Hugging Face's logo

shawn24
/
Ladder

TensorBoard
English
custom
Model card Files
xet
Metrics Community
shawn24 commited on
Commit
d70d529
·
verified ·
1 Parent(s): 220c97c

Delete folder MetaShift after moving to output/

Browse files
Files changed (43) hide show
  1. MetaShift/.DS_Store +0 -0
  2. MetaShift/resnet_sup_in1k_attrNo/.DS_Store +0 -0
  3. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/.DS_Store +0 -0
  4. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/args.json +0 -30
  5. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/.DS_Store +0 -0
  6. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/aligner_30.pth +0 -3
  7. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/aligner_out.txt +0 -38
  8. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/cat_error_top_50_sent_diff_emb.txt +0 -50
  9. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/cat_hypothesis_dict.pkl +0 -3
  10. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/cat_prompt_dict.pkl +0 -3
  11. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/dog_error_top_50_sent_diff_emb.txt +0 -50
  12. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/dog_hypothesis_dict.pkl +0 -3
  13. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/dog_prompt_dict.pkl +0 -3
  14. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/ladder_validate_slices_w_LLM-cat.txt +0 -55
  15. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/ladder_validate_slices_w_LLM-dog.txt +0 -55
  16. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/prompt.txt +0 -96
  17. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/sent_emb_captions_gpt-4o.npy +0 -3
  18. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/sentences_captions_gpt-4o.pkl +0 -3
  19. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_additional_info.csv +0 -875
  20. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_additional_info.pkl +0 -3
  21. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_cat_dataframe_mitigation.csv +0 -0
  22. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_classifier_embeddings.npy +0 -3
  23. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_clip_embeddings.npy +0 -3
  24. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_dog_dataframe_mitigation.csv +0 -0
  25. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_additional_info.csv +0 -2269
  26. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_additional_info.pkl +0 -3
  27. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_cat_dataframe_mitigation.csv +0 -0
  28. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_classifier_embeddings.npy +0 -3
  29. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_clip_embeddings.npy +0 -3
  30. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_dog_dataframe_mitigation.csv +0 -0
  31. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_additional_info.csv +0 -350
  32. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_additional_info.pkl +0 -3
  33. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_cat_dataframe_mitigation.csv +0 -350
  34. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_classifier_embeddings.npy +0 -3
  35. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_clip_embeddings.npy +0 -3
  36. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_dog_dataframe_mitigation.csv +0 -350
  37. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/done +0 -1
  38. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/err.txt +0 -390
  39. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/events.out.tfevents.1712698653.dv004.ib.bridges2.psc.edu +0 -3
  40. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/events.out.tfevents.1712700841.dv004.ib.bridges2.psc.edu +0 -3
  41. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/final_results.pkl +0 -3
  42. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/model.pkl +0 -3
  43. MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/out.txt +0 -555
MetaShift/.DS_Store DELETED
Binary file (6.15 kB)
 
MetaShift/resnet_sup_in1k_attrNo/.DS_Store DELETED
Binary file (6.15 kB)
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/.DS_Store DELETED
Binary file (6.15 kB)
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/args.json DELETED
@@ -1,30 +0,0 @@
1
- {
2
- "dataset": "MetaShift",
3
- "algorithm": "ERM",
4
- "output_folder_name": "resnet_sup_in1k_attrNo",
5
- "train_attr": "no",
6
- "data_dir": "/ocean/projects/asc170022p/shg121/PhD/Multimodal-mistakes-debug/data",
7
- "output_dir": "/ocean/projects/asc170022p/shg121/PhD/Multimodal-mistakes-debug/out/MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0",
8
- "hparams": null,
9
- "hparams_seed": 0,
10
- "seed": 0,
11
- "steps": null,
12
- "tb_log_all": false,
13
- "stage1_folder": "vanilla",
14
- "stage1_algo": "ERM",
15
- "use_es": false,
16
- "es_strategy": "metric",
17
- "es_metric": "min_group:accuracy",
18
- "es_patience": 5,
19
- "resume": "",
20
- "pretrained": "",
21
- "checkpoint_freq": null,
22
- "skip_model_save": false,
23
- "cmnist_label_prob": 0.5,
24
- "cmnist_attr_prob": 0.5,
25
- "cmnist_spur_prob": 0.2,
26
- "cmnist_flip_prob": 0.25,
27
- "image_arch": "resnet_sup_in1k",
28
- "text_arch": "bert-base-uncased",
29
- "store_name": "MetaShift_ERM_hparams0_seed0"
30
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/.DS_Store DELETED
Binary file (6.15 kB)
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/aligner_30.pth DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:677b97962208f6ca046fa544a65383b6917efe53dce0f288d2da43456c694c74
3
- size 4197802
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/aligner_out.txt DELETED
@@ -1,38 +0,0 @@
1
- 2024-10-29 21:03:27,452 - Train size: classifier [(2268, 2048)], clip [(2268, 512)]
2
- 2024-10-29 21:03:27,452 - Valid size: classifier [(349, 2048)], clip [(349, 512)]
3
- 2024-10-29 21:03:27,452 - Training linear aligner ...
4
- 2024-10-29 21:03:27,452 - Linear alignment train: ((2268, 2048)) --> ((2268, 512)).
5
- 2024-10-29 21:03:27,452 - Linear alignment test: ((349, 2048)) --> ((349, 512)).
6
- 2024-10-29 21:03:29,742 - Initial MSE, R^2: 6.782, -0.507
7
- 2024-10-29 21:03:30,008 - Epoch number, 0, train loss: 3.215, test MSE: 2.050, test_r2: 0.544, best MSE: 2.050
8
- 2024-10-29 21:03:30,167 - Epoch number, 1, train loss: 1.870, test MSE: 1.760, test_r2: 0.609, best MSE: 1.760
9
- 2024-10-29 21:03:30,334 - Epoch number, 2, train loss: 1.651, test MSE: 1.654, test_r2: 0.632, best MSE: 1.654
10
- 2024-10-29 21:03:30,489 - Epoch number, 3, train loss: 1.554, test MSE: 1.588, test_r2: 0.647, best MSE: 1.588
11
- 2024-10-29 21:03:30,644 - Epoch number, 4, train loss: 1.499, test MSE: 1.567, test_r2: 0.652, best MSE: 1.567
12
- 2024-10-29 21:03:30,803 - Epoch number, 5, train loss: 1.452, test MSE: 1.532, test_r2: 0.659, best MSE: 1.532
13
- 2024-10-29 21:03:30,953 - Epoch number, 6, train loss: 1.418, test MSE: 1.500, test_r2: 0.667, best MSE: 1.500
14
- 2024-10-29 21:03:31,111 - Epoch number, 7, train loss: 1.390, test MSE: 1.489, test_r2: 0.669, best MSE: 1.489
15
- 2024-10-29 21:03:31,269 - Epoch number, 8, train loss: 1.368, test MSE: 1.474, test_r2: 0.672, best MSE: 1.474
16
- 2024-10-29 21:03:31,422 - Epoch number, 9, train loss: 1.349, test MSE: 1.464, test_r2: 0.675, best MSE: 1.464
17
- 2024-10-29 21:03:31,580 - Epoch number, 10, train loss: 1.331, test MSE: 1.472, test_r2: 0.673, best MSE: 1.464
18
- 2024-10-29 21:03:31,734 - Epoch number, 11, train loss: 1.318, test MSE: 1.467, test_r2: 0.674, best MSE: 1.464
19
- 2024-10-29 21:03:31,893 - Epoch number, 12, train loss: 1.305, test MSE: 1.442, test_r2: 0.680, best MSE: 1.442
20
- 2024-10-29 21:03:32,050 - Epoch number, 13, train loss: 1.292, test MSE: 1.439, test_r2: 0.680, best MSE: 1.439
21
- 2024-10-29 21:03:32,198 - Epoch number, 14, train loss: 1.276, test MSE: 1.427, test_r2: 0.683, best MSE: 1.427
22
- 2024-10-29 21:03:32,346 - Epoch number, 15, train loss: 1.265, test MSE: 1.422, test_r2: 0.684, best MSE: 1.422
23
- 2024-10-29 21:03:32,509 - Epoch number, 16, train loss: 1.260, test MSE: 1.429, test_r2: 0.683, best MSE: 1.422
24
- 2024-10-29 21:03:32,656 - Epoch number, 17, train loss: 1.253, test MSE: 1.423, test_r2: 0.684, best MSE: 1.422
25
- 2024-10-29 21:03:32,818 - Epoch number, 18, train loss: 1.242, test MSE: 1.420, test_r2: 0.684, best MSE: 1.420
26
- 2024-10-29 21:03:32,977 - Epoch number, 19, train loss: 1.234, test MSE: 1.415, test_r2: 0.686, best MSE: 1.415
27
- 2024-10-29 21:03:33,139 - Epoch number, 20, train loss: 1.223, test MSE: 1.403, test_r2: 0.688, best MSE: 1.403
28
- 2024-10-29 21:03:33,296 - Epoch number, 21, train loss: 1.217, test MSE: 1.398, test_r2: 0.689, best MSE: 1.398
29
- 2024-10-29 21:03:33,441 - Epoch number, 22, train loss: 1.211, test MSE: 1.402, test_r2: 0.688, best MSE: 1.398
30
- 2024-10-29 21:03:33,592 - Epoch number, 23, train loss: 1.204, test MSE: 1.396, test_r2: 0.690, best MSE: 1.396
31
- 2024-10-29 21:03:33,740 - Epoch number, 24, train loss: 1.199, test MSE: 1.394, test_r2: 0.690, best MSE: 1.394
32
- 2024-10-29 21:03:33,893 - Epoch number, 25, train loss: 1.194, test MSE: 1.388, test_r2: 0.691, best MSE: 1.388
33
- 2024-10-29 21:03:34,047 - Epoch number, 26, train loss: 1.187, test MSE: 1.402, test_r2: 0.688, best MSE: 1.388
34
- 2024-10-29 21:03:34,199 - Epoch number, 27, train loss: 1.183, test MSE: 1.393, test_r2: 0.690, best MSE: 1.388
35
- 2024-10-29 21:03:34,349 - Epoch number, 28, train loss: 1.179, test MSE: 1.389, test_r2: 0.691, best MSE: 1.388
36
- 2024-10-29 21:03:34,505 - Epoch number, 29, train loss: 1.173, test MSE: 1.393, test_r2: 0.690, best MSE: 1.388
37
- 2024-10-29 21:03:34,516 - Aligner weights saved to /restricted/projectnb/batmanlab/shawn24/PhD/Ladder/out/MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/aligner_30.pth
38
- 2024-10-29 21:03:34,516 - Saved aligner to /restricted/projectnb/batmanlab/shawn24/PhD/Ladder/out/MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/cat_error_top_50_sent_diff_emb.txt DELETED
@@ -1,50 +0,0 @@
1
- 1. A cat is sleeping on a desk next to a stack of CDs and a computer monitor, with blinds in the background
2
- 2. A fluffy cat is lying on a desk next to a laptop and trackball mouse, appearing curious or relaxed
3
- 3. A cat is sleeping on a desk next to a computer monitor displaying code, with a closed laptop and a white keyboard nearby
4
- 4. A cat is sitting intently in front of a computer monitor on a wooden table, with a plant in the background
5
- 5. The image shows a tabby and white cat lying on a cushioned surface, with a bookshelf in the background
6
- 6. A fluffy tabby cat is comfortably sitting on a desk beside a laptop, surrounded by papers
7
- 7. A tabby cat is lounging comfortably on top of a wooden shelf, appearing relaxed
8
- 8. A fluffy cat is lying comfortably on a striped bed, surrounded by dim indoor lighting
9
- 9. A gray and white cat is lying on a keyboard in front of a computer screen, appearing curious and relaxed
10
- 10. A grey and white cat with green eyes is resting on a soft, floral-patterned surface
11
- 11. A tabby cat is lying on a bed, playfully engaging with a colorful toy using its paws
12
- 12. A cat is sitting on a closed suitcase on a bed in a dimly lit room
13
- 13. A tabby cat is lying on a bed next to a window, looking toward the camera with sunlight streaming in
14
- 14. The image shows a cat lounging on a bed with a cylindrical pillow, surrounded by computer accessories and pens
15
- 15. A tabby cat with white paws and chest rests comfortably on a white chair
16
- 16. An orange kitten is sleeping next to a computer mouse on a colorful blue and yellow mouse pad
17
- 17. A black cat with bright green eyes is lying on a desk with a keyboard and monitor
18
- 18. A cat with a light, mixed coat color is lying on a patterned bedspread, appearing relaxed and comfortable
19
- 19. A ginger cat is curled up comfortably in a bathroom sink, surrounded by toiletries
20
- 20. A cat is intently staring at a laptop displaying an abstract green and black desktop background
21
- 21. A fluffy, light-colored cat is standing on a bed covered with a colorful blanket, near a stack of books or magazines
22
- 22. A fluffy cat is curled up inside a terracotta planter, resting beside a pair of boots in an outdoor setting
23
- 23. A brown cat is relaxing on an overturned drum next to a bookshelf filled with various books
24
- 24. A tabby cat is lying on its back on a floral-patterned couch, appearing relaxed and asleep
25
- 25. A cat is sitting beside an open laptop on a cluttered desk
26
- 26. A cat is sleeping on its back next to a blue laptop and a computer mouse
27
- 27. A cat is lying on a cushion behind a glass window, basking in sunlight with its head tilted back
28
- 28. A cat sits on furniture, watching a television news broadcast in a cozy room with bookshelves in the background
29
- 29. A cluttered workspace includes a desktop computer, various items like a keyboard and mouse, and a cat curled up on the desk
30
- 30. A tabby cat is lounging on a couch, resting its paw on a remote control
31
- 31. A gray cat is sitting on a black laptop, partially covering the keyboard with its body
32
- 32. A gray cat with closed eyes is sitting on a shelf next to some books
33
- 33. A tabby cat is lying on a carpet between two white sneakers, with its paws forward
34
- 34. A cat is eating food from a bowl placed on a blue mat, with canned food visible in the background
35
- 35. A cat is curiously exploring by placing its head inside a toilet bowl, with its hind legs and tail visible
36
- 36. A black and white cat is sitting on a laptop keyboard with the screen partially visible
37
- 37. A cat is curled up sleeping on a person's lap next to a laptop
38
- 38. A fluffy cat is comfortably lying inside an open suitcase
39
- 39. A cat is sitting on a blue and wooden structure, possibly a boat, with a teal wall in the background
40
- 40. A cat peeks through a curtain, looking out of a window with a snowy landscape outside
41
- 41. A collage of four images shows a cat sitting on a couch in a dimly lit room
42
- 42. A cat is lounging on the back of a wooden chair in a room with a Christmas tree and various furniture pieces
43
- 43. A cat crouches against a wall, holding a prey in its mouth
44
- 44. A person is sitting on a couch holding a relaxed cat, both appear calm and comfortable
45
- 45. A cat is standing on the edge of a toilet seat in a bathroom with tiled flooring
46
- 46. A cat is sitting on a laptop keyboard in a dimly lit room, with fruit on the table and artwork on the wall
47
- 47. A fluffy black cat is lounging on a pillow next to a table with a TV remote on it
48
- 48. A cat is comfortably napping on a person, next to a laptop on a small stand in a room with vertical blinds
49
- 49. A gray cat is sleeping on a teal suitcase in a room, surrounded by bags and boxes
50
- 50. A cat is comfortably sleeping on someone's lap in a cozy setting with a table nearby holding various items, including a laptop and drinks
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/cat_hypothesis_dict.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:afed3b6f4ce294471ac0aa45d0aa96b9eba8fa97ae82bc8f99332f40fd3432e9
3
- size 719
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/cat_prompt_dict.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:163cab6446f86c8c9a9cc43915a682cb2b1e5c930efee47d6b755e0d99416792
3
- size 1750
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/dog_error_top_50_sent_diff_emb.txt DELETED
@@ -1,50 +0,0 @@
1
- 1. A dog with a black and white coat is carrying a frisbee in its mouth while standing on grass
2
- 2. A person is playing with a dog outdoors, using a yellow frisbee, with a crowd and mountains in the background
3
- 3. A golden retriever is walking along a beach with a frisbee in its mouth
4
- 4. A child walks near a resting dog with a tennis ball in a park setting
5
- 5. A large white dog carries a green frisbee in its mouth, while a smaller dog runs next to it on a grassy area
6
- 6. A dog with a pink bandana stands on grass near a metal water bowl in a sunny park setting
7
- 7. A happy dog with its tongue out stands next to a red and blue ball in a grassy field
8
- 8. A dog leaps into the air to catch a frisbee while an individual crouches on the grass, holding another frisbee
9
- 9. A brown and white dog is playing with a red and white basketball on a grassy area next to a gravel path
10
- 10. A tan and white dog is running on a sandy beach with a red frisbee in its mouth
11
- 11. A person is standing in a grassy park with three dogs playing nearby
12
- 12. A dog is jumping in the air to catch a yellow frisbee in a grassy area with a play structure in the background
13
- 13. A person is riding a horse across a grassy field, accompanied by a dog
14
- 14. A dog is playing outside on the grass, holding an object in its mouth near a fenced area
15
- 15. A group of people wearing matching pink shirts pose together on a grassy field, some holding a frisbee, suggesting a team activity or sport
16
- 16. A man helps a young child pet a golden retriever on a riverside walkway, while two people and another dog sit nearby
17
- 17. A dog is walking on a sandy shore towards a line of green trees, under a cloudy sky
18
- 18. A dog is jumping in the air to catch a pink frisbee in a grassy field
19
- 19. A person walks a dog along a sandy beach with gentle waves approaching the shore
20
- 20. A dog is carrying a baseball bat on a field near a catcher, while players stand in the background
21
- 21. A dog is standing confidently on a surfboard in the water
22
- 22. A dog is leaping off a dock towards a red ball in the water, with a wooded shoreline in the background
23
- 23. A man is navigating a small motorized inflatable boat on a lake or river, accompanied by a dog at the bow with trees and other boats in the background
24
- 24. A person wearing a helmet is riding a bicycle through a grassy field, accompanied by a black dog
25
- 25. A person stands by a small boat on a pebble beach, with a dog sniffing the ground nearby, against a backdrop of buildings and greenery
26
- 26. A person is skateboarding on the street, accompanied by a running dog
27
- 27. A dog is herding a group of sheep in a grassy field near a fenced area
28
- 28. A cow and two dogs are walking along a sandy beach near the ocean shoreline
29
- 29. A herding dog is guiding a group of cows near a white fence on a grassy field
30
- 30. A dog is lying on grass, holding a large, striped frisbee in its mouth
31
- 31. A group of people and dogs are enjoying time at a sandy lakeside, with some dogs in the water and others on the shore
32
- 32. A black French Bulldog and a large black Poodle are standing together on grass, both on leashes, with the Poodle looking to the side
33
- 33. A group of people is gathered in a park, some with coffee cups, while a leashed dog stands among them
34
- 34. A person with a dog stands on a beach with kite surfers in the ocean
35
- 35. A brown and white dog walks near a puddle while a white horse grazes in the background on a grassy area
36
- 36. Two golden retrievers are lying on the grass, playing with a frisbee together
37
- 37. A group of people is horseback riding through a forested trail, accompanied by a dog
38
- 38. A black and white dog is energetically leaping in the air to catch an orange frisbee on a grassy field
39
- 39. A dog wearing a green and black backpack is standing on a rock in a stream, attached to a red leash
40
- 40. A dog wearing sunglasses and a bandana is seated on a stationary motorcycle, with a person standing nearby on a brick pavement
41
