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```
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7607 Solitaire.py abb966d0aa4f23599204a1a75f3179903c175eac637c0847bcc772374f380126|9e3ee44bd5d7ec196b513afad0856ed4f2cd6bf3e0fe9b6bc0130bb7731d04c8
63970 cards2.png f2959fc211d97c9b16bf049eb5c9e99167b033f887c43f9ae20adb13717c7899|b42371c35b39715277627114132f8b261283b2b1d59e47b7066e06f4df5a4ad6
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5281 sol.py dbea0e836eb158a26407ca3b74289d24fa7eae16ed1093eb7c598767a150fccc|e50319aa995f934a4cbd52bc28d8f49f376e62470170690cdbed7352f67a7942
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18025 LICENSE a01259a1b522cf0de95824f9860613b453153eebac468e96196d5d7dba84786c
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61 README.md 277247b410300ee16477b12ca54ad878d81c8061f6134e2e1cadccaf299de3a3
-469 affine_break_parameter_trials.csv 1a9d635d0af2f41fc6f1e83ae87d6372034259321ba288a11fb024e98ed52f4f|dd9c840434de596a30c84e79de26a9824b36c217a84876c2aab0579b76999735
+569 affine_break_parameter_trials.csv 6593e15e63e3e5632222fece8971ab473a7cdcf43020cd279e6f926cac0445fd|80705a44f33ed99ee08ed7d80e18945e84a2ae60afa1b39c331fa6bf58e21115
6488666 big.txt fa066c7d40f0f201ac4144e652aa62430e58a6b3805ec70650f678da5804e87b
16832 cadenus-ciphers.ipynb 50e49b3ec5e6440b86fe13472b9f4ab9e133e5665c7280b2abf7a6b57bb8a89a
-514 caesar_break_parameter_trials.csv 6586223bcc00e06e3ff79d107202d6c29ef962a6dd544add00610c5907407e85|1cb7cc77831ef3ef4f994a9ea77e82a841b38acdde45ede9cedbe7a54f1e8e46
+514 caesar_break_parameter_trials.csv cafee5b7e752807f856090b527870fc3a189de325e41c09c2933fe7bf5efcbe5|54761ff222587ae788d3213505308b631a8a3f17793beb3fbf492bfdaba320c3
318 cipher-training.sublime-project 58e5d5b4e54fb29abecaef2d41266e3355adccb8b6a70bd595e509bd07c16587
42922 cipher.py 58637b8946b4fb973b19a374a2066a896d86c928dacaa1ccd2252e6f8bb6e810
-28908 cipherbreak.py 0fb22645ddce4e04c7e441a1f7bdc0e4a397a3c9b2cfb3098bcb213e79a361c9
+28937 cipherbreak.py 5ba9424badfed9721f8f6f044e636b693105fbbdb03ad65907c10c25a26ac760
11564 count_1edit.txt 3bf563ef032ba151ec1a4b2d1f33f50c49f4a47e4dc5b8152394bc5b63f57655|b5fbacbebcc25f5011ce97bc9ac967a09c50eef28b4aa98379a6c426df6ac08b
223 count_1l.txt 335388d457db6ef1da05d8b55ab879e9be7d4e021085efc8d9dfeac0e4a79aa9
4956241 count_1w.txt 51df159fd3de12b20e403c108f526e96dbd723d9cabdd5f17955cdc16059e690
5566017 count_2w.txt 781c0596c3eea532d30bef9f3dba1d5137d652f00376260822c761a7584dfb8c
220441 count_3l.txt 8702c95530c7d0d182ab94dc03ed7681fcf969819f6db011a58de31411dc6365
320508 count_big.txt 3ba257fba1934bd138413d8274e79b56c5992431a27692fd562929aa43ec01a3
-3355 find_best_affine_break_parameters.py 6b11004bb93ac26ec7d42d33504e758edbaf9d55365ae2e4ca2fca7589263f25
-3027 find_best_caesar_break_parameters.py 0347d80309179d937a88fd1c8684490a513ccd086366c5a0dd55b8a2fe5c565f
+3348 find_best_affine_break_parameters.py b08fbccd56f7a3243cde14bd895e8ca417b89cd0faaced9a0e68ecb372ffcc14
+3020 find_best_caesar_break_parameters.py 7119e2eea7c138e133b6f2df691af9e1128ff10d2b6ae16f32b0c0b35e488d76
