Linear Regression Prediction

Compute the predictions of a linear model: $\widehat{y}=Xw+b\backslash hat\; y=Xw+b$, where X is an $n\times dn\backslash times\; d$ matrix, w a length-$dd$ weight vector and b a scalar bias.

Implement solve(X, w, b).

Returns. Return a NumPy array of $nn$ predictions.

Examples

Example 1

Input:

solve([[1.0, 2.0], [3.0, 4.0]], [1.0, 1.0], 0.0)

Output:

[3.0, 7.0]

Example 2

Input:

solve([[1.0], [2.0], [3.0]], [2.0], 1.0)

Output:

[3.0, 5.0, 7.0]