import random
from math import log

import numpy as np
from numpy.typing import NDArray

from gp import Chromosome
from gp.crossovers import crossover_subtree
from gp.fitness import (
    MAEFitness,
    MSEFitness,
    NRMSEFitness,
    PenalizedFitness,
    RMSEFitness,
)
from gp.ga import GARunConfig, genetic_algorithm
from gp.mutations import (
    grow_mutation,
    hoist_mutation,
    node_replacement_mutation,
    shrink_mutation,
)
from gp.ops import ADD, COS, DIV, EXP, MUL, NEG, POW, SIN, SQUARE, SUB
from gp.population import ramped_initialization
from gp.primitive import Const, Var
from gp.selection import roulette_selection, tournament_selection

NUM_VARS = 8
TEST_POINTS = 10000
MAX_DEPTH = 10
MAX_GENERATIONS = 200
SEED = 17
np.random.seed(SEED)
random.seed(SEED)
X = np.random.uniform(-5.536, 5.536, size=(TEST_POINTS, NUM_VARS))
# axes = [np.linspace(-5.536, 5.536, TEST_POINTS) for _ in range(NUM_VARS)]
# X = np.array(np.meshgrid(*axes)).T.reshape(-1, NUM_VARS)
operations = [SQUARE, SIN, COS, EXP, ADD, SUB, MUL, DIV, POW]
# operations = [SQUARE, ADD, SUB, MUL]
terminals = [Var(f"x{i}") for i in range(1, NUM_VARS + 1)]


def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
    """
    Векторизованная версия функции:
    f(x) = sum_{i=1}^n sum_{j=1}^i x_j^2
    x имеет форму (n_samples, n_vars)
    """
    # Префиксные суммы квадратов по оси переменных
    x_sq = x**2
    prefix_sums = np.cumsum(x_sq, axis=1)
    # Суммируем по i (ось 1)
    return np.sum(prefix_sums, axis=1)


# fitness_function = MSEFitness(target_function, lambda: X)
# fitness_function = HuberFitness(target_function, lambda: X, delta=0.5)
# fitness_function = PenalizedFitness(
#     target_function, lambda: X, base_fitness=fitness, lambda_=0.1
# )
# fitness_function = NRMSEFitness(target_function, lambda: X)
fitness_function = RMSEFitness(target_function, lambda: X)

# fitness_function = PenalizedFitness(
#     target_function, lambda: X, base_fitness=fitness_function, lambda_=0.0001
# )


def adaptive_mutation(
    chromosome: Chromosome,
    generation: int,
    max_generations: int,
    max_depth: int,
) -> Chromosome:
    r = random.random()

    if r < 0.4:
        return grow_mutation(chromosome, max_depth=max_depth)
    elif r < 0.7:
        return node_replacement_mutation(chromosome)
    elif r < 0.85:
        return hoist_mutation(chromosome)

    return shrink_mutation(chromosome)


init_population = ramped_initialization(
    20, [i for i in range(MAX_DEPTH - 9, MAX_DEPTH + 1)], terminals, operations
)

print("Population size:", len(init_population))

config = GARunConfig(
    fitness_func=fitness_function,
    crossover_fn=lambda p1, p2: crossover_subtree(p1, p2, max_depth=MAX_DEPTH),
    mutation_fn=lambda chrom, gen_num: adaptive_mutation(
        chrom, gen_num, MAX_GENERATIONS, MAX_DEPTH
    ),
    # selection_fn=roulette_selection,
    selection_fn=lambda p, f: tournament_selection(p, f, k=3),
    init_population=init_population,
    seed=SEED,
    pc=0.85,
    pm=0.15,
    elitism=15,
    max_generations=MAX_GENERATIONS,
    log_every_generation=True,
)

result = genetic_algorithm(config)


# Выводим результаты
print(f"Лучшая особь: {result.best_generation.best}")
print(result.best_generation.best.root.to_str_tree())
print(f"Лучшее значение фитнеса: {result.best_generation.best_fitness:.6f}")
print(f"Количество поколений: {result.generations_count}")
print(f"Время выполнения: {result.time_ms:.2f} мс")
print("Population size:", len(init_population))

mse_fitness = MSEFitness(target_function, lambda: X)
print(f"MSE: {mse_fitness(result.best_generation.best):.6f}")
rmse_fitness = RMSEFitness(target_function, lambda: X)
print(f"RMSE: {rmse_fitness(result.best_generation.best):.6f}")
mae_fitness = MAEFitness(target_function, lambda: X)
print(f"MAE: {mae_fitness(result.best_generation.best):.6f}")
nrmse_fitness = NRMSEFitness(target_function, lambda: X)
print(f"NRMSE: {nrmse_fitness(result.best_generation.best):.6f}")