1236 find_wikipedia_titles.py f040bf855dfec7fff9d8e5eba2fb509179bc53bc02a20b26b7fc61fef983aa45
37128 hill-ciphers.ipynb ce802c2be807b4565858b568d3a82c65a3957aa625344189f8f2a055237b3fdd
5645 language_models.py bfd5b60cdef8af20cdb061b24a1691f569984be3be333782c3d76e3370e16d14
368 lettercount.py ed36497d62cf75b91994055e4a18848b2fabe5ce793cd76a77fabfc94d81d4f3
592 make-cracking-dictionary.py 71791e64e4853cd9ca292cb436bbe8c72dd60f509811174df93ed2067683d5c1
-7077 norms.py a657a36c1741e6f3a513386b318fcc99e6b11f98ec64a48284b47462ff2acf30
+7070 norms.py c80289c5769e518ea40a7de85c869febab2e6d40c64a596bcd2a13ea2a9ece1a
8411 norms.pyc ac7a18765c7bcc27e406d8f38d943408097b3384a271502185d53482e6ec0da7|002b186e716cec64869a00bd2d72e16614931e696daa0cf3529d634a0f270e42
112847 plot-caesar-parameters.ipynb 639459b4b2e434f9f0852c012ed9a8a8d87bd1cb6c2d65ca5abfdb0e42c3dea6
23863 railfence-experiment-1.ipynb e34a61cef19b3249a3d6d731fe054769c02233b0be661161ebf45bada3b7b97b
18628 railfence-experiment-2.ipynb 8c4d90c1c934b764deba0956bb4be81868e463789dc8fe02e5621ab3a95c0889
881 segment.py 94d257cc6151861ef3d3033c4d2d03d8c121b0a982344abf400f65fd507fed28
4538523 shakespeare.txt 6f9c770efced5c3d87efa6197cd3091b982341372e36c6357f865df91ddecde6
+592309 sherlock-holmes.txt 0027de6f4110440ea51d67a2f3af3484898c630808f13b1d4db108e6283e67a3|2034ee1ebdec47e839607124d22b674d4614e1cc6209d758f7b6e99e69ae8e08
451530 spell-errors.txt a4abe6ce6c24280f9a8d0485cbf78ddd2e58279ca01293692630a08ba4b13407
69351 unknown-word-probability-investigation.ipynb 8a9cd7163f10bf2bfb3e286445eddcfc953f80abfdef4e29dac27617a53c3d41
3291641 war-and-peace.txt 3ed0f41cfdf660846878943bad5b9d575bcae1e4a92ee9a7f43d3c9dba2af344|6799e48d3fd0a6f4c40b9951ec86de6da81f0b9cd36e413490ac511542ca54d3
+<<<<<<< HEAD
,message_length
scoring, 300, 100, 50, 30, 20, 10, 5
Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
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
+=======
+"name",100,50,30,20,10,5\r
+"Pletters",4996,4997,4984,4900,4063,2358\r
+"cosine_similarity + euclidean_scaled",4998,4986,4914,4659,3528,2198\r
+"cosine_similarity + normalised",4997,4993,4917,4659,3557,2084\r
+"l1 + euclidean_scaled",4998,4992,4951,4755,3767,2192\r
+"l1 + normalised",4998,4996,4936,4767,3596,2161\r
+"l2 + euclidean_scaled",4998,4990,4926,4683,3567,2179\r
+"l2 + normalised",4995,4993,4920,4672,3610,2135\r
+"l3 + euclidean_scaled",4996,4964,4822,4457,3167,2018\r
+"l3 + normalised",4999,4973,4797,4351,2872,1989\r
+>>>>>>> 883806c... More tweaking
with Pool() as pool:
helper_args = [(message, trans, fillcolumnwise, emptycolumnwise,
fitness)
- for trans in translist.keys()
+ for trans in translist
for fillcolumnwise in [True, False]
for emptycolumnwise in [True, False]]
# Gotcha: the helper function here needs to be defined at the top level
(5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
(6, 1, 0, 4, 5, 3, 2): ['keyword']}, \
patterns=[(1, 2)]) # doctest: +ELLIPSIS
- (((2, 0, 5, 3, 1, 4, 6), (1, 2)), -709.4646722...)
