Parameters —
- vector_a: [array_like], if a is complex, its complex conjugate is used to calculate the point product.
- 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:
| # 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
"" " |
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
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