structure.py
2.77 KB
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from operator import itemgetter as ig
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
self.Likeliness = .0 # Probability of Markov model
self.pastCount = 1
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=ig(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 (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 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