Given a vector of real numbers r = (r1, r2, . . ., rn) We can standardize the vector using the formulation: v₂ = ****, where m is the mean of the vector r, and s is the standard deviation of r. The vector v = (v1, V2, … .., Un) will be the scaled vector. Write a Python function scale_vec(r) that takes the vector r as input and returns the scaled vector v. Sample inputs and outputs: • Input: np. array([1, 3, 5]), output: [-1.22474487 0. 1.22474487] • Input: np.array([3.3, 1.2, -2.7, −0.6]), output: [1.35457092 0.40637128 -1.35457092 -0.40637128] Hint: Use numpy.mean and numpy.std with default parameters. # Write your function here Let's test your function.
Given a vector of real numbers r = (r1, r2, . . ., rn) We can standardize the vector using the formulation: v₂ = ****, where m is the mean of the vector r, and s is the standard deviation of r. The vector v = (v1, V2, … .., Un) will be the scaled vector. Write a Python function scale_vec(r) that takes the vector r as input and returns the scaled vector v. Sample inputs and outputs: • Input: np. array([1, 3, 5]), output: [-1.22474487 0. 1.22474487] • Input: np.array([3.3, 1.2, -2.7, −0.6]), output: [1.35457092 0.40637128 -1.35457092 -0.40637128] Hint: Use numpy.mean and numpy.std with default parameters. # Write your function here Let's test your function.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
Related questions
Question
![Given a vector of real numbers r = (r1, r2, ..., rm). We can standardize the vector using the formulation: vi = "im, where m
ri-m
is the mean of the vector r, and s is the standard deviation of r. The vector v = (v1, v2, ..., Un) will be the scaled vector.
Write a Python function scale_vec (r) that takes the vector r as input and returns the scaled vector v.
Sample inputs and outputs:
● Input: np.array([1, 3, 5]), output: [-1.22474487 0. 1.22474487]
• Input: np. array([3.3, 1.2, -2.7, -0.6]), output: [1.35457092 0.40637128 -1.35457092
-0.40637128]
Hint: Use numpy.mean and numpy.std with default parameters.
# Write your function here.
Let's test your function.
[ ] import numpy as np
print (scale_vec (np.array([1, 3, 5])))
print (scale_vec (np.array([3.3, 1.2, -2.7, -0.6])))](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9b63d5f0-a313-4df7-92fd-2acb696a8a17%2Fd0e69991-c8fe-4acd-9c84-20842a4fdfe1%2Fhb1npy_processed.png&w=3840&q=75)
Transcribed Image Text:Given a vector of real numbers r = (r1, r2, ..., rm). We can standardize the vector using the formulation: vi = "im, where m
ri-m
is the mean of the vector r, and s is the standard deviation of r. The vector v = (v1, v2, ..., Un) will be the scaled vector.
Write a Python function scale_vec (r) that takes the vector r as input and returns the scaled vector v.
Sample inputs and outputs:
● Input: np.array([1, 3, 5]), output: [-1.22474487 0. 1.22474487]
• Input: np. array([3.3, 1.2, -2.7, -0.6]), output: [1.35457092 0.40637128 -1.35457092
-0.40637128]
Hint: Use numpy.mean and numpy.std with default parameters.
# Write your function here.
Let's test your function.
[ ] import numpy as np
print (scale_vec (np.array([1, 3, 5])))
print (scale_vec (np.array([3.3, 1.2, -2.7, -0.6])))
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