--- /dev/null
+import random
+import collections
+from cipher import *
+from cipherbreak import *
+import itertools
+
+print('Loading...')
+
+corpus = sanitise(''.join([open('shakespeare.txt', 'r').read(),
+ open('sherlock-holmes.txt', 'r').read(),
+ open('war-and-peace.txt', 'r').read()]))
+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': 'l2'},
+ {'func': norms.cosine_distance, 'name': 'cosine_distance'},
+ {'func': norms.harmonic_mean, 'name': 'harminic_mean'},
+ {'func': norms.geometric_mean, 'name': 'geometric_mean'},
+ {'func': norms.inverse_log_pl, 'name': 'inverse_log_pl'}]
+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'}]
+message_lengths = [300, 100, 50, 30, 20, 10, 5]
+
+trials = 5000
+
+scores = collections.defaultdict(int)
+
+def eval_all():
+ list(itertools.starmap(eval_one_parameter_set,
+ itertools.product(metrics, scalings, message_lengths)))
+
+def eval_one_parameter_set(metric, scaling, message_length):
+ for i 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, score) = caesar_break(sample_ciphertext,
+ metric=metric['func'],
+ target_counts=scaling['corpus_frequency'],
+ message_frequency_scaling=scaling['scaling'])
+ if found_key == key:
+ scores[(metric['name'], scaling['name'], message_length)] += 1
+ return scores[(metric['name'], scaling['name'], message_length)]
+
+def show_results():
+ with open('caesar_break_parameter_trials.csv', 'w') as f:
+ for (k, v) in scores.items():
+ print(str(k)[1:-1], v, sep=",", file=f)
+
+eval_all()
+show_results()