Using Homework3.py as a base, implement the functionality required for K-Means clustering (50%) and K-Nearest Neighbor classification (50%). You may use numpy or any math library you prefer, though this is not necessary. You are not permitted to call k-Means or k-NN classifiers from other packages to implement your own.
The provided Python file will output your k-Means cluster centers and assess your kNN classifier accuracy using Leave-One-Out-Cross Validation. You are to complete this assignment on your own (without collaboration).
Submit your fully implemented Homework3.py file, as well as the
hw3_kmeans_*.pkl file containing your cluster centers to Moodle for full credit.
