def fitness_func(self, juece, origin_adata, adata):
'''适应度函数，可以根据个体的两个染色体计算出该个体的适应度'''
a = []
i = 0
while True:
temp = []
if (i + 4 <= len(juece)):
temp.append(list(juece[i:i + 4]))
i += 4
a.append(temp)
else:
break
pici = inputdata.cul_pici(a, 1)
dic, b_dic = inputdata.daikuan(origin_adata, pici)
target = inputdata.jisuan(origin_adata, dic, b_dic)
return 1 / (target + 1)
def fitness_func(juece, origin_adata, adata):
'''适应度函数，可以根据个体的两个染色体计算出该个体的适应度'''
pici = inputdata.cul_pici([juece], 1)
dic, b_dic = inputdata.daikuan(origin_adata, pici)
xiaohao = inputdata.jisuan(origin_adata, dic, b_dic)
return xiaohao
arr = []
arr.append(ar)
origin_data = inputdata.get_data()
train_data = inputdata.get_input_data(origin_data)
split = int(len(origin_data) * 0.8)
xunlian_data = origin_data[0:split]
pre_data = origin_data[split:len(origin_data)]
pre_time = []
xunlian_time = []
#训练组80%的数据
for i in range(len(xunlian_data)):
xunlian_adata = []
xunlian_adata.append(np.array(xunlian_data[i]))
pici = inputdata.cul_pici(arr, 1)
dic, b_dic = inputdata.daikuan(xunlian_adata, pici)
# print("每个批次任务的带宽分配情况：", b_dic)
time_ = inputdata.jisuan(xunlian_adata, dic, b_dic)
xunlian_time.append(time_)
print("随机匹配的训练80%任务的时间列表长度", len(xunlian_time))
Utils.save("random_xunlian80.npy", xunlian_time)
#预测后20%组数据
for i in range(len(pre_data)):
pre_adata = []
pre_adata.append(np.array(pre_data[i]))
pici = inputdata.cul_pici(arr, 1)
dic, b_dic = inputdata.daikuan(pre_adata, pici)
# print("每个批次任务的带宽分配情况：", b_dic)
time_ = inputdata.jisuan(pre_adata, dic, b_dic)
pre_time.append(time_)
