Done challenge 8

[cipher-tools.git] / caesar_break_parameter_trials.csv

diff --git a/caesar_break_parameter_trials.csv b/caesar_break_parameter_trials.csv
index df9b836ebdfec843d0f42cb319729dfd1a45836c..e18f92c5604d97855b83cff0fdce99d17c7385a1 100644 (file)
--- a/caesar_break_parameter_trials.csv
+++ b/caesar_break_parameter_trials.csv
@@ -1,144 +1,106 @@
-l1, normalised_english_counts, normalise, 3000, 0.9616
-l1, normalised_english_counts, normalise, 1000, 0.9562
-l1, normalised_english_counts, normalise, 300, 0.9598
-l1, normalised_english_counts, normalise, 100, 0.9622
-l1, normalised_english_counts, normalise, 50, 0.9584
-l1, normalised_english_counts, normalise, 30, 0.953
-l1, normalised_english_counts, normalise, 20, 0.917
-l1, normalised_english_counts, normalise, 10, 0.7328
-l1, normalised_english_counts, normalise, 5, 0.4394
-l1, normalised_english_counts, scale, 3000, 0.9618
-l1, normalised_english_counts, scale, 1000, 0.9574
-l1, normalised_english_counts, scale, 300, 0.9624
-l1, normalised_english_counts, scale, 100, 0.9566
-l1, normalised_english_counts, scale, 50, 0.959
-l1, normalised_english_counts, scale, 30, 0.9476
-l1, normalised_english_counts, scale, 20, 0.8968
-l1, normalised_english_counts, scale, 10, 0.6844
-l1, normalised_english_counts, scale, 5, 0.4298
-l1, scaled_english_counts, normalise, 3000, 0.957
-l1, scaled_english_counts, normalise, 1000, 0.9662
-l1, scaled_english_counts, normalise, 300, 0.9604
-l1, scaled_english_counts, normalise, 100, 0.9602
-l1, scaled_english_counts, normalise, 50, 0.9578
-l1, scaled_english_counts, normalise, 30, 0.9504
-l1, scaled_english_counts, normalise, 20, 0.9174
-l1, scaled_english_counts, normalise, 10, 0.7204
-l1, scaled_english_counts, normalise, 5, 0.4506
-l1, scaled_english_counts, scale, 3000, 0.9584
-l1, scaled_english_counts, scale, 1000, 0.9586
-l1, scaled_english_counts, scale, 300, 0.964
-l1, scaled_english_counts, scale, 100, 0.9582
-l1, scaled_english_counts, scale, 50, 0.9606
-l1, scaled_english_counts, scale, 30, 0.944
-l1, scaled_english_counts, scale, 20, 0.915
-l1, scaled_english_counts, scale, 10, 0.7324
-l1, scaled_english_counts, scale, 5, 0.4446
-l2, normalised_english_counts, normalise, 3000, 0.953
-l2, normalised_english_counts, normalise, 1000, 0.962
-l2, normalised_english_counts, normalise, 300, 0.9638
-l2, normalised_english_counts, normalise, 100, 0.9632
-l2, normalised_english_counts, normalise, 50, 0.9604
-l2, normalised_english_counts, normalise, 30, 0.95
-l2, normalised_english_counts, normalise, 20, 0.892
-l2, normalised_english_counts, normalise, 10, 0.7124
-l2, normalised_english_counts, normalise, 5, 0.4406
-l2, normalised_english_counts, scale, 3000, 0.9626
-l2, normalised_english_counts, scale, 1000, 0.956
-l2, normalised_english_counts, scale, 300, 0.962
-l2, normalised_english_counts, scale, 100, 0.9572
-l2, normalised_english_counts, scale, 50, 0.9526
-l2, normalised_english_counts, scale, 30, 0.9478
-l2, normalised_english_counts, scale, 20, 0.9046
-l2, normalised_english_counts, scale, 10, 0.6896
-l2, normalised_english_counts, scale, 5, 0.4308
-l2, scaled_english_counts, normalise, 3000, 0.9574
-l2, scaled_english_counts, normalise, 1000, 0.9568
-l2, scaled_english_counts, normalise, 300, 0.9536
-l2, scaled_english_counts, normalise, 100, 0.9624
-l2, scaled_english_counts, normalise, 50, 0.9606
-l2, scaled_english_counts, normalise, 30, 0.9384
-l2, scaled_english_counts, normalise, 20, 0.8914
-l2, scaled_english_counts, normalise, 10, 0.6892
-l2, scaled_english_counts, normalise, 5, 0.4196
-l2, scaled_english_counts, scale, 3000, 0.9532
-l2, scaled_english_counts, scale, 1000, 0.9588
-l2, scaled_english_counts, scale, 300, 0.9644
-l2, scaled_english_counts, scale, 100, 0.9572
-l2, scaled_english_counts, scale, 50, 0.9586
-l2, scaled_english_counts, scale, 30, 0.9436
-l2, scaled_english_counts, scale, 20, 0.9036
-l2, scaled_english_counts, scale, 10, 0.693
-l2, scaled_english_counts, scale, 5, 0.4376
-l3, normalised_english_counts, normalise, 3000, 0.9626
-l3, normalised_english_counts, normalise, 1000, 0.9582
-l3, normalised_english_counts, normalise, 300, 0.9542
-l3, normalised_english_counts, normalise, 100, 0.9606
-l3, normalised_english_counts, normalise, 50, 0.953
-l3, normalised_english_counts, normalise, 30, 0.9248
-l3, normalised_english_counts, normalise, 20, 0.8684
-l3, normalised_english_counts, normalise, 10, 0.6106
-l3, normalised_english_counts, normalise, 5, 0.4064
-l3, normalised_english_counts, scale, 3000, 0.961
