Metadata-Version: 2.1
Name: resautonet
Version: 0.1.1
Summary: Library for Autoencoder-based Residual Deep Network
Home-page: UNKNOWN
Author: Lianfa Li
Author-email: lspatial@gmail.com
License: UNKNOWN
Description: # Library of Autoencoder-based Residual Deep Network (resautonet)
        
        [![Build Status](https://travis-ci.org/pybind/cmake_example.svg?branch=master)](https://travis-ci.org/pybind/cmake_example)
        [![Build status](https://ci.appveyor.com/api/projects/status/57nnxfm4subeug43/branch/master?svg=true)](https://ci.appveyor.com/project/dean0x7d/cmake-example/branch/master)
        
        The python library of autoencoder based residual deep network (resautonet). 
        Current version just supports the KERAS package of deep learning and 
        will extend to the others in the future. 
        
        ## Major modules
        
        **model**
        
        * resAutoencoder: major class to obtain a autoencoder-based residual 
              deep network by setting the arguments. See the class and its 
              member functions' help for details.  
        * pmetrics: functions for regression metrics like rsquared and RMSE. 
        
        **peranalysis**
        
        * mulParPerAnalysis: major class for parallel performance analysis 
              You can setup many configure parameters for each network (a duty)
              and then run them to the effects in a parallel way. See this class 
              and its member functions' help for details.  
        
        **data**
        
        * data: function to access each of two datasets,  
                 sim': simulated dataset in the format of Pandas's Data Frame,
                 'pm2.5':string, the name for a real dataset of the 2015 PM2.5 
                    and the relevant covariates for the Beijing-Tianjin-Tangshan
                    area. It is sampled by the fraction of 0.8 from the
                   the original dataset (stratified by the julian day).
                 See this function's help for details.  
        * simdata: function to simulate the test dataset,  
                 The simulated dataset generated according to the formula:
                     y=x1+x2*np.sqrt(x3)+x4+np.power((x5/500),0.3)-x6+
                        np.sqrt(x7)+x8+noise
                 See this function's help for details.
        
        ## Installation
        
        You can directly install it using the following command for the latest version:
             pip install resautonet -U  
        You can also clone the repository and then install:
        
        ```bash
        git clone --recursive https://github.com/lspatial/resautonet.git
        cd package 
        pip install ./setup.py install 
        ```
        
        With the `setup.py` file included in this example, the `pip install` command will
        invoke CMake and build the resautonet module as specified in `CMakeLists.txt`.
        
        
        ## Note for installation and use 
        
        **Compiler requirements**
        
        resautonet requires a C++11 compliant compiler to be available.
        
        **Runtime requirements**
        
        resautonet requires installation of Keras with support of Tensorflow or other 
        backend system of deep learning (to support Keras). Also Pandas and Numpy should 
        be installed. 
        
        
        ## Use case 
        The homepage of the github for the package, resautonet provides two specific 
        examples for use of autoencoder based residual deep network:  
        https://github.com/lspatial/resautonet 
        
        
        ## License
        
        The resautonet is provided under a MIT license that can be found in the LICENSE
        file. By using, distributing, or contributing to this project, you agree to the
        terms and conditions of this license.
        
        ## Test call
        
        ```python
        import resautonet as r
        #Load the sample dataset for PM2.5  
        simdata=r.data('pm2.5')
        simdata.head()
        ```
        ## Collaboration
        
        Welcome to contact Dr. Lianfa Li (Email: lspatial@gmail.com). 
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