轉載自 https://blog.csdn.net/u012609509/article/details/70230204 Python中的幾種矩陣乘法 1. 同線性代數中矩陣乘法的定義: np.dot() np.dot(A, B):對於二維矩陣,計算真正意義上的矩陣乘積,同線性代數中矩陣乘法的定義 ...
轉載自 https://blog.csdn.net/u012609509/article/details/70230204
Python中的幾種矩陣乘法
1. 同線性代數中矩陣乘法的定義: np.dot()
np.dot(A, B):對於二維矩陣,計算真正意義上的矩陣乘積,同線性代數中矩陣乘法的定義。對於一維矩陣,計算兩者的內積。見如下Python代碼:
import numpy as np
# 2-D array: 2 x 3
two_dim_matrix_one = np.array([[1, 2, 3], [4, 5, 6]])
# 2-D array: 3 x 2
two_dim_matrix_two = np.array([[1, 2], [3, 4], [5, 6]])
two_multi_res = np.dot(two_dim_matrix_one, two_dim_matrix_two)
print('two_multi_res: %s' %(two_multi_res))
# 1-D array
one_dim_vec_one = np.array([1, 2, 3])
one_dim_vec_two = np.array([4, 5, 6])
one_result_res = np.dot(one_dim_vec_one, one_dim_vec_two)
print('one_result_res: %s' %(one_result_res))
結果如下:
two_multi_res: [[22 28]
[49 64]]
one_result_res: 32
2. 對應元素相乘 element-wise product: np.multiply(), 或 *
在Python中,實現對應元素相乘,有2種方式,一個是np.multiply(),另外一個是*,這種方式要求連個矩陣的的形狀shape相同。見如下Python代碼:
import numpy as np
# 2-D array: 2 x 3
two_dim_matrix_one = np.array([[1, 2, 3], [4, 5, 6]])
another_two_dim_matrix_one = np.array([[7, 8, 9], [4, 7, 1]])
# 對應元素相乘 element-wise product
element_wise = two_dim_matrix_one * another_two_dim_matrix_one
print('element wise product: %s' %(element_wise))
# 對應元素相乘 element-wise product
element_wise_2 = np.multiply(two_dim_matrix_one, another_two_dim_matrix_one)
print('element wise product: %s' % (element_wise_2))
結果如下:
element wise product: [[ 7 16 27]
[16 35 6]]
element wise product: [[ 7 16 27]
[16 35 6]]