Write a Python function mat_to_prob (R) that returns the matrix P = (P₁, P2, ..., Pm) where P; is the i-th row of matrix P, and P; is the probability vector obtained from R; using the formulation in Question 1. In other words, convert each row of the input matrix into a probability vector. Sample inputs and outputs:
Write a Python function mat_to_prob (R) that returns the matrix P = (P₁, P2, ..., Pm) where P; is the i-th row of matrix P, and P; is the probability vector obtained from R; using the formulation in Question 1. In other words, convert each row of the input matrix into a probability vector. Sample inputs and outputs:
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
![Write a Python function mat_to_prob (R) that returns the matrix P = (P₁, P2, ..., Pm) where Pi is the i-th row of matrix P, and Pi is
the probability vector obtained from R; using the formulation in Question 1. In other words, convert each row of the input matrix into a
probability vector.
Sample inputs and outputs:
• Input: np.array([[4, 6], [3.5, 9.1]])
Output:
●
[[0.11920292 0.88079708]
[0.00368424 0.99631576]]
Input: np.array([[2, 3.1, 5], [10, 3.7, 12], [4, 5.5, 0]]))
Output:
[[4.15115123e-02 1.24707475e-01 8.33781013e-01]
[1.19176835e-01 2.18844992e-04 8.80604320e-01]
[1.81818026e-01 8.14851861e-01 3.33011331e-03]]
Hint: use numpy.sum with an appropriate axis and keepdims settings. You should also check broadcasting in numpy.
[ ] # Write your function here
Let's test your function.
[ ] # Convert input from 2-d list to np.array first before calling your function
print (mat_to_prob(np.array([[4, 6], [3.5, 9.1]])))
print (mat_to_prob (np.array([[2, 3.1, 5], [10, 3.7, 12], [4, 5.5, 0]])))](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9b63d5f0-a313-4df7-92fd-2acb696a8a17%2F53a2c428-dd62-4388-8503-762b2fa50977%2Fi5ntphr_processed.png&w=3840&q=75)
Transcribed Image Text:Write a Python function mat_to_prob (R) that returns the matrix P = (P₁, P2, ..., Pm) where Pi is the i-th row of matrix P, and Pi is
the probability vector obtained from R; using the formulation in Question 1. In other words, convert each row of the input matrix into a
probability vector.
Sample inputs and outputs:
• Input: np.array([[4, 6], [3.5, 9.1]])
Output:
●
[[0.11920292 0.88079708]
[0.00368424 0.99631576]]
Input: np.array([[2, 3.1, 5], [10, 3.7, 12], [4, 5.5, 0]]))
Output:
[[4.15115123e-02 1.24707475e-01 8.33781013e-01]
[1.19176835e-01 2.18844992e-04 8.80604320e-01]
[1.81818026e-01 8.14851861e-01 3.33011331e-03]]
Hint: use numpy.sum with an appropriate axis and keepdims settings. You should also check broadcasting in numpy.
[ ] # Write your function here
Let's test your function.
[ ] # Convert input from 2-d list to np.array first before calling your function
print (mat_to_prob(np.array([[4, 6], [3.5, 9.1]])))
print (mat_to_prob (np.array([[2, 3.1, 5], [10, 3.7, 12], [4, 5.5, 0]])))
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