In this article, you will learn how to create dot product of two arrays in using numpy.dot() function.
Syntax: numpy.dot(a, b, out=None)
Dot product of two arrays. Specifically,
If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).
If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred.
If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy.multiply(a, b) or a * b is preferred.
If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.
If a is an N-D array and b is an M-D array (where M>=2), it is a sum product over the last axis of a and the second-to-last axis of b:
dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
a : array_like
b : array_like
out : ndarray, optional
Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible.
output : ndarray
Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. If out is given, then it is returned.
If the last dimension of a is not the same size as the second-to-last dimension of b.
#example program on np.dot() function
import numpy as np print('single array result\n',np.dot(3, 4)) # two dimentionl array a=np.array([[1, 2], [ 5, 6]]) b=np.array([[1, 4], [ 5, 8]]) print(a.shape) print(b.shape) c=np.dot(a,b) print('two dimentionl arrayresult\n',c) # three dimentionl array a=np.array([[1, 2,3], [4, 5, 6], [7,8,9]]) b=np.array([[1, 2,3], [4, 5, 6], [7,8,9]]) print(a.shape) print(b.shape) c=np.dot(a,b) print('three dimentionl arrayresult\n',c)