Update README.md

README.md

@@ -41,7 +41,7 @@ Code for nonlinear NOTEARS has been added. See [[2](https://arxiv.org/abs/1909.1
 
 ## tl;dr Structure learning in <60 lines
 
-Check out [`notears.py`](src/notears.py) for a complete, end-to-end implementation of the NOTEARS algorithm in fewer than **60 lines**.
+Check out [`linear.py`](src/linear.py) for a complete, end-to-end implementation of the NOTEARS algorithm in fewer than **60 lines**.
 
 This includes L2, Logistic, and Poisson loss functions with L1 penalty. 
 
@@ -73,11 +73,14 @@ space of DAGs.
 - `numpy`
 - `scipy`
 - `python-igraph`: Install [igraph C core](https://igraph.org/c/) and `pkg-config` first.
- 
+- `torch': Optional, only used for nonlinear model.
 
 ## Contents (New version)
 
-- `notears.py` - the 60-line implementation of NOTEARS with l1 regularization for various losses
+- `linear.py` - the 60-line implementation of NOTEARS with l1 regularization for various losses
+- `nonlinear.py` - nonlinear NOTEARS using MLP or basis expansion
+- `locally_connected.py` - special layer structure used for MLP
+- `lbfgsb_scipy.py` - wrapper for scipy's LBFGS-B
 - `utils.py` - graph simulation, data simulation, and accuracy evaluation
 
 
@@ -87,7 +90,7 @@ The simplest way to try out NOTEARS is to run a simple example:
 ```bash
 $ git clone https://github.com/xunzheng/notears.git
 $ cd notears/
-$ python src/notears.py
+$ python src/linear.py
 ```
 This runs the l1-regularized NOTEARS on a randomly generated 20-node Erdos-Renyi graph with 100 samples. 
 Within a few seconds, you should see output like this:
@@ -102,7 +105,7 @@ The data, ground truth graph, and the estimate will be stored in `X.csv`, `W_tru
 Alternatively, if you have a CSV data file `X.csv`, you can install the package and run the algorithm as a command:
 ```bash
 $ pip install git+git:https://github.com/xunzheng/notears
-$ notears_linear_l1 X.csv
+$ notears_linear X.csv
 ```
 The output graph will be stored in `W_est.csv`.