- 41. A fluffy brown dog with a red backpack stands on a forest path, with trees and people in the background
42
- 42. Two women are walking on a sidewalk; one is holding a dog and they are in a green, outdoor setting
43
- 43. A dog is in motion, enthusiastically chasing a yellow ball in a grassy yard
44
- 44. A group of horses is running toward a black dog in a rural setting, with colorful buildings in the background
45
- 45. A dog with a black and brown coat sits on a wooden bench in an outdoor setting, with people and bicycles in the background
46
- 46. They are both smiling and wearing sunglasses, with the man in a hat and the woman holding the dog's leash
47
- 47. A person is sitting on a rock near a stream, accompanied by a brown dog, surrounded by lush greenery
48
- 48. Two people are standing together on a concrete surface, with skateboards nearby and a dog in the foreground
49
- 49. A black and white image shows a dog walking down wide stone steps, followed by a child near a riverside with boats lined up at the water's edge
50
- 50. A man in a floral shirt is paddleboarding on the ocean alongside a dog wearing an orange vest
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/dog_hypothesis_dict.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:a5536b9d154053c0e96f4e5393450dac3359cd2e033000d1f6718b0283007c7b
3
- size 712
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/dog_prompt_dict.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:977739e18bfb8b026b1609f23c223f656e0be8028713feee805a292f0671b08e
3
- size 1611
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/ladder_validate_slices_w_LLM-cat.txt DELETED
@@ -1,55 +0,0 @@
1
- Hypothesis Dictionary:
2
- {'H1': 'The classifier is making mistake as it is biased toward desk', 'H2': 'The classifier is making mistake as it is biased toward laptop', 'H3': 'The classifier is making mistake as it is biased toward monitor', 'H4': 'The classifier is making mistake as it is biased toward keyboard', 'H5': 'The classifier is making mistake as it is biased toward bookshelf', 'H6': 'The classifier is making mistake as it is biased toward cushion', 'H7': 'The classifier is making mistake as it is biased toward bed', 'H8': 'The classifier is making mistake as it is biased toward suitcase', 'H9': 'The classifier is making mistake as it is biased toward plant', 'H10': 'The classifier is making mistake as it is biased toward mouse'}
3
-
4
- Prompt Dictionary:
5
- {'H1_desk': ['A cat is sitting on a desk', 'A dog is sitting on a desk', 'A desk with a cat and a computer', 'A desk with a dog and a computer', 'A cluttered desk with a cat'], 'H2_laptop': ['A cat is lying next to a laptop', 'A dog is lying next to a laptop', 'A laptop with a cat beside it', 'A laptop with a dog beside it', 'A cat sitting on a laptop'], 'H3_monitor': ['A cat is in front of a computer monitor', 'A dog is in front of a computer monitor', 'A computer monitor with a cat nearby', 'A computer monitor with a dog nearby', 'A cat looking at a computer monitor'], 'H4_keyboard': ['A cat is lying on a keyboard', 'A dog is lying on a keyboard', 'A keyboard with a cat on it', 'A keyboard with a dog on it', 'A cat playing with a keyboard'], 'H5_bookshelf': ['A cat is near a bookshelf', 'A dog is near a bookshelf', 'A bookshelf with a cat nearby', 'A bookshelf with a dog nearby', 'A cat sitting on a bookshelf'], 'H6_cushion': ['A cat is lying on a cushion', 'A dog is lying on a cushion', 'A cushion with a cat on it', 'A cushion with a dog on it', 'A cat sleeping on a cushion'], 'H7_bed': ['A cat is lying on a bed', 'A dog is lying on a bed', 'A bed with a cat on it', 'A bed with a dog on it', 'A cat sleeping on a bed'], 'H8_suitcase': ['A cat is sitting on a suitcase', 'A dog is sitting on a suitcase', 'A suitcase with a cat on it', 'A suitcase with a dog on it', 'A cat sleeping in a suitcase'], 'H9_plant': ['A cat is near a plant', 'A dog is near a plant', 'A plant with a cat nearby', 'A plant with a dog nearby', 'A cat sitting next to a plant'], 'H10_mouse': ['A cat is playing with a mouse', 'A dog is playing with a mouse', 'A computer mouse with a cat nearby', 'A computer mouse with a dog nearby', 'A cat sitting next to a computer mouse']}
6
- ==============================================
7
- 0 H1_desk
8
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.75
9
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9580645161290322
10
- ==============================================
11
- ==============================================
12
- 1 H2_laptop
13
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7547169811320755
14
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9605263157894737
15
- ==============================================
16
- ==============================================
17
- 2 H3_monitor
18
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7833333333333333
19
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9586206896551724
20
- ==============================================
21
- ==============================================
22
- 3 H4_keyboard
23
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7222222222222222
24
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9735099337748344
25
- ==============================================
26
- ==============================================
27
- 4 H5_bookshelf
28
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7572815533980582
29
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9576547231270358
30
- ==============================================
31
- ==============================================
32
- 5 H6_cushion
33
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7627118644067796
34
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9657534246575342
35
- ==============================================
36
- ==============================================
37
- 6 H7_bed
38
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.75
39
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9664429530201343
40
- ==============================================
41
- ==============================================
42
- 7 H8_suitcase
43
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7777777777777778
44
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9647887323943662
45
- ==============================================
46
- ==============================================
47
- 8 H9_plant
48
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7947019867549668
49
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.972972972972973
50
- ==============================================
51
- ==============================================
52
- 9 H10_mouse
53
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7391304347826086
54
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9728813559322034
55
- ==============================================
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/ladder_validate_slices_w_LLM-dog.txt DELETED
@@ -1,55 +0,0 @@
1
- Hypothesis Dictionary:
2
- {'H1': 'The classifier is making mistake as it is biased toward frisbee', 'H2': 'The classifier is making mistake as it is biased toward grass', 'H3': 'The classifier is making mistake as it is biased toward beach', 'H4': 'The classifier is making mistake as it is biased toward water', 'H5': 'The classifier is making mistake as it is biased toward park', 'H6': 'The classifier is making mistake as it is biased toward ball', 'H7': 'The classifier is making mistake as it is biased toward leash', 'H8': 'The classifier is making mistake as it is biased toward person', 'H9': 'The classifier is making mistake as it is biased toward jumping', 'H10': 'The classifier is making mistake as it is biased toward running'}
3
-
4
- Prompt Dictionary:
5
- {'H1_frisbee': ['A dog playing with a frisbee', 'A dog catching a frisbee in the air', 'A dog holding a frisbee in its mouth', 'A dog running with a frisbee', 'A dog standing next to a frisbee'], 'H2_grass': ['A dog standing on grass', 'A dog playing on a grassy field', 'A dog lying on grass', 'A dog running on grass', 'A dog sitting on grass'], 'H3_beach': ['A dog walking on a beach', 'A dog running on a sandy beach', 'A dog playing on the beach', 'A dog near the ocean on a beach', 'A dog on a beach with waves'], 'H4_water': ['A dog swimming in water', 'A dog playing in water', 'A dog near a body of water', 'A dog jumping into water', 'A dog standing in water'], 'H5_park': ['A dog in a park setting', 'A dog playing in a park', 'A dog walking in a park', 'A dog resting in a park', 'A dog running in a park'], 'H6_ball': ['A dog playing with a ball', 'A dog chasing a ball', 'A dog holding a ball in its mouth', 'A dog standing next to a ball', 'A dog catching a ball'], 'H7_leash': ['A dog on a leash', 'A person walking a dog on a leash', 'A dog being led by a leash', 'A dog standing with a leash', 'A dog sitting with a leash'], 'H8_person': ['A person playing with a dog', 'A person walking a dog', 'A person standing next to a dog', 'A person petting a dog', 'A person holding a dog'], 'H9_jumping': ['A dog jumping in the air', 'A dog leaping to catch something', 'A dog jumping over an obstacle', 'A dog in mid-air', 'A dog jumping with excitement'], 'H10_running': ['A dog running fast', 'A dog sprinting across a field', 'A dog running alongside a person', 'A dog running with other dogs', 'A dog running happily']}
6
- ==============================================
7
- 0 H1_frisbee
8
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.725
9
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9825581395348837
10
- ==============================================
11
- ==============================================
12
- 1 H2_grass
13
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7664233576642335
14
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9785932721712538
15
- ==============================================
16
- ==============================================
17
- 2 H3_beach
18
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7301587301587301
19
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.985207100591716
20
- ==============================================
21
- ==============================================
22
- 3 H4_water
23
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7905405405405406
24
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9746835443037974
25
- ==============================================
26
- ==============================================
27
- 4 H5_park
28
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7633587786259542
29
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.975975975975976
30
- ==============================================
31
- ==============================================
32
- 5 H6_ball
33
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.781021897810219
34
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9724770642201835
35
- ==============================================
36
- ==============================================
37
- 6 H7_leash
38
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.782608695652174
39
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9723926380368099
40
- ==============================================
41
- ==============================================
42
- 7 H8_person
43
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.7686567164179104
44
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9757575757575757
45
- ==============================================
46
- ==============================================
47
- 8 H9_jumping
48
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.8066666666666666
49
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9681528662420382
50
- ==============================================
51
- ==============================================
52
- 9 H10_running
53
- Accuracy on the error slice (where attribute absent, the hypothesis failed): 0.78125
54
- Accuracy on the bias aligned slice (where attribute present, the hypothesis passed): 0.9672619047619048
55
- ==============================================
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/prompt.txt DELETED
@@ -1,96 +0,0 @@
1
-
2
- Context: Cat vs Dog classification from images using a deep neural network
3
- Analysis post training: On a validation set,
4
- a. Get the difference between the image embeddings of correct and incorrectly classified samples.
5
- b. Retrieve the top K sentences from the captions of the images that matches closely to the embedding difference in step a.
6
- c. The sentence list is given below in the descending order of similarity with the embedding difference:
7
- 1. A cat is sleeping on a desk next to a stack of CDs and a computer monitor, with blinds in the background
8
- 2. A fluffy cat is lying on a desk next to a laptop and trackball mouse, appearing curious or relaxed
9
- 3. A cat is sleeping on a desk next to a computer monitor displaying code, with a closed laptop and a white keyboard nearby
10
- 4. A cat is sitting intently in front of a computer monitor on a wooden table, with a plant in the background
11
- 5. The image shows a tabby and white cat lying on a cushioned surface, with a bookshelf in the background
12
- 6. A fluffy tabby cat is comfortably sitting on a desk beside a laptop, surrounded by papers
13
- 7. A tabby cat is lounging comfortably on top of a wooden shelf, appearing relaxed
14
- 8. A fluffy cat is lying comfortably on a striped bed, surrounded by dim indoor lighting
15
- 9. A gray and white cat is lying on a keyboard in front of a computer screen, appearing curious and relaxed
16
- 10. A grey and white cat with green eyes is resting on a soft, floral-patterned surface
17
- 11. A tabby cat is lying on a bed, playfully engaging with a colorful toy using its paws
18
- 12. A cat is sitting on a closed suitcase on a bed in a dimly lit room
19
- 13. A tabby cat is lying on a bed next to a window, looking toward the camera with sunlight streaming in
20
- 14. The image shows a cat lounging on a bed with a cylindrical pillow, surrounded by computer accessories and pens
21
- 15. A tabby cat with white paws and chest rests comfortably on a white chair
22
- 16. An orange kitten is sleeping next to a computer mouse on a colorful blue and yellow mouse pad
23
- 17. A black cat with bright green eyes is lying on a desk with a keyboard and monitor
24
- 18. A cat with a light, mixed coat color is lying on a patterned bedspread, appearing relaxed and comfortable
25
- 19. A ginger cat is curled up comfortably in a bathroom sink, surrounded by toiletries
26
- 20. A cat is intently staring at a laptop displaying an abstract green and black desktop background
27
- 21. A fluffy, light-colored cat is standing on a bed covered with a colorful blanket, near a stack of books or magazines
28
- 22. A fluffy cat is curled up inside a terracotta planter, resting beside a pair of boots in an outdoor setting
29
- 23. A brown cat is relaxing on an overturned drum next to a bookshelf filled with various books
30
- 24. A tabby cat is lying on its back on a floral-patterned couch, appearing relaxed and asleep
31
- 25. A cat is sitting beside an open laptop on a cluttered desk
32
- 26. A cat is sleeping on its back next to a blue laptop and a computer mouse
33
- 27. A cat is lying on a cushion behind a glass window, basking in sunlight with its head tilted back
34
- 28. A cat sits on furniture, watching a television news broadcast in a cozy room with bookshelves in the background
35
- 29. A cluttered workspace includes a desktop computer, various items like a keyboard and mouse, and a cat curled up on the desk
36
- 30. A tabby cat is lounging on a couch, resting its paw on a remote control
37
- 31. A gray cat is sitting on a black laptop, partially covering the keyboard with its body
38
- 32. A gray cat with closed eyes is sitting on a shelf next to some books
39
- 33. A tabby cat is lying on a carpet between two white sneakers, with its paws forward
40
- 34. A cat is eating food from a bowl placed on a blue mat, with canned food visible in the background
41
- 35. A cat is curiously exploring by placing its head inside a toilet bowl, with its hind legs and tail visible
42
- 36. A black and white cat is sitting on a laptop keyboard with the screen partially visible
43
- 37. A cat is curled up sleeping on a person's lap next to a laptop
44
- 38. A fluffy cat is comfortably lying inside an open suitcase
45
- 39. A cat is sitting on a blue and wooden structure, possibly a boat, with a teal wall in the background
46
- 40. A cat peeks through a curtain, looking out of a window with a snowy landscape outside
47
- 41. A collage of four images shows a cat sitting on a couch in a dimly lit room
48
- 42. A cat is lounging on the back of a wooden chair in a room with a Christmas tree and various furniture pieces
49
- 43. A cat crouches against a wall, holding a prey in its mouth
50
- 44. A person is sitting on a couch holding a relaxed cat, both appear calm and comfortable
51
- 45. A cat is standing on the edge of a toilet seat in a bathroom with tiled flooring
52
- 46. A cat is sitting on a laptop keyboard in a dimly lit room, with fruit on the table and artwork on the wall
53
- 47. A fluffy black cat is lounging on a pillow next to a table with a TV remote on it
54
- 48. A cat is comfortably napping on a person, next to a laptop on a small stand in a room with vertical blinds
55
- 49. A gray cat is sleeping on a teal suitcase in a room, surrounded by bags and boxes
56
- 50. A cat is comfortably sleeping on someone's lap in a cozy setting with a table nearby holding various items, including a laptop and drinks
57
-
58
-
59
- These sentences represent the features present in the correctly classified samples but missing in the misclassified samples.
60
- Task:
61
- The task is to reason why the model is making mistakes on the misclassified samples based on the sentences for the class label 'cat'. To do so, consider the attributes present in the above captions regarding to the specific bird species. Attributes are all the concepts other than the class label (i.e, cat). So come up with the list of hypotheses based based on these attributes to reason why a model makes systematic mistakes. For the hypotheses, you should be the following python dictionary template, no extra sentence:
62
-
63
- hypothesis_dict = {
64
- "H1": "The classifier is making mistake as it is biased toward <attribute>",
65
- "H2": "The classifier is making mistake as it is biased toward <attribute>",
66
- "H3": "The classifier is making mistake as it is biased toward <attribute>",
67
- ...
68
- }
69
-
70
- You must follow the following rules to construct the hypotheses:
71
- 1. You must pick specific attributes, e.g, blue, not generic attributes like color.
72
- 2. Your hypotheses must be based on the attributes present in the captions, nothing else.
73
- 3. You must pay close attention to the attributes that are consistently present in the sentences. These attributes are likely to be the cause of the systematic mistakes on the misclassified samples.
74
- 4. You must construct as many hypotheses possible.
75
-
76
- Next you have to test the hypothesis. To effectively test Hypothesis 1 (H1) using the CLIP language encoder, you need to create prompts that explicitly validate H1. These prompts will help to generate text embeddings that capture the essence of the hypothesis, which can be used to compute similarity with the image embeddings from the dataset. The goal is to see if the images for which the model makes mistakes are those that aligns with H1 or violates H1. The prompts are python list. Remember, your focus is only the specific bird.
