def test_getPositionInArray():
test_loc = Location(2, 2)
assert test_loc.getPositionInArray() == 0
test_loc = Location(2, 6)
assert test_loc.getPositionInArray() == 1
test_loc = Location(10, 10)
assert test_loc.getPositionInArray() == 8
def test_Location():
loc1 = Location(1, 2)
loc2 = Location(3, 4)
assert loc1 + loc2 == Location(4, 6)
assert loc1 * 2 == Location(2, 4)
loc2 -= Location(1, 1)
assert loc2 == Location(2, 3)
loc1 *= 3
assert loc1 == Location(3, 6)
def main():
#### N-Lateration ####
NLat_result = NLateration(dataset, step=1)
printf("\r\nN-Lateration :\nComputed location : " +
NLat_result[0].toString())
printf("With distance = " + str(round(NLat_result[1], 2)) + " m")
#### K neighbours ####
printf("\nK-neighbors of test sample : ")
neighborsCells = KNeighbors(Tf, testSample)
for k in neighborsCells:
printf("(", k.location.x, ";", k.location.y, ")")
#### Distances ####
print("\nDistances : " + str(testSample.distances))
#### Barycenter ####
printf("\r\nWeighted barycenter :")
a = resolve_barycenter(neighborsCells, testSample.distances)
printf(a.toString())
#### Markov ####
MM = MarkovModel(Tf)
# small set fixed definition
MM.path([
8, 7, 8, 7, 8, 7, 8, 5, 8, 2, 9, 8, 1, 9, 8, 9, 5, 4, 3, 2, 3, 2, 4, 5,
4, 5, 6, 6, 7, 6, 9, 5, 9, 3, 2, 4, 3, 5, 3, 4, 3, 3, 5, 6, 7, 6, 7, 6,
5, 4, 3, 4, 3, 4
])
# larger set random generation
for k in range(0, 100):
MM.moveToCellID(floor(random() * 9 + 1))
printf("\r\n")
MM.printValues()
printf("\r\nPERCENTAGES : \r\n")
MM.printPercentages()
printf("\r\ncurrent cell is {output.GREEN}" + str(MM.previousCell) +
"{output.NORMAL} , most likely next cell is {output.GREEN}" +
str(MM.getMostLikely()) +
"{output.NORMAL} which is located at {output.GREEN}" +
str(Location.fromID(MM.getMostLikely()).toString()) +
"{output.NORMAL}")
while (1):
printf("Input next location ID (between 1 and 9)\r\n>>", end='')
in_char = int(input())
printf(output.CLEAR)
if in_char > 0 and in_char < 10:
MM.moveToCellID(in_char)
MM.printValues()
printf("\r\nPERCENTAGES : \r\n")
MM.printPercentages()
printf(
"\r\ncurrent cell is {output.GREEN}#" + str(MM.previousCell) +
"{output.NORMAL} , most likely next cell is {output.GREEN}" +
str(MM.getMostLikely()) +
"{output.NORMAL} which is located at {output.GREEN}" +
str(Location.fromID(MM.getMostLikely()).toString()) +
"{output.NORMAL}")
else:
printf("invalid ID")
break
from structure import output, printf, RSSVector, Location, newCell, KNeighbors, resolve_barycenter, MarkovModel, NLateration
from random import random
from math import floor
Tf = [] #cells table
#dataset : list of tuples representing emitters (Location, distance)
dataset = [(Location(.5, .5, .5), 3.0), (Location(4.0, .0, .0), 2.0),
(Location(4.0, 5.0, 5.0), 4.2), (Location(3.0, 3.0, 3.0), 2.5)]
testSample = RSSVector(-26, -42, -13, -46)
#known fingerprints
Tf = [[newCell(-38,-27,-54,-13,2,2),newCell(-74,-62,-48,-33,2,6),newCell(-13,-28,-12,-40,2,10) ],\
