Parameters —

1. vector_a: [array_like], if a is complex, its complex conjugate is used to calculate the point product.
2. vector_b: [array_like], if b is complex, its complex conjugate is used to compute the dot product.

Return — point Product of vectors a and b.

Code 1:

# Python program illustrating # numpy.vdot () method   import numpy as geek    # 1D array vector_a =   2 + 3j vector_b = 4 + 5j   product = geek.vdot (vector_a, vector_b) print ( "Dot Product :" , product)

Output:

 Dot Product: (23-2j) 

How does Code1 work?
vector_a = 2 + 3j
vector_b = 4 + 5j

According to the method, take the conjugation of vector_a, i.e. 2 — 3j

now product point = 2 (4 — 5j) + 3j (4 — 5j)
= 8 — 10j + 12j + 15
= 23 — 2j

Code 2:

Output:

 Dot Product: 55 Dot Product: 55 

Links:
https:// docs .scipy.org / doc / numpy-dev / reference / generated / numpy.vdot.html # numpy.vdot

,
This article is courtesy of Mohit Gupta_OMG

# Python program illustrating # numpy.vdot () 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.vdot (vector_a, vector_b) print ( "Dot Product :" , product)   product = geek.vdot (vector_b, vector_a) print ( "Dot Product :" , product)   "" " How Code 2 works: array is aligned   1 * 2 + 4 * 4 + 5 * 5 + 6 * 2 = 55 "" "