77
-
78
- Do this for all the hypothesis. Your final response should follow the following list of dictionaries, nothing else:
79
-
80
-
81
- prompt_dict = {
82
- "H1_<attribute>": [List of prompts],
83
- "H2_<attribute>": [List of prompts]
84
- ...
85
- }
86
-
87
-
88
-
89
- Each attribute hypothesis should contain 5 prompts.
90
-
91
- So final response should follow the below format strictly (nothing else, no extra sentence):
92
- ```python
93
- hypothesis_dict
94
- prompt_dict
95
- ```
96
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/sent_emb_captions_gpt-4o.npy DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:fc2013c7df730b96ab6bba67986ae38fc981d1bdd151eb58d3427d28c9de7ab5
3
- size 477312
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/sentences_captions_gpt-4o.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:dd0a820cc5e8340a911e432544c81d1939c03cb2f6c5bad6b056351b432505e3
3
- size 42464
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_additional_info.csv DELETED
@@ -1,875 +0,0 @@
1
- out_put_GT,out_put_predict,attribute_bg_predict,idx,gs
2
- 1.0,1.0,1.0,0,"y=1,a=1"
3
- 1.0,1.0,1.0,1,"y=1,a=1"
4
- 1.0,1.0,1.0,2,"y=1,a=1"
5
- 1.0,1.0,1.0,3,"y=1,a=1"
6
- 1.0,1.0,1.0,4,"y=1,a=1"
7
- 1.0,1.0,1.0,5,"y=1,a=1"
8
- 1.0,1.0,1.0,6,"y=1,a=1"
9
- 1.0,1.0,1.0,7,"y=1,a=1"
10
- 1.0,1.0,1.0,8,"y=1,a=1"
11
- 1.0,1.0,1.0,9,"y=1,a=1"
12
- 1.0,1.0,1.0,10,"y=1,a=1"
13
- 1.0,1.0,1.0,11,"y=1,a=1"
14
- 1.0,1.0,1.0,12,"y=1,a=1"
15
- 1.0,1.0,1.0,13,"y=1,a=1"
16
- 1.0,1.0,1.0,14,"y=1,a=1"
17
- 1.0,0.0,1.0,15,"y=1,a=1"
18
- 1.0,1.0,1.0,16,"y=1,a=1"
19
- 1.0,1.0,1.0,17,"y=1,a=1"
20
- 1.0,1.0,1.0,18,"y=1,a=1"
21
- 1.0,1.0,1.0,19,"y=1,a=1"
22
- 1.0,1.0,1.0,20,"y=1,a=1"
23
- 1.0,1.0,1.0,21,"y=1,a=1"
24
- 1.0,1.0,1.0,22,"y=1,a=1"
25
- 1.0,1.0,1.0,23,"y=1,a=1"
26
- 1.0,1.0,1.0,24,"y=1,a=1"
27
- 1.0,1.0,1.0,25,"y=1,a=1"
28
- 1.0,1.0,1.0,26,"y=1,a=1"
29
- 1.0,1.0,1.0,27,"y=1,a=1"
30
- 1.0,1.0,1.0,28,"y=1,a=1"
31
- 1.0,1.0,1.0,29,"y=1,a=1"
32
- 1.0,1.0,1.0,30,"y=1,a=1"
33
- 1.0,1.0,1.0,31,"y=1,a=1"
34
- 1.0,1.0,1.0,32,"y=1,a=1"
35
- 1.0,1.0,1.0,33,"y=1,a=1"
36
- 1.0,1.0,1.0,34,"y=1,a=1"
37
- 1.0,1.0,1.0,35,"y=1,a=1"
38
- 1.0,1.0,1.0,36,"y=1,a=1"
39
- 1.0,1.0,1.0,37,"y=1,a=1"
40
- 1.0,1.0,1.0,38,"y=1,a=1"
41
- 1.0,1.0,1.0,39,"y=1,a=1"
42
- 1.0,1.0,1.0,40,"y=1,a=1"
43
- 1.0,1.0,1.0,41,"y=1,a=1"
44
- 1.0,1.0,1.0,42,"y=1,a=1"
45
- 1.0,0.0,1.0,43,"y=1,a=1"
46
- 1.0,1.0,1.0,44,"y=1,a=1"
47
- 1.0,0.0,1.0,45,"y=1,a=1"
48
- 1.0,1.0,1.0,46,"y=1,a=1"
49
- 1.0,1.0,1.0,47,"y=1,a=1"
50
- 1.0,1.0,1.0,48,"y=1,a=1"
51
- 1.0,1.0,1.0,49,"y=1,a=1"
52
- 1.0,1.0,1.0,50,"y=1,a=1"
53
- 1.0,1.0,1.0,51,"y=1,a=1"
54
- 1.0,1.0,1.0,52,"y=1,a=1"
55
- 1.0,1.0,1.0,53,"y=1,a=1"
56
- 1.0,1.0,1.0,54,"y=1,a=1"
57
- 1.0,1.0,1.0,55,"y=1,a=1"
58
- 1.0,1.0,1.0,56,"y=1,a=1"
59
- 1.0,1.0,1.0,57,"y=1,a=1"
60
- 1.0,1.0,1.0,58,"y=1,a=1"
61
- 1.0,1.0,1.0,59,"y=1,a=1"
62
- 1.0,1.0,1.0,60,"y=1,a=1"
63
- 1.0,1.0,1.0,61,"y=1,a=1"
64
- 1.0,1.0,1.0,62,"y=1,a=1"
65
- 1.0,1.0,1.0,63,"y=1,a=1"
66
- 1.0,1.0,1.0,64,"y=1,a=1"
67
- 1.0,0.0,1.0,65,"y=1,a=1"
68
- 1.0,1.0,1.0,66,"y=1,a=1"
69
- 1.0,1.0,1.0,67,"y=1,a=1"
70
- 1.0,1.0,1.0,68,"y=1,a=1"
71
- 1.0,1.0,1.0,69,"y=1,a=1"
72
- 1.0,1.0,1.0,70,"y=1,a=1"
73
- 1.0,0.0,1.0,71,"y=1,a=1"
74
- 1.0,1.0,1.0,72,"y=1,a=1"
75
- 1.0,1.0,1.0,73,"y=1,a=1"
76
- 1.0,1.0,1.0,74,"y=1,a=1"
77
- 1.0,1.0,1.0,75,"y=1,a=1"
78
- 1.0,1.0,1.0,76,"y=1,a=1"
79
- 1.0,1.0,1.0,77,"y=1,a=1"
80
- 1.0,1.0,1.0,78,"y=1,a=1"
81
- 1.0,1.0,1.0,79,"y=1,a=1"
82
- 1.0,1.0,1.0,80,"y=1,a=1"
83
- 1.0,1.0,1.0,81,"y=1,a=1"
84
- 1.0,1.0,1.0,82,"y=1,a=1"
85
- 1.0,1.0,1.0,83,"y=1,a=1"
86
- 1.0,1.0,1.0,84,"y=1,a=1"
87
- 1.0,1.0,1.0,85,"y=1,a=1"
88
- 1.0,1.0,1.0,86,"y=1,a=1"
89
- 1.0,1.0,1.0,87,"y=1,a=1"
90
- 1.0,1.0,1.0,88,"y=1,a=1"
91
- 1.0,1.0,1.0,89,"y=1,a=1"
92
- 1.0,0.0,1.0,90,"y=1,a=1"
93
- 1.0,1.0,1.0,91,"y=1,a=1"
94
- 1.0,1.0,1.0,92,"y=1,a=1"
95
- 1.0,1.0,1.0,93,"y=1,a=1"
96
- 1.0,1.0,1.0,94,"y=1,a=1"
97
- 1.0,0.0,1.0,95,"y=1,a=1"
98
- 1.0,1.0,1.0,96,"y=1,a=1"
99
- 1.0,1.0,1.0,97,"y=1,a=1"
100
- 1.0,1.0,1.0,98,"y=1,a=1"
101
- 1.0,1.0,1.0,99,"y=1,a=1"
102
- 1.0,1.0,1.0,100,"y=1,a=1"
103
- 1.0,1.0,1.0,101,"y=1,a=1"
104
- 1.0,1.0,1.0,102,"y=1,a=1"
105
- 1.0,1.0,1.0,103,"y=1,a=1"
106
- 1.0,1.0,1.0,104,"y=1,a=1"
107
- 1.0,1.0,1.0,105,"y=1,a=1"
108
- 1.0,1.0,1.0,106,"y=1,a=1"
109
- 1.0,1.0,1.0,107,"y=1,a=1"
110
- 1.0,1.0,1.0,108,"y=1,a=1"
111
- 1.0,1.0,1.0,109,"y=1,a=1"
112
- 1.0,1.0,1.0,110,"y=1,a=1"
113
- 1.0,1.0,1.0,111,"y=1,a=1"
114
- 1.0,1.0,1.0,112,"y=1,a=1"
115
- 1.0,1.0,1.0,113,"y=1,a=1"
116
- 1.0,1.0,1.0,114,"y=1,a=1"
117
- 1.0,1.0,1.0,115,"y=1,a=1"
118
- 1.0,1.0,1.0,116,"y=1,a=1"
119
- 1.0,1.0,1.0,117,"y=1,a=1"
120
- 1.0,1.0,1.0,118,"y=1,a=1"
121
- 1.0,1.0,1.0,119,"y=1,a=1"
122
- 1.0,1.0,1.0,120,"y=1,a=1"
123
- 1.0,1.0,1.0,121,"y=1,a=1"
124
- 1.0,1.0,1.0,122,"y=1,a=1"
125
- 1.0,1.0,1.0,123,"y=1,a=1"
126
- 1.0,1.0,1.0,124,"y=1,a=1"
127
- 1.0,1.0,1.0,125,"y=1,a=1"
128
- 1.0,1.0,1.0,126,"y=1,a=1"
129
- 1.0,1.0,1.0,127,"y=1,a=1"
130
- 1.0,1.0,1.0,128,"y=1,a=1"
131
- 1.0,1.0,1.0,129,"y=1,a=1"
132
- 1.0,1.0,1.0,130,"y=1,a=1"
133
- 1.0,1.0,1.0,131,"y=1,a=1"
134
- 1.0,1.0,1.0,132,"y=1,a=1"
135
- 1.0,1.0,1.0,133,"y=1,a=1"
136
- 1.0,1.0,1.0,134,"y=1,a=1"
137
- 1.0,1.0,1.0,135,"y=1,a=1"
138
- 1.0,1.0,1.0,136,"y=1,a=1"
139
- 1.0,1.0,1.0,137,"y=1,a=1"
140
- 1.0,1.0,1.0,138,"y=1,a=1"
141
- 1.0,1.0,1.0,139,"y=1,a=1"
142
- 1.0,1.0,1.0,140,"y=1,a=1"
143
- 1.0,1.0,1.0,141,"y=1,a=1"
144
- 1.0,1.0,1.0,142,"y=1,a=1"
145
- 1.0,1.0,1.0,143,"y=1,a=1"
146
- 1.0,0.0,1.0,144,"y=1,a=1"
147
- 1.0,1.0,1.0,145,"y=1,a=1"
148
- 1.0,1.0,1.0,146,"y=1,a=1"
149
- 1.0,0.0,1.0,147,"y=1,a=1"
150
- 1.0,1.0,1.0,148,"y=1,a=1"
151
- 1.0,1.0,1.0,149,"y=1,a=1"
152
- 1.0,1.0,1.0,150,"y=1,a=1"
153
- 1.0,1.0,1.0,151,"y=1,a=1"
154
- 1.0,1.0,1.0,152,"y=1,a=1"
155
- 1.0,1.0,1.0,153,"y=1,a=1"
156
- 1.0,1.0,1.0,154,"y=1,a=1"
157
- 1.0,1.0,1.0,155,"y=1,a=1"
158
- 1.0,1.0,1.0,156,"y=1,a=1"
159
- 1.0,1.0,1.0,157,"y=1,a=1"
160
- 1.0,1.0,1.0,158,"y=1,a=1"
161
- 1.0,1.0,1.0,159,"y=1,a=1"
162
- 1.0,1.0,1.0,160,"y=1,a=1"
163
- 1.0,1.0,1.0,161,"y=1,a=1"
164
- 1.0,1.0,1.0,162,"y=1,a=1"
165
- 1.0,1.0,1.0,163,"y=1,a=1"
166
- 1.0,1.0,1.0,164,"y=1,a=1"
167
- 1.0,1.0,1.0,165,"y=1,a=1"
168
- 1.0,1.0,1.0,166,"y=1,a=1"
169
- 1.0,1.0,1.0,167,"y=1,a=1"
170
- 1.0,1.0,1.0,168,"y=1,a=1"
171
- 1.0,1.0,1.0,169,"y=1,a=1"
172
- 1.0,1.0,1.0,170,"y=1,a=1"
173
- 1.0,1.0,1.0,171,"y=1,a=1"
174
- 1.0,1.0,1.0,172,"y=1,a=1"
175
- 1.0,1.0,1.0,173,"y=1,a=1"
176
- 1.0,1.0,1.0,174,"y=1,a=1"
177
- 1.0,0.0,1.0,175,"y=1,a=1"
178
- 1.0,1.0,1.0,176,"y=1,a=1"
179
- 1.0,1.0,1.0,177,"y=1,a=1"
180
- 1.0,1.0,1.0,178,"y=1,a=1"
181
- 1.0,1.0,1.0,179,"y=1,a=1"
182
- 1.0,1.0,1.0,180,"y=1,a=1"
183
- 1.0,1.0,1.0,181,"y=1,a=1"
184
- 1.0,1.0,1.0,182,"y=1,a=1"
185
- 1.0,1.0,1.0,183,"y=1,a=1"
186
- 1.0,1.0,1.0,184,"y=1,a=1"
187
- 1.0,1.0,1.0,185,"y=1,a=1"
188
- 1.0,1.0,1.0,186,"y=1,a=1"
189
- 1.0,1.0,1.0,187,"y=1,a=1"
190
- 1.0,1.0,1.0,188,"y=1,a=1"
191
- 1.0,1.0,1.0,189,"y=1,a=1"
192
- 1.0,1.0,1.0,190,"y=1,a=1"
193
- 1.0,1.0,1.0,191,"y=1,a=1"
194
- 1.0,1.0,1.0,192,"y=1,a=1"
195
- 1.0,1.0,1.0,193,"y=1,a=1"
196
- 1.0,1.0,1.0,194,"y=1,a=1"
197
- 1.0,1.0,1.0,195,"y=1,a=1"
198
- 1.0,1.0,1.0,196,"y=1,a=1"
199
- 1.0,1.0,1.0,197,"y=1,a=1"
200
- 1.0,1.0,1.0,198,"y=1,a=1"
201
- 1.0,1.0,1.0,199,"y=1,a=1"
202
- 1.0,1.0,1.0,200,"y=1,a=1"
203
- 1.0,0.0,1.0,201,"y=1,a=1"
204
- 1.0,1.0,1.0,202,"y=1,a=1"
205
- 1.0,0.0,1.0,203,"y=1,a=1"
206
- 1.0,1.0,1.0,204,"y=1,a=1"
207
- 1.0,1.0,1.0,205,"y=1,a=1"
208
- 1.0,1.0,1.0,206,"y=1,a=1"
209
- 1.0,1.0,1.0,207,"y=1,a=1"
210
- 1.0,1.0,1.0,208,"y=1,a=1"
211
- 1.0,1.0,1.0,209,"y=1,a=1"
212
- 1.0,1.0,1.0,210,"y=1,a=1"
213
- 1.0,1.0,1.0,211,"y=1,a=1"
214
- 1.0,1.0,1.0,212,"y=1,a=1"
215
- 1.0,1.0,1.0,213,"y=1,a=1"
216
- 1.0,1.0,1.0,214,"y=1,a=1"
217
- 1.0,1.0,1.0,215,"y=1,a=1"
218
- 1.0,1.0,1.0,216,"y=1,a=1"
219
- 1.0,1.0,1.0,217,"y=1,a=1"
220
- 1.0,1.0,1.0,218,"y=1,a=1"
221
- 1.0,0.0,1.0,219,"y=1,a=1"
222
- 1.0,1.0,1.0,220,"y=1,a=1"
223
- 1.0,1.0,1.0,221,"y=1,a=1"
224
- 1.0,1.0,1.0,222,"y=1,a=1"
225
- 1.0,1.0,1.0,223,"y=1,a=1"
226
- 1.0,1.0,1.0,224,"y=1,a=1"
227
- 1.0,1.0,1.0,225,"y=1,a=1"
228
- 1.0,1.0,1.0,226,"y=1,a=1"
229
- 1.0,1.0,1.0,227,"y=1,a=1"
230
- 1.0,1.0,1.0,228,"y=1,a=1"
231
- 1.0,1.0,1.0,229,"y=1,a=1"
232
- 1.0,1.0,1.0,230,"y=1,a=1"
233
- 1.0,1.0,1.0,231,"y=1,a=1"
234
- 1.0,1.0,1.0,232,"y=1,a=1"
235
- 1.0,1.0,1.0,233,"y=1,a=1"
236
- 1.0,1.0,1.0,234,"y=1,a=1"
237
- 1.0,1.0,1.0,235,"y=1,a=1"
238
- 1.0,1.0,1.0,236,"y=1,a=1"
239
- 1.0,1.0,1.0,237,"y=1,a=1"
240
