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 isthe probability vector obtained from R; using the formulation in Question 1. In other words, convert each row of the input matrix into aprobability 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 hereLet's test your function.[ ] # Convert input from 2-d list to np.array first before calling your functionprint (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]])))

The provided Python function, mat_to_prob, utilizes NumPy to convert rows of an input matrix into…

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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