Metadata-Version: 2.1
Name: resautonet
Version: 0.2.1
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)
        
        The python library of autoencoder based residual deep network (resautonet). 
        Current version (2.0) 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). 
Platform: UNKNOWN
Classifier: Development Status :: 6 - Mature
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Description-Content-Type: text/markdown
