numpy.dot() in Python

numpy.dot(vector_a, vector_b, out = None) returns the dot product of vectors a and b. It can handle 2D arrays but considers them as matrix and will perform matrix multiplication. For N dimensions it is a sum-product over the last axis of a and the second-to-last of b :

dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])  

Parameters

1. vector_a : [array_like] if a is complex its complex conjugate is used for the calculation of the dot product. 
2. vector_b : [array_like] if b is complex its complex conjugate is used for the calculation of the dot product. 
3. out : [array, optional] output argument must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). 

Dot Product of vectors a and b. if vector_a and vector_b are 1D, then scalar is returned

Code 1:

# Python Program illustrating # numpy.dot() method import numpy as geek # Scalars product = geek.dot(5, 4) print("Dot Product of scalar values : ", product) # 1D array vector_a = 2 + 3j vector_b = 4 + 5j product = geek.dot(vector_a, vector_b) print("Dot Product : ", product) 

Output:

Dot Product of scalar values : 20 Dot Product : (-7+22j)

How Code1 works ? vector_a = 2 + 3j vector_b = 4 + 5j now dot product = 2(4 + 5j) + 3j(4 +5j) = 8 + 10j + 12j - 15 = -7 + 22j

Code 2:

# Python Program illustrating # numpy.dot() method import numpy as geek # 1D array vector_a = geek.array([[1, 4], [5, 6]]) vector_b = geek.array([[2, 4], [5, 2]]) product = geek.dot(vector_a, vector_b) print("Dot Product : \n", product) product = geek.dot(vector_b, vector_a) print("\nDot Product : \n", product) """  Code 2 : as normal matrix multiplication """ 

Output:

Dot Product : [[22 12] [40 32]] Dot Product : [[22 32] [15 32]]