NumPy - inner() function

The NumPy inner() function returns the inner product of two arrays. For 1-D arrays, it returns ordinary inner product (without complex conjugation). For higher dimensions a sum product over the last axes is returned.

Syntax

numpy.inner(a, b)

Parameters

Return Value

Returns the inner product of a and b.

Exception

Raises ValueError exception, if the last dimension of a and b has different size.

Example: inner() function with scalars

The example below shows the result when two scalars are used with inner() function.

import numpy as np
print(np.inner(5, 10))

The output of the above code will be:

50

Example: inner() function with 1-D arrays

When two 1-D arrays are used, the function returns inner product of the arrays.

import numpy as np
Arr1 = [5, 8]
Arr2 = [10, 20]

#returns 5*10 + 8*20 = 210
print(np.inner(Arr1, Arr2))

The output of the above code will be:

210

Example: inner() function with complex numbers

The inner() function can be used with complex numbers. Consider the following example.

import numpy as np
Arr1 = np.array([1+2j, 1+3j])
Arr2 = np.array([2+2j, 2+3j])

Arr3 = np.inner(Arr1, Arr2)

print(Arr3)

The output of the above code will be:

(-9+15j)

The inner product is calculated as:

= (1+2j)*(2+2j) + (1+3j)*(2+3j)
= (-2+6j) + (-7+9j)
= (-9+15j)

Example: inner() function with matrix

When two matrix are used, the function performs inner product on last axes of the matrix.

import numpy as np
Arr1 = np.array([[1, 2], 
                 [3, 4]])
Arr2 = np.array([[10, 20], 
                 [30, 40]])
Arr3 = np.inner(Arr1, Arr2)

print(Arr3)

The output of the above code will be:

[[ 50 110]
 [110 250]]

The inner product is calculated as:

[[1*10+2*20 1*30+2*40]
 [3*10+4*20 3*30+4*40]]

= [[ 50 110]
   [110 250]]