def mutual_info_classif(X, y, discrete_features='auto', n_neighbors=3,
                        copy=True, random_state=None):
    check_classification_targets(y)
    return _estimate_mi(X, y, discrete_features, True, n_neighbors,
                        copy, random_state)
Returns the reward the agent has gained as the difference between the last action and the current one.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/feature_selection/mutual_info_.py/mutual_info_classif--crossover with--dataset/ETHPy150Open ntasfi/PyGame-Learning-Environment/ple/ple.py/PLE._getReward