Answer :
The assignment requires implementing two functions: (NN) Nearest Neighbor and (KNN) K-Nearest Neighbor with L1 (Manhattan) and L2 (Euclidean) distance metrics. The next step is to call one of these functions with different values of K and either L1 or L2 distance.
To fulfill your assignment, you can write functions for Nearest Neighbor (NN) and k-Nearest Neighbor (KNN) algorithms with L1 and L2 distance calculations. 1. NN Function: The NN function finds the nearest neighbor to a given query point using either L1 or L2 distance. It calculates the distance between the query point and each data point in the dataset using the chosen distance metric. The function then selects the data point with the minimum distance as the nearest neighbor and returns it. 2. KNN Function: The KNN function extends the NN function by finding the k nearest neighbors to a query point. It follows a similar approach as the NN function but keeps track of the k nearest neighbors based on their distances. It returns the k nearest neighbors as output.
You can call either the NN or KNN function with different values of k (number of neighbors) and choose either L1 or L2 distance calculation to analyze the impact of different parameters on the nearest neighbor search.
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