80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
#!/bin/python3
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import argparse
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import subprocess
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import re
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import tempfile
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from gen_values import generate_test_file
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def run_agcd(input_file):
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cmd = ["./target/release/approximate-gcd", "agcd", input_file]
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try:
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result = subprocess.run(cmd, capture_output=True, text=True, check=True)
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output = result.stdout
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match = re.search(r"Recovered p: (\d+)", output)
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return int(match.group(1)) if match else None
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except subprocess.CalledProcessError as e:
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print(f"Error running command for input_file={input_file}: {e}")
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return None
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except (AttributeError, ValueError) as e:
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print(f"Error parsing output for input_file={input_file}: {e}")
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return None
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def plot_curves(noise_bits, p_bits, test_numbers, success_rates):
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plt.figure(figsize=(10, 6))
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plt.plot(test_numbers, success_rates, marker='o')
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plt.xlabel('Number of Test Values')
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plt.ylabel('Success Rate')
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plt.title(f'Success Rate vs. Number of Test Values\n(noise_bits={noise_bits}, p_bits={p_bits})')
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plt.grid(True)
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plt.ylim(-0.1, 1.1)
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plt.xticks(test_numbers)
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plt.savefig('success_rate_plot.png')
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plt.show()
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def main():
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parser = argparse.ArgumentParser(description='Test AGCD with varying number of test values.')
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parser.add_argument('--noise_bits', type=int, default=0, help='Number of noise bits')
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parser.add_argument('--p_bits', type=int, default=10000, help='Number of bits for p')
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parser.add_argument('--min_values', type=int, default=2, help='Minimum number of test values')
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parser.add_argument('--max_values', type=int, default=100, help='Maximum number of test values')
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args = parser.parse_args()
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noise_bits = args.noise_bits
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p_bits = args.p_bits
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test_numbers = range(args.min_values, args.max_values + 1)
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success_rates = []
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num_trials = 100
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for num_values in test_numbers:
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successes = 0
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for _ in range(num_trials):
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# Create temporary test file
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with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.txt') as tmp_file:
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true_p = generate_test_file(noise_bits, num_values, p_bits, tmp_file.name)
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# Run AGCD
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recovered_p = run_agcd(tmp_file.name)
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# Check if recovery was successful
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if recovered_p is not None and abs(recovered_p-true_p) <= 2000:
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successes += 1
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# Clean up
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os.unlink(tmp_file.name)
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success_rate = successes / num_trials
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success_rates.append(success_rate)
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print(f"Number of values: {num_values}, Success rate: {success_rate:.3f} ({successes}/{num_trials})")
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# Plot the results
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plot_curves(noise_bits, p_bits, test_numbers, success_rates)
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if __name__ == "__main__":
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main()
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