Added experiment for checking which metrics work best for using unigram frequency...
[cipher-tools.git] / caesar_break_parameter_trials.csv
1 l1, normalised_english_counts, normalise, 3000, 0.9616
2 l1, normalised_english_counts, normalise, 1000, 0.9562
3 l1, normalised_english_counts, normalise, 300, 0.9598
4 l1, normalised_english_counts, normalise, 100, 0.9622
5 l1, normalised_english_counts, normalise, 50, 0.9584
6 l1, normalised_english_counts, normalise, 30, 0.953
7 l1, normalised_english_counts, normalise, 20, 0.917
8 l1, normalised_english_counts, normalise, 10, 0.7328
9 l1, normalised_english_counts, normalise, 5, 0.4394
10 l1, normalised_english_counts, scale, 3000, 0.9618
11 l1, normalised_english_counts, scale, 1000, 0.9574
12 l1, normalised_english_counts, scale, 300, 0.9624
13 l1, normalised_english_counts, scale, 100, 0.9566
14 l1, normalised_english_counts, scale, 50, 0.959
15 l1, normalised_english_counts, scale, 30, 0.9476
16 l1, normalised_english_counts, scale, 20, 0.8968
17 l1, normalised_english_counts, scale, 10, 0.6844
18 l1, normalised_english_counts, scale, 5, 0.4298
19 l1, scaled_english_counts, normalise, 3000, 0.957
20 l1, scaled_english_counts, normalise, 1000, 0.9662
21 l1, scaled_english_counts, normalise, 300, 0.9604
22 l1, scaled_english_counts, normalise, 100, 0.9602
23 l1, scaled_english_counts, normalise, 50, 0.9578
24 l1, scaled_english_counts, normalise, 30, 0.9504
25 l1, scaled_english_counts, normalise, 20, 0.9174
26 l1, scaled_english_counts, normalise, 10, 0.7204
27 l1, scaled_english_counts, normalise, 5, 0.4506
28 l1, scaled_english_counts, scale, 3000, 0.9584
29 l1, scaled_english_counts, scale, 1000, 0.9586
30 l1, scaled_english_counts, scale, 300, 0.964
31 l1, scaled_english_counts, scale, 100, 0.9582
32 l1, scaled_english_counts, scale, 50, 0.9606
33 l1, scaled_english_counts, scale, 30, 0.944
34 l1, scaled_english_counts, scale, 20, 0.915
35 l1, scaled_english_counts, scale, 10, 0.7324
36 l1, scaled_english_counts, scale, 5, 0.4446
37 l2, normalised_english_counts, normalise, 3000, 0.953
38 l2, normalised_english_counts, normalise, 1000, 0.962
39 l2, normalised_english_counts, normalise, 300, 0.9638
40 l2, normalised_english_counts, normalise, 100, 0.9632
41 l2, normalised_english_counts, normalise, 50, 0.9604
42 l2, normalised_english_counts, normalise, 30, 0.95
43 l2, normalised_english_counts, normalise, 20, 0.892
44 l2, normalised_english_counts, normalise, 10, 0.7124
45 l2, normalised_english_counts, normalise, 5, 0.4406
46 l2, normalised_english_counts, scale, 3000, 0.9626
47 l2, normalised_english_counts, scale, 1000, 0.956
48 l2, normalised_english_counts, scale, 300, 0.962
49 l2, normalised_english_counts, scale, 100, 0.9572
50 l2, normalised_english_counts, scale, 50, 0.9526
51 l2, normalised_english_counts, scale, 30, 0.9478
52 l2, normalised_english_counts, scale, 20, 0.9046
53 l2, normalised_english_counts, scale, 10, 0.6896
54 l2, normalised_english_counts, scale, 5, 0.4308
55 l2, scaled_english_counts, normalise, 3000, 0.9574
56 l2, scaled_english_counts, normalise, 1000, 0.9568
57 l2, scaled_english_counts, normalise, 300, 0.9536
58 l2, scaled_english_counts, normalise, 100, 0.9624
59 l2, scaled_english_counts, normalise, 50, 0.9606
60 l2, scaled_english_counts, normalise, 30, 0.9384
61 l2, scaled_english_counts, normalise, 20, 0.8914
62 l2, scaled_english_counts, normalise, 10, 0.6892
63 l2, scaled_english_counts, normalise, 5, 0.4196
64 l2, scaled_english_counts, scale, 3000, 0.9532
65 l2, scaled_english_counts, scale, 1000, 0.9588
66 l2, scaled_english_counts, scale, 300, 0.9644
67 l2, scaled_english_counts, scale, 100, 0.9572
68 l2, scaled_english_counts, scale, 50, 0.9586
69 l2, scaled_english_counts, scale, 30, 0.9436
70 l2, scaled_english_counts, scale, 20, 0.9036
71 l2, scaled_english_counts, scale, 10, 0.693
72 l2, scaled_english_counts, scale, 5, 0.4376
73 l3, normalised_english_counts, normalise, 3000, 0.9626
74 l3, normalised_english_counts, normalise, 1000, 0.9582
75 l3, normalised_english_counts, normalise, 300, 0.9542
