Get the Inner product of a One-Dimensional and a Two-Dimensional array in Python

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To get the Inner product of two arrays, use the numpy.inner() method in Python. Ordinary inner product of vectors for 1-D arrays, in higher dimensions a sum product over the last axes. The parameters are 1 and b, two vectors. If a and b are nonscalar, their last dimensions must match.

Steps

At first, import the required libraries −

import numpy as np

Creating two numpy One-Dimensional array using the array() method −

arr1 = np.arange(2).reshape((1,1,2))
arr2 = np.arange(6).reshape((3,2))

Display the arrays −

print("Array1...\n",arr1)
print("\nArray2...\n",arr2)

Check the Dimensions of both the arrays −

print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)

Check the Shape of both the arrays −

print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)

To get the Inner product of two arrays, use the numpy.inner() method −

print("\nResult (Inner Product)...\n",np.inner(arr1, arr2))

Example

import numpy as np

# Creating two numpy One-Dimensional array using the array() method
arr1 = np.arange(2).reshape((1,1,2))
arr2 = np.arange(6).reshape((3,2))

# Display the arrays
print("Array1...\n",arr1)
print("\nArray2...\n",arr2)

# Check the Dimensions of both the arrays
print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)

# Check the Shape of both the arrays
print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)

# To get the Inner product of two arrays, use the numpy.inner() method in Python
# Ordinary inner product of vectors for 1-D arrays, in higher dimensions a sum product over the last axes.
print("\nResult (Inner Product)...\n",np.inner(arr1, arr2))

Output

Array1...
[[[0 1]]]

Array2...
[[0 1]
[2 3]
[4 5]]

Dimensions of Array1...
3

Dimensions of Array2...
2

Shape of Array1...
(1, 1, 2)

Shape of Array2...
(3, 2)

Result (Inner Product)...
[[[1 3 5]]]
raja
Updated on 02-Mar-2022 07:45:17

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