Question

我目前正在尝试同时遍历三个列表：

list_weight = [0.9,0.3,0.6,0.4]
list_reliability = [0.8,0.5,0.2,0.8]
belief_CRED0 = [create_belief_matrix ('ACBA').iloc[0]] 

belief_CRED0
Out[40]: 
[1    0.562500
 2    0.562500
 3    0.391304
 4    0.391304
 Name: CRED0, dtype: float64]

首先我创建了一个嵌套循环：

for belief in belief_CRED0:
    for weight in list_weight:
        for reliability in list_reliability:
            m = [(1/(1+weight-reliability))*(weight*belief)]
print(m)

但结果完全没了。所以我尝试这样做：

for belief, weight, reliability in zip(belief_CRED0, list_weight, list_reliability):
    m = [(1/(1+weight-reliability))*(weight*belief)]
print(m)

但结果也是错误的：

m
Out[42]: 
[1    0.460227
 2    0.460227
 3    0.320158
 4    0.320158
 Name: CRED0, dtype: float64]

从出现的结果来看，循环似乎只使用相应列表中的第一个权重和可靠性（权重= 0.9，可靠性= 0.8）。

正确的输出应该是：

[1    0.460227
 2    0.210937
 3    0.16770171
 4    0.26086933

我该怎么办？

Answer 1

你的for循环中的小错误zip（这是BTW的最佳方式）。累积结果......而不是保持分配给m。

m = []
for belief, weight, reliability in zip(belief_CRED0, list_weight, list_reliability):
    m.append(weight*belief/(1+weight-reliability))
print(m)

Answer 2

如果它们都是pandas.Series或numpy.array，那么您可以直接执行此操作，例如：

>>> weight = pd.Series(list_weight, index=range(1, 5))
>>> reliability = pd.Series(list_reliability, index=range(1, 5))
>>> 1/(1+weight-reliability)*(weight*belief_CRED0)
1    0.460227
2    0.210937
3    0.167702
4    0.260869
dtype: float64

与numpy类似：

>>> weight = np.array(list_weight)
>>> reliability = np.array(list_reliability)
>>> 1/(1+weight-reliability)*(weight*belief_CRED0)
1    0.460227
2    0.210937
3    0.167702
4    0.260869
Name: CRED0, dtype: float64

同时循环三个列表：嵌套循环不起作用

2 个答案: