Finished the revision of norms, with the revised method for finding the best paramete...

diff --git a/caesar_break_parameter_trials.csv b/caesar_break_parameter_trials.csv
index 465cb85fd81d9dc6365a40883134c0ba48015b13..6f71f0779797eb302a00da9557f6b23fd20447ae 100644 (file)
--- a/caesar_break_parameter_trials.csv
+++ b/caesar_break_parameter_trials.csv
@@ -1,23 +1,93 @@
 ,message_length
-metric+scaling, 300,100,50,30,20,10,5
-l1:normalised, 0.9988, 0.9996, 0.9984, 0.9896, 0.953, 0.736, 0.44
-l1:euclidean_scaled, 0.9996, 1.0, 0.9988, 0.9896, 0.9518, 0.7536, 0.4418
-l1:normalised_with_identity, 0.9606, 0.9922, 0.988, 0.9644, 0.909, 0.7028, 0.4288
-l2:normalised, 0.9996, 0.9994, 0.9984, 0.981, 0.9302, 0.723, 0.4354
-l2:euclidean_scaled, 0.9992, 0.9992, 0.9984, 0.9836, 0.9298, 0.7116, 0.423
-l2:normalised_with_identity, 1.0, 0.9998, 0.9982, 0.986, 0.9322, 0.722, 0.4262
-l3:normalised, 0.9998, 0.999, 0.9952, 0.9536, 0.8742, 0.5964, 0.4078
-l3:euclidean_scaled, 0.9992, 0.9992, 0.9958, 0.9672, 0.8894, 0.6276, 0.4014
-l3:normalised_with_identity, 0.9998, 0.998, 0.97, 0.9002, 0.7686, 0.5484, 0.391
-cosine_distance:normalised, 0.999, 0.9994, 0.9984, 0.9854, 0.934, 0.7092, 0.4338
-cosine_distance:euclidean_scaled, 0.9996, 0.9992, 0.999, 0.9822, 0.9342, 0.7114, 0.4326
-cosine_distance:normalised_with_identity, 0.9994, 0.9994, 0.9984, 0.986, 0.9354, 0.7166, 0.4294
-harmonic_mean:normalised, 0.8154, 0.8382, 0.7618, 0.2696, 0.8678, 0.6736, 0.4566
-harmonic_mean:euclidean_scaled, 0.4756, 0.5108, 0.686, 0.6098, 0.5342, 0.4322, 0.3568
-harmonic_mean:normalised_with_identity, 0.9574, 0.969, 0.952, 0.9254, 0.897, 0.7368, 0.4434
-geometric_mean:normalised, 0.9996, 0.9996, 0.9914, 0.9178, 0.9368, 0.7114, 0.4562
-geometric_mean:euclidean_scaled, 0.9998, 0.999, 0.9962, 0.9534, 0.8824, 0.6548, 0.443
-geometric_mean:normalised_with_identity, 0.9426, 0.9872, 0.9848, 0.9694, 0.9358, 0.7654, 0.4582
-inverse_log_pl:normalised, 0.9994, 0.9996, 0.9992, 0.996, 0.98, 0.8088, 0.488
-inverse_log_pl:euclidean_scaled, 0.9998, 0.9998, 0.9996, 0.996, 0.9826, 0.817, 0.481
-inverse_log_pl:normalised_with_identity, 0.999, 0.9996, 0.9992, 0.9978, 0.9802, 0.8106, 0.483
+scoring, 300, 100, 50, 30, 20, 10, 5
+Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
+Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
+Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
+Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
+Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
+Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
+Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
+cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
+cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
+cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
+cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
+cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
+cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
+cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
+cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
+cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
+cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
+cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
+cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
+cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
+cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
+geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
+geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
+geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
+geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
+geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
+geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
+geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
+geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
+geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
+geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
+geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
+geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
+geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
+geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
+harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
+harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
+harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
+harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
+harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
+harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
+harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
+harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
+harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
+harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
+harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
+harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
+harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
+harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
+l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
+l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
+l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
+l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
+l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
+l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
+l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
+l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
+l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
+l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
+l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
+l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
+l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
+l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
+l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
+l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
+l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
+l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
+l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
+l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
+l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
+l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
+l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
+l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
+l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
+l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
+l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
+l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
+l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
+l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
+l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
+l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
+l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
+l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
+l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
+l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
+l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
+l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
+l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
+l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
+l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
+l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122

diff --git a/cipherbreak.py b/cipherbreak.py
index 3993a42b5b8f786e9352160032389ac5ae58d476..03165e5ecca6985e6a2ead4912520e8d6000d50b 100644 (file)
--- a/cipherbreak.py
+++ b/cipherbreak.py
@@ -57,15 +57,6 @@ def frequencies(text):
     return collections.Counter(c for c in text)
 
 
-def frequency_compare(text, target_frequency, frequency_scaling, metric):
-    counts = frequency_scaling(frequencies(text))
-    return -1 * metric(target_frequency, counts)
-
-def euclidean_compare(text):
-    return frequency_compare(text, norms.euclidean_scale(english_counts),
-            norms.euclidean_scale, norms.euclidean_distance)
-
-
 def caesar_break(message, fitness=Pletters):
     """Breaks a Caesar cipher using frequency analysis
     

diff --git a/find_best_caesar_break_parameters.py b/find_best_caesar_break_parameters.py
index edab90fcc2c55bfe5dc6b74b425413837030f1cd..9ed53488dde8161ea13b1b6caa35b8aa60eb0fa0 100644 (file)
--- a/find_best_caesar_break_parameters.py
+++ b/find_best_caesar_break_parameters.py
@@ -11,55 +11,80 @@ corpus_length = len(corpus)
 
 euclidean_scaled_english_counts = norms.euclidean_scale(english_counts)
 
