Done challenge 4
[cipher-tools.git] / caesar_break_parameter_trials.csv
index ba7ee273a0682301beab9d4da6d0db1cba45f0ec..e18f92c5604d97855b83cff0fdce99d17c7385a1 100644 (file)
-l1, normalised_english_counts, normalise, 300, 0.9992
-l1, normalised_english_counts, normalise, 100, 0.9996
-l1, normalised_english_counts, normalise, 50, 0.9992
-l1, normalised_english_counts, normalise, 30, 0.9914
-l1, normalised_english_counts, normalise, 20, 0.9532
-l1, normalised_english_counts, normalise, 10, 0.7442
-l1, normalised_english_counts, normalise, 5, 0.4358
-l1, normalised_english_counts, scale, 300, 1.0
-l1, normalised_english_counts, scale, 100, 0.999
-l1, normalised_english_counts, scale, 50, 0.9988
-l1, normalised_english_counts, scale, 30, 0.9848
-l1, normalised_english_counts, scale, 20, 0.9316
-l1, normalised_english_counts, scale, 10, 0.715
-l1, normalised_english_counts, scale, 5, 0.436
-l1, scaled_english_counts, normalise, 300, 0.9994
-l1, scaled_english_counts, normalise, 100, 0.9998
-l1, scaled_english_counts, normalise, 50, 0.999
-l1, scaled_english_counts, normalise, 30, 0.9868
-l1, scaled_english_counts, normalise, 20, 0.9482
-l1, scaled_english_counts, normalise, 10, 0.7434
-l1, scaled_english_counts, normalise, 5, 0.4532
-l1, scaled_english_counts, scale, 300, 0.9996
-l1, scaled_english_counts, scale, 100, 1.0
-l1, scaled_english_counts, scale, 50, 0.9988
-l1, scaled_english_counts, scale, 30, 0.9874
-l1, scaled_english_counts, scale, 20, 0.9488
-l1, scaled_english_counts, scale, 10, 0.745
-l1, scaled_english_counts, scale, 5, 0.4548
-l2, normalised_english_counts, normalise, 300, 0.9994
-l2, normalised_english_counts, normalise, 100, 0.9992
-l2, normalised_english_counts, normalise, 50, 0.9978
-l2, normalised_english_counts, normalise, 30, 0.9836
-l2, normalised_english_counts, normalise, 20, 0.9318
-l2, normalised_english_counts, normalise, 10, 0.7072
-l2, normalised_english_counts, normalise, 5, 0.4294
-l2, normalised_english_counts, scale, 300, 0.9988
-l2, normalised_english_counts, scale, 100, 0.9998
-l2, normalised_english_counts, scale, 50, 0.9978
-l2, normalised_english_counts, scale, 30, 0.9868
-l2, normalised_english_counts, scale, 20, 0.9364
-l2, normalised_english_counts, scale, 10, 0.7136
-l2, normalised_english_counts, scale, 5, 0.446
-l2, scaled_english_counts, normalise, 300, 0.9992
-l2, scaled_english_counts, normalise, 100, 0.9996
-l2, scaled_english_counts, normalise, 50, 0.9984
-l2, scaled_english_counts, normalise, 30, 0.9854
-l2, scaled_english_counts, normalise, 20, 0.9328
-l2, scaled_english_counts, normalise, 10, 0.7122
-l2, scaled_english_counts, normalise, 5, 0.4328
-l2, scaled_english_counts, scale, 300, 1.0
-l2, scaled_english_counts, scale, 100, 0.9998
-l2, scaled_english_counts, scale, 50, 0.9972
-l2, scaled_english_counts, scale, 30, 0.9842
-l2, scaled_english_counts, scale, 20, 0.9356
-l2, scaled_english_counts, scale, 10, 0.7126
-l2, scaled_english_counts, scale, 5, 0.4318
-l3, normalised_english_counts, normalise, 300, 0.9996
-l3, normalised_english_counts, normalise, 100, 0.999
-l3, normalised_english_counts, normalise, 50, 0.994
-l3, normalised_english_counts, normalise, 30, 0.9658
-l3, normalised_english_counts, normalise, 20, 0.8926