+ (((2, 0, 5, 3, 1, 4, 6), (1, 2), <AmscoFillStyle.continuous: 1>), -709.4646722...)
>>> amsco_break(amsco_transposition_encipher(sanitise( \
"It is a truth universally acknowledged, that a single man in \
possession of a good fortune, must be in want of a wife. However \
(5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
(6, 1, 0, 4, 5, 3, 2): ['keyword']}, \
patterns=[(1, 2), (2, 1)], fitness=Ptrigrams) # doctest: +ELLIPSIS
- (((2, 0, 5, 3, 1, 4, 6), (2, 1)), -997.0129085...)
+ (((2, 0, 5, 3, 1, 4, 6), (2, 1), <AmscoFillStyle.continuous: 1>), -997.0129085...)
"""
with Pool() as pool:
helper_args = [(message, trans, pattern, fillstyle, fitness)
- for trans in translist.keys()
+ for trans in translist
for pattern in patterns
for fillstyle in fillstyles]
# Gotcha: the helper function here needs to be defined at the top level
def plot_frequency_histogram(freqs, sort_key=None):
- x = range(len(freqs.keys()))
- y = [freqs[l] for l in sorted(freqs.keys(), key=sort_key)]
+ x = range(len(freqs))
+ y = [freqs[l] for l in sorted(freqs, key=sort_key)]
f = plt.figure()
ax = f.add_axes([0.1, 0.1, 0.9, 0.9])
ax.bar(x, y, align='center')
ax.set_xticks(x)
- ax.set_xticklabels(sorted(freqs.keys(), key=sort_key))
+ ax.set_xticklabels(sorted(freqs, key=sort_key))
f.show()
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)
-# 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'}]
+ {'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'}]
-message_lengths = [300, 100, 50, 30, 20, 10, 5]
+message_lengths = [100, 50, 30, 20, 10, 5]
trials = 5000
return score
return frequency_compare
-
def scoring_functions():
return [{'func': make_frequency_compare_function(s['corpus_frequency'],
s['scaling'], m['func'], m['invert']),
def show_results():
with open('caesar_break_parameter_trials.csv', 'w') as f:
- print(',message_length', 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)
+ 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_scores()
show_results()
>>> sorted(normalise({1: 1, 2: 2, 3: 1}).items())
[(1, 0.25), (2, 0.5), (3, 0.25)]
"""
- length = sum([f for f in frequencies.values()])
+ length = sum(f for f in frequencies.values())
return collections.defaultdict(int, ((k, v / length)
for (k, v) in frequencies.items()))
return len(frequencies1) / total
-def cosine_distance(frequencies1, frequencies2):
+def cosine_similarity(frequencies1, frequencies2):
"""Finds the distances between two frequency profiles, expressed as dictionaries.
Assumes every key in frequencies1 is also in frequencies2
- >>> cosine_distance({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
+ >>> cosine_similarity({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
1.0000000000...
- >>> cosine_distance({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
+ >>> cosine_similarity({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
1.0000000000...
- >>> cosine_distance({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
+ >>> cosine_similarity({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
0.5773502691...
- >>> cosine_distance({'a':0, 'b':1}, {'a':1, 'b':1}) # doctest: +ELLIPSIS
+ >>> cosine_similarity({'a':0, 'b':1}, {'a':1, 'b':1}) # doctest: +ELLIPSIS
0.7071067811...
"""
numerator = 0
for k in frequencies1:
numerator += frequencies1[k] * frequencies2[k]
length1 += frequencies1[k]**2
- for k in frequencies2.keys():
- length2 += frequencies2[k]
+ for k in frequencies2:
+ length2 += frequencies2[k]**2
return numerator / (length1 ** 0.5 * length2 ** 0.5)
projects / cipher-tools.git / commitdiff