How should I practice One-Nearest-Neighbor Classification with Manhattan Distance?

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One-Nearest-Neighbor Classification with Manhattan Distance

OpenAI● EasyFULLTIMEPHONE SCREEN

Problem statement

You are given a nonempty set of labeled training samples and a set of query samples. Each sample is an integer feature vector.

For every query, find the single training sample with the smallest Manhattan distance:

distance(a, b) = sum(abs(a[i] - b[i]))

The problem statement continues

Pro

Examples

Example 1

trainingFeatures = [[0,0],[3,0],[2,3]]trainingLabels = [10,20,30]queries = [[2,1],[2,3]]return = [20,30]

The first query is two units from both the first and second rows, so the earlier of those tied nearest rows supplies label 20 only after checking all distances: its distances are 3, 2, and 2, making the second row the earliest minimum. The second query exactly matches the third row.

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