[newCell(-34,-27,-38,-41,6,2), newCell(-64,-48,-72,-35,6,6), newCell(-45,-37,-20,-15,6,10)], \
[newCell(-17,-50,-44,-33,10,2), newCell(-27,-28,-32,-45,10,6), newCell(-30,-20,-60,-40,10,10)]]
def main():
#### N-Lateration ####
NLat_result = NLateration(dataset, step=1)
printf("\r\nN-Lateration :\nComputed location : " +
NLat_result[0].toString())
printf("With distance = " + str(round(NLat_result[1], 2)) + " m")
#### K neighbours ####
printf("\nK-neighbors of test sample : ")
neighborsCells = KNeighbors(Tf, testSample)
for k in neighborsCells:
printf("(", k.location.x, ";", k.location.y, ")")
def test_fromID():
test_loc = Location.fromID(3)
assert test_loc == Location(2, 10)
test_loc = Location.fromID(9)
assert test_loc == Location(10, 10)
def test_newCell():
testCell = newCell(-38, -27, -54, -13, 2, 2)
assert testCell.location == Location(2, 2)
assert testCell.v == RSSVector(-38, -27, -54, -13)
def test_NLateration():
test_result = NLateration(testEmitters)
assert test_result[0] == Location(3.3, 1.5, 1.1)
assert round(test_result[1], 2) == 1.19
from structure import KNeighbors, resolve_barycenter, Location, newCell, MarkovModel, RSSVector, NLateration
from io import StringIO
import sys
testEmitters = [(Location(.5, .5, .5), 3.0), (Location(4.0, .0, .0), 2.0),
(Location(4.0, 5.0, 5.0), 4.2), (Location(3.0, 3.0, 3.0), 2.5)]
Tf = [[newCell(-38,-27,-54,-13,2,2),newCell(-74,-62,-48,-33,2,6),newCell(-13,-28,-12,-40,2,10) ],\
[newCell(-34,-27,-38,-41,6,2), newCell(-64,-48,-72,-35,6,6), newCell(-45,-37,-20,-15,6,10)], \
[newCell(-17,-50,-44,-33,10,2), newCell(-27,-28,-32,-45,10,6), newCell(-30,-20,-60,-40,10,10)]]
testSample = RSSVector(-26, -42, -13, -46)
testCells = [Tf[0][2], Tf[2][1], Tf[1][0], Tf[2][0]]
testDistances = [34, 35, 53, 61]
def test_KNeighbors():
assert KNeighbors(Tf, testSample)[0].location.toString() == "(2 ; 10 ; 0)"
assert KNeighbors(Tf, testSample)[1].location.toString() == "(10 ; 6 ; 0)"
assert KNeighbors(Tf, testSample)[2].location.toString() == "(6 ; 2 ; 0)"
assert KNeighbors(Tf, testSample)[3].location.toString() == "(10 ; 2 ; 0)"
def test_resolve_barycenter():
result = resolve_barycenter(testCells, testDistances)
print(testSample.distances)
assert round(result.x, 2) == 6.67
assert round(result.y, 2) == 5.75
print("Importing Libs")
from structure import NLateration, Location
from math import floor
from PIL import Image, ImageDraw
import sys
import time
global_step = .05
upscale_factor = 1
upscale_method = Image.NEAREST
dataset = [(Location(.5, .5, 0), 3.0), (Location(5.0, 7.0, .0), 4.0),
(Location(3.0, .0, 5.0), 4)]
colorRange = 1.0 # [0.0, 1.0]
#NLat_result = NLateration(dataset, step=global_step)
frames = []
max = NLateration(dataset, step=global_step)
def createFrame(x, y, arr, nbr):
img = Image.new("HSV", (y, x))
img.putdata(arr[nbr])
return img
def exportGif(includeReverse=False):
print(
str(round(1000 * (time.time() - start), 2)) + "ms",
" - Calculating Array")
NLat_result = NLateration(dataset,
step=global_step,