Easy

Linear Prediction

Easy

~12 min

code completion

A linear model makes predictions with a simple dot product plus a bias:

\overset{y}{^} = X w + b

where:

X

is the feature matrix of shape (m, n)

w

is the weight vector of shape (n,)

b

is the scalar bias (intercept)

This is the forward pass of linear regression.

Your task:

Implement linear_predict(X, weights, bias) that returns the prediction vector $\overset{y}{^}$ of shape (m,).

Example Tests

2x2 matrix with equal weights and bias

Input: {"X":[[1,2],[3,4]],"bias":1,"weights":[0.5,0.5]}

Expected: [2.5,4.5]

3 samples, integer weights, no bias

Input: {"X":[[1,0],[0,1],[1,1]],"bias":0,"weights":[2,3]}

Expected: [2,3,5]

Negative weights

Input: {"X":[[2,3]],"bias":5,"weights":[-1,2]}

Expected: [9]

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