From b52ac6313a75277d82ba5eecc3a694827a3a894f Mon Sep 17 00:00:00 2001 From: Sam Hadow Date: Sun, 25 May 2025 18:45:09 +0200 Subject: [PATCH] script update --- scripts/gen_values.py | 2 +- scripts/script.py | 100 ++++++++++++++++++++++++++++-------------- 2 files changed, 68 insertions(+), 34 deletions(-) diff --git a/scripts/gen_values.py b/scripts/gen_values.py index 9b991f7..c491543 100644 --- a/scripts/gen_values.py +++ b/scripts/gen_values.py @@ -11,7 +11,7 @@ def generate_test_values(noise_bits, number, p_bits): max_noise = (1 << noise_bits) - 1 # 2^noise_bits - 1 - a = [str(p * random.randint(1, 2) + random.randint(0, max_noise)) for _ in range(number)] + a = [str(p * random.randint(1, max_noise) + random.randint(0, max_noise)) for _ in range(number)] return noise_bits, a, p diff --git a/scripts/script.py b/scripts/script.py index ad6300b..836c4de 100644 --- a/scripts/script.py +++ b/scripts/script.py @@ -6,74 +6,108 @@ import tempfile import os import numpy as np import matplotlib.pyplot as plt +import time +import math from gen_values import generate_test_file def run_agcd(input_file): cmd = ["./target/release/approximate-gcd", "agcd", input_file] - try: + start = time.perf_counter() result = subprocess.run(cmd, capture_output=True, text=True, check=True) + duration = time.perf_counter() - start output = result.stdout match = re.search(r"Recovered p: (\d+)", output) - return int(match.group(1)) if match else None + return int(match.group(1)) if match else None, duration except subprocess.CalledProcessError as e: print(f"Error running command for input_file={input_file}: {e}") - return None + return None, 0.0 except (AttributeError, ValueError) as e: print(f"Error parsing output for input_file={input_file}: {e}") - return None + return None, 0.0 -def plot_curves(noise_bits, p_bits, test_numbers, success_rates): - plt.figure(figsize=(10, 6)) - plt.plot(test_numbers, success_rates, marker='o') - plt.xlabel('Number of Test Values') - plt.ylabel('Success Rate') - plt.title(f'Success Rate vs. Number of Test Values\n(noise_bits={noise_bits}, p_bits={p_bits})') - plt.grid(True) - plt.ylim(-0.1, 1.1) - plt.xticks(test_numbers) - plt.savefig('success_rate_plot.png') +def plot_all(noise_bits, p_bits, test_numbers, success_rates, mean_distances, mean_times): + num_points = len(test_numbers) + max_ticks = 20 + step = max(1, num_points // max_ticks) + xticks = list(test_numbers)[::step] + if test_numbers[-1] not in xticks: + xticks.append(test_numbers[-1]) + + fig, axs = plt.subplots(3, 1, figsize=(10, 15), sharex=True) + + # Success rate plot + axs[0].plot(test_numbers, success_rates, linestyle='-') + axs[0].set_ylabel('Success Rate') + axs[0].set_ylim(-0.1, 1.1) + axs[0].grid(True) + axs[0].set_title(f'Success Rate (noise_bits={noise_bits}, p_bits={p_bits})') + + # Mean distance plot + axs[1].plot(test_numbers, mean_distances, linestyle='-') + axs[1].set_ylabel('Mean Distance to True p') + axs[1].grid(True) + axs[1].set_title(f'Mean Distance (noise_bits={noise_bits}, p_bits={p_bits})') + + # Mean runtime plot + axs[2].plot(test_numbers, mean_times, linestyle='-') + axs[2].set_ylabel('Mean Runtime (s)') + axs[2].set_xlabel('Number of Test Values') + axs[2].grid(True) + axs[2].set_title(f'Mean Runtime (noise_bits={noise_bits}, p_bits={p_bits})') + + axs[2].set_xticks(xticks) + + fig.tight_layout() + plt.savefig('agcd_plots.png') plt.show() def main(): parser = argparse.ArgumentParser(description='Test AGCD with varying number of test values.') - parser.add_argument('--noise_bits', type=int, default=0, help='Number of noise bits') - parser.add_argument('--p_bits', type=int, default=10000, help='Number of bits for p') - parser.add_argument('--min_values', type=int, default=2, help='Minimum number of test values') - parser.add_argument('--max_values', type=int, default=100, help='Maximum number of test values') + parser.add_argument('--noise-bits', type=int, default=1, help='Number of noise bits') + parser.add_argument('--p-bits', type=int, default=10000, help='Number of bits for p') + parser.add_argument('--min-values', type=int, default=2, help='Minimum number of test values') + parser.add_argument('--max-values', type=int, default=50, help='Maximum number of test values') + parser.add_argument('--trials', type=int, default=50, help='Number of trials per setting') args = parser.parse_args() noise_bits = args.noise_bits p_bits = args.p_bits test_numbers = range(args.min_values, args.max_values + 1) + success_rates = [] - num_trials = 100 + mean_distances = [] + mean_times = [] for num_values in test_numbers: successes = 0 - for _ in range(num_trials): - # Create temporary test file + distances = [] + runtimes = [] + + for _ in range(args.trials): with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.txt') as tmp_file: true_p = generate_test_file(noise_bits, num_values, p_bits, tmp_file.name) + recovered_p, duration = run_agcd(tmp_file.name) + os.unlink(tmp_file.name) - # Run AGCD - recovered_p = run_agcd(tmp_file.name) - - # Check if recovery was successful - if recovered_p is not None and abs(recovered_p-true_p) <= 2000: + if recovered_p is not None: + diff = abs(recovered_p - true_p) + threshold = math.isqrt(true_p.bit_length()) + if diff <= threshold: successes += 1 + distances.append(diff) + runtimes.append(duration) - # Clean up - os.unlink(tmp_file.name) + success_rates.append(successes / args.trials) + mean_distances.append(np.mean(distances) if distances else float('nan')) + mean_times.append(np.mean(runtimes)) - success_rate = successes / num_trials - success_rates.append(success_rate) - print(f"Number of values: {num_values}, Success rate: {success_rate:.3f} ({successes}/{num_trials})") + print(f"Values: {num_values}, Success rate: {success_rates[-1]:.3f} ({successes}/{args.trials}), " + f"Mean distance: {mean_distances[-1]:.2f}, Mean time: {mean_times[-1]:.4f}s") - # Plot the results - plot_curves(noise_bits, p_bits, test_numbers, success_rates) + plot_all(noise_bits, p_bits, list(test_numbers), success_rates, mean_distances, mean_times) if __name__ == "__main__": main()