Linear Regression Prediction
MediumMachine LearningMathArrayMatrix
Description
Given a weight vector w, a bias b, and a 2D array X of feature rows, return the linear-regression predictions: for each row, the dot product of w with the row plus b. Round each result to 4 decimal places.
Examples
Input:
[1,2], 0, [[1,1],[2,2]]Output:
[3,6]Explanation:
Each prediction is the weighted sum of that row’s features plus the shared bias term.
Input:
[1], 0, [[5],[10]]Output:
[5,10]Explanation:
Each prediction is the weighted sum of that row’s features plus the shared bias term.
Input:
[2,-1], 1, [[3,4],[1,1]]Output:
[3,2]Explanation:
Each prediction is the weighted sum of that row’s features plus the shared bias term.
Constraints
- •
1 ≤ rows ≤ 10⁴ - •
each row length equals w length