introduction

Numpy Defined matrix type , Use this matrix Type creates a matrix object , Their addition, subtraction, multiplication and division operations are calculated in matrix mode by default , So the usage and Matlab Very similar . But because of NumPy At the same time ndarray and matrix object , Therefore, users can easily confuse the two . This is against it. Python Of " Explicit is better than implicit ", Therefore, the use of matrix, ad locum , We still use ndarray To introduce .

Matrix and vector product

numpy.dot(a, b[, out]) Calculate the product of two matrices , If it is a one-dimensional array, it is their inner product .

import numpy as np

x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 3, 4, 5, 6])
z = np.dot(x, y) #x1*y1 + x2*y2 +...+ xn*yn
print(z)  # 70

x = np.array([[1, 2, 3], [3, 4, 5], [6, 7, 8]])
print(x)
# [[1 2 3]
# [3 4 5]
# [6 7 8]]

y = np.array([[5, 4, 2], [1, 7, 9], [0, 4, 5]])
print(y)
# [[5 4 2]
# [1 7 9]
# [0 4 5]]

z = np.dot(x, y)  # matrix multiplication 
print(z)
# [[ 7 30 35]
# [ 19 60 67]
# [ 37 105 115]]

z = np.dot(y, x)
print(z)
# [[ 29 40 51]
# [ 76 93 110]
# [ 42 51 60]]