"""
Karlinska Sleepiness Score (KSS) prediction model.
"""


def karlinska_sleepiness_score(features):
    """
    Predict KSS (1-9) from a set of features.

    This is a placeholder model. In a real implementation, you would train
    a regression model on labeled data. Here we provide a simple heuristic.

    Parameters
    ----------
    features : dict
        Dictionary containing relevant metrics:
        - 'perclos': PERCLOS value (0-100)
        - 'blink_frequency': blinks per minute
        - 'pupil_variance': variance of pupil diameter
        - 'head_movement': variance of head pose angles
        - etc.

    Returns
    -------
    float
        Estimated KSS (1-9, higher = sleepier).
    """
    # Simple weighted sum (to be calibrated)
    score = 1.0
    if 'perclos' in features:
        score += 0.05 * features['perclos']
    if 'blink_frequency' in features:
        score += 0.1 * features['blink_frequency']
    if 'pupil_variance' in features:
        score += 0.5 * features['pupil_variance']  # normalized?
    if 'head_movement' in features:
        score += 2.0 * features['head_movement']

    # Clip to 1-9
    return max(1, min(9, score))