- 1.0,1.0,1.0,238,"y=1,a=1"
241
- 1.0,1.0,1.0,239,"y=1,a=1"
242
- 1.0,1.0,1.0,240,"y=1,a=1"
243
- 1.0,1.0,1.0,241,"y=1,a=1"
244
- 1.0,1.0,1.0,242,"y=1,a=1"
245
- 1.0,1.0,1.0,243,"y=1,a=1"
246
- 1.0,1.0,1.0,244,"y=1,a=1"
247
- 1.0,0.0,1.0,245,"y=1,a=1"
248
- 1.0,1.0,1.0,246,"y=1,a=1"
249
- 1.0,1.0,1.0,247,"y=1,a=1"
250
- 1.0,1.0,1.0,248,"y=1,a=1"
251
- 1.0,1.0,1.0,249,"y=1,a=1"
252
- 1.0,1.0,1.0,250,"y=1,a=1"
253
- 1.0,1.0,1.0,251,"y=1,a=1"
254
- 1.0,1.0,1.0,252,"y=1,a=1"
255
- 1.0,1.0,1.0,253,"y=1,a=1"
256
- 1.0,1.0,1.0,254,"y=1,a=1"
257
- 1.0,1.0,1.0,255,"y=1,a=1"
258
- 1.0,1.0,1.0,256,"y=1,a=1"
259
- 1.0,1.0,1.0,257,"y=1,a=1"
260
- 1.0,1.0,1.0,258,"y=1,a=1"
261
- 1.0,1.0,1.0,259,"y=1,a=1"
262
- 1.0,1.0,1.0,260,"y=1,a=1"
263
- 1.0,1.0,1.0,261,"y=1,a=1"
264
- 1.0,1.0,1.0,262,"y=1,a=1"
265
- 1.0,1.0,1.0,263,"y=1,a=1"
266
- 1.0,1.0,1.0,264,"y=1,a=1"
267
- 1.0,1.0,1.0,265,"y=1,a=1"
268
- 1.0,1.0,1.0,266,"y=1,a=1"
269
- 1.0,1.0,1.0,267,"y=1,a=1"
270
- 1.0,0.0,1.0,268,"y=1,a=1"
271
- 1.0,1.0,1.0,269,"y=1,a=1"
272
- 1.0,1.0,1.0,270,"y=1,a=1"
273
- 1.0,1.0,1.0,271,"y=1,a=1"
274
- 1.0,0.0,1.0,272,"y=1,a=1"
275
- 1.0,1.0,1.0,273,"y=1,a=1"
276
- 1.0,0.0,1.0,274,"y=1,a=1"
277
- 1.0,1.0,1.0,275,"y=1,a=1"
278
- 1.0,1.0,1.0,276,"y=1,a=1"
279
- 1.0,1.0,1.0,277,"y=1,a=1"
280
- 1.0,1.0,1.0,278,"y=1,a=1"
281
- 1.0,1.0,1.0,279,"y=1,a=1"
282
- 1.0,1.0,1.0,280,"y=1,a=1"
283
- 1.0,1.0,1.0,281,"y=1,a=1"
284
- 1.0,0.0,1.0,282,"y=1,a=1"
285
- 1.0,1.0,1.0,283,"y=1,a=1"
286
- 1.0,1.0,1.0,284,"y=1,a=1"
287
- 1.0,1.0,1.0,285,"y=1,a=1"
288
- 1.0,1.0,1.0,286,"y=1,a=1"
289
- 1.0,1.0,1.0,287,"y=1,a=1"
290
- 1.0,1.0,1.0,288,"y=1,a=1"
291
- 1.0,1.0,1.0,289,"y=1,a=1"
292
- 1.0,1.0,1.0,290,"y=1,a=1"
293
- 1.0,1.0,1.0,291,"y=1,a=1"
294
- 1.0,1.0,1.0,292,"y=1,a=1"
295
- 1.0,1.0,1.0,293,"y=1,a=1"
296
- 1.0,0.0,1.0,294,"y=1,a=1"
297
- 1.0,0.0,1.0,295,"y=1,a=1"
298
- 1.0,1.0,1.0,296,"y=1,a=1"
299
- 1.0,1.0,1.0,297,"y=1,a=1"
300
- 1.0,1.0,1.0,298,"y=1,a=1"
301
- 1.0,1.0,1.0,299,"y=1,a=1"
302
- 1.0,0.0,1.0,300,"y=1,a=1"
303
- 1.0,1.0,1.0,301,"y=1,a=1"
304
- 1.0,1.0,1.0,302,"y=1,a=1"
305
- 1.0,1.0,1.0,303,"y=1,a=1"
306
- 1.0,1.0,1.0,304,"y=1,a=1"
307
- 1.0,1.0,1.0,305,"y=1,a=1"
308
- 1.0,1.0,1.0,306,"y=1,a=1"
309
- 1.0,1.0,1.0,307,"y=1,a=1"
310
- 1.0,1.0,1.0,308,"y=1,a=1"
311
- 1.0,1.0,1.0,309,"y=1,a=1"
312
- 1.0,1.0,1.0,310,"y=1,a=1"
313
- 1.0,1.0,1.0,311,"y=1,a=1"
314
- 1.0,0.0,1.0,312,"y=1,a=1"
315
- 1.0,1.0,1.0,313,"y=1,a=1"
316
- 1.0,1.0,1.0,314,"y=1,a=1"
317
- 1.0,0.0,1.0,315,"y=1,a=1"
318
- 1.0,1.0,1.0,316,"y=1,a=1"
319
- 1.0,1.0,1.0,317,"y=1,a=1"
320
- 1.0,1.0,1.0,318,"y=1,a=1"
321
- 1.0,1.0,1.0,319,"y=1,a=1"
322
- 1.0,1.0,1.0,320,"y=1,a=1"
323
- 1.0,1.0,1.0,321,"y=1,a=1"
324
- 1.0,1.0,1.0,322,"y=1,a=1"
325
- 1.0,1.0,1.0,323,"y=1,a=1"
326
- 1.0,1.0,1.0,324,"y=1,a=1"
327
- 1.0,1.0,1.0,325,"y=1,a=1"
328
- 1.0,1.0,1.0,326,"y=1,a=1"
329
- 1.0,1.0,1.0,327,"y=1,a=1"
330
- 1.0,1.0,1.0,328,"y=1,a=1"
331
- 1.0,1.0,1.0,329,"y=1,a=1"
332
- 1.0,0.0,1.0,330,"y=1,a=1"
333
- 1.0,1.0,1.0,331,"y=1,a=1"
334
- 1.0,1.0,1.0,332,"y=1,a=1"
335
- 1.0,1.0,1.0,333,"y=1,a=1"
336
- 1.0,1.0,1.0,334,"y=1,a=1"
337
- 1.0,1.0,1.0,335,"y=1,a=1"
338
- 1.0,1.0,1.0,336,"y=1,a=1"
339
- 1.0,0.0,1.0,337,"y=1,a=1"
340
- 1.0,1.0,1.0,338,"y=1,a=1"
341
- 1.0,1.0,1.0,339,"y=1,a=1"
342
- 1.0,1.0,1.0,340,"y=1,a=1"
343
- 1.0,1.0,1.0,341,"y=1,a=1"
344
- 1.0,1.0,1.0,342,"y=1,a=1"
345
- 1.0,1.0,1.0,343,"y=1,a=1"
346
- 1.0,1.0,1.0,344,"y=1,a=1"
347
- 0.0,0.0,0.0,345,"y=0,a=0"
348
- 0.0,0.0,0.0,346,"y=0,a=0"
349
- 0.0,0.0,0.0,347,"y=0,a=0"
350
- 0.0,0.0,0.0,348,"y=0,a=0"
351
- 0.0,0.0,0.0,349,"y=0,a=0"
352
- 0.0,1.0,0.0,350,"y=0,a=0"
353
- 0.0,0.0,0.0,351,"y=0,a=0"
354
- 0.0,0.0,0.0,352,"y=0,a=0"
355
- 0.0,0.0,0.0,353,"y=0,a=0"
356
- 0.0,0.0,0.0,354,"y=0,a=0"
357
- 0.0,0.0,0.0,355,"y=0,a=0"
358
- 0.0,0.0,0.0,356,"y=0,a=0"
359
- 0.0,0.0,0.0,357,"y=0,a=0"
360
- 0.0,0.0,0.0,358,"y=0,a=0"
361
- 0.0,0.0,0.0,359,"y=0,a=0"
362
- 0.0,0.0,0.0,360,"y=0,a=0"
363
- 0.0,0.0,0.0,361,"y=0,a=0"
364
- 0.0,0.0,0.0,362,"y=0,a=0"
365
- 0.0,0.0,0.0,363,"y=0,a=0"
366
- 0.0,0.0,0.0,364,"y=0,a=0"
367
- 0.0,0.0,0.0,365,"y=0,a=0"
368
- 0.0,0.0,0.0,366,"y=0,a=0"
369
- 0.0,0.0,0.0,367,"y=0,a=0"
370
- 0.0,0.0,0.0,368,"y=0,a=0"
371
- 0.0,0.0,0.0,369,"y=0,a=0"
372
- 0.0,0.0,0.0,370,"y=0,a=0"
373
- 0.0,0.0,0.0,371,"y=0,a=0"
374
- 0.0,0.0,0.0,372,"y=0,a=0"
375
- 0.0,0.0,0.0,373,"y=0,a=0"
376
- 0.0,0.0,0.0,374,"y=0,a=0"
377
- 0.0,0.0,0.0,375,"y=0,a=0"
378
- 0.0,0.0,0.0,376,"y=0,a=0"
379
- 0.0,0.0,0.0,377,"y=0,a=0"
380
- 0.0,0.0,0.0,378,"y=0,a=0"
381
- 0.0,0.0,0.0,379,"y=0,a=0"
382
- 0.0,0.0,0.0,380,"y=0,a=0"
383
- 0.0,0.0,0.0,381,"y=0,a=0"
384
- 0.0,0.0,0.0,382,"y=0,a=0"
385
- 0.0,0.0,0.0,383,"y=0,a=0"
386
- 0.0,0.0,0.0,384,"y=0,a=0"
387
- 0.0,0.0,0.0,385,"y=0,a=0"
388
- 0.0,0.0,0.0,386,"y=0,a=0"
389
- 0.0,0.0,0.0,387,"y=0,a=0"
390
- 0.0,0.0,0.0,388,"y=0,a=0"
391
- 0.0,0.0,0.0,389,"y=0,a=0"
392
- 0.0,0.0,0.0,390,"y=0,a=0"
393
- 0.0,0.0,0.0,391,"y=0,a=0"
394
- 0.0,0.0,0.0,392,"y=0,a=0"
395
- 0.0,0.0,0.0,393,"y=0,a=0"
396
- 0.0,1.0,0.0,394,"y=0,a=0"
397
- 0.0,0.0,0.0,395,"y=0,a=0"
398
- 0.0,0.0,0.0,396,"y=0,a=0"
399
- 0.0,0.0,0.0,397,"y=0,a=0"
400
- 0.0,0.0,0.0,398,"y=0,a=0"
401
- 0.0,0.0,0.0,399,"y=0,a=0"
402
- 0.0,0.0,0.0,400,"y=0,a=0"
403
- 0.0,0.0,0.0,401,"y=0,a=0"
404
- 0.0,0.0,0.0,402,"y=0,a=0"
405
- 0.0,0.0,0.0,403,"y=0,a=0"
406
- 0.0,0.0,0.0,404,"y=0,a=0"
407
- 0.0,0.0,0.0,405,"y=0,a=0"
408
- 0.0,0.0,0.0,406,"y=0,a=0"
409
- 0.0,0.0,0.0,407,"y=0,a=0"
410
- 0.0,0.0,0.0,408,"y=0,a=0"
411
- 0.0,0.0,0.0,409,"y=0,a=0"
412
- 0.0,0.0,0.0,410,"y=0,a=0"
413
- 0.0,0.0,0.0,411,"y=0,a=0"
414
- 0.0,0.0,0.0,412,"y=0,a=0"
415
- 0.0,0.0,0.0,413,"y=0,a=0"
416
- 0.0,0.0,0.0,414,"y=0,a=0"
417
- 0.0,0.0,0.0,415,"y=0,a=0"
418
- 0.0,0.0,0.0,416,"y=0,a=0"
419
- 0.0,0.0,0.0,417,"y=0,a=0"
420
- 0.0,0.0,0.0,418,"y=0,a=0"
421
- 0.0,0.0,0.0,419,"y=0,a=0"
422
- 0.0,0.0,0.0,420,"y=0,a=0"
423
- 0.0,0.0,0.0,421,"y=0,a=0"
424
- 0.0,0.0,0.0,422,"y=0,a=0"
425
- 0.0,0.0,0.0,423,"y=0,a=0"
426
- 0.0,0.0,0.0,424,"y=0,a=0"
427
- 0.0,0.0,0.0,425,"y=0,a=0"
428
- 0.0,0.0,0.0,426,"y=0,a=0"
429
- 0.0,0.0,0.0,427,"y=0,a=0"
430
- 0.0,0.0,0.0,428,"y=0,a=0"
431
- 0.0,0.0,0.0,429,"y=0,a=0"
432
- 0.0,0.0,0.0,430,"y=0,a=0"
433
- 0.0,0.0,0.0,431,"y=0,a=0"
434
- 0.0,0.0,0.0,432,"y=0,a=0"
435
- 0.0,0.0,0.0,433,"y=0,a=0"
436
- 0.0,0.0,0.0,434,"y=0,a=0"
437
- 0.0,0.0,0.0,435,"y=0,a=0"
438
- 0.0,0.0,0.0,436,"y=0,a=0"
439
- 0.0,0.0,0.0,437,"y=0,a=0"
440
- 0.0,0.0,0.0,438,"y=0,a=0"
441
- 0.0,0.0,0.0,439,"y=0,a=0"
442
- 0.0,0.0,0.0,440,"y=0,a=0"
443
- 0.0,0.0,0.0,441,"y=0,a=0"
444
- 0.0,0.0,0.0,442,"y=0,a=0"
445
- 0.0,0.0,0.0,443,"y=0,a=0"
446
- 0.0,0.0,0.0,444,"y=0,a=0"
447
- 0.0,0.0,0.0,445,"y=0,a=0"
448
- 0.0,0.0,0.0,446,"y=0,a=0"
449
- 0.0,0.0,0.0,447,"y=0,a=0"
450
- 0.0,0.0,0.0,448,"y=0,a=0"
451
- 0.0,0.0,0.0,449,"y=0,a=0"
452
- 0.0,0.0,0.0,450,"y=0,a=0"
453
- 0.0,0.0,0.0,451,"y=0,a=0"
454
- 0.0,0.0,0.0,452,"y=0,a=0"
455
- 0.0,0.0,0.0,453,"y=0,a=0"
456
- 0.0,0.0,0.0,454,"y=0,a=0"
457
- 0.0,0.0,0.0,455,"y=0,a=0"
458
- 0.0,0.0,0.0,456,"y=0,a=0"
459
- 0.0,0.0,0.0,457,"y=0,a=0"
460
- 0.0,0.0,0.0,458,"y=0,a=0"
461
- 0.0,0.0,0.0,459,"y=0,a=0"
462
- 0.0,0.0,0.0,460,"y=0,a=0"
463
- 0.0,0.0,0.0,461,"y=0,a=0"
464
- 0.0,0.0,0.0,462,"y=0,a=0"
465
- 0.0,0.0,0.0,463,"y=0,a=0"
466
- 0.0,0.0,0.0,464,"y=0,a=0"
467
- 0.0,0.0,0.0,465,"y=0,a=0"
468
- 0.0,0.0,0.0,466,"y=0,a=0"
469
- 0.0,0.0,0.0,467,"y=0,a=0"
470
- 0.0,0.0,0.0,468,"y=0,a=0"
471
- 0.0,0.0,0.0,469,"y=0,a=0"
472
- 0.0,0.0,0.0,470,"y=0,a=0"
473
- 0.0,0.0,0.0,471,"y=0,a=0"
474
- 0.0,0.0,0.0,472,"y=0,a=0"
475
- 0.0,0.0,0.0,473,"y=0,a=0"
476
- 0.0,0.0,0.0,474,"y=0,a=0"
477
- 0.0,0.0,0.0,475,"y=0,a=0"
478
- 0.0,1.0,0.0,476,"y=0,a=0"
479
- 0.0,0.0,0.0,477,"y=0,a=0"
480
- 0.0,0.0,0.0,478,"y=0,a=0"
481
- 0.0,0.0,0.0,479,"y=0,a=0"
482
- 0.0,0.0,0.0,480,"y=0,a=0"
483
- 0.0,0.0,0.0,481,"y=0,a=0"
484
- 0.0,0.0,0.0,482,"y=0,a=0"
485
- 0.0,0.0,0.0,483,"y=0,a=0"
486
- 0.0,0.0,0.0,484,"y=0,a=0"
487
- 0.0,0.0,0.0,485,"y=0,a=0"
488
- 0.0,0.0,0.0,486,"y=0,a=0"
489
- 0.0,0.0,0.0,487,"y=0,a=0"
490
- 0.0,0.0,0.0,488,"y=0,a=0"
491
- 0.0,0.0,0.0,489,"y=0,a=0"
492
- 0.0,0.0,0.0,490,"y=0,a=0"
493
- 0.0,0.0,0.0,491,"y=0,a=0"
494
- 0.0,0.0,0.0,492,"y=0,a=0"
495
- 0.0,0.0,0.0,493,"y=0,a=0"
496
- 0.0,0.0,0.0,494,"y=0,a=0"
497
- 0.0,0.0,0.0,495,"y=0,a=0"
498
- 0.0,0.0,0.0,496,"y=0,a=0"
499
- 0.0,0.0,0.0,497,"y=0,a=0"
500
- 0.0,0.0,0.0,498,"y=0,a=0"
501
- 0.0,0.0,0.0,499,"y=0,a=0"
502
- 0.0,0.0,0.0,500,"y=0,a=0"
503
- 0.0,0.0,0.0,501,"y=0,a=0"
504
- 0.0,0.0,0.0,502,"y=0,a=0"
505
- 0.0,0.0,0.0,503,"y=0,a=0"
506
