structure.py
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from operator import itemgetter as ig
from math import floor
class RSSVector():
distances = []
def __init__(self, n1, n2, n3, n4):
'''
:param n1: AP1 RSSI
:param n2: AP2 RSSI
:param n3: AP3 RSSI
:param n4: AP4 RSSI
'''
self.n1 = n1
self.n2 = n2
self.n3 = n3
self.n4 = n4
def __eq__(self, v2):
return True if v2.n1 == self.n1 and v2.n2 == self.n2 \
and v2.n3 == self.n3 and v2.n4 == self.n4 else False
class Location():
def __init__(self, x, y, z=0):
self.x = x
self.y = y
self.z = z
def __eq__(self, loc2):
if self.x == loc2.x and self.y == loc2.y and self.z == loc2.z:
return True
else:
return False
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 __isub__(self, value):
return self + -1 * value
def __itruediv__(self, divider):
returnValue = Location(self.x, self.y, self.z)
returnValue.x /= divider
returnValue.y /= divider
returnValue.z /= divider
return returnValue
def toString(self):
return "(" + str(self.x) + " ; " + str(self.y) + " ; " + str(self.z) + ")"
def getPositionInArray(self, arraySize=3):
'''
Returns the unique ID of a fingerprint given its location
:param arraySize: (Optional) dimension of the array
'''
temp = Location(self.x, self.y)
temp /= 2
temp -= Location(1,1)
return floor((temp.x * arraySize + temp.y)/2)
@staticmethod
def fromID(id, arraySize=3):
'''
Returns the Location of a fingerprint of known ID
:param: ID to resolve
:param arraySize: (Optional) dimension of the array
'''
id -= 1
y=id % 3
x=floor((id - y) / 3)
returnValue = Location(x, y)
returnValue *= 2
returnValue += Location(1,1)
returnValue *= 2
return returnValue
class Cell():
def __init__(self, v_, loc):
'''
:param v_: RSSI vector of the fingerprint
:param loc: Location of the fingerprint
'''
self.v = v_
self.location = loc
class MarkovValue():
def __init__(self, nb=0, percentage=0.0):
'''
:param nb: Counter of incoming/outgoing movements
:param percentage: probability of being the next movement [0.0 , 1.0]
'''
self.nb = nb
self.percentage = percentage # Probability of Markov model (100% <=> 1.0)
class MarkovModel():
def __init__(self,cells):
'''
:param cells: an array containing all the cells of the model
'''
self.MarkovValues = [] #table of the coefficients of the Markov Model
self.cells = cells
self.previousCell = 0
for i in range (0, 11):
self.MarkovValues.append([])
for _ in range (0, 10):
self.MarkovValues[i].append(MarkovValue())
self.MarkovValues[10][0].nb = 1 #initial position sigma increment
def moveToCellID(self, nextCell):
'''
Registers a movement from the current cell to a specified location by its ID
:param nextCell: The ID of the new location
'''
self.MarkovValues[nextCell][self.previousCell].nb += 1
self.MarkovValues[10][nextCell].nb += 1
self.refreshPercentage(self.previousCell)
self.previousCell = nextCell
def moveToCell(self, nextCell):
'''
Registers a movement from the current cell to another based on the Location of its fingerprint
:param nextCell: The location of the new cell
'''
self.moveToCellID(nextCell.location.getPositionInArray()+1)
def refreshPercentage(self, col):
'''
Refreshes the probabilities of a column after a counter is changed
Needed after every change to the nb field
:param col: the # of the column to refresh
'''
if self.MarkovValues[10][col].nb:
for k in range(0,10):
self.MarkovValues[k][col].percentage = self.MarkovValues[k][col].nb / self.MarkovValues[10][col].nb
def printValues(self):
'''
Prints the counters of the Markov Model in a human-readable table form
'''
print("\t? \t1 \t2 \t3\t4 \t5 \t6 \t7 \t8 \t9")
print("---------------------------------------------------------------------------------", end='')
for i in range (0, 11):
print("\r\n", end='')
if i == 10 or i == 1:
print("---------------------------------------------------------------------------------\r\n",end='')
print(i, end='\t')
for k in range (0,10):
if not self.MarkovValues[i][k].nb:
print("\033[0;31;40m", end='')
else:
print("\033[0;32;40m", end='')
print(self.MarkovValues[i][k].nb, end='\t')
print("\033[1;37;40m", end='')
print("")
def printPercentages(self):
'''
Prints the percentages of the Markov Model in a human-readable table form
'''
print("\t? \t1 \t2 \t3\t4 \t5 \t6 \t7 \t8 \t9")
print("---------------------------------------------------------------------------------", end='')
for i in range (1, 10):
print("\r\n", i, end='\t')
for k in range (0,10):
if not self.MarkovValues[i][k].percentage:
print("\033[0;31;40m", end='')
elif k != self.previousCell or self.getMostLikely() != i:
print("\033[0;32;40m", end='')
else:
print("\033[4;30;47m", end='')
print(str(floor(self.MarkovValues[i][k].percentage * 100)), end='%')
print("\033[1;37;40m\t", end='')
print("")
def getMostLikely(self):
'''
Returns the ID of the most likely next location
Convert to coordinates using the Location.fromID() function
:return: ID of the most likely next location
'''
return self.getMostLikelyFromCell(self.previousCell)
def getMostLikelyFromCell(self, currentCell):
'''
Returns the ID of the most likely next location with a given previous cell ID
Typically called by getMostLikely() function
Convert to coordinates using the Location.fromID() function
:param currentCell: ID of the last cell
:return: ID of the most likely next location
'''
max=0
max_id=0
for k in range(1,10):
if self.MarkovValues[k][currentCell].nb > max:
max = self.MarkovValues[k][currentCell].nb
max_id = k
return max_id
def path(self, locationIDs):
'''
shorthand for defining multiple movements betweens cells
:param LocationIDs: Array containing the different cell IDs in order of movement
'''
for loc in locationIDs:
self.moveToCellID(loc)
def newCell(n1, n2, n3, n4, l1, l2):
'''
Shorthand for Cell creation
:param n1: AP1 RSSI
:param n2: AP2 RSSI
:param n3: AP3 RSSI
:param n4: AP4 RSSI
:param L1: x coordinate of the fingerprinting location
:param L2: y coordinate of the fingerprinting location
:return: Cell with given characteristics
'''
return Cell(RSSVector(n1,n2,n3,n4), Location(l1,l2))
def KNeighbors(fingerprints, sample):
'''
Returns the 4 closest cells to the given sample and fills sample distance data
:param fingerprints: 2D array of all the cells
:param sample: Mobile terminal sample
:return: the 4 nearest cells
'''
distances, neighbours = [], []
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))
for k in range (0,4):
neighbours.append(distances[k][1])
sample.distances.append(distances[k][0])
return neighbours
def resolve_barycenter(nC, d):
'''
Returns the weighted barycenter of the 4 neighbouring cells
:param nC: (neighborCells) Array containing the 4 closest cells
:param d: distances of the sample of the mobile terminal
:return: Estimated location of the mobile terminal (return None if error)
'''
return None if len(nC) != 4 or len(d) != 4 else \
1 / (1+d[0]/d[1]+d[0]/d[2]+d[0]/d[3])*nC[0].location \
+ 1 / (1+d[1]/d[0]+d[1]/d[2]+d[1]/d[3])*nC[1].location \
+ 1 / (1+d[2]/d[1]+d[2]/d[0]+d[2]/d[3])*nC[2].location \
+ 1 / (1+d[3]/d[1]+d[3]/d[2]+d[3]/d[0])*nC[3].location