import random
+import collections
from cipher import *
+from cipherbreak import *
+import itertools
-
-corpus = sanitise(''.join([open('shakespeare.txt', 'r').read(), open('sherlock-holmes.txt', 'r').read(), open('war-and-peace.txt', 'r').read()]))
+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)
-scaled_english_counts = norms.scale(english_counts)
-
+euclidean_scaled_english_counts = norms.euclidean_scale(english_counts)
-metrics = [norms.l1, norms.l2, norms.l3, norms.cosine_distance, norms.harmonic_mean, norms.geometric_mean]
-corpus_frequencies = [normalised_english_counts, scaled_english_counts]
-scalings = [norms.normalise, norms.scale]
+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'}]
+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]
-metric_names = ['l1', 'l2', 'l3', 'cosine_distance', 'harmonic_mean', 'geometric_mean']
-corpus_frequency_names = ['normalised_english_counts', 'scaled_english_counts']
-scaling_names = ['normalise', 'scale']
-
trials = 5000
scores = collections.defaultdict(int)
-for metric in range(len(metrics)):
- scores[metric_names[metric]] = collections.defaultdict(int)
- for corpus_freqency in range(len(corpus_frequencies)):
- scores[metric_names[metric]][corpus_frequency_names[corpus_freqency]] = collections.defaultdict(int)
- for scaling in range(len(scalings)):
- scores[metric_names[metric]][corpus_frequency_names[corpus_freqency]][scaling_names[scaling]] = collections.defaultdict(int)
- 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=metrics[metric],
- target_frequencies=corpus_frequencies[corpus_freqency],
- message_frequency_scaling=scalings[scaling])
- if found_key == key:
- scores[metric_names[metric]][corpus_frequency_names[corpus_freqency]][scaling_names[scaling]][message_length] += 1
- print(', '.join([metric_names[metric],
- corpus_frequency_names[corpus_freqency],
- scaling_names[scaling],
- str(message_length),
- str(scores[metric_names[metric]][corpus_frequency_names[corpus_freqency]][scaling_names[scaling]][message_length] / trials) ]))
-
-
-with open('caesar_break_parameter_trials.csv', 'w') as f:
- for metric in range(len(metrics)):
- for corpus_freqency in range(len(corpus_frequencies)):
- for scaling in range(len(scalings)):
- for message_length in message_lengths:
- print(', '.join([metric_names[metric],
- corpus_frequency_names[corpus_freqency],
- scaling_names[scaling],
- str(message_length),
- str(scores[metric_names[metric]][corpus_frequency_names[corpus_freqency]][scaling_names[scaling]][message_length] / trials) ]),
- file=f)
-
-
\ No newline at end of file
+
+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()