- 0.0,0.0,0.0,504,"y=0,a=0"
507
- 0.0,0.0,0.0,505,"y=0,a=0"
508
- 0.0,0.0,0.0,506,"y=0,a=0"
509
- 0.0,0.0,0.0,507,"y=0,a=0"
510
- 0.0,0.0,0.0,508,"y=0,a=0"
511
- 0.0,0.0,0.0,509,"y=0,a=0"
512
- 0.0,0.0,0.0,510,"y=0,a=0"
513
- 0.0,0.0,0.0,511,"y=0,a=0"
514
- 0.0,0.0,0.0,512,"y=0,a=0"
515
- 0.0,0.0,0.0,513,"y=0,a=0"
516
- 0.0,0.0,0.0,514,"y=0,a=0"
517
- 0.0,0.0,0.0,515,"y=0,a=0"
518
- 0.0,1.0,0.0,516,"y=0,a=0"
519
- 0.0,0.0,0.0,517,"y=0,a=0"
520
- 0.0,0.0,0.0,518,"y=0,a=0"
521
- 0.0,0.0,0.0,519,"y=0,a=0"
522
- 0.0,0.0,0.0,520,"y=0,a=0"
523
- 0.0,0.0,0.0,521,"y=0,a=0"
524
- 0.0,0.0,0.0,522,"y=0,a=0"
525
- 0.0,0.0,0.0,523,"y=0,a=0"
526
- 0.0,0.0,0.0,524,"y=0,a=0"
527
- 0.0,0.0,0.0,525,"y=0,a=0"
528
- 0.0,0.0,0.0,526,"y=0,a=0"
529
- 0.0,0.0,0.0,527,"y=0,a=0"
530
- 0.0,0.0,0.0,528,"y=0,a=0"
531
- 0.0,0.0,0.0,529,"y=0,a=0"
532
- 0.0,0.0,0.0,530,"y=0,a=0"
533
- 0.0,0.0,0.0,531,"y=0,a=0"
534
- 0.0,0.0,0.0,532,"y=0,a=0"
535
- 0.0,0.0,0.0,533,"y=0,a=0"
536
- 0.0,0.0,0.0,534,"y=0,a=0"
537
- 0.0,0.0,0.0,535,"y=0,a=0"
538
- 0.0,0.0,0.0,536,"y=0,a=0"
539
- 0.0,0.0,0.0,537,"y=0,a=0"
540
- 0.0,0.0,0.0,538,"y=0,a=0"
541
- 0.0,0.0,0.0,539,"y=0,a=0"
542
- 0.0,0.0,0.0,540,"y=0,a=0"
543
- 0.0,0.0,0.0,541,"y=0,a=0"
544
- 0.0,0.0,0.0,542,"y=0,a=0"
545
- 0.0,0.0,0.0,543,"y=0,a=0"
546
- 0.0,0.0,0.0,544,"y=0,a=0"
547
- 0.0,0.0,0.0,545,"y=0,a=0"
548
- 0.0,0.0,0.0,546,"y=0,a=0"
549
- 0.0,0.0,0.0,547,"y=0,a=0"
550
- 0.0,0.0,0.0,548,"y=0,a=0"
551
- 0.0,0.0,0.0,549,"y=0,a=0"
552
- 0.0,0.0,0.0,550,"y=0,a=0"
553
- 0.0,0.0,0.0,551,"y=0,a=0"
554
- 0.0,0.0,0.0,552,"y=0,a=0"
555
- 0.0,0.0,0.0,553,"y=0,a=0"
556
- 0.0,0.0,0.0,554,"y=0,a=0"
557
- 0.0,0.0,0.0,555,"y=0,a=0"
558
- 0.0,0.0,0.0,556,"y=0,a=0"
559
- 0.0,0.0,0.0,557,"y=0,a=0"
560
- 0.0,0.0,0.0,558,"y=0,a=0"
561
- 0.0,0.0,0.0,559,"y=0,a=0"
562
- 0.0,0.0,0.0,560,"y=0,a=0"
563
- 0.0,0.0,0.0,561,"y=0,a=0"
564
- 0.0,0.0,0.0,562,"y=0,a=0"
565
- 0.0,0.0,0.0,563,"y=0,a=0"
566
- 0.0,0.0,0.0,564,"y=0,a=0"
567
- 0.0,0.0,0.0,565,"y=0,a=0"
568
- 0.0,0.0,0.0,566,"y=0,a=0"
569
- 0.0,0.0,0.0,567,"y=0,a=0"
570
- 0.0,0.0,0.0,568,"y=0,a=0"
571
- 0.0,0.0,0.0,569,"y=0,a=0"
572
- 0.0,0.0,0.0,570,"y=0,a=0"
573
- 0.0,0.0,0.0,571,"y=0,a=0"
574
- 0.0,0.0,0.0,572,"y=0,a=0"
575
- 0.0,0.0,0.0,573,"y=0,a=0"
576
- 0.0,0.0,0.0,574,"y=0,a=0"
577
- 0.0,0.0,0.0,575,"y=0,a=0"
578
- 0.0,0.0,0.0,576,"y=0,a=0"
579
- 0.0,0.0,0.0,577,"y=0,a=0"
580
- 0.0,0.0,0.0,578,"y=0,a=0"
581
- 0.0,0.0,0.0,579,"y=0,a=0"
582
- 0.0,0.0,0.0,580,"y=0,a=0"
583
- 0.0,0.0,0.0,581,"y=0,a=0"
584
- 0.0,0.0,0.0,582,"y=0,a=0"
585
- 0.0,0.0,0.0,583,"y=0,a=0"
586
- 0.0,0.0,0.0,584,"y=0,a=0"
587
- 0.0,0.0,0.0,585,"y=0,a=0"
588
- 0.0,0.0,0.0,586,"y=0,a=0"
589
- 0.0,0.0,0.0,587,"y=0,a=0"
590
- 0.0,0.0,0.0,588,"y=0,a=0"
591
- 0.0,0.0,0.0,589,"y=0,a=0"
592
- 0.0,0.0,0.0,590,"y=0,a=0"
593
- 0.0,0.0,0.0,591,"y=0,a=0"
594
- 0.0,0.0,0.0,592,"y=0,a=0"
595
- 0.0,0.0,0.0,593,"y=0,a=0"
596
- 0.0,0.0,0.0,594,"y=0,a=0"
597
- 0.0,0.0,0.0,595,"y=0,a=0"
598
- 0.0,0.0,0.0,596,"y=0,a=0"
599
- 0.0,0.0,0.0,597,"y=0,a=0"
600
- 0.0,0.0,0.0,598,"y=0,a=0"
601
- 0.0,0.0,0.0,599,"y=0,a=0"
602
- 0.0,0.0,0.0,600,"y=0,a=0"
603
- 0.0,0.0,0.0,601,"y=0,a=0"
604
- 0.0,0.0,0.0,602,"y=0,a=0"
605
- 0.0,0.0,0.0,603,"y=0,a=0"
606
- 0.0,0.0,0.0,604,"y=0,a=0"
607
- 0.0,0.0,0.0,605,"y=0,a=0"
608
- 0.0,0.0,0.0,606,"y=0,a=0"
609
- 0.0,0.0,0.0,607,"y=0,a=0"
610
- 0.0,0.0,0.0,608,"y=0,a=0"
611
- 0.0,0.0,0.0,609,"y=0,a=0"
612
- 0.0,0.0,0.0,610,"y=0,a=0"
613
- 0.0,0.0,0.0,611,"y=0,a=0"
614
- 0.0,0.0,0.0,612,"y=0,a=0"
615
- 0.0,0.0,0.0,613,"y=0,a=0"
616
- 0.0,0.0,0.0,614,"y=0,a=0"
617
- 0.0,0.0,0.0,615,"y=0,a=0"
618
- 0.0,1.0,0.0,616,"y=0,a=0"
619
- 0.0,0.0,0.0,617,"y=0,a=0"
620
- 1.0,1.0,0.0,618,"y=1,a=0"
621
- 1.0,0.0,0.0,619,"y=1,a=0"
622
- 1.0,0.0,0.0,620,"y=1,a=0"
623
- 1.0,1.0,0.0,621,"y=1,a=0"
624
- 1.0,1.0,0.0,622,"y=1,a=0"
625
- 1.0,1.0,0.0,623,"y=1,a=0"
626
- 1.0,1.0,0.0,624,"y=1,a=0"
627
- 1.0,1.0,0.0,625,"y=1,a=0"
628
- 1.0,1.0,0.0,626,"y=1,a=0"
629
- 1.0,1.0,0.0,627,"y=1,a=0"
630
- 1.0,1.0,0.0,628,"y=1,a=0"
631
- 1.0,1.0,0.0,629,"y=1,a=0"
632
- 1.0,1.0,0.0,630,"y=1,a=0"
633
- 1.0,1.0,0.0,631,"y=1,a=0"
634
- 1.0,1.0,0.0,632,"y=1,a=0"
635
- 1.0,1.0,0.0,633,"y=1,a=0"
636
- 1.0,0.0,0.0,634,"y=1,a=0"
637
- 1.0,1.0,0.0,635,"y=1,a=0"
638
- 1.0,1.0,0.0,636,"y=1,a=0"
639
- 1.0,1.0,0.0,637,"y=1,a=0"
640
- 1.0,1.0,0.0,638,"y=1,a=0"
641
- 1.0,1.0,0.0,639,"y=1,a=0"
642
- 1.0,1.0,0.0,640,"y=1,a=0"
643
- 1.0,1.0,0.0,641,"y=1,a=0"
644
- 1.0,0.0,0.0,642,"y=1,a=0"
645
- 1.0,0.0,0.0,643,"y=1,a=0"
646
- 1.0,0.0,0.0,644,"y=1,a=0"
647
- 1.0,1.0,0.0,645,"y=1,a=0"
648
- 1.0,1.0,0.0,646,"y=1,a=0"
649
- 1.0,1.0,0.0,647,"y=1,a=0"
650
- 1.0,1.0,0.0,648,"y=1,a=0"
651
- 1.0,1.0,0.0,649,"y=1,a=0"
652
- 1.0,1.0,0.0,650,"y=1,a=0"
653
- 1.0,1.0,0.0,651,"y=1,a=0"
654
- 1.0,1.0,0.0,652,"y=1,a=0"
655
- 1.0,1.0,0.0,653,"y=1,a=0"
656
- 1.0,1.0,0.0,654,"y=1,a=0"
657
- 1.0,1.0,0.0,655,"y=1,a=0"
658
- 1.0,1.0,0.0,656,"y=1,a=0"
659
- 1.0,0.0,0.0,657,"y=1,a=0"
660
- 1.0,1.0,0.0,658,"y=1,a=0"
661
- 1.0,1.0,0.0,659,"y=1,a=0"
662
- 1.0,1.0,0.0,660,"y=1,a=0"
663
- 1.0,1.0,0.0,661,"y=1,a=0"
664
- 1.0,1.0,0.0,662,"y=1,a=0"
665
- 1.0,1.0,0.0,663,"y=1,a=0"
666
- 1.0,0.0,0.0,664,"y=1,a=0"
667
- 1.0,1.0,0.0,665,"y=1,a=0"
668
- 1.0,1.0,0.0,666,"y=1,a=0"
669
- 1.0,0.0,0.0,667,"y=1,a=0"
670
- 1.0,0.0,0.0,668,"y=1,a=0"
671
- 1.0,1.0,0.0,669,"y=1,a=0"
672
- 1.0,1.0,0.0,670,"y=1,a=0"
673
- 1.0,0.0,0.0,671,"y=1,a=0"
674
- 1.0,1.0,0.0,672,"y=1,a=0"
675
- 1.0,1.0,0.0,673,"y=1,a=0"
676
- 1.0,1.0,0.0,674,"y=1,a=0"
677
- 1.0,1.0,0.0,675,"y=1,a=0"
678
- 1.0,0.0,0.0,676,"y=1,a=0"
679
- 1.0,1.0,0.0,677,"y=1,a=0"
680
- 1.0,1.0,0.0,678,"y=1,a=0"
681
- 1.0,1.0,0.0,679,"y=1,a=0"
682
- 1.0,1.0,0.0,680,"y=1,a=0"
683
- 1.0,1.0,0.0,681,"y=1,a=0"
684
- 1.0,0.0,0.0,682,"y=1,a=0"
685
- 0.0,0.0,1.0,683,"y=0,a=1"
686
- 0.0,0.0,1.0,684,"y=0,a=1"
687
- 0.0,0.0,1.0,685,"y=0,a=1"
688
- 0.0,0.0,1.0,686,"y=0,a=1"
689
- 0.0,0.0,1.0,687,"y=0,a=1"
690
- 0.0,0.0,1.0,688,"y=0,a=1"
691
- 0.0,0.0,1.0,689,"y=0,a=1"
692
- 0.0,0.0,1.0,690,"y=0,a=1"
693
- 0.0,0.0,1.0,691,"y=0,a=1"
694
- 0.0,0.0,1.0,692,"y=0,a=1"
695
- 0.0,1.0,1.0,693,"y=0,a=1"
696
- 0.0,0.0,1.0,694,"y=0,a=1"
697
- 0.0,0.0,1.0,695,"y=0,a=1"
698
- 0.0,0.0,1.0,696,"y=0,a=1"
699
- 0.0,0.0,1.0,697,"y=0,a=1"
700
- 0.0,0.0,1.0,698,"y=0,a=1"
701
- 0.0,1.0,1.0,699,"y=0,a=1"
702
- 0.0,1.0,1.0,700,"y=0,a=1"
703
- 0.0,0.0,1.0,701,"y=0,a=1"
704
- 0.0,0.0,1.0,702,"y=0,a=1"
705
- 0.0,1.0,1.0,703,"y=0,a=1"
706
- 0.0,0.0,1.0,704,"y=0,a=1"
707
- 0.0,0.0,1.0,705,"y=0,a=1"
708
- 0.0,0.0,1.0,706,"y=0,a=1"
709
- 0.0,0.0,1.0,707,"y=0,a=1"
710
- 0.0,1.0,1.0,708,"y=0,a=1"
711
- 0.0,0.0,1.0,709,"y=0,a=1"
712
- 0.0,0.0,1.0,710,"y=0,a=1"
713
- 0.0,1.0,1.0,711,"y=0,a=1"
714
- 0.0,0.0,1.0,712,"y=0,a=1"
715
- 0.0,0.0,1.0,713,"y=0,a=1"
716
- 0.0,0.0,1.0,714,"y=0,a=1"
717
- 0.0,0.0,1.0,715,"y=0,a=1"
718
- 0.0,0.0,1.0,716,"y=0,a=1"
719
- 0.0,0.0,1.0,717,"y=0,a=1"
720
- 0.0,0.0,1.0,718,"y=0,a=1"
721
- 0.0,0.0,1.0,719,"y=0,a=1"
722
- 0.0,0.0,1.0,720,"y=0,a=1"
723
- 0.0,1.0,1.0,721,"y=0,a=1"
724
- 0.0,0.0,1.0,722,"y=0,a=1"
725
- 0.0,0.0,1.0,723,"y=0,a=1"
726
- 0.0,0.0,1.0,724,"y=0,a=1"
727
- 0.0,0.0,1.0,725,"y=0,a=1"
728
- 0.0,0.0,1.0,726,"y=0,a=1"
729
- 0.0,0.0,1.0,727,"y=0,a=1"
730
- 0.0,1.0,1.0,728,"y=0,a=1"
731
- 0.0,1.0,1.0,729,"y=0,a=1"
732
- 0.0,1.0,1.0,730,"y=0,a=1"
733
- 0.0,0.0,1.0,731,"y=0,a=1"
734
- 0.0,1.0,1.0,732,"y=0,a=1"
735
- 0.0,0.0,1.0,733,"y=0,a=1"
736
- 0.0,0.0,1.0,734,"y=0,a=1"
737
- 0.0,0.0,1.0,735,"y=0,a=1"
738
- 0.0,0.0,1.0,736,"y=0,a=1"
739
- 0.0,0.0,1.0,737,"y=0,a=1"
740
- 0.0,0.0,1.0,738,"y=0,a=1"
741
- 0.0,1.0,1.0,739,"y=0,a=1"
742
- 0.0,0.0,1.0,740,"y=0,a=1"
743
- 0.0,0.0,1.0,741,"y=0,a=1"
744
- 0.0,0.0,1.0,742,"y=0,a=1"
745
- 0.0,0.0,1.0,743,"y=0,a=1"
746
- 0.0,0.0,1.0,744,"y=0,a=1"
747
- 0.0,0.0,1.0,745,"y=0,a=1"
748
- 0.0,0.0,1.0,746,"y=0,a=1"
749
- 0.0,0.0,1.0,747,"y=0,a=1"
750
- 0.0,0.0,1.0,748,"y=0,a=1"
751
- 0.0,0.0,1.0,749,"y=0,a=1"
752
- 0.0,0.0,1.0,750,"y=0,a=1"
753
- 0.0,0.0,1.0,751,"y=0,a=1"
754
- 0.0,0.0,1.0,752,"y=0,a=1"
755
- 0.0,1.0,1.0,753,"y=0,a=1"
756
- 0.0,0.0,1.0,754,"y=0,a=1"
757
- 0.0,0.0,1.0,755,"y=0,a=1"
758
- 0.0,0.0,1.0,756,"y=0,a=1"
759
- 0.0,0.0,1.0,757,"y=0,a=1"
760
- 0.0,0.0,1.0,758,"y=0,a=1"
761
- 0.0,1.0,1.0,759,"y=0,a=1"
762
- 0.0,0.0,1.0,760,"y=0,a=1"
763
- 0.0,0.0,1.0,761,"y=0,a=1"
764
- 0.0,0.0,1.0,762,"y=0,a=1"
765
- 0.0,0.0,1.0,763,"y=0,a=1"
766
- 0.0,0.0,1.0,764,"y=0,a=1"
767
- 0.0,0.0,1.0,765,"y=0,a=1"
768
- 0.0,0.0,1.0,766,"y=0,a=1"
769
- 0.0,0.0,1.0,767,"y=0,a=1"
770
- 0.0,0.0,1.0,768,"y=0,a=1"
771
- 0.0,0.0,1.0,769,"y=0,a=1"
772
