Deleted unnecessary files

[cipher-training.git] / find_best_affine_break_parameters.py

diff --git a/find_best_affine_break_parameters.py b/find_best_affine_break_parameters.py
deleted file mode 100644 (file)
index e3d6bad..0000000
--- a/find_best_affine_break_parameters.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import random
-import collections
-from cipher import *
-from cipherbreak import *
-import itertools
-import csv
-
-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, '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'}]
-message_lengths = [2000, 1000, 500, 250, 100, 50, 20]
-
-trials = 5000
-
-scores = {}
-
-
-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, ': ', end='', flush=True)
-    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)]
-        multiplier = random.choice([x for x in range(1, 26, 2) if x != 13])
-        adder = random.randint(0, 25)
-        one_based = random.choice([True, False])
-        key = (multiplier, adder, one_based)
-        ciphertext = affine_encipher(sample, multiplier, adder, one_based)
-        found_key, _ = affine_break(ciphertext, scoring_function['func'])
-        if found_key == key:
-            scores[scoring_function['name']][message_length] += 1 
-    print(scores[scoring_function['name']][message_length], '/', trials)
-    return scores[scoring_function['name']][message_length]
-
-def show_results():
-    with open('affine_break_parameter_trials.csv', 'w') as f:
-        writer = csv.DictWriter(f, ['name'] + message_lengths, 
-            quoting=csv.QUOTE_NONNUMERIC)
-        writer.writeheader()
-        for scoring in sorted(scores.keys()):
-            scores[scoring]['name'] = scoring
-            writer.writerow(scores[scoring])
-
-print('Starting...')
-eval_scores()
-show_results()