2025-05-18 19:26:14 +02:00

73 lines
2.6 KiB
Python

#!/bin/python3
import argparse, subprocess, re, tempfile, os
import numpy as np
import matplotlib.pyplot as plt
from gen_values import generate_test_file
def run_agcd(input_file):
cmd = ["./target/release/approximate-gcd", "agcd", input_file]
try:
result = subprocess.run(cmd, capture_output=True, text=True, check=True)
output = result.stdout
match = re.search(r"Recovered p: (\d+)", output)
return int(match.group(1)) if match else None
except subprocess.CalledProcessError as e:
print(f"Error running command for input_file={input_file}: {e}")
return None
except (AttributeError, ValueError) as e:
print(f"Error parsing output for input_file={input_file}: {e}")
return None
def plot_curves(noise_bits, p_bits, test_numbers, successes):
plt.figure(figsize=(10, 6))
plt.plot(test_numbers, successes, marker='o')
plt.xlabel('Number of Test Values')
plt.ylabel('Success (1 = Correct, 0 = Incorrect)')
plt.title(f'Success vs. Number of Test Values\n(noise_bits={noise_bits}, p_bits={p_bits})')
plt.grid(True)
plt.ylim(-0.1, 1.1)
plt.yticks([0, 1])
plt.xticks(test_numbers)
plt.savefig('success_plot.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=8, help='Number of noise bits')
parser.add_argument('--p_bits', type=int, default=128, 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')
args = parser.parse_args()
noise_bits = args.noise_bits
p_bits = args.p_bits
test_numbers = range(args.min_values, args.max_values + 1)
successes = []
for num_values in test_numbers:
# Create temporary test file
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)
# Run AGCD
recovered_p = run_agcd(tmp_file.name)
# Check if recovery was successful
success = 1 if recovered_p != None and abs(recovered_p - true_p) <= 4 else 0
successes.append(success)
# Clean up
os.unlink(tmp_file.name)
print(f"Number of values: {num_values}, Success: {'Yes' if success else 'No'}")
# Plot the results
plot_curves(noise_bits, p_bits, test_numbers, successes)
if __name__ == "__main__":
main()