2. max(1,1,2,3,0) min(1,1,2,3,0) 3. max( x , key=abs ) min( x , key=abs ) 4. count = 0 result = 0 for num in nums: if num == 1: count += 1 else: result = max(count,result) count = 0 return max(count,result)编辑...
python内置的一些类型中 可变对象：list dict set 不可变对象：tuple string int float bool 举一个例子...
elif operator == '/': # 确保能整除 num1 = num2 * random.randint(1, max_num // num2) # 计算正确答案 if operator == '+': answer = num1 + num2 elif operator == '-': answer = num1 - num2 elif operator == '*': answer = num1 * num2 elif operator == '/': answer =...
```python def find_max_and_positions(lst): max_val = float('-inf') # 初始化最大值为负无穷 positions = [] # 用来保存最大值的位置 for i, num in enumerate(lst): if num > max_val: max_val = num positions = [i] # 更新最大值,并记录位置 elif num == max_val: positions.append...
```pythondef find_max(numbers): if not numbers: raise ValueError("传入的列表不能为空") max_num = numbers[0] for num in numbers: if num > max_num: max_num = num return max_num``` 原代码的问题在于当传入的numbers为空列表时,会触发IndexError异常(尝试访问numbers[0])。改进方案添加了...
```python def find_max_min(lst): if len(lst) == 0: return None max_value = lst[0] min_value = lst[0] for num in lst: if num > max_value: max_value = num if num < min_value: min_value = num return max_value, min_value ...
In[01]:import numpy as np In[02]: t1 = np.arange(12) In[03]: t1 Out[04]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) In[04]: t1.shape # 查看数组的形状 Out[04]: (12,) In[05]: t2 = np.array([[1,2,3],[4,5,6]]) ...
"""max_num = lst[0] # 初始化最大值为列表中的第一个元素 for num in lst:if num > max_num:max_num = num return max_num 示例用法 lst = [4, 2, 1, 6, 7]max_num = get_max(lst)print(max_num) # 输出 7 在该示例代码中，函数 get_max 接受一个列表 lst 作为参数...
self.num_layers = len(sizes) weights_scale = 0.01 self.weights = [np.random.randn(ch1, ch2) * weights_scale for ch1, ch2 in zip(sizes[:-1], sizes[1:])] self.biases = [np.random.randn(1, ch) * weights_scale for ch in sizes[1:]] ...
Python 3.x 返回迭代器。 所以在python 3.x中需要再次转换一下 ls= list(map(float, data)) 2、计算数组的Avg(平均值),并保留1位小数 avg = round(sum(ls) / len(ls), 1) 保留n位小数:round(numdata, n) 3、计算数组的Max、Min max_val= max(ls, key=abs)min_val= min(ls, key=abs) ...