# numpy.vdot () in Python

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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 `

,

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 ` # 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 ` ` "" "`