dot

Description

Return the 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 multiplication and using multiply 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.

Mandatory Input Parameters

Parameter	Type	Description
a, b	array_like	Arrays for dot product

Optional Input Parameters

Parameter	Type	Default Value	Description
out	ndarray	None	Output argument. It must have the right type, must be C-contiguous, and its dtype must be the dtype that is returned for dot(a,b). This is a performance feature. If these conditions are not met, an exception is raised, instead of attempting to be flexible.

Return Value

Type	Description
ndarray	Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays, a scalar is returned; otherwise an array is returned. If *out* is given, *out* is returned.

Examples

>>> import numpy as np
>>> np.dot(7, 9)
63
>>> 
>>> np.dot([2j,3j], [2j, 3j])
(-13+0j)
>>> 
>>> a = [[1,0], [0,1]]
>>> b = [[4,1], [2,2]]
>>> np.dot(a, b)
array([[4, 1],
       [2, 2]])
>>> 
>>> a = np.arange(3*4*5*6).reshape(3,4,5,6)
>>> a = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
>>> 
>>> 
>>> 
>>> a = np.arange(3*4*5*6).reshape(3,4,5,6)
>>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
>>> np.dot(a, b)[2,3,2,1,2,2]
499128
>>> 
>>> sum(a[2,3,2,:] * b[1,2,:,2])
499128
>>>

Parent topic: Basic Statistics Functions