Sparse Vector in Python | Dataford Interview Questions

OpenShift-related analytics can represent high-dimensional feature vectors where most coordinates are zero. Design a Python SparseVector class that stores only nonzero values and supports efficient updates, lookups, and dot products.

Implement:

__init__(self, values), where values is a dictionary mapping integer indices to nonzero numeric values.
get(self, index), returning the value at index, or 0 if it is not stored.
set(self, index, value), updating an index. If value is zero, remove the index from storage.
dot(self, other), returning the dot product with another sparse vector of the same logical dimension.

For automated evaluation, also implement sparse_vector_operations(vector_a, vector_b). It receives two dictionaries, constructs sparse vectors, and returns their dot product.

Constraints

Each input dictionary contains at most 10^5 entries
Indices are distinct nonnegative integers less than 10^9
Stored values are nonzero integers in the range [-10^9, 10^9]
Both vectors have the same logical dimension
The dot product must not iterate through every possible coordinate

Function Signature

def sparse_vector_operations(vector_a, vector_b):

Your question is Sparse Vector in Python. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem