diff --git a/.gitignore b/.gitignore
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index 0000000..7a60b85
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+__pycache__/
+*.pyc
diff --git a/README.mk b/README.mk
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index 0000000..0db86ed
--- /dev/null
+++ b/README.mk
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+[UTBM - LO53] - Python fingerprinting
+Achille Dantz <achille@dantz.fr>
+
+This program aims to compute the approximate location of a mobile terminal for a known set of Wi-Fi accesspoints in an ideal 12x12m space for which we have 9 fingerprinting values (known RSSI for each accesspoint at a given location)
diff --git a/main.py b/main.py
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--- /dev/null
+++ b/main.py
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+
+from structure import *
+import sys 
+
+Tf = [] #cells table
+testSample = RSSVector(-26, -42, -13, -46)
+
+
+#cells Table initialization
+for i in range (0, 3):
+    Tf.append([])
+    for k in range (0,3):
+        Tf[i].append([])
+
+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))
+Tf[1][0] = Cell(RSSVector(-34,-27,-38,-41), Location(6,2))
+Tf[1][1] = Cell(RSSVector(-64,-48,-72,-35), Location(6,6))
+Tf[1][2] = Cell(RSSVector(-45,-37,-20,-15), Location(6,10))
+Tf[2][0] = Cell(RSSVector(-17,-50,-44,-33), Location(10,2))
+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)
+
+
+        print("\nk neighbors of test sample : ")
+        neighborsCells = KNeighbors(Tf, testSample)
+        for k in neighborsCells:
+                print("(", k.location.x, ";", k.location.y, ")")
+
+        print ("\ndistances : " + str(testSample.distances))
+
+        testLoc = Location(3,4,5)
+        loc2 = Location(1,2,3)
+        print((testLoc+loc2).toString())
+
+        a = resolve_barycenter(neighborsCells, testSample)
+        print(a.toString)
+        return 0;
+
+
+main(sys.argv)
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diff --git a/structure.py b/structure.py
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+++ b/structure.py
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+from operator import itemgetter
+
+class RSSVector():
+    distances = []
+    def __init__(self, n1, n2, n3, n4):
+        self.n1 = n1
+        self.n2 = n2
+        self.n3 = n3
+        self.n4 = n4
+
+class Location():
+    def __init(self):
+        self.x = self.y = self.z = 0
+
+    def __init__(self, x, y, z=0):
+        self.x = x
+        self.y = y
+        self.z = z
+    
+    def __mul__(self, multiplier):
+        returnValue = Location(self.x, self.y, self.z)
+        returnValue.x *= multiplier 
+        returnValue.y *= multiplier
+        returnValue.z *= multiplier
+        return returnValue
+    def __rmul__(self, multiplier):
+        return self * multiplier
+
+    def __add__(self, added):
+        returnValue = Location(self.x, self.y, self.z)
+        returnValue.x += added.x
+        returnValue.y += added.y
+        returnValue.z += added.z
+        return returnValue
+
+    def toString(self):
+        return "(" + str(self.x) + " ; " + str(self.y) + " ; " + str(self.z) + ")"
+
+
+class Cell():
+    def __init__(self, v_, loc):
+        self.v = v_
+        self.location = loc
+
+
+def KNeighbors(fingerprints, sample):  
+    '''
+    Returns the 4 closest cells to the given sample and fills sample distance data
+    :param Cell[3][3] fingerprints: 2D array of all the cells
+    :param RSSIVector sample: Mobile terminal sample
+    :return Cell[4]: the 4 nearest cells
+    '''
+    distances = []
+    for row in fingerprints:
+        for currentItem in row:
+            dist = abs(currentItem.v.n1 - sample.n1) \
+                    + abs(currentItem.v.n2 - sample.n2) \
+                    + abs(currentItem.v.n3 - sample.n3) \
+                    + abs(currentItem.v.n4 - sample.n4) 
+            distances.append((dist, currentItem))
+            distances = sorted(distances, key=itemgetter(0))
+    neighbours = []
+    for k in range (0,4):
+        neighbours.append(distances[k][1])
+        sample.distances.append(distances[k][0])
+    return neighbours
+
+
+
+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 
+    '''
+    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
+     
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