def get_data(self):
self.tokenID = read()
epoch_time = time.time()
self.ts = str(datetime.datetime.fromtimestamp(epoch_time))
pass
return pos2
def oneGeneration(self):
# generational
newPop = []
for _ in range(self.__param['popSize']):
p1 = self.__population[self.selection()]
p2 = self.__population[self.selection()]
off = p1.crossover(p2)
off.mutation()
newPop.append(off)
self.__population = newPop
self.evaluation()
net = read('data/50p_easy_01_tsp.txt')
MIN = 1
noDim = net["noNodes"]
# initialise de GA parameters
gaParam = {'popSize': 100, 'noGen': 1000, 'pc': 0.8, 'pm': 0.1}
# problem parameters
problParam = {
'min': MIN,
'max': noDim,
'function': modularity,
'noDim': noDim,
'noBits': 8,
'noNodes': net['noNodes']
}
# store the best/average solution of each iteration (for a final plot used to anlyse the GA's convergence)
from ACO import ACO
from Graph import Graph
from Read import read, readTSP
from random import seed
from random import randint
net = read('data/easy.txt')
graph = Graph(net['mat'], net['noNodes'])
aco = ACO(100, 150, 1, 1, 0.5, 5)
bestSolution, bestDistance = aco.solve(graph)
print(str(bestDistance) + ": ")
for i in range(len(bestSolution)):
print(str(bestSolution[i]) + " ")
f.write(x)
else:
f.write(x)
elif s1 in x:
break
elif s2 in x:
f.write(x)
f.close()
break
else:
f.write(x)
file.close()
arr=np.zeros(30,dtype=int)
m=1
while m<c+1:
read(m,arr)
m=m+1
while True:
speak.Speak("Would you like to know which questions remain unsolved or revise your answers? Answering no will end the paper")
answer=stt()
if answer=="no":
break
else:
speak.Speak("Choose Option: 1. Revise answers\n2. Unsolved Questions\n")
ans=stt()
if int(ans)==2:
unsolved(arr,c)
elif int(ans)==1:
revise(arr,c)
else: