from cipher import *
from cipherbreak import *
import itertools
+import csv
corpus = sanitise(''.join([open('shakespeare.txt', 'r').read(),
open('sherlock-holmes.txt', 'r').read(),
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'}]
+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_similarity, 'invert': False, 'name': 'cosine_similarity'}]
+ # {'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'}]
-message_lengths = [300, 100, 50, 30, 20, 10, 5]
+ 'name': 'euclidean_scaled'}]
+message_lengths = [100, 50, 30, 20, 10, 5]
trials = 5000
-scores = collections.defaultdict(int)
+scores = {}
-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):
+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_scores():
+ [eval_one_score(f, l)
+ for f in scoring_functions()
+ for l in message_lengths]
+
+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)
+ writer = csv.DictWriter(f, ['name'] + message_lengths,
+ quoting=csv.QUOTE_NONNUMERIC)
+ writer.writeheader()
+ for scoring in sorted(scores):
+ scores[scoring]['name'] = scoring
+ writer.writerow(scores[scoring])
-eval_all()
+eval_scores()
show_results()