Numpy ndarray.dot() function | Python

The numpy.ndarray.dot() function computes the dot product of two arrays. It is widely used in linear algebra, machine learning and deep learning for operations like matrix multiplication and vector projections.

Example:

import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
result = np.dot(a, b)
print(result)

32

Understanding the Dot Product

• If both inputs are 1D arrays, dot() computes the inner product, resulting in a scalar.
• If either input is an N-dimensional array, dot() performs matrix multiplication.
• If one input is a scalar, dot() performs element-wise multiplication.

Syntax : numpy.ndarray.dot(arr, out=None)

Parameters:

• arr (array_like) : The input array for the dot product.
• out (ndarray, optional): Output argument (stores the result).

Returns:

• A scalar, vector or matrix depending on input shape.

Code Implementation

Code #1 : Using numpy.ndarray.dot() for Matrix Multiplication

import numpy as geek
arr1 = geek.eye(3)
arr = geek.ones((3, 3)) * 3
gfg = arr1.dot(arr)
print(gfg)

[[3. 3. 3.]
 [3. 3. 3.]
 [3. 3. 3.]]

Code #2 : Performing Multiple Dot Products

import numpy as geek
arr1 = geek.eye(3)
arr = geek.ones((3, 3)) * 3
gfg = arr1.dot(arr).dot(arr)
print(gfg)

[[27. 27. 27.]
 [27. 27. 27.]
 [27. 27. 27.]]

In this article, we explored the numpy.ndarray.dot() function, which computes the dot product of two arrays. We demonstrated its application using identity matrices and uniform arrays, highlighting its significance in matrix operations and numerical computing.

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Article Tags :

• Machine Learning
• python
• Python numpy-ndarray
• Python-numpy

Practice Tags :

• Machine Learning
• python