【Python】numpy中的矩阵运算

import numpy as np

a = np.array([0, 2, 4])
a * 2    # array([0, 4, 8])

a = [0, 2, 4]
a * 2    # [0, 2, 4, 0, 2, 4]

import array
array.array('i', [0, 2, 4]) * 2    # array('i', [0, 2, 4, 0, 2, 4])

[i*2 for i in a]    # [0, 4, 8]

res = []
for i in a:
    res.append(i * 2)
res    # [0, 4, 8]

data = range(10**6)
%%time
res = []
for i in data:
    res.append(i * 2)
res    # Wall time: 171 ms

%time res = [i*2 for i in data]    # Wall time: 110 ms

arr = np.array(data)
%time res = arr * 2    # Wall time: 19.1 ms

a = np.array([0, 2, 4])

a + 2    # array([2, 4, 6])
a - 2    # array([-2,  0,  2])
a * 2    # array([0, 4, 8])
a / 2    # array([0., 1., 2.])

X = np.arange(1, 16).reshape((3, 5))
X.shape
X // 2    # 整除
X ** 2    # 幂运算
X % 2    # 模运算
1 / X    # 求倒数
np.abs(X - 2)    # 求绝对值
np.sin(X)    # 求正弦
np.cos(X)    # 求余弦
np.tan(X)    # 求正切
np.exp(X)    # X的每个函数的e次方

from math import e
e ** 3    # 20.085536923187664即(2.00855369e+01)
np.p(3, X)    # 3为底的X次方
3 ** X
np.log(X)    # 以自然对数e为底
np.log2(X)    # 以2为底
2 ** 1.5849625    # 2.999999998500398
np.log10(X)    # 以10为底
10 ** 0.30103    # 2.0000000199681045

A = np.arange(4).reshape((2, -1))
B = np.full((2, 2), 10)
A + B    # numpy中对应元素做运算
A - B
A * B
A / B

A = np.arange(4).reshape((2, -1))
B = np.array([10, 20, 30, 40]).reshape(2, -1)

A.dot(B)    # 标准矩阵乘法运算：用A矩阵中的每一行和B矩阵中的每一列对应位置做相乘，然后相加
invA = np.linalg.inv(A)    # 矩阵的逆
A.dot(invA)
invA.dot(A)

A = np.arange(6).reshape((2, -1))
invA = np.linalg.inv(A)
invA    # LinAlgError: Last 2 dimensions of the array must be square，不是方阵，不能求得逆矩阵，只能求伪逆矩阵

pinvA = np.linalg.pinv(A)    # 伪逆矩阵
pinvA
A.dot(pinvA)

A.T    # 矩阵的转置，行变列，列变行

注：代码来自《Python全栈工程师特训班》课程