Реализуйте Gaussian Naive Bayes классификатор.
Для каждого класса вычислите P(c), μ, σ по каждому признаку. Предсказание через формулу Байеса.
def naive_bayes_predict(X_train: list[list[float]], y_train: list[int],
X_test: list[list[float]], n_classes: int) -> list[int]:X_train = [[1,2],[2,1],[1,1],[5,6],[6,5],[5,5]]
y_train = [0,0,0,1,1,1]
naive_bayes_predict(X_train, y_train, [[1.5,1.5],[5.5,5.5]], 2) → [0, 1]X_train = [[1,2],[2,1],[1,1],[5,6],[6,5],[5,5]]y_train = [0,0,0,1,1,1]X_test = [[1.5,1.5],[5.5,5.5]]n_classes = 2[0,1]X_train = [[1],[2],[3],[7],[8],[9]]y_train = [0,0,0,1,1,1]X_test = [[2],[5],[8]]n_classes = 2[0,0,1]X_train = [[1,1],[1,2],[2,1],[5,5],[5,6],[6,5],[9,9],[9,10],[10,9]]y_train = [0,0,0,1,1,1,2,2,2]X_test = [[1,1],[5,5],[10,10]]n_classes = 3[0,1,2]