diff --git a/caliGraph.py b/caliGraph.py
index dbbf3b6..b10961f 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -1058,20 +1058,17 @@ def evaluateFitness(books, batchSize=-1, debugPrint=False):
             for wt in weights:
                 scoreB = sum([a*(1.001 if wt==w[0] else 1)*weights[w[0]]*(w[1] if len(w)>1 else 1) for a,w in zip(G.nodes[b]['_act'], G.nodes[b]['_wgh'])])/sum([(1.001 if wt==w[0] else 1)*weights[w[0]]*(w[1] if len(w)>1 else 1) for w in G.nodes[b]['_wgh']])
                 gradient[wt] += ((rating - G.nodes[b]['score'])**2 - (rating - scoreB)**2)*1000
-        if 'score' in G.nodes[b] and G.nodes[b]['score'] != None:
-            score = G.nodes[b]['score']
-            if score > 10.0:
-                boundsLoss += (score - 10)**2
-            elif score < 0.0:
-                boundsLoss += (score)**2
-            # reward seperation linearly
-            linSepLoss.append(abs(score - mu))
-    regressionLoss = sum([(1-w)**2 for w in weights.values()])
+    regressionLoss = sum([max(0,abs(w)-1)**2 for w in weights.values()]) # no punishment if w within -1 and 1
+    for wt in weights:
+        if abs(weights[wt]) > 1.0:
+            gradient[wt] -= weights[wt]*10
+        else:
+            gradient[wt] -= weights[wt]*1
     for g in gradient:
         gradient[g] /= len(errSq)
     if debugPrint:
-        print(sum(errSq)/len(errSq), 0.001*regressionLoss, 0.1*boundsLoss/len(ratedBooks), 0.5*sum(linSepLoss)/len(linSepLoss))
-    fit = sum(errSq)/len(errSq) + 0.001*regressionLoss + 0.1*boundsLoss/len(ratedBooks) - 0.5*sum(linSepLoss)/len(linSepLoss)
+        print(sum(errSq)/len(errSq), 0.001*regressionLoss)
+    fit = sum(errSq)/len(errSq) + 0.001*regressionLoss
     return fit, gradient
 
 def train(initGamma, full=True):
@@ -1171,7 +1168,7 @@ def cliInterface():
     p_show.add_argument('-d', type=float, default=2.1, help='depth of expansion')
 
     p_train = cmds.add_parser('train', description="TODO", aliases=[])
-    p_train.add_argument('-g', type=float, default=0.1, help='learning rate gamma')
+    p_train.add_argument('-g', type=float, default=0.2, help='learning rate gamma')
     p_train.add_argument('--full', action="store_true")
 
     p_prog = cmds.add_parser('progress', description="TODO", aliases=[])