| """ |
| 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() |
