diff --git a/README.md b/README.md
index 239ca74..bdf9fc1 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,7 @@ Currently, we are still working on developing a conda package, which might take
 Scikit-learn>=0.21
 Keras>=2.2.4 
 Cvxpy>=1.0.0
+NILMTK-0.3
 
 
 **Note: For faster computation of neural-networks, it is suggested that you install keras-gpu, since it can take advantage of GPUs. The algorithms AFHMM, AFHMM_SAC and DSC are CPU intensive, use a system with good CPU for these algorithms.**
diff --git a/disaggregate/rnn.py b/disaggregate/rnn.py
index 3aa461e..7bf3948 100644
--- a/disaggregate/rnn.py
+++ b/disaggregate/rnn.py
@@ -50,6 +50,7 @@ def partial_fit(self,train_main,train_appliances,do_preprocessing=True,
         # If no appliance wise parameters are provided, then copmute them using the first chunk
         if len(self.appliance_params) == 0:
             self.set_appliance_params(train_appliances)
+            
 
         print("...............RNN partial_fit running...............")
         # Do the pre-processing, such as  windowing and normalizing