What is the array square sum in NumPy?

In NumPy, the array square sum refers to the sum of the squares of all elements within a NumPy array. It's a fundamental operation in linear algebra and data analysis, often used in various mathematical and statistical computations.

NumPy provides a convenient function, numpy.sum(), combined with element-wise squaring, to efficiently compute this sum. Here's how you can calculate the array square sum:

1. Square each element of the array: Using the operator or numpy.square() function, you can compute the square of each element in the array.

2. Sum the squared elements: Utilize the numpy.sum() function to add up all the squared elements, resulting in the array square sum.

Here's an example demonstrating how to calculate the array square sum using NumPy:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

# Method 1: Using the operator
square_sum_1 = np.sum(arr2)
print("Square sum (using ):", square_sum_1)

# Method 2: Using numpy.square()
square_sum_2 = np.sum(np.square(arr))
print("Square sum (using numpy.square()):", square_sum_2)

In the example above:

- An array arr is initialized with values [1, 2, 3, 4, 5].

- The array square sum is calculated using two methods: the operator for squaring and the numpy.square() function. Both methods yield the same result.

- The numpy.sum() function is used to sum the squared elements efficiently.

The array square sum is a valuable operation in various applications such as calculating the magnitude of a vector, computing the sum of squared errors in statistical models, and performing other mathematical transformations on data.

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