bkz using LLL oracle

scripts/gen_values.py

@@ -3,6 +3,7 @@ import random
 import argparse
 
 def generate_test_values(noise_bits, number, p_bits):
+    sys.set_int_max_str_digits(10000000)
 
     p = random.randint(2**(p_bits-1), 2**p_bits)
     while p % 2 == 0:
@@ -10,7 +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, 100) + random.randint(0, max_noise)) for _ in range(number)]
+    a = [str(p * random.randint(1, 2) + random.randint(0, max_noise)) for _ in range(number)]
 
     return noise_bits, a, p
 

scripts/script.py

@@ -1,5 +1,9 @@
 #!/bin/python3
-import argparse, subprocess, re, tempfile, os
+import argparse
+import subprocess
+import re
+import tempfile
+import os
 import numpy as np
 import matplotlib.pyplot as plt
 from gen_values import generate_test_file
@@ -21,24 +25,22 @@ def run_agcd(input_file):
         print(f"Error parsing output for input_file={input_file}: {e}")
         return None
 
-
-def plot_curves(noise_bits, p_bits, test_numbers, successes):
+def plot_curves(noise_bits, p_bits, test_numbers, success_rates):
     plt.figure(figsize=(10, 6))
-    plt.plot(test_numbers, successes, marker='o')
+    plt.plot(test_numbers, success_rates, 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.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.yticks([0, 1])
     plt.xticks(test_numbers)
-    plt.savefig('success_plot.png')
+    plt.savefig('success_rate_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('--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')
     args = parser.parse_args()
@@ -46,27 +48,32 @@ def main():
     noise_bits = args.noise_bits
     p_bits = args.p_bits
     test_numbers = range(args.min_values, args.max_values + 1)
-    successes = []
+    success_rates = []
+    num_trials = 100
 
     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)
+        successes = 0
+        for _ in range(num_trials):
+            # 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)
+                # 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)
+                # Check if recovery was successful
+                if recovered_p is not None and abs(recovered_p-true_p) <= 2000:
+                    successes += 1
 
-            # Clean up
-            os.unlink(tmp_file.name)
+                # Clean up
+                os.unlink(tmp_file.name)
 
-        print(f"Number of values: {num_values}, Success: {'Yes' if success else 'No'}")
+        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})")
 
     # Plot the results
-    plot_curves(noise_bits, p_bits, test_numbers, successes)
+    plot_curves(noise_bits, p_bits, test_numbers, success_rates)
 
 if __name__ == "__main__":
     main()