def bestBeeQuality(self):
distance = 0
i = 0
pos = -1
while (i < self.bees_number):
max_val = self.beeList[i].fitness
nbUn = Solution.nbrUn(self.beeList[i].solution.get_state())
while ((i < self.bees_number)
and (self.beeList[i].solution.get_accuracy(
self.beeList[i].solution.get_state()) == max_val)):
distanceTemp = self.distanceTabou(self.beeList[i])
nbUnTemp = Solution.nbrUn(self.beeList[i].solution.get_state())
if (distanceTemp > distance) or ((distanceTemp == distance) and
(nbUnTemp < nbUn)):
if ((distanceTemp == distance) and (nbUnTemp < nbUn)):
print("We pick the solution with less features")
nbUn = nbUnTemp
distance = distanceTemp
pos = i
i += 1
if (pos != -1):
return self.beeList[pos]
bee = Bee(-1, self.data, self.locIterations,
Bee.Rand(self.data.nb_attribs))
return bee
def searchArea(self):
i = 0
h = 0
self.beeList = []
while ((i < self.bees_number) and (i < self.flip)):
#print ("First method to generate")
solution = self.refSolution.solution.get_state()
k = 0
while ((self.flip * k + h) < len(solution)):
solution[self.flip * k +
h] = ((solution[self.flip * k + h] + 1) % 2)
k += 1
newBee = Bee(i, self.data, self.locIterations, solution)
self.beeList.append(newBee)
i += 1
h = h + 1
h = 0
while ((i < self.bees_number) and (i < 2 * self.flip)):
#print("Second method to generate")
solution = self.refSolution.solution.get_state()
k = 0
while ((k < int(len(solution) / self.flip))
and (self.flip * k + h < len(solution))):
solution[int(self.data.nb_attribs / self.flip) * h + k] = (
(solution[int(self.data.nb_attribs / self.flip) * h + k] +
1) % 2)
k += 1
newBee = Bee(i, self.data, self.locIterations, solution)
self.beeList.append(newBee)
i += 1
h = h + 1
while (i < self.bees_number):
#print("Random method to generate")
solution = self.refSolution.solution.get_state()
indice = random.randint(0, len(solution) - 1)
solution[indice] = ((solution[indice] + 1) % 2)
newBee = Bee(i, self.data, self.locIterations, solution)
self.beeList.append(newBee)
i += 1
for bee in (self.beeList):
lista = [
j for j, n in enumerate(bee.solution.get_state()) if n == 1
]
if (len(lista) == 0):
bee.setSolution(Bee.Rand(self.data.nb_attribs))
def bestBeeDiversity(self):
max_val = 0
for i in range(len(self.beeList)):
if (self.distanceTabou(self.beeList[i]) > max_val):
max_val = self.distanceTabou(self.beeList[i])
if (max_val == 0):
bee = Bee(-1, self.data, self.locIterations,
Bee.Rand(self.data.nb_attribs))
return bee
i = 0
while (i < len(self.beeList)
and self.distanceTabou(self.beeList[i]) != max_val):
i += 1
return self.beeList[i]
def __init__(self, problem, flip, max_chance, bees_number, maxIterations,
locIterations):
self.data = problem
self.flip = flip
self.max_chance = max_chance
self.nbChance = max_chance
self.bees_number = bees_number
self.maxIterations = maxIterations
self.locIterations = locIterations
self.beeList = []
self.refSolution = Bee(-1, self.data, self.locIterations,
Bee.Rand(self.data.nb_attribs))
self.bestSolution = self.refSolution
self.tabou = []
self.feature_count = {i: 0 for i in range(self.data.nb_attribs)}
Solution.solutions.clear()
