Compute the Inner Product of Vectors for 1D Arrays using NumPy in Python

Inner product (or dot product) of two 1D arrays is the sum of the products of their corresponding elements. NumPy provides the inner() function to calculate this efficiently.

Example: In this example, we compute the inner product of two small arrays with values.

import numpy as np

x = np.array([1, 2])
y = np.array([3, 4])
res = np.inner(x, y)
print(res)

11

Explanation: np.inner(x, y) multiplies corresponding elements (1*3 + 2*4) and sums them to get 11.

Syntax

numpy.inner(array1, array2)

• Parameters: array1, array2 -> 1D arrays whose inner product is to be calculated.
• Returns: A single scalar value representing the inner product.

Examples

Example 1: Calculating the inner product of two small arrays.

import numpy as np

a = np.array([6, 2])
b = np.array([2, 5])
res = np.inner(a, b)
print(res)

22

Explanation: np.inner(a, b) multiplies corresponding elements (6*2 + 2*5) and sums them to get 22.

Example 2: In this example, the inner product of two arrays with more elements is calculated.

import numpy as np

a = np.array([1, 3, 5])
b = np.array([0, 1, 5])
res = np.inner(a, b)
print(res)

28

Explanation: np.inner(a, b) calculates (1*0 + 3*1 + 5*5) which equals 28.

Example 3: In this example, we compute the inner product of arrays containing varied values.

import numpy as np

a = np.array([1, 2, 2, 8])
b = np.array([2, 1, 0, 6])
res = np.inner(a, b)
print(res)

52

Explanation: np.inner(a, b) multiplies elements (1*2 + 2*1 + 2*0 + 8*6) and sums them to give 52.