numpy.dot () in Python

numpy.dot (vector_a, vector_b, out = None) returns the point product of vectors a and b. It can handle two-dimensional arrays, but treats them like a matrix and does matrix multiplication. For N measurements, this is the sum of the products along the last a axis and the second longest 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 to compute the dot product.
  2. vector_b: [array_like], if b is complex, its complex conjugate used to compute the dot product.
  3. out: [array, optional] The output argument must be C-contiguous and its dtype must be the dtype that will be returned for dot (a, b).

Return —

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

Code 1 —

# Program Python 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)

Exit —

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

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

is now a dot product
= 2 (4 + 5j) + 3j (4 — 5j)
= 8 + 10j + 12j — 15
= -7 + 22j

Code 2 —

# Program Python 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 :" , product)

  

product = geek.dot ( vector_b, vector_a)

print ( " Dot Product : " , product)

  
"" "
Code 2: as normal matrix multiplication
"" "

Exit —

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

Links —
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.dot.html
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This article is provided by Mohit Gupta_OMG