def train_optimzation_model(self, train_daylist, distinct):
daytest = self.select_test_day(train_daylist)
y_train = []
X_train = []
for day in daytest:
for slice in range(144):
dateslice = day + '-' + str(slice + 1)
#feature, label = self.generateFeatureLabel(dateslice, distinct)
feature, label = self.feature.generate(dateslice, distinct)
#print(feature,label)
#print(feature1,label1)
#print("-----------")
if feature != None:
X_train.append(feature)
y_train.append(label)
opt = optimization()
opt.fit(X_train, y_train)
return opt
if __name__ == '__main__':
print("Bacterial Foraging Optimization Algorithm")
print("Dimension: ", dimension)
# For tracking global minima. ;)
results = {'c_space': c_space, 'c_prob': c_prob, 'c_tumble': c_tumble}
res = {}
for i in range(num):
best = INF # the best solution found during the search
fe_count = 0 # number of objective function evaluations
# search space [-100, 100]^dimension
c_tumble, c_space, c_prob, space = initialize_space(
space, -100.0, 100.0)
# random initialization within the search space
population, c_space, fe_count, best = initialize_population(
i+1, population, c_space, fe_count, best)
# minimization of objective function
best, iteration_values = optimization(
i+1, population, c_space, fe_count, best, c_prob, c_tumble)
if best == 0.0:
results[str(i+1)] = 1.0
else:
results[str(i+1)] = best
res[str(i+1)] = iteration_values
# with open('data.py', 'a') as f:
# f.write(dumps(results, sort_keys=True) + '\n')
with open('data_graphs.py', 'a') as f:
f.write(dumps(res, sort_keys=True) + '\n')
print("Updated!")
1: "sphere",
2: "rastrigin",
3: "rosenbrock",
4: "schwefel",
5: "quartic",
6: "ackley",
7: "schaffer",
8: "griewank",
9: "matyas",
10: "trid"
}
results = {}
for i in range(num):
best = INF # the best solution found during the search
fe_count = 0 # number of objective function evaluations
# search space [-100, 100]^dimension
space = initialize_space(space, -100.0, 100.0)
# random initialization within the search space
population, fe_count, best = initialize_population(
i+1, population, space, fe_count, best)
# minimization of objective function
best = optimization(i+1, population, space, fe_count, best)
if best == 0.0:
results[func_dict[i+1]] = 1.0
else:
results[func_dict[i+1]] = best
with open('data.py', 'a') as f:
f.write(dumps(results, sort_keys=True) + '\n')
print("Updated!")