- 0.0,0.0,1.0,770,"y=0,a=1"
773
- 0.0,0.0,1.0,771,"y=0,a=1"
774
- 0.0,0.0,1.0,772,"y=0,a=1"
775
- 0.0,0.0,1.0,773,"y=0,a=1"
776
- 0.0,0.0,1.0,774,"y=0,a=1"
777
- 0.0,0.0,1.0,775,"y=0,a=1"
778
- 0.0,0.0,1.0,776,"y=0,a=1"
779
- 0.0,0.0,1.0,777,"y=0,a=1"
780
- 0.0,0.0,1.0,778,"y=0,a=1"
781
- 0.0,0.0,1.0,779,"y=0,a=1"
782
- 0.0,0.0,1.0,780,"y=0,a=1"
783
- 0.0,1.0,1.0,781,"y=0,a=1"
784
- 0.0,0.0,1.0,782,"y=0,a=1"
785
- 0.0,0.0,1.0,783,"y=0,a=1"
786
- 0.0,0.0,1.0,784,"y=0,a=1"
787
- 0.0,0.0,1.0,785,"y=0,a=1"
788
- 0.0,1.0,1.0,786,"y=0,a=1"
789
- 0.0,0.0,1.0,787,"y=0,a=1"
790
- 0.0,1.0,1.0,788,"y=0,a=1"
791
- 0.0,0.0,1.0,789,"y=0,a=1"
792
- 0.0,0.0,1.0,790,"y=0,a=1"
793
- 0.0,0.0,1.0,791,"y=0,a=1"
794
- 0.0,0.0,1.0,792,"y=0,a=1"
795
- 0.0,0.0,1.0,793,"y=0,a=1"
796
- 0.0,0.0,1.0,794,"y=0,a=1"
797
- 0.0,0.0,1.0,795,"y=0,a=1"
798
- 0.0,0.0,1.0,796,"y=0,a=1"
799
- 0.0,0.0,1.0,797,"y=0,a=1"
800
- 0.0,0.0,1.0,798,"y=0,a=1"
801
- 0.0,0.0,1.0,799,"y=0,a=1"
802
- 0.0,0.0,1.0,800,"y=0,a=1"
803
- 0.0,0.0,1.0,801,"y=0,a=1"
804
- 0.0,0.0,1.0,802,"y=0,a=1"
805
- 0.0,0.0,1.0,803,"y=0,a=1"
806
- 0.0,0.0,1.0,804,"y=0,a=1"
807
- 0.0,0.0,1.0,805,"y=0,a=1"
808
- 0.0,0.0,1.0,806,"y=0,a=1"
809
- 0.0,0.0,1.0,807,"y=0,a=1"
810
- 0.0,0.0,1.0,808,"y=0,a=1"
811
- 0.0,0.0,1.0,809,"y=0,a=1"
812
- 0.0,0.0,1.0,810,"y=0,a=1"
813
- 0.0,0.0,1.0,811,"y=0,a=1"
814
- 0.0,0.0,1.0,812,"y=0,a=1"
815
- 0.0,0.0,1.0,813,"y=0,a=1"
816
- 0.0,0.0,1.0,814,"y=0,a=1"
817
- 0.0,1.0,1.0,815,"y=0,a=1"
818
- 0.0,1.0,1.0,816,"y=0,a=1"
819
- 0.0,0.0,1.0,817,"y=0,a=1"
820
- 0.0,0.0,1.0,818,"y=0,a=1"
821
- 0.0,0.0,1.0,819,"y=0,a=1"
822
- 0.0,1.0,1.0,820,"y=0,a=1"
823
- 0.0,0.0,1.0,821,"y=0,a=1"
824
- 0.0,0.0,1.0,822,"y=0,a=1"
825
- 0.0,0.0,1.0,823,"y=0,a=1"
826
- 0.0,1.0,1.0,824,"y=0,a=1"
827
- 0.0,0.0,1.0,825,"y=0,a=1"
828
- 0.0,0.0,1.0,826,"y=0,a=1"
829
- 0.0,1.0,1.0,827,"y=0,a=1"
830
- 0.0,0.0,1.0,828,"y=0,a=1"
831
- 0.0,0.0,1.0,829,"y=0,a=1"
832
- 0.0,0.0,1.0,830,"y=0,a=1"
833
- 0.0,0.0,1.0,831,"y=0,a=1"
834
- 0.0,0.0,1.0,832,"y=0,a=1"
835
- 0.0,0.0,1.0,833,"y=0,a=1"
836
- 0.0,0.0,1.0,834,"y=0,a=1"
837
- 0.0,0.0,1.0,835,"y=0,a=1"
838
- 0.0,1.0,1.0,836,"y=0,a=1"
839
- 0.0,0.0,1.0,837,"y=0,a=1"
840
- 0.0,0.0,1.0,838,"y=0,a=1"
841
- 0.0,1.0,1.0,839,"y=0,a=1"
842
- 0.0,1.0,1.0,840,"y=0,a=1"
843
- 0.0,0.0,1.0,841,"y=0,a=1"
844
- 0.0,0.0,1.0,842,"y=0,a=1"
845
- 0.0,0.0,1.0,843,"y=0,a=1"
846
- 0.0,0.0,1.0,844,"y=0,a=1"
847
- 0.0,0.0,1.0,845,"y=0,a=1"
848
- 0.0,0.0,1.0,846,"y=0,a=1"
849
- 0.0,0.0,1.0,847,"y=0,a=1"
850
- 0.0,0.0,1.0,848,"y=0,a=1"
851
- 0.0,0.0,1.0,849,"y=0,a=1"
852
- 0.0,1.0,1.0,850,"y=0,a=1"
853
- 0.0,0.0,1.0,851,"y=0,a=1"
854
- 0.0,1.0,1.0,852,"y=0,a=1"
855
- 0.0,1.0,1.0,853,"y=0,a=1"
856
- 0.0,1.0,1.0,854,"y=0,a=1"
857
- 0.0,0.0,1.0,855,"y=0,a=1"
858
- 0.0,0.0,1.0,856,"y=0,a=1"
859
- 0.0,0.0,1.0,857,"y=0,a=1"
860
- 0.0,1.0,1.0,858,"y=0,a=1"
861
- 0.0,0.0,1.0,859,"y=0,a=1"
862
- 0.0,0.0,1.0,860,"y=0,a=1"
863
- 0.0,0.0,1.0,861,"y=0,a=1"
864
- 0.0,1.0,1.0,862,"y=0,a=1"
865
- 0.0,0.0,1.0,863,"y=0,a=1"
866
- 0.0,0.0,1.0,864,"y=0,a=1"
867
- 0.0,0.0,1.0,865,"y=0,a=1"
868
- 0.0,1.0,1.0,866,"y=0,a=1"
869
- 0.0,1.0,1.0,867,"y=0,a=1"
870
- 0.0,1.0,1.0,868,"y=0,a=1"
871
- 0.0,0.0,1.0,869,"y=0,a=1"
872
- 0.0,0.0,1.0,870,"y=0,a=1"
873
- 0.0,0.0,1.0,871,"y=0,a=1"
874
- 0.0,0.0,1.0,872,"y=0,a=1"
875
- 0.0,0.0,1.0,873,"y=0,a=1"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_additional_info.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:4b07ccd3a336ee56714ddb0f531e7ac32e087d3ac1d3d62c810617dcdcd5b472
3
- size 28691
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_cat_dataframe_mitigation.csv DELETED
The diff for this file is too large to render. See raw diff
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_classifier_embeddings.npy DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:4ebe9a20f38f20e750d6163ffe508861dc59c4224332cb7faa85c516106281fc
3
- size 7159936
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_clip_embeddings.npy DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:b62801f57b51672b5f202bb86ed0b442f216507d8274e1582f8443c34f236609
3
- size 895104
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/test_dog_dataframe_mitigation.csv DELETED
The diff for this file is too large to render. See raw diff
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_additional_info.csv DELETED
@@ -1,2269 +0,0 @@
1
- out_put_GT,out_put_predict,attribute_bg_predict,idx,gs
2
- 1.0,1.0,1.0,0,"y=1,a=1"
3
- 1.0,1.0,1.0,1,"y=1,a=1"
4
- 1.0,1.0,1.0,2,"y=1,a=1"
5
- 1.0,1.0,1.0,3,"y=1,a=1"
6
- 1.0,1.0,1.0,4,"y=1,a=1"
7
- 1.0,1.0,1.0,5,"y=1,a=1"
8
- 1.0,1.0,1.0,6,"y=1,a=1"
9
- 1.0,1.0,1.0,7,"y=1,a=1"
10
- 1.0,1.0,1.0,8,"y=1,a=1"
11
- 1.0,1.0,1.0,9,"y=1,a=1"
12
- 1.0,1.0,1.0,10,"y=1,a=1"
13
- 1.0,1.0,1.0,11,"y=1,a=1"
14
- 1.0,1.0,1.0,12,"y=1,a=1"
15
- 1.0,1.0,1.0,13,"y=1,a=1"
16
- 1.0,1.0,1.0,14,"y=1,a=1"
17
- 1.0,1.0,1.0,15,"y=1,a=1"
18
- 1.0,1.0,1.0,16,"y=1,a=1"
19
- 1.0,1.0,1.0,17,"y=1,a=1"
20
- 1.0,1.0,1.0,18,"y=1,a=1"
21
- 1.0,1.0,1.0,19,"y=1,a=1"
22
- 1.0,1.0,1.0,20,"y=1,a=1"
23
- 1.0,1.0,1.0,21,"y=1,a=1"
24
- 1.0,1.0,1.0,22,"y=1,a=1"
25
- 1.0,1.0,1.0,23,"y=1,a=1"
26
- 1.0,1.0,1.0,24,"y=1,a=1"
27
- 1.0,1.0,1.0,25,"y=1,a=1"
28
- 1.0,1.0,1.0,26,"y=1,a=1"
29
- 1.0,1.0,1.0,27,"y=1,a=1"
30
- 1.0,1.0,1.0,28,"y=1,a=1"
31
- 1.0,1.0,1.0,29,"y=1,a=1"
32
- 1.0,1.0,1.0,30,"y=1,a=1"
33
- 1.0,1.0,1.0,31,"y=1,a=1"
34
- 1.0,1.0,1.0,32,"y=1,a=1"
35
- 1.0,1.0,1.0,33,"y=1,a=1"
36
- 1.0,1.0,1.0,34,"y=1,a=1"
37
- 1.0,1.0,1.0,35,"y=1,a=1"
38
- 1.0,1.0,1.0,36,"y=1,a=1"
39
- 1.0,1.0,1.0,37,"y=1,a=1"
40
- 1.0,1.0,1.0,38,"y=1,a=1"
41
- 1.0,1.0,1.0,39,"y=1,a=1"
42
- 1.0,1.0,1.0,40,"y=1,a=1"
43
- 1.0,1.0,1.0,41,"y=1,a=1"
44
- 1.0,1.0,1.0,42,"y=1,a=1"
45
- 1.0,1.0,1.0,43,"y=1,a=1"
46
- 1.0,1.0,1.0,44,"y=1,a=1"
47
- 1.0,1.0,1.0,45,"y=1,a=1"
48
- 1.0,1.0,1.0,46,"y=1,a=1"
49
- 1.0,1.0,1.0,47,"y=1,a=1"
50
- 1.0,1.0,1.0,48,"y=1,a=1"
51
- 1.0,1.0,1.0,49,"y=1,a=1"
52
- 1.0,1.0,1.0,50,"y=1,a=1"
53
- 1.0,1.0,1.0,51,"y=1,a=1"
54
- 1.0,1.0,1.0,52,"y=1,a=1"
55
- 1.0,1.0,1.0,53,"y=1,a=1"
56
- 1.0,1.0,1.0,54,"y=1,a=1"
57
- 1.0,1.0,1.0,55,"y=1,a=1"
58
- 1.0,1.0,1.0,56,"y=1,a=1"
59
- 1.0,1.0,1.0,57,"y=1,a=1"
60
- 1.0,1.0,1.0,58,"y=1,a=1"
61
- 1.0,1.0,1.0,59,"y=1,a=1"
62
- 1.0,1.0,1.0,60,"y=1,a=1"
63
- 1.0,1.0,1.0,61,"y=1,a=1"
64
- 1.0,1.0,1.0,62,"y=1,a=1"
65
- 1.0,1.0,1.0,63,"y=1,a=1"
66
- 1.0,1.0,1.0,64,"y=1,a=1"
67
- 1.0,1.0,1.0,65,"y=1,a=1"
68
- 1.0,1.0,1.0,66,"y=1,a=1"
69
- 1.0,1.0,1.0,67,"y=1,a=1"
70
- 1.0,1.0,1.0,68,"y=1,a=1"
71
- 1.0,1.0,1.0,69,"y=1,a=1"
72
- 1.0,1.0,1.0,70,"y=1,a=1"
73
- 1.0,1.0,1.0,71,"y=1,a=1"
74
- 1.0,1.0,1.0,72,"y=1,a=1"
75
- 1.0,1.0,1.0,73,"y=1,a=1"
76
- 1.0,1.0,1.0,74,"y=1,a=1"
77
- 1.0,1.0,1.0,75,"y=1,a=1"
78
- 1.0,1.0,1.0,76,"y=1,a=1"
79
- 1.0,1.0,1.0,77,"y=1,a=1"
80
- 1.0,1.0,1.0,78,"y=1,a=1"
81
- 1.0,1.0,1.0,79,"y=1,a=1"
82
- 1.0,1.0,1.0,80,"y=1,a=1"
83
- 1.0,1.0,1.0,81,"y=1,a=1"
84
- 1.0,1.0,1.0,82,"y=1,a=1"
85
- 1.0,1.0,1.0,83,"y=1,a=1"
86
- 1.0,1.0,1.0,84,"y=1,a=1"
87
- 1.0,1.0,1.0,85,"y=1,a=1"
88
- 1.0,1.0,1.0,86,"y=1,a=1"
89
- 1.0,1.0,1.0,87,"y=1,a=1"
90
- 1.0,1.0,1.0,88,"y=1,a=1"
91
- 1.0,1.0,1.0,89,"y=1,a=1"
92
- 1.0,1.0,1.0,90,"y=1,a=1"
93
- 1.0,1.0,1.0,91,"y=1,a=1"
94
- 1.0,1.0,1.0,92,"y=1,a=1"
95
- 1.0,1.0,1.0,93,"y=1,a=1"
96
- 1.0,1.0,1.0,94,"y=1,a=1"
97
- 1.0,1.0,1.0,95,"y=1,a=1"
98
- 1.0,1.0,1.0,96,"y=1,a=1"
99
- 1.0,1.0,1.0,97,"y=1,a=1"
100
- 1.0,1.0,1.0,98,"y=1,a=1"
101
- 1.0,1.0,1.0,99,"y=1,a=1"
102
- 1.0,1.0,1.0,100,"y=1,a=1"
103
- 1.0,1.0,1.0,101,"y=1,a=1"
104
- 1.0,1.0,1.0,102,"y=1,a=1"
105
- 1.0,1.0,1.0,103,"y=1,a=1"
106
- 1.0,1.0,1.0,104,"y=1,a=1"
107
- 1.0,1.0,1.0,105,"y=1,a=1"
108
- 1.0,1.0,1.0,106,"y=1,a=1"
109
- 1.0,1.0,1.0,107,"y=1,a=1"
110
- 1.0,1.0,1.0,108,"y=1,a=1"
111
- 1.0,1.0,1.0,109,"y=1,a=1"
112
- 1.0,1.0,1.0,110,"y=1,a=1"
113
- 1.0,1.0,1.0,111,"y=1,a=1"
114
- 1.0,1.0,1.0,112,"y=1,a=1"
115
- 1.0,1.0,1.0,113,"y=1,a=1"
116
- 1.0,1.0,1.0,114,"y=1,a=1"
117
- 1.0,1.0,1.0,115,"y=1,a=1"
118
- 1.0,1.0,1.0,116,"y=1,a=1"
119
- 1.0,1.0,1.0,117,"y=1,a=1"
120
- 1.0,1.0,1.0,118,"y=1,a=1"
121
- 1.0,1.0,1.0,119,"y=1,a=1"
122
- 1.0,1.0,1.0,120,"y=1,a=1"
123
- 1.0,1.0,1.0,121,"y=1,a=1"
124
- 1.0,1.0,1.0,122,"y=1,a=1"
125
- 1.0,1.0,1.0,123,"y=1,a=1"
126
- 1.0,1.0,1.0,124,"y=1,a=1"
127
- 1.0,1.0,1.0,125,"y=1,a=1"
128
- 1.0,1.0,1.0,126,"y=1,a=1"
129
- 1.0,1.0,1.0,127,"y=1,a=1"
130
- 1.0,1.0,1.0,128,"y=1,a=1"
131
- 1.0,1.0,1.0,129,"y=1,a=1"
132
- 1.0,1.0,1.0,130,"y=1,a=1"
133
- 1.0,1.0,1.0,131,"y=1,a=1"
134
- 1.0,1.0,1.0,132,"y=1,a=1"
135
- 1.0,1.0,1.0,133,"y=1,a=1"
136
