import collections
from cipher import *
from cipherbreak import *
+import itertools
corpus = sanitise(''.join([open('shakespeare.txt', 'r').read(),
open('sherlock-holmes.txt', 'r').read(),
scores = collections.defaultdict(int)
-with open('caesar_break_parameter_trials.csv', 'w') as f:
- print('metric,scaling,message_length,score', file = f)
- for metric in metrics:
- for scaling in scalings:
- for message_length in message_lengths:
- 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
- print(', '.join([metric['name'],
- scaling['name'],
- str(message_length),
- str(scores[(metric['name'], scaling['name'], message_length)] / trials) ]),
- file = f)
-print()
+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:
+ 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)
+
+eval_all()
+show_results()