"""Doğruluk ve kalibrasyon ölçüleri (yalnızca numpy)."""
import numpy as np


def ece(conf, correct, bins=15):
    conf, correct = np.asarray(conf, float), np.asarray(correct, float)
    edges = np.linspace(0, 1, bins + 1)
    out = 0.0
    for lo, hi in zip(edges[:-1], edges[1:]):
        m = (conf > lo) & (conf <= hi) if lo > 0 else (conf >= lo) & (conf <= hi)
        if m.any():
            out += m.mean() * abs(conf[m].mean() - correct[m].mean())
    return float(out)


def brier(conf, correct):
    conf, correct = np.asarray(conf, float), np.asarray(correct, float)
    return float(np.mean((conf - correct) ** 2))


def auroc(conf, correct):
    """Güvenin doğru/yanlışı ayırma gücü (Mann-Whitney)."""
    conf, correct = np.asarray(conf, float), np.asarray(correct, bool)
    pos, neg = conf[correct], conf[~correct]
    if len(pos) == 0 or len(neg) == 0:
        return float("nan")
    order = np.argsort(np.concatenate([pos, neg]), kind="mergesort")
    ranks = np.empty(len(order))
    allv = np.concatenate([pos, neg])[order]
    i = 0
    r = np.empty(len(order))
    while i < len(allv):
        j = i
        while j + 1 < len(allv) and allv[j + 1] == allv[i]:
            j += 1
        r[i:j + 1] = (i + j) / 2 + 1
        i = j + 1
    ranks[order] = r
    return float((ranks[:len(pos)].sum() - len(pos) * (len(pos) + 1) / 2) / (len(pos) * len(neg)))


def risk_coverage(conf, correct, coverages=(1.0, 0.8, 0.6, 0.4, 0.2)):
    """En emin olduğu adımların %c'sinde doğruluk."""
    conf, correct = np.asarray(conf, float), np.asarray(correct, float)
    order = np.argsort(-conf, kind="stable")
    out = {}
    for c in coverages:
        k = max(1, int(round(c * len(order))))
        out[f"acc@{int(c * 100)}%"] = float(correct[order[:k]].mean())
    return out


def bootstrap_ci(correct, n=1000, seed=0):
    correct = np.asarray(correct, float)
    rng = np.random.default_rng(seed)
    means = [correct[rng.integers(0, len(correct), len(correct))].mean() for _ in range(n)]
    return float(np.percentile(means, 2.5)), float(np.percentile(means, 97.5))


def summarize(conf, correct):
    d = {"n": int(len(correct)), "acc": float(np.mean(correct)),
         "acc_ci95": bootstrap_ci(correct), "ece": ece(conf, correct),
         "brier": brier(conf, correct), "auroc": auroc(conf, correct)}
    d.update(risk_coverage(conf, correct))
    return d