Commit 8d00808c authored by Erik Frisk's avatar Erik Frisk

Polish the instructions in the README.md

parent 645a67ea
......@@ -3,22 +3,28 @@ This repository contains data and code to generate results from the paper
_Residual Selection for Consistency Based Diagnosis Using Machine Learning Models_
by Erik Frisk and Mattias Krysander, Department of Electrical Engineering, Linköping University, Sweden
The repository includes code in both Matlab and Python (in the form of a Jupyter notebook).
presented at Safeprocess-2018, Warszaw, Poland.
Note that the generated plots are not identical to the results in the paper where a specific Matlab implementation of the machine learning algorithms were used. However, the methodology is the same and the results are similar.
The repository includes code in Matlab and Python. Note that the generated plots are not identical to the results in the paper where a specific Matlab implementation of the machine learning algorithms were used. However, the methodology is the same and the results are similar.
If you have any questions, you are welcome to contact any of the authors
If you have any questions, you are welcome to contact any of the authors<br>
Erik Frisk (erik.frisk@lliu.se) or
Mattias Krysander (mattias.krysander@liu.se)
# Matlab installation instructions
Clone the git repository and run the file ```main.m``` in the ```code``` directory.
# Get files
To get the files, either download the zip-archive or clone the git repository by
```bash
% git clone https://gitlab.liu.se/erifr93/MLTestSelection.git
```
# Matlab instructions
Run the file ```main.m``` in the ```code``` directory.
# Python installation instructions
# Python instructions
You must pick the mechanism by which you install packages, here the Virtualenv approach is described. Below is a generic approach that should work on linux, MacOS, or Windows.
You must choose the mechanism by which you install packages, here the [Virtualenv](https://virtualenv.pypa.io/) approach is described but it should be straightforward to modify for, e.g., [conda](https://www.continuum.io/downloads) or other python package managers. Below is a generic approach that should work on linux, MacOS, or Windows.
First, clone the git repository. Then, create a virtual environment
First, after getting the files, create a virtual environment
```bash
% python3 -m venv env
```
......@@ -28,13 +34,14 @@ and activate the virtual environment
```
Then install the necessary packages
```bash
% (env) pip install numpy matplotlib scipy sklearn jupyterlab
(env) % pip install numpy matplotlib scipy sklearn jupyterlab
```
Now, start 'JupyterLab' (or Jupyter Notebook if you prefer) and start exploring the notebook ```main.ipynb``` in the ```code``` directory by starting
Now, start [Jupyter Lab](https://jupyterlab.readthedocs.io/) or [Jupyter Notebook](https://jupyter-notebook.readthedocs.io/) if you prefer by
```bash
% (env) jupyter lab
(env) % jupyter lab
```
or
or
```bash
% (env) jupyter notebook
(env) % jupyter notebook
```
Then, start exploring the notebook ```main.ipynb``` in the ```code``` directory.
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment