Code RoomCosine similarity sparse vectors
MediumPrep Room Coding #232

Cosine similarity sparse vectors

CodingML systemsMid–Senior~20 min

Compute the cosine similarity of two sparse vectors given as dicts mapping index/key to a nonzero numeric weight. Cosine = dot(a,b) / (||a|| * ||b||), where the dot product sums a[k]*b[k] over keys present in both, and each norm is the square root of the sum of squared weights. If either vector has zero norm (empty dict), return 0.0. Return the similarity rounded to 6 decimals.

Implement
sparse_cosine(a: dict, b: dict) → float
Examples
in[{"x":1,"y":2},{"y":2,"z":1}]out0.8
What a strong answer looks like

State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.

0:00 of about 20 min
InputExpectedGot
[{"x":1,"y":2},{"y":2,"z":1}]0.8not run yetsample