From df47ed4cb2b7e1d05f86b20899244f2350bac05c Mon Sep 17 00:00:00 2001
From: Steve Nyemba <steve@the-phi.com>
Date: Tue, 31 Dec 2019 23:34:04 -0600
Subject: [PATCH] documentation

---
 README.md | 36 ++++++++++++++++++++----------------
 1 file changed, 20 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index b42b1f7..f5c5e5d 100644
--- a/README.md
+++ b/README.md
@@ -12,32 +12,33 @@ This package is designed to generate synthetic data from a dataset from an origi
 ## Usage
 
 After installing the easiest way to get started is as follows (using pandas). The process is as follows:
-1. Train the GAN on the original/raw dataset
 
+**Train the GAN on the original/raw dataset**
 
-import pandas as pd
-import data.maker
 
-df      = pd.read_csv('sample.csv')
-column  = 'gender'
-id      = 'id' 
-context = 'demo'
-data.maker.train(context=context,data=df,column=column,id=id,logs='logs')
+    import pandas as pd
+    import data.maker
+
+    df      = pd.read_csv('sample.csv')
+    column  = 'gender'
+    id      = 'id' 
+    context = 'demo'
+    data.maker.train(context=context,data=df,column=column,id=id,logs='logs')
 
 The trainer will store the data on disk (for now) in a structured folder that will hold training models that will be used to generate the synthetic data.
 
 
-2. Generate a candidate dataset from the learnt features
+**Generate a candidate dataset from the learned features**
 
 
-import pandas as pd
-import data.maker
+        import pandas as pd
+        import data.maker
 
-df  = pd.read_csv('sample.csv')
-id  = 'id'
-column = 'gender'
-context = 'demo'
-data.maker.generate(data=df,id=id,column=column,logs='logs')
+        df  = pd.read_csv('sample.csv')
+        id  = 'id'
+        column = 'gender'
+        context = 'demo'
+        data.maker.generate(data=df,id=id,column=column,logs='logs')
 
 ## Limitations
 
@@ -46,11 +47,14 @@ GANS will generate data assuming the original data has all the value space neede
 - No new data will be created
     
         Assuming we have a dataset with an gender attribute with values [M,F]. 
+            
         The synthetic data will not be able to generate genders outside [M,F]
+        
 - Not advised on continuous values
 
         GANS work well on discrete values and thus are not advised to be used.
         e.g:measurements (height, blood pressure, ...)
+- For now will only perform on a single feature.
 
 ## Credits :