknorm-py
        
        [![PyPIl](https://img.shields.io/pypi/l/liknorm-py.svg?style=flat-square)](https://pypi.python.org/pypi/liknorm-py/)
        [![PyPIv](https://img.shields.io/pypi/v/liknorm-py.svg?style=flat-square)](https://pypi.python.org/pypi/liknorm-py/)
        [![Anaconda-Server Badge](https://anaconda.org/conda-forge/liknorm-py/badges/version.svg)](https://anaconda.org/conda-forge/liknorm-py)
        
        Liknorm Python wrapper.
        
        ## Install
        
        The recommended way of installing it is via
        [conda](http://conda.pydata.org/docs/index.html)
        ```bash
        conda install -c conda-forge liknorm-py
        ```
        
        An alternative way would be via [pip](https://pypi.python.org/pypi/pip).
        First you need to install [liknorm](http://liknorm.readthedocs.io/)
        library and then
        ```bash
        pip install liknorm-py
        ```
        
        ## Running the tests
        
        After installation, you can test it
        ```
        python -c "import liknorm; liknorm.test()"
        ```
        as long as you have [pytest](http://docs.pytest.org/en/latest/).
        
        ## Example
        
        ```python
        >>> from numpy import empty
        >>> from numpy.random import RandomState
        >>> from liknorm import LikNormMachine
        >>>
        >>> machine = LikNormMachine('bernoulli')
        >>> random = RandomState(0)
        >>> outcome = random.randint(0, 2, 5)
        >>> tau = random.rand(5)
        >>> eta = random.randn(5) * tau
        >>>
        >>> log_zeroth = empty(5)
        >>> mean = empty(5)
        >>> variance = empty(5)
        >>>
        >>> moments = {'log_zeroth': log_zeroth, 'mean': mean, 'variance': variance}
        >>> machine.moments(outcome, eta, tau, moments)
        >>>
        >>> print('%.3f %.3f %.3f' % (log_zeroth[0], mean[0], variance[0]))
        -0.671 -0.515 0.946
        ```
        
        ## Authors
        
        * **Danilo Horta** - [https://github.com/Horta](https://github.com/Horta)
        
        ## License
        
        This project is licensed under the MIT License - see the
        [LICENSE](LICENSE) file for details
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
