"""
Minimal usage example for hv-locality.

    python example.py

Diagnoses three functions at the same I/O dimensions, prints a verdict
matrix, and profiles two synthetic tasks.
"""

import numpy as np

from hv_locality import (
    HVLocality,
    task_profile,
    _identity_encoder_factory,
    _weak_avalanche_factory,
    _random_hash_factory,
)


N_IN = 64
N_OUT = 512


def _verdict_row(m, reports, funcs, k_intra):
    row = [f"{k_intra:>8}"]
    for name, _ in funcs:
        v = m.verdict(reports[name], k_intra)
        row.append(f"{v['tier']:>16}")
    return "  ".join(row)


def main() -> None:
    m = HVLocality()

    funcs = [
        ("identity",       _identity_encoder_factory(N_OUT)),
        ("weak_avalanche", _weak_avalanche_factory(N_OUT, mix_rate=0.1)),
        ("random_hash",    _random_hash_factory(N_OUT)),
    ]

    reports = {}
    print("=" * 70)
    print("Diagnostic summary")
    print("=" * 70)
    print(f"  {'function':<18}  {'ε':>7}  {'L(1)':>7}  "
          f"{'L(10)':>7}  {'k*':>4}  {'L_∞':>7}")
    print("  " + "-" * 62)
    for name, f in funcs:
        rpt = m.diagnose(f, N_IN, N_OUT, function_name=name, seed=0)
        reports[name] = rpt
        print(f"  {name:<18}  {rpt.avalanche_epsilon:>7.4f}  "
              f"{rpt.L_at_1:>7.4f}  {rpt.L_at_10:>7.4f}  "
              f"{rpt.saturation_k:>4}  {rpt.plateau_value:>7.4f}")

    print()
    print("=" * 70)
    print("Verdict matrix")
    print("=" * 70)
    print(f"  {'k_intra':>8}  " + "  ".join(f"{n:>16}" for n, _ in funcs))
    print("  " + "-" * (8 + 18 * len(funcs)))
    for k in [1, 5, 10, 20, 50]:
        print(_verdict_row(m, reports, funcs, k))

    print()
    print("=" * 70)
    print("Task profile: synthetic 10-class 64-dim")
    print("=" * 70)
    rng = np.random.default_rng(42)
    X_list, y_list = [], []
    for c in range(10):
        center = rng.standard_normal(64) * 2.0
        X_c = center + rng.standard_normal((100, 64)) * 1.0
        X_list.append(X_c)
        y_list.append(np.full(100, c))
    X = np.concatenate(X_list, axis=0).astype(np.float32)
    y = np.concatenate(y_list, axis=0)
    prof = task_profile(X, y, n_bits=64, seed=0)
    print(f"  k_intra (median) : {prof['k_intra']['median']:.2f}")
    print(f"  k_inter (median) : {prof['k_inter']['median']:.2f}")

    print()
    print("=" * 70)
    print("Task profile: MNIST-like 8x8 bit patterns")
    print("=" * 70)
    rng = np.random.default_rng(7)
    X_list, y_list = [], []
    for c in range(10):
        proto = rng.integers(0, 2, size=64).astype(np.float32)
        noise = (rng.random((50, 64)) < 0.15).astype(np.float32)
        X_c = np.clip(proto + noise - 2 * proto * noise, 0, 1)
        X_list.append(X_c)
        y_list.append(np.full(50, c))
    X = np.concatenate(X_list, axis=0)
    y = np.concatenate(y_list, axis=0)
    prof = task_profile(X, y, n_bits=64, seed=0)
    print(f"  k_intra (median) : {prof['k_intra']['median']:.2f}")
    print(f"  k_inter (median) : {prof['k_inter']['median']:.2f}")


if __name__ == "__main__":
    main()