numpy.dot(vector_a, vector_b, out = None) returns the dot product of vectors a and b. It can handle 2D arrays but considers 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
- vector_a :[array_like] if a is complex its complex conjugate is used for the calculation of the dot product.
- vector_b :[array_like] if b is complex its complex conjugate is used for the calculation of the dot product.
- out :[array, optional] output argument must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b).
Dot Product of vectors a and b. if vector_a and vector_b are 1D, then scalar is returned
Code 1:
Python# Python Program illustrating# numpy.dot() methodimportnumpyasgeek# Scalarsproduct=geek.dot(5,4)print("Dot Product of scalar values : ",product)# 1D arrayvector_a=2+3jvector_b=4+5jproduct=geek.dot(vector_a,vector_b)print("Dot Product : ",product)Output:
Dot Product of scalar values : 20Dot Product : (-7+22j)
How Code1 works ? vector_a = 2 + 3j vector_b = 4 + 5jnow dot product = 2(4 + 5j) + 3j(4 +5j) = 8 + 10j + 12j - 15 = -7 + 22j
Code 2:
Python# Python Program illustrating# numpy.dot() methodimportnumpyasgeek# 1D arrayvector_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"""Output:
Dot Product : [[22 12] [40 32]]Dot Product : [[22 32] [15 32]]