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. Print color_homophily. Here's the code to get you started: from collections import Counter import numpy as np def marginal prob(chars): Enter code here! def chance homophily(chars): Enter code here! favorite colors= ( "ankit": "red", "xiaoyu": "blue", "mary's "blue" color_homophily chance homophily (favorite colors) print (color_homophily) What is the chance homophily of the favorite colors dictionary?

. Print color_homophily. Here's the code to get you started: from collections import Counter import numpy as np def marginal prob(chars): Enter code here! def chance homophily(chars): Enter code here! favorite colors= ( "ankit": "red", "xiaoyu": "blue", "mary's "blue" color_homophily chance homophily (favorite colors) print (color_homophily) What is the chance homophily of the favorite colors dictionary?

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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• Print color_homophily. Here's the code to get you started: from collections import Counter import numpy as np def marginal prob(chara): Enter code here! def chance homophily(chars): * Enter code here! favorite colors = { "ankit": "red", "xiaoyu": "blue", "mary" i "blue" > color_homophily chance homophily favorite colors) print (color_homophily) What is the chance homophily of the favorite colors dictionary? Submit You have used 0 of 10 attempts Save Hi
Transcribed Image Text:• Print color_homophily. Here's the code to get you started: from collections import Counter import numpy as np def marginal prob(chara): Enter code here! def chance homophily(chars): * Enter code here! favorite colors = { "ankit": "red", "xiaoyu": "blue", "mary" i "blue" > color_homophily chance homophily favorite colors) print (color_homophily) What is the chance homophily of the favorite colors dictionary? Submit You have used 0 of 10 attempts Save Hi
Homework due Sep 15, 2022 03:59 +04 Exercise 1 In Exercise 1, we will calculate the chance homophily for an arbitrary characteristic. Homophily is the proportion of edges in the network whose constituent nodes share that characteristic. How much homophily do we expect by chance? If characteristics are distributed completely randomly, the probability that two nodes and y share characteristic a is the probability both nodes have characteristica. which is the marginal probability of a squared. The total probability that nodes x and y share their characteristic is therefore the sum of the square of the marginal probabilities of each characteristic in the network. Instructions • Create a function marginal_prob that takes a dictionary chars with personal IDs as keys and characteristics as values; it should return a dictionary with characteristics as keys and their marginal probability (frequency of occurence of a characteristic divided by the sum of frequencies of each characteristic) as values. • Create a function chance_homophily(chars) that takes a dictionary chars defined as above and computes the chance homophily (homophily due to chance alone) for that characteristic. • A sample of three peoples' favorite colors is given in favorite colors. Use your function to compute the chance homophily in this group, and store it as color_homophily. • Print color_homophily. Here's the code to get you started: rece
Transcribed Image Text:Homework due Sep 15, 2022 03:59 +04 Exercise 1 In Exercise 1, we will calculate the chance homophily for an arbitrary characteristic. Homophily is the proportion of edges in the network whose constituent nodes share that characteristic. How much homophily do we expect by chance? If characteristics are distributed completely randomly, the probability that two nodes and y share characteristic a is the probability both nodes have characteristica. which is the marginal probability of a squared. The total probability that nodes x and y share their characteristic is therefore the sum of the square of the marginal probabilities of each characteristic in the network. Instructions • Create a function marginal_prob that takes a dictionary chars with personal IDs as keys and characteristics as values; it should return a dictionary with characteristics as keys and their marginal probability (frequency of occurence of a characteristic divided by the sum of frequencies of each characteristic) as values. • Create a function chance_homophily(chars) that takes a dictionary chars defined as above and computes the chance homophily (homophily due to chance alone) for that characteristic. • A sample of three peoples' favorite colors is given in favorite colors. Use your function to compute the chance homophily in this group, and store it as color_homophily. • Print color_homophily. Here's the code to get you started: rece
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