Updated for challenge 9

[cipher-tools.git] / caesar_break_parameter_trials.csv

diff --git a/caesar_break_parameter_trials.csv b/caesar_break_parameter_trials.csv
index df9b836ebdfec843d0f42cb319729dfd1a45836c..30c1a336c30447a3c9de2493d8f3ae127c7618ad 100644 (file)
--- a/caesar_break_parameter_trials.csv
+++ b/caesar_break_parameter_trials.csv
@@ -1,144 +1,12 @@
-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
+"name",100,50,30,20,10,5\r
+"Pbigrams",99975,99972,99962,99831,95323,67277\r
+"Pletters",99952,99937,99683,97936,81597,47758\r
+"Ptrigrams",99991,99994,99990,99944,97994,74922\r
+"cosine_similarity + l1_scaled",99948,99764,98358,93346,71183,43193\r
+"cosine_similarity + l2_scaled",99946,99768,98345,93399,71353,43259\r
+"l1 + l1_scaled",99949,99879,98944,95454,72617,42940\r
+"l1 + l2_scaled",99945,99889,98996,95350,74966,44413\r
+"l2 + l1_scaled",99946,99822,98336,93457,71287,43350\r
+"l2 + l2_scaled",99957,99796,98274,93471,71413,43288\r
+"l3 + l1_scaled",99942,99324,95766,87384,59770,40661\r
+"l3 + l2_scaled",99922,99445,96568,89109,63241,39819\r