X-Git-Url: https://git.njae.me.uk/?a=blobdiff_plain;f=caesar_break_parameter_trials.csv;h=e18f92c5604d97855b83cff0fdce99d17c7385a1;hb=4e49d9a8c3379d16d1d721a14b072396568ecbcc;hp=df9b836ebdfec843d0f42cb319729dfd1a45836c;hpb=26d9d2228e47a6ff8b8696d37c0a8d6d6b906c67;p=cipher-tools.git diff --git a/caesar_break_parameter_trials.csv b/caesar_break_parameter_trials.csv index df9b836..e18f92c 100644 --- 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 +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 +"Pletters",4996,4997,4984,4900,4063,2358 +"cosine_similarity + euclidean_scaled",4998,4986,4914,4659,3528,2198 +"cosine_similarity + normalised",4997,4993,4917,4659,3557,2084 +"l1 + euclidean_scaled",4998,4992,4951,4755,3767,2192 +"l1 + normalised",4998,4996,4936,4767,3596,2161 +"l2 + euclidean_scaled",4998,4990,4926,4683,3567,2179 +"l2 + normalised",4995,4993,4920,4672,3610,2135 +"l3 + euclidean_scaled",4996,4964,4822,4457,3167,2018 +"l3 + normalised",4999,4973,4797,4351,2872,1989 +>>>>>>> 883806c... More tweaking