diff --git a/main.py b/main.py
index 89c79ea..accae22 100644
--- a/main.py
+++ b/main.py
@@ -3,8 +3,13 @@ from structure import *
 import sys 
 
 Tf = [] #cells table
+
 testSample = RSSVector(-26, -42, -13, -46)
+testSample2 = RSSVector(-26, -42, -13, -46)
+testSample3 = RSSVector(-26, -42, -13, -46)
 
+testSamples = []
+testSamples.extend([testSample, testSample2, testSample3])
 
 #cells Table initialization
 for i in range (0, 3):
@@ -12,6 +17,7 @@ for i in range (0, 3):
     for k in range (0,3):
         Tf[i].append([])
 
+#known fingerprints 
 Tf[0][0] = Cell(RSSVector(-38,-27,-54,-13), Location(2,2))
 Tf[0][1] = Cell(RSSVector(-74,-62,-48,-33), Location(2,6))
 Tf[0][2] = Cell(RSSVector(-13,-28,-12,-40), Location(2,10))
@@ -23,24 +29,20 @@ Tf[2][1] = Cell(RSSVector(-27,-28,-32,-45), Location(10,6))
 Tf[2][2] = Cell(RSSVector(-30,-20,-60,-40), Location(10,10))
 
 def main(args):
-
-        print(Tf[1][1].v.n2)
-
-
+        #### K neighbours ####
         print("\nk neighbors of test sample : ")
         neighborsCells = KNeighbors(Tf, testSample)
         for k in neighborsCells:
                 print("(", k.location.x, ";", k.location.y, ")")
 
+        #### Distances ####
         print ("\ndistances : " + str(testSample.distances))
 
-        testLoc = Location(3,4,5)
-        loc2 = Location(1,2,3)
-        print((testLoc+loc2).toString())
-
+        #### Barycenter ####
         a = resolve_barycenter(neighborsCells, testSample)
-        print(a.toString)
-        return 0;
+        print(a.toString())
 
+        #### Markov ####
 
+        return 0;
 main(sys.argv)
\ No newline at end of file
diff --git a/structure.py b/structure.py
index 5c6aba1..81296e9 100644
--- a/structure.py
+++ b/structure.py
@@ -1,4 +1,4 @@
-from operator import itemgetter
+from operator import itemgetter as ig
 
 class RSSVector():
     distances = []
@@ -41,7 +41,8 @@ class Cell():
     def __init__(self, v_, loc):
         self.v = v_
         self.location = loc
-
+        self.Likeliness = .0 # Probability of Markov model
+        self.pastCount = 1
 
 def KNeighbors(fingerprints, sample):  
     '''
@@ -58,7 +59,7 @@ def KNeighbors(fingerprints, sample):
                     + abs(currentItem.v.n3 - sample.n3) \
                     + abs(currentItem.v.n4 - sample.n4) 
             distances.append((dist, currentItem))
-            distances = sorted(distances, key=itemgetter(0))
+            distances = sorted(distances, key=ig(0))
     neighbours = []
     for k in range (0,4):
         neighbours.append(distances[k][1])
@@ -72,9 +73,12 @@ def resolve_barycenter(neighbourCells, sample):
     Returns the weighted barycenter of the 4 neighbouring cells
     :param Cell[4] neighbourCells: Array containing the 4 closest cells
     :param RSSIVector sample: Sample of the mobile terminal
-    :return Location: Estimated location of the mobile terminal 
+    :return Location: Estimated location of the mobile terminal (return None if error)
     '''
     d = sample.distances #shorter notation
-    a1,a2,a3,a4 = 1 / (1+d[0]/d[1]+d[0]/d[2]+d[0]/d[3]), 1 / (1+d[1]/d[0]+d[1]/d[2]+d[1]/d[3]), 1 / (1+d[2]/d[1]+d[2]/d[0]+d[2]/d[3]), 1 / (1+d[3]/d[1]+d[3]/d[2]+d[3]/d[0])
-    return a1*neighbourCells[0].location + a2*neighbourCells[1].location + a3*neighbourCells[1].location + a4*neighbourCells[1].location
+    a1,a2,a3,a4 = 1 / (1+d[0]/d[1]+d[0]/d[2]+d[0]/d[3]),\
+                  1 / (1+d[1]/d[0]+d[1]/d[2]+d[1]/d[3]),\
+                  1 / (1+d[2]/d[1]+d[2]/d[0]+d[2]/d[3]),\
+                  1 / (1+d[3]/d[1]+d[3]/d[2]+d[3]/d[0])
+    return None if a1+a2+a3+a4 != 1.0 else a1*neighbourCells[0].location + a2*neighbourCells[1].location + a3*neighbourCells[2].location + a4*neighbourCells[3].location 
      
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libgit2 0.21.4