-l3, normalised_english_counts, normalise, 10, 0.6252
-l3, normalised_english_counts, normalise, 5, 0.3974
-l3, normalised_english_counts, scale, 300, 0.9996
-l3, normalised_english_counts, scale, 100, 0.998
-l3, normalised_english_counts, scale, 50, 0.9828
-l3, normalised_english_counts, scale, 30, 0.9334
-l3, normalised_english_counts, scale, 20, 0.8304
-l3, normalised_english_counts, scale, 10, 0.5968
-l3, normalised_english_counts, scale, 5, 0.4114
-l3, scaled_english_counts, normalise, 300, 0.9994
-l3, scaled_english_counts, normalise, 100, 0.9984
-l3, scaled_english_counts, normalise, 50, 0.9876
-l3, scaled_english_counts, normalise, 30, 0.9284
-l3, scaled_english_counts, normalise, 20, 0.8322
-l3, scaled_english_counts, normalise, 10, 0.579
-l3, scaled_english_counts, normalise, 5, 0.3466
-l3, scaled_english_counts, scale, 300, 1.0
-l3, scaled_english_counts, scale, 100, 0.999
-l3, scaled_english_counts, scale, 50, 0.994
-l3, scaled_english_counts, scale, 30, 0.9688
-l3, scaled_english_counts, scale, 20, 0.8952
-l3, scaled_english_counts, scale, 10, 0.6416
-l3, scaled_english_counts, scale, 5, 0.4042
-cosine_distance, normalised_english_counts, normalise, 300, 0.9994
-cosine_distance, normalised_english_counts, normalise, 100, 1.0
-cosine_distance, normalised_english_counts, normalise, 50, 0.9978
-cosine_distance, normalised_english_counts, normalise, 30, 0.9856
-cosine_distance, normalised_english_counts, normalise, 20, 0.9374
-cosine_distance, normalised_english_counts, normalise, 10, 0.7212
-cosine_distance, normalised_english_counts, normalise, 5, 0.4282
-cosine_distance, normalised_english_counts, scale, 300, 0.9998
-cosine_distance, normalised_english_counts, scale, 100, 0.9994
-cosine_distance, normalised_english_counts, scale, 50, 0.9972
-cosine_distance, normalised_english_counts, scale, 30, 0.9846
-cosine_distance, normalised_english_counts, scale, 20, 0.9324
-cosine_distance, normalised_english_counts, scale, 10, 0.7144
-cosine_distance, normalised_english_counts, scale, 5, 0.4284
-cosine_distance, scaled_english_counts, normalise, 300, 0.9994
-cosine_distance, scaled_english_counts, normalise, 100, 0.9996
-cosine_distance, scaled_english_counts, normalise, 50, 0.9978
-cosine_distance, scaled_english_counts, normalise, 30, 0.9856
-cosine_distance, scaled_english_counts, normalise, 20, 0.935
-cosine_distance, scaled_english_counts, normalise, 10, 0.7232
-cosine_distance, scaled_english_counts, normalise, 5, 0.415
-cosine_distance, scaled_english_counts, scale, 300, 0.9982
-cosine_distance, scaled_english_counts, scale, 100, 0.9988
-cosine_distance, scaled_english_counts, scale, 50, 0.9976
-cosine_distance, scaled_english_counts, scale, 30, 0.9844
-cosine_distance, scaled_english_counts, scale, 20, 0.9314
-cosine_distance, scaled_english_counts, scale, 10, 0.7102
-cosine_distance, scaled_english_counts, scale, 5, 0.4376
-harmonic_mean, normalised_english_counts, normalise, 300, 0.4684
-harmonic_mean, normalised_english_counts, normalise, 100, 0.5068
-harmonic_mean, normalised_english_counts, normalise, 50, 0.6978
-harmonic_mean, normalised_english_counts, normalise, 30, 0.593
-harmonic_mean, normalised_english_counts, normalise, 20, 0.536
-harmonic_mean, normalised_english_counts, normalise, 10, 0.4284
