update readme

README.md

@@ -12,20 +12,33 @@ This repo contains a C++ implementation of our paper, "Hawkes Process Modeling o
 >  year={2017}
 > }
 
+---
+
 ## Data
 Each row in the data file represent the trajectory of one patient, which is a sequence of ordered time-event pairs separated by whitespace:
 > t_1 m_1 t_2 m_2 ... t_n m_n
 
-where 0 <= t_1 <= ... <= t_n and m_i in {1,...,numOfVariables}. We assume {1,...,numOfOutcomes} to be the indices for the adverse outcomes and {numOfOutcomes+1,...,numOfVariables} to be the idices for the drugs of interest.
+where 0 <= t_1 <= ... <= t_n and m_i in {1,...,numOfVariables}. 
+
+We assume {1,...,numOfOutcomes} to be the indices for the adverse outcomes and {numOfOutcomes+1,...,numOfVariables} to be the idices for the drugs of interest.
 
 The Marshfield Clinic EHR data is not publicly available due to patients' privacy. We provide an example synthetic data generated from a Poisson autoregressive model. 
 
+---
+
 ## Code Usage
 1. Compile
 ```
 g++ --std=c++14 Hawkes.cpp -o Hawkes.out
 ```
 2. Example run
 ```
-./Hawkes.out -f "file_to_data" -k num_of_kernels -w length_of_windows -l lasso_regularizations 
+./Hawkes.out -f path_to_data -k num_of_kernels -w length_of_windows -l lasso_regularizations
 ```
+
+---
+### License
+
+The MIT License (MIT)
+
+Copyright (c) 2017 Michael Rose