diff --git a/gen-bn b/gen_bn
similarity index 100%
rename from gen-bn
rename to gen_bn
diff --git a/main.py b/main.py
index 01981f0..9465f83 100644
--- a/main.py
+++ b/main.py
@@ -5,23 +5,74 @@
 
 import json
 import random
-from collections import defaultdict
+import math
+import sys
+import os
+
+#pwd = os.getcwd()
+#sys.path.append(pwd +'/gen-bn/')
+#sys.path.append("./gen-bn")
+
+import gen_bn.gen_bn
+
+#from collections import defaultdict
 
 def main():
+    #Generate a new BN. Specify type and number of nodes in network
+    gen_json("dag", 5)
+    
+    #Get our BN
     bayes_net = import_bayes()
-    ev = {"0" : True, "4" : True}
-    W = likelihood_weighting(1, ev, bayes_net, 10)
+    
+    #Generate random evidence
+    E = gen_ev(bayes_net)
+    
+    #Get W from LW
+    W = likelihood_weighting(1, E, bayes_net, 10)
+    
+    #Print if desired
     print()
     for key, value in W.items():
         print(key, ' : ', value)
 
+#Generate a new BN.
+#Input: Type ("dag", or "polytree")
+#Input: Number of nodes in the network
+def gen_json(type, num_nodes):
+    os.chdir("./gen_bn")
+    os.system('python gen_bn.py' + ' ' + type + ' ' + str(num_nodes))
+ 
 #Import the BN from the json
 def import_bayes():
-    with open ("gen-bn/bn.json") as json_file:
+    with open ("gen_bn/bn.json") as json_file:
         bayes_json = json.load(json_file)
     json_file.close()
     return bayes_json
 
+#Generate a random set of evidence
+def gen_ev(bayes_net):
+    total_nodes = len(bayes_net)
+    #Arbitrarily, let's only generate total_nodes/2 (rounded up) evidence variables at most, but at least 1
+    num_ev = random.randint(1, int(math.ceil(total_nodes/2)))
+    fixed_ev = []
+    #Go through and generate nodes that will be fixed
+    for i in range(num_ev):
+        fixed_var = random.randint(0, total_nodes-1)
+        if fixed_var not in fixed_ev:
+            fixed_ev.append(fixed_var)
+    #Now generate random values for the ev
+    #Randomly generate a double. >=0.5 will be "True", <0.5 will be "False"
+    E = {}
+    for i in fixed_ev:
+        val_p = random.random()
+        if val_p >= 0.5:
+            E[str(i)] = True
+        else:
+            E[str(i)] = False
+    
+    return E
+
+
 #Checks if node has parents
 def is_root(node, BN):
     return (BN[node]["parents"]) == []