Update README.md
Fixed the implicit/explicit thing, and changed `make list` to `make help`, as well as some visual changes.
@@ -8,9 +8,9 @@ see our blog post [Using machine learning to classify devices on your network](h
## Using Docker to evaluate, train, and test
-_Note: we are aware that issues currently exist that render the RandomForest model
+_**Note:** we are aware of issues that currently render the RandomForest model
unuseable, and we are working to fix this as soon as possible. You can check the
-progress on this fix via issue #104._
+progress on this fix via [issue #104](https://github.com/CyberReboot/PoseidonML/issues/104)._
We currently have two different models available on Docker Hub -- RandomForest and
OneLayer -- tagged `randomforest` and `onelayer`, respectively. OneLayer is used by
@@ -35,14 +35,19 @@ be mapped into the Docker container at runtime. The `train` and `test` function
require a directory of PCAP files along with a `label_assignments.json` for
those PCAPs. Additionally, you will need to map a place to save the models.
-Here's an example of explicit `eval` and implicit `onelayer`:
+Here's an example of implicitly calling `eval_onelayer`:
+```
+export PCAP=[path/to/file.pcap]
+make run
+```
+And an example of explicitly calling `eval_onelayer`:
```
export PCAP=[path/to/file.pcap]
-make run $PCAP
+make eval_onelayer
```
-Use `make list` to see the possible options.
+Use `make help` to see the possible options.
Output is currently JSON to STDOUT.
