Python嵌套for循环-内部循环迭代器类型更改

在此函数中，

exs

被假定为浮动列表的列表。它表示我所有训练示例的列表，每个示例都是一个浮点数列表（length

num_vars

），表示感知器输入

target

被假定为浮点数列表（length

num_vars

），表示目标函数的系数

def gradDesc(exs, target, num_vars, n=0.5, its=256):
    import random
    weights = []
    # Create and initialize delWeights to 0. Make its size num_vars.
    delWeights = [0.0]*num_vars


    # Initializes the weights to a real number in [-1,1]. Also makes weights
    # contain num_vars entries.
    for i in range(num_vars):
        weights.append(random.uniform(-1,1))

    # To make the printouts look nicer
    print("Iteration\tError")
    print("---------\t-----")

    for i in range(its):

        # Reset delWeights to 0
        for j in range(num_vars):
            delWeights[j] = 0

        for e in exs:

            # Plug e into the current hypothesis and get the output.
            output = test_hypo(weights, e, num_vars)

            print("delWeights: ", delWeights)
            for dw in delWeights:
                print("type(dw): ", type(dw))
                delWeights[dw] = delWeights[dw] + n*(test_hypo(target, e, num_vars) - output)*e[dw]

        for w in weights:
            weights[w] = weights[w] + delWeights[dw]

        # Print out the error every tenth iteration
        if i % 10 == 0:
            print(i + "\t" + train_err(exs, target, weights, num_vars))

    # Print out the final hypothesis
    print(i + "\t" + train_err(exs, target, weights, num_vars))

    return weights

问题是，在给定（有限）测试输入的情况下，当我尝试运行此程序时

trainers = 
[[1, 2.7902232015508766, -4.624194135789617], 
[1, -7.964359679418456, 2.1940274082288624], 
[1, 8.445941538761794, -8.86567924774781], 
... other sub-lists following this same format ...]

及

我得到了这个奇怪的输出：

gradDesc(trainers, target, num_vars)
Iteration       Error
---------       -----
delWeights:  [0, 0, 0]
type(dw):  <class 'int'>
type(dw):  <class 'int'>
type(dw):  <class 'int'>
delWeights:  [0.0, 0, 0]
type(dw):  <class 'float'>
Traceback (most recent call last):

  File "<ipython-input-19-97298b385113>", line 1, in <module>
    gradDesc(trainers, target, num_vars)

  File "C:/Users/Me/.spyder-py3/Machine Learning/gradDesc.py", line 107, in gradDesc
    delWeights[dw] = delWeights[dw] + n*(test_hypo(target, e, num_vars) - output)*e[dw]

TypeError: list indices must be integers or slices, not float

gradDesc（训练师、目标、数值变量）
迭代误差
---------       -----
增量权重：[0,0,0]
类型（dw）：
类型（dw）：
类型（dw）：
增量权重：[0.0,0,0]
类型（dw）：
回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
gradDesc（训练师、目标、数值变量）
文件“C:/Users/Me/.spyder-py3/Machine Learning/gradDesc.py”，第107行，gradDesc中
delWeights[dw]=delWeights[dw]+n*（测试hypo（目标，e，数值）-输出）*e[dw]
TypeError:列表索引必须是整数或片，而不是浮点

---

所以我的问题是：

dw

的类型为什么在第二次迭代中通过exs中e的

循环从int变为float？
你的意思是使用
范围内i（len（delWeights））的
而不是delWeights中dw的
循环通过
delWeight
的索引吗

for dw in delWeights
循环遍历
delWeights
中的所有值，因此循环的第一次迭代可能会为
delWeights[dw]+n*（test_hypo（target，e，num_vars）-output）*e[dw
中的一个
delWeights
索引分配一个浮点值

delWeights[dw] = delWeights[dw] + n*(test_hypo(target, e, num_vars) - output)*e[dw]

将
delWeights[dw]
设置为浮点，因为
e[dw]
是浮点。因此，下次在delWeights:
循环中对dw执行
时，
dw
是一个浮点

使用
delWeights
的元素作为索引是没有意义的。如果要在列表上迭代并获取索引，则应使用
enumerate（）

为什么在delweights:
循环中的dw的
之外使用
dw
变量？为什么首先将
delweights
初始化为
0.0
的列表，然后在
for i in range（its）：
循环中用
0
替换它？为什么不
delWeights=[0]*num_vars
？
delWeights[dw] = delWeights[dw] + n*(test_hypo(target, e, num_vars) - output)*e[dw]

for i, dw in enumerate(delWeights):
    delWeights[i] = dw + n*(test_hypo(target, e, num_vars) - output)*e[i]