KNN Regression

Predict a numeric target for a query point as the average target of its k nearest training rows (squared Euclidean distance). Distance ties are resolved in favour of the earlier training row.

Implement solve(X,y,q,k).

Returns. Return the prediction as a Python float.

Examples

Example 1

Input:

solve([[0.0], [1.0], [2.0], [10.0]], [1.0, 2.0, 3.0, 100.0], [1.2], 2)

Output:

2.5

Example 2

Input:

solve([[0.0], [4.0]], [10.0, 20.0], [1.0], 2)

Output:

15.0