Given a vector of real numbers r = (r₁, r2, . . . , rn). We can convert this vector into a probability vector ‚ Pn) using the formulation: p¡ = e'¹/(Σï-₁ e¹¹), for all i. p = (P₁, P2, · Write a Python function vec_to_prob(r) that takes the vector r as input and returns the vector p. Both r and p will be numpy arrays. You can assume r is non-empty. Sample inputs and outputs: • Input: np.array([4, 6]), output: [0.11920292 0.88079708] • Input: np.array([3.4, 6.2, 7.1, 9.8]), output: [0.00151576 0.02492606 0.06130823 0.91224995] Hint: use numpy.sum [ ] # Write your function here Let's test your function. [ ] # Convert input from list to np.array first before calling your function to avoid errors print (vec_to_prob(np.array ( [4, 6]))) print (vec_to_prob (np.array( [3.4, 6.2, 7.1, 9.8]))) print (vec_to_prob(np.array([3, 5.5, 0])))
Given a vector of real numbers r = (r₁, r2, . . . , rn). We can convert this vector into a probability vector ‚ Pn) using the formulation: p¡ = e'¹/(Σï-₁ e¹¹), for all i. p = (P₁, P2, · Write a Python function vec_to_prob(r) that takes the vector r as input and returns the vector p. Both r and p will be numpy arrays. You can assume r is non-empty. Sample inputs and outputs: • Input: np.array([4, 6]), output: [0.11920292 0.88079708] • Input: np.array([3.4, 6.2, 7.1, 9.8]), output: [0.00151576 0.02492606 0.06130823 0.91224995] Hint: use numpy.sum [ ] # Write your function here Let's test your function. [ ] # Convert input from list to np.array first before calling your function to avoid errors print (vec_to_prob(np.array ( [4, 6]))) print (vec_to_prob (np.array( [3.4, 6.2, 7.1, 9.8]))) print (vec_to_prob(np.array([3, 5.5, 0])))
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
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Question
![Given a vector of real numbers r = (r1, V2, . . ., rn). We can convert this vector into a probability vector
P = (P1, P2, . . ., Pn) using the formulation: p; = e¹¹/(Σï-₁ e¹¹), for all i.
Write a Python function vec_to_prob(r) that takes the vector r as input and returns the vector p. Both r and p will
be numpy arrays. You can assume r is non-empty.
Sample inputs and outputs:
Input: np.array([4, 6]), output: [0.11920292 0.88079708]
• Input: np.array([3.4, 6.2, 7.1, 9.8]), output: [0.00151576 0.02492606 0.06130823 0.91224995]
Hint: use numpy.sum
[ ] # Write your function here
Let's test your function.
[ ] # Convert input from list to np.array first before calling your function to avoid errors
print (vec_to_prob(np.array([4, 6])))
print (vec_to_prob (np. array ( [3.4, 6.2, 7.1, 9.8])))
print (vec_to_prob (np.array([3, 5.5, 0])))](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9b63d5f0-a313-4df7-92fd-2acb696a8a17%2F374944d9-5825-4a56-b93a-209356edaa8a%2Fn6or6j_processed.png&w=3840&q=75)
Transcribed Image Text:Given a vector of real numbers r = (r1, V2, . . ., rn). We can convert this vector into a probability vector
P = (P1, P2, . . ., Pn) using the formulation: p; = e¹¹/(Σï-₁ e¹¹), for all i.
Write a Python function vec_to_prob(r) that takes the vector r as input and returns the vector p. Both r and p will
be numpy arrays. You can assume r is non-empty.
Sample inputs and outputs:
Input: np.array([4, 6]), output: [0.11920292 0.88079708]
• Input: np.array([3.4, 6.2, 7.1, 9.8]), output: [0.00151576 0.02492606 0.06130823 0.91224995]
Hint: use numpy.sum
[ ] # Write your function here
Let's test your function.
[ ] # Convert input from list to np.array first before calling your function to avoid errors
print (vec_to_prob(np.array([4, 6])))
print (vec_to_prob (np. array ( [3.4, 6.2, 7.1, 9.8])))
print (vec_to_prob (np.array([3, 5.5, 0])))
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