- 1.0,1.0,1.0,134,"y=1,a=1"
137
- 1.0,1.0,1.0,135,"y=1,a=1"
138
- 1.0,1.0,1.0,136,"y=1,a=1"
139
- 1.0,1.0,1.0,137,"y=1,a=1"
140
- 1.0,1.0,1.0,138,"y=1,a=1"
141
- 1.0,1.0,1.0,139,"y=1,a=1"
142
- 1.0,1.0,1.0,140,"y=1,a=1"
143
- 1.0,1.0,1.0,141,"y=1,a=1"
144
- 1.0,1.0,1.0,142,"y=1,a=1"
145
- 1.0,1.0,1.0,143,"y=1,a=1"
146
- 1.0,1.0,1.0,144,"y=1,a=1"
147
- 1.0,1.0,1.0,145,"y=1,a=1"
148
- 1.0,1.0,1.0,146,"y=1,a=1"
149
- 1.0,1.0,1.0,147,"y=1,a=1"
150
- 1.0,1.0,1.0,148,"y=1,a=1"
151
- 1.0,1.0,1.0,149,"y=1,a=1"
152
- 1.0,1.0,1.0,150,"y=1,a=1"
153
- 1.0,1.0,1.0,151,"y=1,a=1"
154
- 1.0,1.0,1.0,152,"y=1,a=1"
155
- 1.0,1.0,1.0,153,"y=1,a=1"
156
- 1.0,1.0,1.0,154,"y=1,a=1"
157
- 1.0,1.0,1.0,155,"y=1,a=1"
158
- 1.0,1.0,1.0,156,"y=1,a=1"
159
- 1.0,1.0,1.0,157,"y=1,a=1"
160
- 1.0,1.0,1.0,158,"y=1,a=1"
161
- 1.0,1.0,1.0,159,"y=1,a=1"
162
- 1.0,1.0,1.0,160,"y=1,a=1"
163
- 1.0,1.0,1.0,161,"y=1,a=1"
164
- 1.0,1.0,1.0,162,"y=1,a=1"
165
- 1.0,1.0,1.0,163,"y=1,a=1"
166
- 1.0,1.0,1.0,164,"y=1,a=1"
167
- 1.0,1.0,1.0,165,"y=1,a=1"
168
- 1.0,1.0,1.0,166,"y=1,a=1"
169
- 1.0,1.0,1.0,167,"y=1,a=1"
170
- 1.0,1.0,1.0,168,"y=1,a=1"
171
- 1.0,1.0,1.0,169,"y=1,a=1"
172
- 1.0,1.0,1.0,170,"y=1,a=1"
173
- 1.0,1.0,1.0,171,"y=1,a=1"
174
- 1.0,1.0,1.0,172,"y=1,a=1"
175
- 1.0,1.0,1.0,173,"y=1,a=1"
176
- 1.0,1.0,1.0,174,"y=1,a=1"
177
- 1.0,1.0,1.0,175,"y=1,a=1"
178
- 1.0,1.0,1.0,176,"y=1,a=1"
179
- 1.0,1.0,1.0,177,"y=1,a=1"
180
- 1.0,1.0,1.0,178,"y=1,a=1"
181
- 1.0,1.0,1.0,179,"y=1,a=1"
182
- 1.0,1.0,1.0,180,"y=1,a=1"
183
- 1.0,1.0,1.0,181,"y=1,a=1"
184
- 1.0,1.0,1.0,182,"y=1,a=1"
185
- 1.0,1.0,1.0,183,"y=1,a=1"
186
- 1.0,1.0,1.0,184,"y=1,a=1"
187
- 1.0,1.0,1.0,185,"y=1,a=1"
188
- 1.0,1.0,1.0,186,"y=1,a=1"
189
- 1.0,1.0,1.0,187,"y=1,a=1"
190
- 1.0,1.0,1.0,188,"y=1,a=1"
191
- 1.0,1.0,1.0,189,"y=1,a=1"
192
- 1.0,1.0,1.0,190,"y=1,a=1"
193
- 1.0,1.0,1.0,191,"y=1,a=1"
194
- 1.0,1.0,1.0,192,"y=1,a=1"
195
- 1.0,1.0,1.0,193,"y=1,a=1"
196
- 1.0,1.0,1.0,194,"y=1,a=1"
197
- 1.0,1.0,1.0,195,"y=1,a=1"
198
- 1.0,1.0,1.0,196,"y=1,a=1"
199
- 1.0,1.0,1.0,197,"y=1,a=1"
200
- 1.0,1.0,1.0,198,"y=1,a=1"
201
- 1.0,1.0,1.0,199,"y=1,a=1"
202
- 1.0,1.0,1.0,200,"y=1,a=1"
203
- 1.0,1.0,1.0,201,"y=1,a=1"
204
- 1.0,1.0,1.0,202,"y=1,a=1"
205
- 1.0,1.0,1.0,203,"y=1,a=1"
206
- 1.0,1.0,1.0,204,"y=1,a=1"
207
- 1.0,1.0,1.0,205,"y=1,a=1"
208
- 1.0,1.0,1.0,206,"y=1,a=1"
209
- 1.0,1.0,1.0,207,"y=1,a=1"
210
- 1.0,1.0,1.0,208,"y=1,a=1"
211
- 1.0,1.0,1.0,209,"y=1,a=1"
212
- 1.0,1.0,1.0,210,"y=1,a=1"
213
- 1.0,1.0,1.0,211,"y=1,a=1"
214
- 1.0,1.0,1.0,212,"y=1,a=1"
215
- 1.0,1.0,1.0,213,"y=1,a=1"
216
- 1.0,1.0,1.0,214,"y=1,a=1"
217
- 1.0,1.0,1.0,215,"y=1,a=1"
218
- 1.0,1.0,1.0,216,"y=1,a=1"
219
- 1.0,1.0,1.0,217,"y=1,a=1"
220
- 1.0,1.0,1.0,218,"y=1,a=1"
221
- 1.0,1.0,1.0,219,"y=1,a=1"
222
- 1.0,1.0,1.0,220,"y=1,a=1"
223
- 1.0,1.0,1.0,221,"y=1,a=1"
224
- 1.0,1.0,1.0,222,"y=1,a=1"
225
- 1.0,1.0,1.0,223,"y=1,a=1"
226
- 1.0,1.0,1.0,224,"y=1,a=1"
227
- 1.0,1.0,1.0,225,"y=1,a=1"
228
- 1.0,1.0,1.0,226,"y=1,a=1"
229
- 1.0,1.0,1.0,227,"y=1,a=1"
230
- 1.0,1.0,1.0,228,"y=1,a=1"
231
- 1.0,1.0,1.0,229,"y=1,a=1"
232
- 1.0,1.0,1.0,230,"y=1,a=1"
233
- 1.0,1.0,1.0,231,"y=1,a=1"
234
- 1.0,1.0,1.0,232,"y=1,a=1"
235
- 1.0,1.0,1.0,233,"y=1,a=1"
236
- 1.0,1.0,1.0,234,"y=1,a=1"
237
- 1.0,1.0,1.0,235,"y=1,a=1"
238
- 1.0,1.0,1.0,236,"y=1,a=1"
239
- 1.0,1.0,1.0,237,"y=1,a=1"
240
- 1.0,1.0,1.0,238,"y=1,a=1"
241
- 1.0,1.0,1.0,239,"y=1,a=1"
242
- 1.0,1.0,1.0,240,"y=1,a=1"
243
- 1.0,1.0,1.0,241,"y=1,a=1"
244
- 1.0,1.0,1.0,242,"y=1,a=1"
245
- 1.0,1.0,1.0,243,"y=1,a=1"
246
- 1.0,1.0,1.0,244,"y=1,a=1"
247
- 1.0,1.0,1.0,245,"y=1,a=1"
248
- 1.0,1.0,1.0,246,"y=1,a=1"
249
- 1.0,1.0,1.0,247,"y=1,a=1"
250
- 1.0,1.0,1.0,248,"y=1,a=1"
251
- 1.0,1.0,1.0,249,"y=1,a=1"
252
- 1.0,1.0,1.0,250,"y=1,a=1"
253
- 1.0,1.0,1.0,251,"y=1,a=1"
254
- 1.0,1.0,1.0,252,"y=1,a=1"
255
- 1.0,1.0,1.0,253,"y=1,a=1"
256
- 1.0,1.0,1.0,254,"y=1,a=1"
257
- 1.0,1.0,1.0,255,"y=1,a=1"
258
- 1.0,1.0,1.0,256,"y=1,a=1"
259
- 1.0,1.0,1.0,257,"y=1,a=1"
260
- 1.0,1.0,1.0,258,"y=1,a=1"
261
- 1.0,1.0,1.0,259,"y=1,a=1"
262
- 1.0,1.0,1.0,260,"y=1,a=1"
263
- 1.0,1.0,1.0,261,"y=1,a=1"
264
- 1.0,1.0,1.0,262,"y=1,a=1"
265
- 1.0,1.0,1.0,263,"y=1,a=1"
266
- 1.0,1.0,1.0,264,"y=1,a=1"
267
- 1.0,1.0,1.0,265,"y=1,a=1"
268
- 1.0,1.0,1.0,266,"y=1,a=1"
269
- 1.0,1.0,1.0,267,"y=1,a=1"
270
- 1.0,1.0,1.0,268,"y=1,a=1"
271
- 1.0,1.0,1.0,269,"y=1,a=1"
272
- 1.0,1.0,1.0,270,"y=1,a=1"
273
- 1.0,1.0,1.0,271,"y=1,a=1"
274
- 1.0,1.0,1.0,272,"y=1,a=1"
275
- 1.0,1.0,1.0,273,"y=1,a=1"
276
- 1.0,1.0,1.0,274,"y=1,a=1"
277
- 1.0,1.0,1.0,275,"y=1,a=1"
278
- 1.0,1.0,1.0,276,"y=1,a=1"
279
- 1.0,1.0,1.0,277,"y=1,a=1"
280
- 1.0,1.0,1.0,278,"y=1,a=1"
281
- 1.0,1.0,1.0,279,"y=1,a=1"
282
- 1.0,1.0,1.0,280,"y=1,a=1"
283
- 1.0,1.0,1.0,281,"y=1,a=1"
284
- 1.0,1.0,1.0,282,"y=1,a=1"
285
- 1.0,1.0,1.0,283,"y=1,a=1"
286
- 1.0,1.0,1.0,284,"y=1,a=1"
287
- 1.0,1.0,1.0,285,"y=1,a=1"
288
- 1.0,1.0,1.0,286,"y=1,a=1"
289
- 1.0,1.0,1.0,287,"y=1,a=1"
290
- 1.0,1.0,1.0,288,"y=1,a=1"
291
- 1.0,1.0,1.0,289,"y=1,a=1"
292
- 1.0,1.0,1.0,290,"y=1,a=1"
293
- 1.0,1.0,1.0,291,"y=1,a=1"
294
- 1.0,1.0,1.0,292,"y=1,a=1"
295
- 1.0,1.0,1.0,293,"y=1,a=1"
296
- 1.0,1.0,1.0,294,"y=1,a=1"
297
- 1.0,1.0,1.0,295,"y=1,a=1"
298
- 1.0,1.0,1.0,296,"y=1,a=1"
299
- 1.0,1.0,1.0,297,"y=1,a=1"
300
- 1.0,1.0,1.0,298,"y=1,a=1"
301
- 1.0,1.0,1.0,299,"y=1,a=1"
302
- 1.0,1.0,1.0,300,"y=1,a=1"
303
- 1.0,1.0,1.0,301,"y=1,a=1"
304
- 1.0,1.0,1.0,302,"y=1,a=1"
305
- 1.0,1.0,1.0,303,"y=1,a=1"
306
- 1.0,1.0,1.0,304,"y=1,a=1"
307
- 1.0,1.0,1.0,305,"y=1,a=1"
308
- 1.0,1.0,1.0,306,"y=1,a=1"
309
- 1.0,1.0,1.0,307,"y=1,a=1"
310
- 1.0,1.0,1.0,308,"y=1,a=1"
311
- 1.0,1.0,1.0,309,"y=1,a=1"
312
- 1.0,1.0,1.0,310,"y=1,a=1"
313
- 1.0,1.0,1.0,311,"y=1,a=1"
314
- 1.0,1.0,1.0,312,"y=1,a=1"
315
- 1.0,1.0,1.0,313,"y=1,a=1"
316
- 1.0,1.0,1.0,314,"y=1,a=1"
317
- 1.0,1.0,1.0,315,"y=1,a=1"
318
- 1.0,1.0,1.0,316,"y=1,a=1"
319
- 1.0,1.0,1.0,317,"y=1,a=1"
320
- 1.0,1.0,1.0,318,"y=1,a=1"
321
- 1.0,1.0,1.0,319,"y=1,a=1"
322
- 1.0,1.0,1.0,320,"y=1,a=1"
323
- 1.0,1.0,1.0,321,"y=1,a=1"
324
- 1.0,1.0,1.0,322,"y=1,a=1"
325
- 1.0,1.0,1.0,323,"y=1,a=1"
326
- 1.0,1.0,1.0,324,"y=1,a=1"
327
- 1.0,1.0,1.0,325,"y=1,a=1"
328
- 1.0,1.0,1.0,326,"y=1,a=1"
329
- 1.0,1.0,1.0,327,"y=1,a=1"
330
- 1.0,1.0,1.0,328,"y=1,a=1"
331
- 1.0,1.0,1.0,329,"y=1,a=1"
332
- 1.0,1.0,1.0,330,"y=1,a=1"
333
- 1.0,1.0,1.0,331,"y=1,a=1"
334
- 1.0,1.0,1.0,332,"y=1,a=1"
335
- 1.0,1.0,1.0,333,"y=1,a=1"
336
- 1.0,1.0,1.0,334,"y=1,a=1"
337
- 1.0,1.0,1.0,335,"y=1,a=1"
338
- 1.0,1.0,1.0,336,"y=1,a=1"
339
- 1.0,1.0,1.0,337,"y=1,a=1"
340
- 1.0,1.0,1.0,338,"y=1,a=1"
341
- 1.0,1.0,1.0,339,"y=1,a=1"
342
- 1.0,1.0,1.0,340,"y=1,a=1"
343
- 1.0,1.0,1.0,341,"y=1,a=1"
344
- 1.0,1.0,1.0,342,"y=1,a=1"
345
- 1.0,1.0,1.0,343,"y=1,a=1"
346
- 1.0,1.0,1.0,344,"y=1,a=1"
347
- 1.0,1.0,1.0,345,"y=1,a=1"
348
- 1.0,1.0,1.0,346,"y=1,a=1"
349
- 1.0,1.0,1.0,347,"y=1,a=1"
350
- 1.0,1.0,1.0,348,"y=1,a=1"
351
- 1.0,1.0,1.0,349,"y=1,a=1"
352
- 1.0,1.0,1.0,350,"y=1,a=1"
353
- 1.0,1.0,1.0,351,"y=1,a=1"
354
- 1.0,1.0,1.0,352,"y=1,a=1"
355
- 1.0,1.0,1.0,353,"y=1,a=1"
356
- 1.0,1.0,1.0,354,"y=1,a=1"
357
- 1.0,1.0,1.0,355,"y=1,a=1"
358
- 1.0,1.0,1.0,356,"y=1,a=1"
359
- 1.0,1.0,1.0,357,"y=1,a=1"
360
- 1.0,1.0,1.0,358,"y=1,a=1"
361
- 1.0,1.0,1.0,359,"y=1,a=1"
362
- 1.0,1.0,1.0,360,"y=1,a=1"
363
- 1.0,1.0,1.0,361,"y=1,a=1"
364
- 1.0,1.0,1.0,362,"y=1,a=1"
365
- 1.0,1.0,1.0,363,"y=1,a=1"
366
- 1.0,1.0,1.0,364,"y=1,a=1"
367
- 1.0,1.0,1.0,365,"y=1,a=1"
368
- 1.0,1.0,1.0,366,"y=1,a=1"
369
- 1.0,1.0,1.0,367,"y=1,a=1"
370
- 1.0,1.0,1.0,368,"y=1,a=1"
371
- 1.0,1.0,1.0,369,"y=1,a=1"
372
- 1.0,1.0,1.0,370,"y=1,a=1"
373
- 1.0,1.0,1.0,371,"y=1,a=1"
374
- 1.0,1.0,1.0,372,"y=1,a=1"
375
- 1.0,1.0,1.0,373,"y=1,a=1"
376
- 1.0,1.0,1.0,374,"y=1,a=1"
377
- 1.0,1.0,1.0,375,"y=1,a=1"
378
- 1.0,1.0,1.0,376,"y=1,a=1"
379
- 1.0,1.0,1.0,377,"y=1,a=1"
380
- 1.0,1.0,1.0,378,"y=1,a=1"
381
- 1.0,1.0,1.0,379,"y=1,a=1"
382
- 1.0,1.0,1.0,380,"y=1,a=1"
383
- 1.0,1.0,1.0,381,"y=1,a=1"
384
- 1.0,1.0,1.0,382,"y=1,a=1"
385
- 1.0,1.0,1.0,383,"y=1,a=1"
386
- 1.0,1.0,1.0,384,"y=1,a=1"
387
- 1.0,1.0,1.0,385,"y=1,a=1"
388
- 1.0,1.0,1.0,386,"y=1,a=1"
389
- 1.0,1.0,1.0,387,"y=1,a=1"
390
- 1.0,1.0,1.0,388,"y=1,a=1"
391
- 1.0,1.0,1.0,389,"y=1,a=1"
392
- 1.0,1.0,1.0,390,"y=1,a=1"
393
- 1.0,1.0,1.0,391,"y=1,a=1"
394
- 1.0,1.0,1.0,392,"y=1,a=1"
395
- 1.0,1.0,1.0,393,"y=1,a=1"
396
- 1.0,1.0,1.0,394,"y=1,a=1"
397
- 1.0,1.0,1.0,395,"y=1,a=1"
398
- 1.0,1.0,1.0,396,"y=1,a=1"
399
- 1.0,1.0,1.0,397,"y=1,a=1"
400
- 1.0,1.0,1.0,398,"y=1,a=1"
401
- 1.0,1.0,1.0,399,"y=1,a=1"
402
