Polish the instructions in the README.md

README.md

@@ -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.