|
import numpy as np a = np.matrix([ [1, 2, 3, 4], [5, 5, 6, 8], [7, 9, 9, 1], [4, 6, 7, 1] ]) #矩阵加减法： e = a + a #or e = a - a #矩阵乘法: b = a * a #not matrix multiplication! #or c = np.dot(a, a) #matrix multiplication #or d = a np.dot(a, a, d) #matrix multiplication #转置矩阵(transpose) g = a.transpose() #or h = a.T #not matrix transpose! #逆矩阵(inverse) #The inverse of a matrix A is the matrix B such that AB=I where I is the identity matrix consisting of ones down the main diagonal. Usually B is denoted B=A-1 . #In SciPy, the matrix inverse of the Numpy array, A, is obtained using linalg.inv (A) , or using A.I f = np.linalg.inv(a) #or f = a ** (-1) #or f = a.I #行列式(determinant) j = np.linalg.det(a) #伴随矩阵(adjoint) #(need more test) m = np.dot(np.linalg.det(a), np.linalg.inv(a)) # A-1 = A'' / |A| ==> A''= A-1|A| #矩阵范数(matrix norms) k = np.linalg.norms(a) |
|
|
