#0157

Compute Ridge Penalty Term

EasyML15 min10 XP

Problem

Given weights and lambda, return the ridge penalty lambda * sum(weight^2). Do not include the bias term if provided separately.

Why This Matters

Regularization is easier to understand when the penalty can be computed directly from model weights.

Function Signature

def ridge_penalty(weights, lam):

Examples

Example 1

Inputweights = [2, -3, 1], lam = 0.5

Output7.0

0.5 * (4 + 9 + 1) = 7.0.

Constraints

Return the exact requested value.
Handle empty or small inputs when the prompt allows them.
Keep the code readable and deterministic.

CodePython

def ridge_penalty(weights, lam):
    """
    Input example: weights = [2, -3, 1], lam = 0.5
    Return: 7.0
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return 0.0

def ridge_penalty(weights, lam):
    """
    Input example: weights = [2, -3, 1], lam = 0.5
    Return: 7.0
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return 0.0

Visible browser tests run here when available.

Testcases1 visible / 3 hidden categories

Core example

Inputweights = [2, -3, 1], lam = 0.5

Expected7.0

0.5 * (4 + 9 + 1) = 7.0.

Hidden Test Categories

empty or smallest inputduplicates or repeated valueslarger realistic input