-l3, normalised_english_counts, scale, 1000, 0.9568
-l3, normalised_english_counts, scale, 300, 0.9566
-l3, normalised_english_counts, scale, 100, 0.9554
-l3, normalised_english_counts, scale, 50, 0.9436
-l3, normalised_english_counts, scale, 30, 0.8936
-l3, normalised_english_counts, scale, 20, 0.8016
-l3, normalised_english_counts, scale, 10, 0.579
-l3, normalised_english_counts, scale, 5, 0.4114
-l3, scaled_english_counts, normalise, 3000, 0.9616
-l3, scaled_english_counts, normalise, 1000, 0.9612
-l3, scaled_english_counts, normalise, 300, 0.9624
-l3, scaled_english_counts, normalise, 100, 0.9524
-l3, scaled_english_counts, normalise, 50, 0.9474
-l3, scaled_english_counts, normalise, 30, 0.9066
-l3, scaled_english_counts, normalise, 20, 0.8004
-l3, scaled_english_counts, normalise, 10, 0.5686
-l3, scaled_english_counts, normalise, 5, 0.3404
-l3, scaled_english_counts, scale, 3000, 0.96
-l3, scaled_english_counts, scale, 1000, 0.96
-l3, scaled_english_counts, scale, 300, 0.9596
-l3, scaled_english_counts, scale, 100, 0.96
-l3, scaled_english_counts, scale, 50, 0.954
-l3, scaled_english_counts, scale, 30, 0.9374
-l3, scaled_english_counts, scale, 20, 0.862
-l3, scaled_english_counts, scale, 10, 0.6276
-l3, scaled_english_counts, scale, 5, 0.399
-cosine_distance, normalised_english_counts, normalise, 3000, 0.9618
-cosine_distance, normalised_english_counts, normalise, 1000, 0.96
-cosine_distance, normalised_english_counts, normalise, 300, 0.9604
-cosine_distance, normalised_english_counts, normalise, 100, 0.9538
-cosine_distance, normalised_english_counts, normalise, 50, 0.9608
-cosine_distance, normalised_english_counts, normalise, 30, 0.9426
-cosine_distance, normalised_english_counts, normalise, 20, 0.9012
-cosine_distance, normalised_english_counts, normalise, 10, 0.6916
-cosine_distance, normalised_english_counts, normalise, 5, 0.4286
-cosine_distance, normalised_english_counts, scale, 3000, 0.9606
-cosine_distance, normalised_english_counts, scale, 1000, 0.9572
-cosine_distance, normalised_english_counts, scale, 300, 0.9628
-cosine_distance, normalised_english_counts, scale, 100, 0.959
-cosine_distance, normalised_english_counts, scale, 50, 0.9542
-cosine_distance, normalised_english_counts, scale, 30, 0.951
-cosine_distance, normalised_english_counts, scale, 20, 0.9028
-cosine_distance, normalised_english_counts, scale, 10, 0.7028
-cosine_distance, normalised_english_counts, scale, 5, 0.44
-cosine_distance, scaled_english_counts, normalise, 3000, 0.9582
-cosine_distance, scaled_english_counts, normalise, 1000, 0.9614
-cosine_distance, scaled_english_counts, normalise, 300, 0.9632
-cosine_distance, scaled_english_counts, normalise, 100, 0.9584
-cosine_distance, scaled_english_counts, normalise, 50, 0.9574
-cosine_distance, scaled_english_counts, normalise, 30, 0.9506
-cosine_distance, scaled_english_counts, normalise, 20, 0.8956
-cosine_distance, scaled_english_counts, normalise, 10, 0.6916
-cosine_distance, scaled_english_counts, normalise, 5, 0.4356
-cosine_distance, scaled_english_counts, scale, 3000, 0.9572
-cosine_distance, scaled_english_counts, scale, 1000, 0.961
-cosine_distance, scaled_english_counts, scale, 300, 0.9596
-cosine_distance, scaled_english_counts, scale, 100, 0.9544
-cosine_distance, scaled_english_counts, scale, 50, 0.9598
-cosine_distance, scaled_english_counts, scale, 30, 0.9414
-cosine_distance, scaled_english_counts, scale, 20, 0.9036
-cosine_distance, scaled_english_counts, scale, 10, 0.6928
-cosine_distance, scaled_english_counts, scale, 5, 0.4178
+<<<<<<< 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
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+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
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+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
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+geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
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+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
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+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
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+l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
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+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
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+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",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