# lotto/app/weight_evolver.py
"""5종 시뮬 점수 가중치 자율 학습 루프.

순수 함수 (clamp/perturb/Dirichlet/score/base-rule) + DB 진입점은 별도 섹션.
"""
from __future__ import annotations
import math
import random
from typing import Any, Dict, List, Optional, Tuple

import numpy as np

MIN_WEIGHT = 0.05
N_METRICS = 5
DEFAULT_UNIFORM = [0.2] * N_METRICS  # cold start

RANK_BY_CORRECT = {6: 1, 5: 3, 4: 4, 3: 5}
RANK_BONUS = {1: 1.0, 2: 0.8, 3: 0.6, 4: 0.3, 5: 0.1}


def clamp_and_normalize(W: List[float], min_w: float = MIN_WEIGHT) -> List[float]:
    """각 값 ≥ min_w + 합=1.0. 보장 안 되면 raise."""
    if len(W) != N_METRICS:
        raise ValueError(f"W must have {N_METRICS} elements")
    # Iteratively clamp then normalize until all values satisfy min_w floor.
    # (Normalizing after clamping can reduce some already-floored values below
    #  min_w when the denominator is large — iterate to convergence.)
    vals = [float(w) for w in W]
    for _ in range(100):  # converges in a few iterations in practice
        clamped = [max(min_w, v) for v in vals]
        total = sum(clamped)
        vals = [v / total for v in clamped]
        if all(v >= min_w - 1e-12 for v in vals):
            break
    return vals


def perturb_weights(
    base: List[float],
    sigma: float = 0.05,
    seed: Optional[int] = None,
) -> List[float]:
    """base에 정규분포 noise(σ) 추가 → clamp+normalize."""
    if seed is not None:
        np.random.seed(seed)
    noise = np.random.normal(0, sigma, size=N_METRICS)
    perturbed = [b + n for b, n in zip(base, noise)]
    return clamp_and_normalize(perturbed)


def dirichlet_weights(
    alpha: float = 2.0,
    seed: Optional[int] = None,
) -> List[float]:
    """Dirichlet(α, α, α, α, α) 샘플 → clamp+normalize."""
    if seed is not None:
        np.random.seed(seed)
    sample = np.random.dirichlet([alpha] * N_METRICS).tolist()
    return clamp_and_normalize(sample)


def generate_weekly_candidates(
    base: Optional[List[float]] = None,
    seed: Optional[int] = None,
) -> List[Dict[str, Any]]:
    """6개 후보 — 4 perturb + 2 dirichlet. day_of_week 0..5 매핑.

    Returns:
        [{"day_of_week": 0, "weight": [...], "source": "perturb"}, ...]
    """
    if base is None:
        base = DEFAULT_UNIFORM[:]
    if seed is not None:
        np.random.seed(seed)

    trials = []
    for i in range(4):
        trials.append({
            "day_of_week": i,
            "weight": perturb_weights(base, sigma=0.05),
            "source": "perturb",
        })
    for i in range(4, 6):
        trials.append({
            "day_of_week": i,
            "weight": dirichlet_weights(alpha=2.0),
            "source": "dirichlet",
        })
    return trials


def count_match(pick: List[int], winning: List[int]) -> int:
    """본번호 6개 일치 개수. 보너스 제외."""
    return len(set(pick) & set(winning[:6]))


def calc_pick_score(pick_numbers: List[int], winning_numbers: List[int]) -> float:
    """correct/6 + RANK_BONUS. 보너스 번호 미고려."""
    correct = count_match(pick_numbers, winning_numbers)
    base = correct / 6.0
    rank = RANK_BY_CORRECT.get(correct)
    bonus = RANK_BONUS.get(rank, 0) if rank else 0
    return base + bonus


def decide_base_update(
    winner_max_correct: int,
    winner_W: List[float],
    current_base: Optional[List[float]],
) -> Tuple[List[float], str]:
    """Hybrid base update rule.

    Returns:
        (new_base, reason) — reason ∈ {'winner_4plus','ema_blend','unchanged','cold_start'}
    """
    if winner_max_correct >= 4:
        return list(winner_W), "winner_4plus"
    if winner_max_correct == 3 and current_base is not None:
        blended = [0.3 * w + 0.7 * c for w, c in zip(winner_W, current_base)]
        return clamp_and_normalize(blended), "ema_blend"
    if current_base is None:
        return DEFAULT_UNIFORM[:], "cold_start"
    return list(current_base), "unchanged"