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。
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]]