Adjacency matrix. Given a relation relation (as a numpy.array, see example below) and number of nodes n, define a function adjacency to construct the adjacency matrix A (as a numpy.array).You can assume that n is a positive integer and treat relation as a list of directed edges.   # Example from p112 of VMLS relation = np.array([[1, 2], [1, 3], [2, 1], [2, 4], [3, 4], [4, 1]]) n = 4 # Modify the function adjacency below with your answer for Q8 def adjacency(relation, n):     A = None     ###########################     ### YOUR CODE HERE ########               ## REMOVE THIS LINE--------     raise NotImplementedError()     ## ------------------------     ###########################     return A

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
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Adjacency matrix. Given a relation relation (as a numpy.array, see example below) and number of nodes n, define a function adjacency to construct the adjacency matrix A (as a numpy.array).You can assume that n is a positive integer and treat relation as a list of directed edges.

 

# Example from p112 of VMLS
relation = np.array([[1, 2], [1, 3], [2, 1], [2, 4], [3, 4], [4, 1]])
n = 4

# Modify the function adjacency below with your answer for Q8
def adjacency(relation, n):
    A = None
    ###########################
    ### YOUR CODE HERE ########
    
    
    ## REMOVE THIS LINE--------
    raise NotImplementedError()
    ## ------------------------
    ###########################
    return A

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