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