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Homework 10: K-Means Clustering Due: 23 April, 2021 11�59pm ET This homework asks you to fill in portions of classes that you can then use to perform k-means analysis. Goals In this assignment you will: Get familiar with using objects and classes by defining some methods and using objects to perform a computation Implement k-means Background Classes and Objects Please see the class notes on classes and objects. k-means Please see the class notes on clustering and specifically k-means. Instructions 0) Set up your repository for this homework. Use the link on Piazza to set up Homework 10. The repository should contain the following files: �. This README. �. cluster.py which contains the definition of the Cluster class and some testing code. �. point.py which contains the definition of the Point class and some testing code. �. kmeans.py which contains the skeleton of the kmeans algorithm and some testing code. 1) Homework Problem 1: Complete Point class https://github.com/ECEDataScience/homework-10-s21-davidinouye#due-23-april-2021-1159pm-et https://github.com/ECEDataScience/homework-10-s21-davidinouye#goals https://github.com/ECEDataScience/homework-10-s21-davidinouye#background https://github.com/ECEDataScience/homework-10-s21-davidinouye#classes-and-objects https://github.com/ECEDataScience/homework-10-s21-davidinouye#k-means https://github.com/ECEDataScience/homework-10-s21-davidinouye#instructions https://github.com/ECEDataScience/homework-10-s21-davidinouye#0-set-up-your-repository-for-this-homework https://github.com/ECEDataScience/homework-10-s21-davidinouye#1-homework-problem-1-complete-point-class https://www.davidinouye.com/course/ece20875-spring-2021/lectures/objects.pdf https://www.davidinouye.com/course/ece20875-spring-2021/lectures/clustering.pdf Complete the missing portions of the Point class, defined in point.py : �. distFrom , which calculates the (Euclidean) distance between the current point and the target point. Be sure to account for the fact that a point may be in more than two dimensions (Euclidean distance generalizes: square the difference in each dimension and take the square root of the sum). It is okay to use math.sqrt() to calculate the square root. �. makePointList , which takes in a data p-by-k input matrix data and returns a list of p Point objects. Hint: Instantiate a point object for every row in the input, data . If you test your code by running python3 point.py , you should get the following: [Point: [0.5 2.5], Point: [0.3 4.5], Point: [-0.5 3. ], Point: [0. 1.2], Point: [10. -5.], Point: [11. -4.5], Point: [ 8. -3.]] 2.009975124224178 (Your floating point numbers may be a little off due to rounding) 2) Homework Problem 2: Complete Cluster class Complete the missing portions of the Cluster class, defined in cluster.py : �. avgDistance , which computes the average distance from the center of the cluster (stored in self.center ) to all of the points currently in the cluster (stored in self.points ). This can most easily be done by summing the distances between each point and the current center and then dividing the sum by the total number of points. �. updateCenter , which updates the center of the cluster (stored in self.center ) to be the average position of all the points in the cluster. Note that if there are no points in the cluster, you should return without updating (i.e., if there are no points, just type the command return . Note that we have defined dim and coords as properties that return information about the center of the cluster -- this means that if you pass a cluster into a method that is expecting a point, operations that access dim and coords will use the center of the cluster. Think about how that might be useful in conjunction with the closest method defined for Point . If you test your code by running python3 cluster.py , you should get the following: Cluster: 0 points and center = [0.5, 3.5] Cluster: 2 points and center = [0.5, 3.5] 1.4976761962286425 Cluster: 2 points and center = [1.75, 2.75] 0.3535533905932738 (Your floating point numbers may be a little off due to rounding) 3) Homework Problem 3: Implement k-means https://github.com/ECEDataScience/homework-10-s21-davidinouye#2-homework-problem-2-complete-cluster-class https://github.com/ECEDataScience/homework-10-s21-davidinouye#3-homework-problem-3-implement-k-means Use the methods in Point and Cluster to implement the missing kmeans method in kmeans.py . The basic recommended procedure is outlined in kmeans.py . If you test your code by running python3 kmeans.py , you should get the following: Cluster: 4 points and center = [0.075 2.8 ] [0.3 4.5] [0. 1.2] [0.5 2.5] [-0.5 3. ] Cluster: 3 points and center = [ 9.66666667 -4.16666667] [ 8. -3.] [10. -5.] [11. -4.5] (Your floating point numbers may be a little off due to rounding) What you need to submit Push your completed versions of kmeans.py , cluster.py , and point.py . https://github.com/ECEDataScience/homework-10-s21-davidinouye#what-you-need-to-submit