Version 0.0.0.9000
DeepGLM is a flexible model that use Deep Feedforward Neuron Network as the basis function for Generalized Linear Model. DeepGLM is designed to work with Cross-Sectional Dataset such as real estate data, cencus data, etc.
For more information about DeepGLM, please read the paper: Minh-Ngoc Tran,Nghia Nguyen, David J. Nott and Robert Kohn (2018) Bayesian Deep Net GLM and GLMM https://arxiv.org/abs/1805.10157
Nghia Nguyen (nghia.nguyen@sydney.edu.au)
Minh-Ngoc Tran (minh-ngoc.tran@sydney.edu.au)
Users can choose either Matlab, R or Python version to train and make prediction with deepGLM.
To use the Toolbox, add the folder called "deepGLM" (with Subfolders) to the MATLAB path.
The toolbox contains the following folders:
- Data: some datasets used in the examples.
- Examples: examples of all the functions included in the toolbox.
- Documents: documentations for the functions in deepGLM toolbox
- deepGLM: all the functions of the toolbox all here. This is the folder you must add to the MATLAB path.
Install deepglm package for R:
- Clone the directory or directly download the zip file deepglm_0.0.0.9000.zip inside deepGLM/R/ subdirectory on github.
- In Rstudio, run the command:
install.packages("D:\deepglm_0.0.0.9000.zip", repos = NULL, type="source")
where D:\deepglm_0.0.0.9000.zip is the package directory in my local machine - To use the package, run the command:
library(deepglm)
deepglm provides two function to train a deepGLM model on training data (deepGLMfit) and to make prediction using a trained deepGLM model on unseen data (deepGLMpredict). In Studio, use command: ?deepGLMfit and ?deepGLMpredict to read the documentation for two functions
Use command example(deepGLMfit) to run the example showing how to run deepGLMpredict and deepGLMpredict on a simulation data
User can run addition examples using scripts in demos folder in the installation directory. For example, the installation directory for deepglm package in my Window machine is: D:\Program Files\R\R-3.4.3\R-3.4.3\library\deepglm
Download the file deepGLM.pyc to your project folder.
Please, cite the toolbox as:
Tran, M.-N., Nguyen, N., Kohn, R., and Nott, D. (2018) Bayesian Deep Net GLM and GLMM. arXiv preprint arXiv:1805.10157