76 l3, normalised_english_counts, normalise, 100, 0.9606
77 l3, normalised_english_counts, normalise, 50, 0.953
78 l3, normalised_english_counts, normalise, 30, 0.9248
79 l3, normalised_english_counts, normalise, 20, 0.8684
80 l3, normalised_english_counts, normalise, 10, 0.6106
81 l3, normalised_english_counts, normalise, 5, 0.4064
82 l3, normalised_english_counts, scale, 3000, 0.961
83 l3, normalised_english_counts, scale, 1000, 0.9568
84 l3, normalised_english_counts, scale, 300, 0.9566
85 l3, normalised_english_counts, scale, 100, 0.9554
86 l3, normalised_english_counts, scale, 50, 0.9436
87 l3, normalised_english_counts, scale, 30, 0.8936
88 l3, normalised_english_counts, scale, 20, 0.8016
89 l3, normalised_english_counts, scale, 10, 0.579
90 l3, normalised_english_counts, scale, 5, 0.4114
91 l3, scaled_english_counts, normalise, 3000, 0.9616
92 l3, scaled_english_counts, normalise, 1000, 0.9612
93 l3, scaled_english_counts, normalise, 300, 0.9624
94 l3, scaled_english_counts, normalise, 100, 0.9524
95 l3, scaled_english_counts, normalise, 50, 0.9474
96 l3, scaled_english_counts, normalise, 30, 0.9066
97 l3, scaled_english_counts, normalise, 20, 0.8004
98 l3, scaled_english_counts, normalise, 10, 0.5686
99 l3, scaled_english_counts, normalise, 5, 0.3404
100 l3, scaled_english_counts, scale, 3000, 0.96
101 l3, scaled_english_counts, scale, 1000, 0.96
102 l3, scaled_english_counts, scale, 300, 0.9596
103 l3, scaled_english_counts, scale, 100, 0.96
104 l3, scaled_english_counts, scale, 50, 0.954
105 l3, scaled_english_counts, scale, 30, 0.9374
106 l3, scaled_english_counts, scale, 20, 0.862
107 l3, scaled_english_counts, scale, 10, 0.6276
108 l3, scaled_english_counts, scale, 5, 0.399
109 cosine_distance, normalised_english_counts, normalise, 3000, 0.9618
110 cosine_distance, normalised_english_counts, normalise, 1000, 0.96
111 cosine_distance, normalised_english_counts, normalise, 300, 0.9604
112 cosine_distance, normalised_english_counts, normalise, 100, 0.9538
113 cosine_distance, normalised_english_counts, normalise, 50, 0.9608
114 cosine_distance, normalised_english_counts, normalise, 30, 0.9426
115 cosine_distance, normalised_english_counts, normalise, 20, 0.9012
116 cosine_distance, normalised_english_counts, normalise, 10, 0.6916
117 cosine_distance, normalised_english_counts, normalise, 5, 0.4286
118 cosine_distance, normalised_english_counts, scale, 3000, 0.9606
119 cosine_distance, normalised_english_counts, scale, 1000, 0.9572
120 cosine_distance, normalised_english_counts, scale, 300, 0.9628
121 cosine_distance, normalised_english_counts, scale, 100, 0.959
122 cosine_distance, normalised_english_counts, scale, 50, 0.9542
123 cosine_distance, normalised_english_counts, scale, 30, 0.951
124 cosine_distance, normalised_english_counts, scale, 20, 0.9028
125 cosine_distance, normalised_english_counts, scale, 10, 0.7028
126 cosine_distance, normalised_english_counts, scale, 5, 0.44
127 cosine_distance, scaled_english_counts, normalise, 3000, 0.9582
128 cosine_distance, scaled_english_counts, normalise, 1000, 0.9614
129 cosine_distance, scaled_english_counts, normalise, 300, 0.9632
130 cosine_distance, scaled_english_counts, normalise, 100, 0.9584
131 cosine_distance, scaled_english_counts, normalise, 50, 0.9574
132 cosine_distance, scaled_english_counts, normalise, 30, 0.9506
133 cosine_distance, scaled_english_counts, normalise, 20, 0.8956
134 cosine_distance, scaled_english_counts, normalise, 10, 0.6916
135 cosine_distance, scaled_english_counts, normalise, 5, 0.4356
136 cosine_distance, scaled_english_counts, scale, 3000, 0.9572
137 cosine_distance, scaled_english_counts, scale, 1000, 0.961
138 cosine_distance, scaled_english_counts, scale, 300, 0.9596
139 cosine_distance, scaled_english_counts, scale, 100, 0.9544
140 cosine_distance, scaled_english_counts, scale, 50, 0.9598
141 cosine_distance, scaled_english_counts, scale, 30, 0.9414
142 cosine_distance, scaled_english_counts, scale, 20, 0.9036
143 cosine_distance, scaled_english_counts, scale, 10, 0.6928
144 cosine_distance, scaled_english_counts, scale, 5, 0.4178