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Matrix multiplication is a fundamental operation in linear algebra. In Python, it can be performed using various methods, including custom functions, list comprehensions, and libraries like NumPy.
Example Using NumPy
The numpy.matmul() function is the most efficient and widely used method for matrix multiplication.
import numpy as np# Define two matricesA = np.array([[1, 2], [3, 4]])B = np.array([[5, 6], [7, 8]])# Perform matrix multiplicationresult = np.matmul(A, B)print(result)# Output: [[19 22]# [43 50]]已复制!✕复制Custom Function for Matrix Multiplication
You can also write a custom function to multiply matrices using nested loops.
def multiply_matrices(A, B):rows_A, cols_A = len(A), len(A[0])rows_B, cols_B = len(B), len(B[0])if cols_A != rows_B:raise ValueError("Number of columns in A must equal number of rows in B.")# Initialize result matrix with zerosresult = [[0 for _ in range(cols_B)] for _ in range(rows_A)]for i in range(rows_A):for j in range(cols_B):for k in range(cols_A):result[i][j] += A[i][k] * B[k][j]return result# Example usageA = [[1, 2], [3, 4]]B = [[5, 6], [7, 8]]print(multiply_matrices(A, B))# Output: [[19, 22], [43, 50]]已复制!✕复制 Python 编写一个程序实现矩阵乘法 - 菜鸟教程
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Within the scope of numerical computing with Python, numpy.matmul stands out as a powerful function for performing matrix multiplication.
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