Python中的矩阵处理
在Python中，我们可以解决不同的矩阵操作和运算。Numpy模块为矩阵运算提供了不同的方法。
add()-将两个矩阵的元素相加。
减去()-减去两个矩阵的元素。
split()-将两个矩阵的元素相除。
乘法()-将两个矩阵的元素相乘。
dot()-它执行矩阵乘法，而不是元素明智的乘法。
sqrt()-矩阵每个元素的平方根。
sum(x,axis)-添加到矩阵中的所有元素。第二个参数是可选的，当我们要计算axis为0时的列总和，而axis为1时要计算行总和时使用它。
“T”-执行指定矩阵的转置。
范例程式码
import numpy
# Two matrices are initialized by value
x = numpy.array([[1, 2], [4, 5]])
y = numpy.array([[7, 8], [9, 10]])
# add()is used to add matrices
print ("Addition of two matrices: ")
print (numpy.add(x,y))
# subtract()is used to subtract matrices
print ("Subtraction of two matrices : ")
print (numpy.subtract(x,y))
# divide()is used to divide matrices
print ("Matrix Division : ")
print (numpy.divide(x,y))
print ("Multiplication of two matrices: ")
print (numpy.multiply(x,y))
print ("The product of two matrices : ")
print (numpy.dot(x,y))
print ("square root is : ")
print (numpy.sqrt(x))
print ("The summation of elements : ")
print (numpy.sum(y))
print ("The column wise summation : ")
print (numpy.sum(y,axis=0))
print ("The row wise summation: ")
print (numpy.sum(y,axis=1))
# using "T" to transpose the matrix
print ("Matrix transposition : ")
print (x.T)输出结果
Addition of two matrices: [[ 8 10] [13 15]] Subtraction of two matrices : [[-6 -6] [-5 -5]] Matrix Division : [[0.14285714 0.25 ] [0.44444444 0.5 ]] Multiplication of two matrices: [[ 7 16] [36 50]] The product of two matrices : [[25 28] [73 82]] square root is : [[1. 1.41421356] [2. 2.23606798]] The summation of elements : 34 The column wise summation : [16 18] The row wise summation: [15 19] Matrix transposition : [[1 4] [2 5]]