Metadata-Version: 2.1
Name: baggingrnet
Version: 0.0.7
Summary: Library of Bagging of Deep Residual Neural Networks
Home-page: UNKNOWN
Author: Lianfa Li
Author-email: lspatial@gmail.com
License: UNKNOWN
Description: # Library of Bagging of Deep Residual Neural Networks (baggingrnet)
        
        [![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)
        
        his package provides The python Library for Bagging of Deep Residual Neural Networks (baggingrnet).
        Current version just supports the KERAS package of deep learning and will extend to the others in the future.
        
        ## Major modules

        **model**

        * model multBagging: Major class to parallel bagging of autoencoder-based deep residual networks.
                              You can setup its aruments for optimal effects.
                              See the class and its member functions' help for details.
                 resAutoencoder: Major class of the base model of autoencoder-based deep residual network.
                                 See the specifics for its details.
                 ensPrediction: Major class to ensemble predictions and optional evaluation for independent test.

        **util**
        * util pmetrics: main metrics including rsquare and rmse etc.

        **data**
        * data data: function to access two sample datas to test and demonstrate parallel training and predictions
                of multiple models by bagging. simData: function to simulate the dataset for a test.

        ## Installation
        
        You can directly install it using the following command for the latest version:
             pip install baggingrnet -U
        You can also clone the repository and then install:
        
        ```bash
        git clone --recursive https://github.com/lspatial/baggingrnet.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 baggingrnet module as specified in `CMakeLists.txt`.
        
        
        ## Note for installation and use 
        
        **Compiler requirements**
        
        baggingrnet requires a C++11 compliant compiler to be available.
        
        **Runtime requirements**
        
        baggingrnet 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, baggingrnet provides two specific
        examples for use of autoencoder based residual deep network:  
        https://github.com/lspatial/baggingrnet
        
        
        ## License
        
        The baggingrnet 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 baggingrnet as r
        #Load the sample dataset for PM2.5  
        simdata=r.data('pm2.5_train')
        simdata.head()
        ```
        ## Collaboration
        
        Welcome to contact Dr. Lianfa Li (Email: lspatial@gmail.com or lilf@lreis.ac.cn).
Platform: UNKNOWN
Description-Content-Type: text/markdown
