mean_squared_error.py

import numpy as np


def mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Calculate the Mean Squared Error (MSE) between two arrays.

    Parameters:
    - y_true: The true values (ground truth).
    - y_pred: The predicted values.

    Returns:
    - mse: The Mean Squared Error between y_true and y_pred.

    Example usage:
    true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
    mse = mean_squared_error(true_values, predicted_values)
    print(f"Mean Squared Error: {mse}")
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")

    squared_errors = np.square(np.subtract(y_true, y_pred))
    mse = np.mean(squared_errors)

    return mse


if __name__ == "__main__":
    import doctest

    doctest.testmod()