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 considering 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).

Return –

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

Code 1 –

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# 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)

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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 – 











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# 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 


"""



















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Output – 


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

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



References – 

https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.dot.html

.

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