Matching-Network Cosine

Problem

Classify a query by cosine similarity to a set of support embeddings.

Return [best_index, round(best_similarity, 4)], keeping the smallest index on ties. Zero vectors have similarity 0.0.

case 1

in: [[1,0],[[1,0],[0,1],[-1,0]]]

expected: [0,1]

case 2

in: [[0,1],[[1,0],[0.6,0.8]]]

expected: [1,0.8]

case 3

in: [[1,0],[[0,1]]]

expected: [0,0]

Starter codePython

def matching_cosine(query, supports):
    # Your code here
    pass

3 test cases · run your code against them in the editor