python写出两个(密集)向量相加的函数

以下是两个密集向量相加的Python函数示例：

def add_dense_vectors(vec1, vec2):
    """
    Add two dense vectors element-wise.
    
    Args:
    vec1 (list): a dense vector represented as a list of numbers
    vec2 (list): another dense vector represented as a list of numbers
    
    Returns:
    list: a dense vector representing the sum of vec1 and vec2
    """
    assert len(vec1) == len(vec2), "The two vectors must have the same length."
    return [vec1[i] + vec2[i] for i in range(len(vec1))]


def main():
    vec1 = [1, 2, 3, 4, 5]
    vec2 = [5, 4, 3, 2, 1]
    result = add_dense_vectors(vec1, vec2)
    print(result)  # Output: [6, 6, 6, 6, 6]


if __name__ == '__main__':
    main()

另一个示例：

import numpy as np

def add_dense_vectors(vec1, vec2):
    """
    Add two dense vectors using NumPy.

    Args:
    vec1 (list): a dense vector represented as a list of numbers
    vec2 (list): another dense vector represented as a list of numbers

    Returns:
    list: a dense vector representing the sum of vec1 and vec2
    """
    assert len(vec1) == len(vec2), "The two vectors must have the same length."
    return list(np.array(vec1) + np.array(vec2))


def main():
    vec1 = [1, 2, 3, 4, 5]
    vec2 = [5, 4, 3, 2, 1]
    result = add_dense_vectors(vec1, vec2)
    print(result)  # Output: [6, 6, 6, 6, 6]


if __name__ == '__main__':
    main()

这两个函数都需要两个相同长度的密集向量作为输入，并返回一个向量，其中每个元素等于相应的两个输入向量的元素之和。第一个函数使用纯Python来实现，而第二个函数使用NumPy库来实现。