-metrics = [{'func': norms.l1, 'name': 'l1'}, 
-    {'func': norms.l2, 'name': 'l2'},
-    {'func': norms.l3, 'name': 'l3'},
-    {'func': norms.cosine_distance, 'name': 'cosine_distance'},
-    {'func': norms.harmonic_mean, 'name': 'harmonic_mean'},
-    {'func': norms.geometric_mean, 'name': 'geometric_mean'},
-    {'func': norms.inverse_log_pl, 'name': 'inverse_log_pl'}]
+# def frequency_compare(text, target_frequency, frequency_scaling, metric):
+#     counts = frequency_scaling(frequencies(text))
+#     return -1 * metric(target_frequency, counts)
+
+# def euclidean_compare(text):
+#     return frequency_compare(text, norms.euclidean_scale(english_counts),
+#             norms.euclidean_scale, norms.euclidean_distance)
+
+metrics = [{'func': norms.l1, 'invert': True, 'name': 'l1'}, 
+    {'func': norms.l2, 'invert': True, 'name': 'l2'},
+    {'func': norms.l3, 'invert': True, 'name': 'l3'},
+    {'func': norms.cosine_distance, 'invert': False, 'name': 'cosine_distance'},
+    {'func': norms.harmonic_mean, 'invert': True, 'name': 'harmonic_mean'},
+    {'func': norms.geometric_mean, 'invert': True, 'name': 'geometric_mean'}]
 scalings = [{'corpus_frequency': normalised_english_counts, 
          'scaling': norms.normalise,
          'name': 'normalised'},
         {'corpus_frequency': euclidean_scaled_english_counts, 
          'scaling': norms.euclidean_scale,
-         'name': 'euclidean_scaled'},
-         {'corpus_frequency': normalised_english_counts,
-         'scaling': norms.identity_scale,
-         'name': 'normalised_with_identity'}]
+         'name': 'euclidean_scaled'}]
 message_lengths = [300, 100, 50, 30, 20, 10, 5]
 
 trials = 5000
 
-scores = collections.defaultdict(int)
+scores = {}
+
+
+def make_frequency_compare_function(target_frequency, frequency_scaling, metric, invert):
+    def frequency_compare(text):
+        counts = frequency_scaling(frequencies(text))
+        if invert:
+            score = -1 * metric(target_frequency, counts)
+        else:
+            score = metric(target_frequency, counts)
+        return score
+    return frequency_compare
+
+
+def scoring_functions():
+    return [{'func': make_frequency_compare_function(s['corpus_frequency'], 
+                s['scaling'], m['func'], m['invert']),
+            'name': '{} + {}'.format(m['name'], s['name'])}
+        for m in metrics
+        for s in scalings] + [{'func': Pletters, 'name': 'Pletters'}]
 
-def eval_all():
-    list(itertools.starmap(eval_one_parameter_set,
-        itertools.product(metrics, scalings, message_lengths)))
+def eval_scores():
+    [eval_one_score(f, l) 
+        for f in scoring_functions()
+        for l in message_lengths]
 
-def eval_one_parameter_set(metric, scaling, message_length):
+def eval_one_score(scoring_function, message_length):
+    print(scoring_function['name'], message_length)
+    if scoring_function['name'] not in scores:
+        scores[scoring_function['name']] = collections.defaultdict(int)
     for _ in range(trials):
         sample_start = random.randint(0, corpus_length - message_length)
         sample = corpus[sample_start:(sample_start + message_length)]
         key = random.randint(1, 25)
-        sample_ciphertext = caesar_encipher(sample, key)
-        found_key, _ = caesar_break(sample_ciphertext, 
-                                          metric=metric['func'], 
-                                          target_counts=scaling['corpus_frequency'], 
-                                          message_frequency_scaling=scaling['scaling'])
+        ciphertext = caesar_encipher(sample, key)
+        found_key, _ = caesar_break(ciphertext, scoring_function['func'])
         if found_key == key:
-            scores[(metric['name'], scaling['name'], message_length)] += 1 
-    return scores[(metric['name'], scaling['name'], message_length)]
+            scores[scoring_function['name']][message_length] += 1 
+    return scores[scoring_function['name']][message_length]
 
 def show_results():
     with open('caesar_break_parameter_trials.csv', 'w') as f:
         print(',message_length', file = f)
-        print('metric+scaling,', ','.join([str(l) for l in message_lengths]), file = f)
-        for (metric, scaling) in itertools.product(metrics, scalings):
-            print('{}:{}'.format(metric['name'], scaling['name']), end='', file=f)
-            for l in message_lengths:
-                print(',', scores[(metric['name'], scaling['name'], l)] / trials, end='', file=f)
-            print('', file = f)
+        print('scoring,', ', '.join([str(l) for l in message_lengths]), file = f)
+        for scoring in sorted(scores.keys()):
+            for length in message_lengths:
+                print(scoring, end='', sep='', file=f)
+                for l in message_lengths:
+                    print(',', scores[scoring][l] / trials, end='', file=f)
+                print('', file = f)
 
-eval_all()
+eval_scores()
 show_results()