diff --git a/.gitignore b/.gitignore
index b0c914a..5efa0bd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,5 +2,5 @@ __pycache__
 *.html
 .venv
 neuralWeights.json
-neuralWeights.json.bak
+neuralWeights.json.*
 .imgLinkCache.json
diff --git a/caliGraph.py b/caliGraph.py
index 97cde1f..be0892a 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -435,8 +435,9 @@ def scoreUnread(G, globMu, globStd):
                 score = 0
                 nb = dict(neuralBins)
                 act = {}
+                jig = {}
                 for b in nb:
-                    act[b] = sum(nb[b])/len(nb[b])
+                    act[b], jig[b] = norm.fit(nb[b])
                     score += act[b] * getWeightForType(b)
                 score /= sum([abs(getWeightForType(b)) for b in nb])
                 node['score'] = math.tanh(score/10)*10
@@ -1200,7 +1201,7 @@ def findNewBooks(G, books, mu, num=-1, minRecSco=5):
 # while batchSize is implemented, we only get a good gonvergence when we disable it (batchSize=-1)
 # but might be necessary to enable later for a larger libary for better training performance...
 # maybe try again for 128 books?
-def evaluateFitness(books, batchSize=16, debugPrint=False, runPagerank=True):
+def evaluateFitness(books, batchSize=16, debugPrint=False, calcPagerank=True):
     global weights
     G = buildBookGraph(books)
     graphAddAuthors(G, books)
@@ -1208,7 +1209,7 @@ def evaluateFitness(books, batchSize=16, debugPrint=False, runPagerank=True):
     graphAddTopLists(G, books)
     graphAddSeries(G, books)
     graphAddTags(G, books)
-    if runPagerank:
+    if calcPagerank:
         runPagerank(G)
 
     ratedBooks = [n for n in list(G.nodes) if 'rating' in G.nodes[n] and G.nodes[n]['rating'] != None]
@@ -1259,7 +1260,7 @@ def train(initGamma, full=True, noPagerank=False):
     gamma = initGamma
     books = loadBooksFromDB()
     bestWeights = copy.copy(weights)
-    mse, gradient = evaluateFitness(books, runPagerank=not noPagerank)
+    mse, gradient = evaluateFitness(books, calcPagerank=not noPagerank)
     delta = math.sqrt(sum(gradient[g]**2 for g in gradient)/len(gradient))
     best_mse = mse
     stagLen = 0
@@ -1277,7 +1278,7 @@ def train(initGamma, full=True, noPagerank=False):
                 weights[wt] += gamma*gradient[wt]/math.sqrt(delta)
             #else:
             #    del weights[wt]
-        mse, gradient = evaluateFitness(books, runPagerank=not noPagerank)
+        mse, gradient = evaluateFitness(books, calcPagerank=not noPagerank)
         if mse < last_mse:
             gamma = gamma*1.25
         else: