这是个人学习笔记 原题: Transpose of a Matrix (easy) - Deep-ML给定一个整型或浮点型的二维数组a,返回其转置后的矩阵b 官方题解 def transpose_matrix (a: list[list[ int | float ]]) -> list[list[ int |…
import numpy as np def transpose_matrix_multiplication(A, B): rows_A, cols_A = A.shape rows_B, cols_B = B.shape if cols_A != rows_B: raise ValueError("The number of columns in matrix A must be equal to the number of rows in matrix B.") C = np.zeros((rows_A, cols_B)...
def transpose1(matrix): cols = len(matrix[0]) return [[row[i] for row in matrix] for i in range(0,cols)] def transpose2(matrix): transposed = [] for i in range(len(matrix[0])): transposed.append([row[i] for row in matrix]) return transposed def transpose3(matrix): transposed...
# 创建一个10000x10000的大矩阵,数据在GPU上 large_matrix=cp.random.rand(10000,10000)# 计算矩阵的转置 transposed_matrix=cp.transpose(large_matrix)# 计算两个矩阵的点积 result_matrix=cp.dot(large_matrix,transposed_matrix)# 将结果矩阵的数据从GPU复制回CPU(如果需要在CPU上进一步处理) result_on_cpu=cp...
例如,类似于vcat(transpose.(a)...)的内容将用作one-liner julia> a = [[1,2,3], [4,5,6], [7,8,9]]3-element Vector{Vector{Int64}}: [1, 2, 3] [4, 5, 6] [7, 8, 9]julia> vcat(transpose.(a)...)3×3 Matrix{Int64}: 1 2 3 4 5 6 7 8 9 但请注意 因为您的内部...
# matrix.H hermitian (conjugate) transpose:返回复数矩阵的共轭元素矩阵 # matrix.I inverse:返回矩阵的逆矩阵 # matrix.A base array:返回矩阵基于的数组 # 矩阵对象的方法: # all([axis, out]) :沿给定的轴判断矩阵所有元素是否为真(非0即为真) ...
1.numpy.transpose() / .transpose() / .T 2.numpy.squeeze(array,axis) / .squeeze(array,axis) 三.一般函数 (一)产生数组 1.等差数组 1.1 numpy.arange(start,end,step) 1.2 numpy.linspace(start,stop,num,endpoint,dtype) 2.常数数组 2.1 numpy.ones(shape,dtype) 2.2 numpy.zeros(shape,dtype) 2.3...
NumPy Matrix transpose() Python numpy module is mostly used to work with arrays in Python. We can use the transpose() function to get the transpose of an array. import numpy as np arr1 = np.array([[1, 2, 3], [4, 5, 6]]) print(f'Original Array:\n{arr1}') arr1_transpose ...
Don't be one of the leeches. Either stand out or kicked out. 先附上github地址: 下面是这个一百天计划里面的学习框架,我在这里放上来。 Day01~15 - Python语言基础 Day01 - 初识Python Python简介 - Python的历史 / Python的优缺点 / Python的应用领域 搭建编程环境 - Windows环境 / Linux环境 / MacO...
>>>a matrix([[1, 2], [3, 4]])>>> a.T#方法一,ndarray也行matrix([[1, 3], [2, 4]])>>> np.transpose(a)#方法二matrix([[1, 3], [2, 4]]) 值得一提的是,matrix中求逆还有一种简便方法(ndarray中不行): >>>a matrix([[1, 2], [3, 4]])>>>a.I matrix([[-2. , 1...