Added cat commands to slides

[cipher-training.git] / caesar_break_parameter_trials.csv

diff --git a/caesar_break_parameter_trials.csv b/caesar_break_parameter_trials.csv
index 6f71f0779797eb302a00da9557f6b23fd20447ae..a414d226273482417beefdb183b480ecb154026c 100644 (file)
--- a/caesar_break_parameter_trials.csv
+++ b/caesar_break_parameter_trials.csv
@@ -1,93 +1,10 @@
-,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
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-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
-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
-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",4995,4997,4971,4893,4092,2401\r
+"cosine_similarity + euclidean_scaled",4997,4980,4915,4671,3590,2174\r
+"cosine_similarity + normalised",4999,4993,4931,4660,3559,2207\r
+"l1 + euclidean_scaled",4997,4995,4950,4761,3751,2175\r
+"l1 + normalised",4999,4993,4950,4770,3662,2188\r
+"l2 + euclidean_scaled",4997,4991,4914,4664,3579,2209\r
+"l2 + normalised",4998,4988,4915,4637,3536,2195\r
+"l3 + euclidean_scaled",4997,4979,4822,4477,3243,1978\r
+"l3 + normalised",4999,4959,4774,4351,2966,1949\r