-harmonic_mean, normalised_english_counts, normalise, 5, 0.3542
-harmonic_mean, normalised_english_counts, scale, 300, 0.3602
-harmonic_mean, normalised_english_counts, scale, 100, 0.57
-harmonic_mean, normalised_english_counts, scale, 50, 0.795
-harmonic_mean, normalised_english_counts, scale, 30, 0.7694
-harmonic_mean, normalised_english_counts, scale, 20, 0.6924
-harmonic_mean, normalised_english_counts, scale, 10, 0.559
-harmonic_mean, normalised_english_counts, scale, 5, 0.39
-harmonic_mean, scaled_english_counts, normalise, 300, 0.1214
-harmonic_mean, scaled_english_counts, normalise, 100, 0.132
-harmonic_mean, scaled_english_counts, normalise, 50, 0.1956
-harmonic_mean, scaled_english_counts, normalise, 30, 0.2686
-harmonic_mean, scaled_english_counts, normalise, 20, 0.258
-harmonic_mean, scaled_english_counts, normalise, 10, 0.2042
-harmonic_mean, scaled_english_counts, normalise, 5, 0.227
-harmonic_mean, scaled_english_counts, scale, 300, 0.7956
-harmonic_mean, scaled_english_counts, scale, 100, 0.5672
-harmonic_mean, scaled_english_counts, scale, 50, 0.4404
-harmonic_mean, scaled_english_counts, scale, 30, 0.3584
-harmonic_mean, scaled_english_counts, scale, 20, 0.3012
-harmonic_mean, scaled_english_counts, scale, 10, 0.2136
-harmonic_mean, scaled_english_counts, scale, 5, 0.1426
-geometric_mean, normalised_english_counts, normalise, 300, 0.9996
-geometric_mean, normalised_english_counts, normalise, 100, 0.9992
-geometric_mean, normalised_english_counts, normalise, 50, 0.9928
-geometric_mean, normalised_english_counts, normalise, 30, 0.9552
-geometric_mean, normalised_english_counts, normalise, 20, 0.8936
-geometric_mean, normalised_english_counts, normalise, 10, 0.6582
-geometric_mean, normalised_english_counts, normalise, 5, 0.4316
-geometric_mean, normalised_english_counts, scale, 300, 0.97
-geometric_mean, normalised_english_counts, scale, 100, 0.9762
-geometric_mean, normalised_english_counts, scale, 50, 0.9724
-geometric_mean, normalised_english_counts, scale, 30, 0.9224
-geometric_mean, normalised_english_counts, scale, 20, 0.8496
-geometric_mean, normalised_english_counts, scale, 10, 0.6846
-geometric_mean, normalised_english_counts, scale, 5, 0.4268
-geometric_mean, scaled_english_counts, normalise, 300, 0.9556
-geometric_mean, scaled_english_counts, normalise, 100, 0.8724
-geometric_mean, scaled_english_counts, normalise, 50, 0.7176
-geometric_mean, scaled_english_counts, normalise, 30, 0.6536
-geometric_mean, scaled_english_counts, normalise, 20, 0.5586
-geometric_mean, scaled_english_counts, normalise, 10, 0.3926
-geometric_mean, scaled_english_counts, normalise, 5, 0.319
-geometric_mean, scaled_english_counts, scale, 300, 0.7822
-geometric_mean, scaled_english_counts, scale, 100, 0.5784
-geometric_mean, scaled_english_counts, scale, 50, 0.4318
-geometric_mean, scaled_english_counts, scale, 30, 0.349
-geometric_mean, scaled_english_counts, scale, 20, 0.2932
-geometric_mean, scaled_english_counts, scale, 10, 0.2098
-geometric_mean, scaled_english_counts, scale, 5, 0.1406
+<<<<<<< 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
+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",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