- 1.0,1.0,1.0,400,"y=1,a=1"
403
- 1.0,1.0,1.0,401,"y=1,a=1"
404
- 1.0,1.0,1.0,402,"y=1,a=1"
405
- 1.0,1.0,1.0,403,"y=1,a=1"
406
- 1.0,1.0,1.0,404,"y=1,a=1"
407
- 1.0,1.0,1.0,405,"y=1,a=1"
408
- 1.0,1.0,1.0,406,"y=1,a=1"
409
- 1.0,1.0,1.0,407,"y=1,a=1"
410
- 1.0,1.0,1.0,408,"y=1,a=1"
411
- 1.0,1.0,1.0,409,"y=1,a=1"
412
- 1.0,1.0,1.0,410,"y=1,a=1"
413
- 1.0,1.0,1.0,411,"y=1,a=1"
414
- 1.0,1.0,1.0,412,"y=1,a=1"
415
- 1.0,1.0,1.0,413,"y=1,a=1"
416
- 1.0,1.0,1.0,414,"y=1,a=1"
417
- 1.0,1.0,1.0,415,"y=1,a=1"
418
- 1.0,1.0,1.0,416,"y=1,a=1"
419
- 1.0,1.0,1.0,417,"y=1,a=1"
420
- 1.0,1.0,1.0,418,"y=1,a=1"
421
- 1.0,1.0,1.0,419,"y=1,a=1"
422
- 1.0,1.0,1.0,420,"y=1,a=1"
423
- 1.0,1.0,1.0,421,"y=1,a=1"
424
- 1.0,1.0,1.0,422,"y=1,a=1"
425
- 1.0,1.0,1.0,423,"y=1,a=1"
426
- 1.0,1.0,1.0,424,"y=1,a=1"
427
- 1.0,1.0,1.0,425,"y=1,a=1"
428
- 1.0,1.0,1.0,426,"y=1,a=1"
429
- 1.0,1.0,1.0,427,"y=1,a=1"
430
- 1.0,1.0,1.0,428,"y=1,a=1"
431
- 1.0,1.0,1.0,429,"y=1,a=1"
432
- 1.0,1.0,1.0,430,"y=1,a=1"
433
- 1.0,1.0,1.0,431,"y=1,a=1"
434
- 1.0,1.0,1.0,432,"y=1,a=1"
435
- 1.0,1.0,1.0,433,"y=1,a=1"
436
- 1.0,1.0,1.0,434,"y=1,a=1"
437
- 1.0,1.0,1.0,435,"y=1,a=1"
438
- 1.0,1.0,1.0,436,"y=1,a=1"
439
- 1.0,1.0,1.0,437,"y=1,a=1"
440
- 1.0,1.0,1.0,438,"y=1,a=1"
441
- 1.0,1.0,1.0,439,"y=1,a=1"
442
- 1.0,1.0,1.0,440,"y=1,a=1"
443
- 1.0,1.0,1.0,441,"y=1,a=1"
444
- 1.0,1.0,1.0,442,"y=1,a=1"
445
- 1.0,1.0,1.0,443,"y=1,a=1"
446
- 1.0,1.0,1.0,444,"y=1,a=1"
447
- 1.0,1.0,1.0,445,"y=1,a=1"
448
- 1.0,1.0,1.0,446,"y=1,a=1"
449
- 1.0,1.0,1.0,447,"y=1,a=1"
450
- 1.0,1.0,1.0,448,"y=1,a=1"
451
- 1.0,1.0,1.0,449,"y=1,a=1"
452
- 1.0,1.0,1.0,450,"y=1,a=1"
453
- 1.0,1.0,1.0,451,"y=1,a=1"
454
- 1.0,1.0,1.0,452,"y=1,a=1"
455
- 1.0,1.0,1.0,453,"y=1,a=1"
456
- 1.0,1.0,1.0,454,"y=1,a=1"
457
- 1.0,1.0,1.0,455,"y=1,a=1"
458
- 1.0,1.0,1.0,456,"y=1,a=1"
459
- 1.0,1.0,1.0,457,"y=1,a=1"
460
- 1.0,1.0,1.0,458,"y=1,a=1"
461
- 1.0,1.0,1.0,459,"y=1,a=1"
462
- 1.0,1.0,1.0,460,"y=1,a=1"
463
- 1.0,1.0,1.0,461,"y=1,a=1"
464
- 1.0,1.0,1.0,462,"y=1,a=1"
465
- 1.0,1.0,1.0,463,"y=1,a=1"
466
- 1.0,1.0,1.0,464,"y=1,a=1"
467
- 1.0,1.0,1.0,465,"y=1,a=1"
468
- 1.0,1.0,1.0,466,"y=1,a=1"
469
- 1.0,1.0,1.0,467,"y=1,a=1"
470
- 1.0,1.0,1.0,468,"y=1,a=1"
471
- 1.0,1.0,1.0,469,"y=1,a=1"
472
- 1.0,1.0,1.0,470,"y=1,a=1"
473
- 1.0,1.0,1.0,471,"y=1,a=1"
474
- 1.0,1.0,1.0,472,"y=1,a=1"
475
- 1.0,1.0,1.0,473,"y=1,a=1"
476
- 1.0,1.0,1.0,474,"y=1,a=1"
477
- 1.0,1.0,1.0,475,"y=1,a=1"
478
- 1.0,1.0,1.0,476,"y=1,a=1"
479
- 1.0,1.0,1.0,477,"y=1,a=1"
480
- 1.0,1.0,1.0,478,"y=1,a=1"
481
- 1.0,1.0,1.0,479,"y=1,a=1"
482
- 1.0,1.0,1.0,480,"y=1,a=1"
483
- 1.0,1.0,1.0,481,"y=1,a=1"
484
- 1.0,1.0,1.0,482,"y=1,a=1"
485
- 1.0,1.0,1.0,483,"y=1,a=1"
486
- 1.0,1.0,1.0,484,"y=1,a=1"
487
- 1.0,1.0,1.0,485,"y=1,a=1"
488
- 1.0,1.0,1.0,486,"y=1,a=1"
489
- 1.0,1.0,1.0,487,"y=1,a=1"
490
- 1.0,1.0,1.0,488,"y=1,a=1"
491
- 1.0,1.0,1.0,489,"y=1,a=1"
492
- 1.0,1.0,1.0,490,"y=1,a=1"
493
- 1.0,1.0,1.0,491,"y=1,a=1"
494
- 1.0,1.0,1.0,492,"y=1,a=1"
495
- 1.0,1.0,1.0,493,"y=1,a=1"
496
- 1.0,1.0,1.0,494,"y=1,a=1"
497
- 1.0,1.0,1.0,495,"y=1,a=1"
498
- 1.0,1.0,1.0,496,"y=1,a=1"
499
- 1.0,1.0,1.0,497,"y=1,a=1"
500
- 1.0,1.0,1.0,498,"y=1,a=1"
501
- 1.0,1.0,1.0,499,"y=1,a=1"
502
- 1.0,1.0,1.0,500,"y=1,a=1"
503
- 1.0,1.0,1.0,501,"y=1,a=1"
504
- 1.0,1.0,1.0,502,"y=1,a=1"
505
- 1.0,1.0,1.0,503,"y=1,a=1"
506
- 1.0,1.0,1.0,504,"y=1,a=1"
507
- 1.0,1.0,1.0,505,"y=1,a=1"
508
- 1.0,1.0,1.0,506,"y=1,a=1"
509
- 1.0,1.0,1.0,507,"y=1,a=1"
510
- 1.0,1.0,1.0,508,"y=1,a=1"
511
- 1.0,1.0,1.0,509,"y=1,a=1"
512
- 1.0,1.0,1.0,510,"y=1,a=1"
513
- 1.0,1.0,1.0,511,"y=1,a=1"
514
- 1.0,1.0,1.0,512,"y=1,a=1"
515
- 1.0,1.0,1.0,513,"y=1,a=1"
516
- 1.0,1.0,1.0,514,"y=1,a=1"
517
- 1.0,1.0,1.0,515,"y=1,a=1"
518
- 1.0,1.0,1.0,516,"y=1,a=1"
519
- 1.0,1.0,1.0,517,"y=1,a=1"
520
- 1.0,1.0,1.0,518,"y=1,a=1"
521
- 1.0,1.0,1.0,519,"y=1,a=1"
522
- 1.0,1.0,1.0,520,"y=1,a=1"
523
- 1.0,1.0,1.0,521,"y=1,a=1"
524
- 1.0,1.0,1.0,522,"y=1,a=1"
525
- 1.0,1.0,1.0,523,"y=1,a=1"
526
- 1.0,1.0,1.0,524,"y=1,a=1"
527
- 1.0,1.0,1.0,525,"y=1,a=1"
528
- 1.0,1.0,1.0,526,"y=1,a=1"
529
- 1.0,1.0,1.0,527,"y=1,a=1"
530
- 1.0,1.0,1.0,528,"y=1,a=1"
531
- 1.0,1.0,1.0,529,"y=1,a=1"
532
- 1.0,1.0,1.0,530,"y=1,a=1"
533
- 1.0,1.0,1.0,531,"y=1,a=1"
534
- 1.0,1.0,1.0,532,"y=1,a=1"
535
- 1.0,1.0,1.0,533,"y=1,a=1"
536
- 1.0,1.0,1.0,534,"y=1,a=1"
537
- 1.0,1.0,1.0,535,"y=1,a=1"
538
- 1.0,1.0,1.0,536,"y=1,a=1"
539
- 1.0,1.0,1.0,537,"y=1,a=1"
540
- 1.0,1.0,1.0,538,"y=1,a=1"
541
- 1.0,1.0,1.0,539,"y=1,a=1"
542
- 1.0,1.0,1.0,540,"y=1,a=1"
543
- 1.0,1.0,1.0,541,"y=1,a=1"
544
- 1.0,1.0,1.0,542,"y=1,a=1"
545
- 1.0,1.0,1.0,543,"y=1,a=1"
546
- 1.0,1.0,1.0,544,"y=1,a=1"
547
- 1.0,1.0,1.0,545,"y=1,a=1"
548
- 1.0,1.0,1.0,546,"y=1,a=1"
549
- 1.0,1.0,1.0,547,"y=1,a=1"
550
- 1.0,1.0,1.0,548,"y=1,a=1"
551
- 1.0,1.0,1.0,549,"y=1,a=1"
552
- 1.0,1.0,1.0,550,"y=1,a=1"
553
- 1.0,1.0,1.0,551,"y=1,a=1"
554
- 1.0,1.0,1.0,552,"y=1,a=1"
555
- 1.0,1.0,1.0,553,"y=1,a=1"
556
- 1.0,1.0,1.0,554,"y=1,a=1"
557
- 1.0,1.0,1.0,555,"y=1,a=1"
558
- 1.0,1.0,1.0,556,"y=1,a=1"
559
- 1.0,1.0,1.0,557,"y=1,a=1"
560
- 1.0,1.0,1.0,558,"y=1,a=1"
561
- 1.0,1.0,1.0,559,"y=1,a=1"
562
- 1.0,1.0,1.0,560,"y=1,a=1"
563
- 1.0,1.0,1.0,561,"y=1,a=1"
564
- 1.0,1.0,1.0,562,"y=1,a=1"
565
- 1.0,1.0,1.0,563,"y=1,a=1"
566
- 1.0,1.0,1.0,564,"y=1,a=1"
567
- 1.0,1.0,1.0,565,"y=1,a=1"
568
- 1.0,1.0,1.0,566,"y=1,a=1"
569
- 1.0,1.0,1.0,567,"y=1,a=1"
570
- 1.0,1.0,1.0,568,"y=1,a=1"
571
- 1.0,1.0,1.0,569,"y=1,a=1"
572
- 1.0,1.0,1.0,570,"y=1,a=1"
573
- 1.0,1.0,1.0,571,"y=1,a=1"
574
- 1.0,1.0,1.0,572,"y=1,a=1"
575
- 1.0,1.0,1.0,573,"y=1,a=1"
576
- 1.0,1.0,1.0,574,"y=1,a=1"
577
- 1.0,1.0,1.0,575,"y=1,a=1"
578
- 1.0,1.0,1.0,576,"y=1,a=1"
579
- 1.0,1.0,1.0,577,"y=1,a=1"
580
- 1.0,1.0,1.0,578,"y=1,a=1"
581
- 1.0,1.0,1.0,579,"y=1,a=1"
582
- 1.0,1.0,1.0,580,"y=1,a=1"
583
- 1.0,1.0,1.0,581,"y=1,a=1"
584
- 1.0,1.0,1.0,582,"y=1,a=1"
585
- 1.0,1.0,1.0,583,"y=1,a=1"
586
- 1.0,1.0,1.0,584,"y=1,a=1"
587
- 1.0,1.0,1.0,585,"y=1,a=1"
588
- 1.0,1.0,1.0,586,"y=1,a=1"
589
- 1.0,1.0,1.0,587,"y=1,a=1"
590
- 1.0,1.0,1.0,588,"y=1,a=1"
591
- 1.0,1.0,1.0,589,"y=1,a=1"
592
- 1.0,1.0,1.0,590,"y=1,a=1"
593
- 1.0,1.0,1.0,591,"y=1,a=1"
594
- 1.0,1.0,1.0,592,"y=1,a=1"
595
- 1.0,1.0,1.0,593,"y=1,a=1"
596
- 1.0,1.0,1.0,594,"y=1,a=1"
597
- 1.0,1.0,1.0,595,"y=1,a=1"
598
- 1.0,1.0,1.0,596,"y=1,a=1"
599
- 1.0,1.0,1.0,597,"y=1,a=1"
600
- 1.0,1.0,1.0,598,"y=1,a=1"
601
- 1.0,1.0,1.0,599,"y=1,a=1"
602
- 1.0,1.0,1.0,600,"y=1,a=1"
603
- 1.0,1.0,1.0,601,"y=1,a=1"
604
- 1.0,1.0,1.0,602,"y=1,a=1"
605
- 1.0,1.0,1.0,603,"y=1,a=1"
606
- 1.0,1.0,1.0,604,"y=1,a=1"
607
- 1.0,1.0,1.0,605,"y=1,a=1"
608
- 1.0,1.0,1.0,606,"y=1,a=1"
609
- 1.0,1.0,1.0,607,"y=1,a=1"
610
- 1.0,1.0,1.0,608,"y=1,a=1"
611
- 1.0,1.0,1.0,609,"y=1,a=1"
612
- 1.0,1.0,1.0,610,"y=1,a=1"
613
- 1.0,1.0,1.0,611,"y=1,a=1"
614
- 1.0,1.0,1.0,612,"y=1,a=1"
615
- 1.0,1.0,1.0,613,"y=1,a=1"
616
- 1.0,1.0,1.0,614,"y=1,a=1"
617
- 1.0,1.0,1.0,615,"y=1,a=1"
618
- 1.0,1.0,1.0,616,"y=1,a=1"
619
- 1.0,1.0,1.0,617,"y=1,a=1"
620
- 1.0,1.0,1.0,618,"y=1,a=1"
621
- 1.0,1.0,1.0,619,"y=1,a=1"
622
- 1.0,1.0,1.0,620,"y=1,a=1"
623
- 1.0,1.0,1.0,621,"y=1,a=1"
624
- 1.0,1.0,1.0,622,"y=1,a=1"
625
- 1.0,1.0,1.0,623,"y=1,a=1"
626
- 1.0,1.0,1.0,624,"y=1,a=1"
627
- 1.0,1.0,1.0,625,"y=1,a=1"
628
- 1.0,1.0,1.0,626,"y=1,a=1"
629
- 1.0,1.0,1.0,627,"y=1,a=1"
630
- 1.0,1.0,1.0,628,"y=1,a=1"
631
- 1.0,1.0,1.0,629,"y=1,a=1"
632
- 1.0,1.0,1.0,630,"y=1,a=1"
633
- 1.0,1.0,1.0,631,"y=1,a=1"
634
- 1.0,1.0,1.0,632,"y=1,a=1"
635
- 1.0,1.0,1.0,633,"y=1,a=1"
636
- 1.0,1.0,1.0,634,"y=1,a=1"
637
- 1.0,1.0,1.0,635,"y=1,a=1"
638
- 1.0,1.0,1.0,636,"y=1,a=1"
639
- 1.0,1.0,1.0,637,"y=1,a=1"
640
- 1.0,1.0,1.0,638,"y=1,a=1"
641
- 1.0,1.0,1.0,639,"y=1,a=1"
642
- 1.0,1.0,1.0,640,"y=1,a=1"
643
- 1.0,1.0,1.0,641,"y=1,a=1"
644
- 1.0,1.0,1.0,642,"y=1,a=1"
645
- 1.0,1.0,1.0,643,"y=1,a=1"
646
- 1.0,1.0,1.0,644,"y=1,a=1"
647
- 1.0,1.0,1.0,645,"y=1,a=1"
648
- 1.0,1.0,1.0,646,"y=1,a=1"
649
- 1.0,1.0,1.0,647,"y=1,a=1"
650
- 1.0,1.0,1.0,648,"y=1,a=1"
651
- 1.0,1.0,1.0,649,"y=1,a=1"
652
- 1.0,1.0,1.0,650,"y=1,a=1"
653
- 1.0,1.0,1.0,651,"y=1,a=1"
654
- 1.0,1.0,1.0,652,"y=1,a=1"
655
- 1.0,1.0,1.0,653,"y=1,a=1"
656
- 1.0,1.0,1.0,654,"y=1,a=1"
657
- 1.0,1.0,1.0,655,"y=1,a=1"
658
- 1.0,1.0,1.0,656,"y=1,a=1"
659
- 1.0,1.0,1.0,657,"y=1,a=1"
660
- 1.0,1.0,1.0,658,"y=1,a=1"
661
- 1.0,1.0,1.0,659,"y=1,a=1"
662
- 1.0,1.0,1.0,660,"y=1,a=1"
663
- 1.0,1.0,1.0,661,"y=1,a=1"
664
- 1.0,1.0,1.0,662,"y=1,a=1"
665
- 1.0,1.0,1.0,663,"y=1,a=1"
666
- 1.0,1.0,1.0,664,"y=1,a=1"
667
- 1.0,1.0,1.0,665,"y=1,a=1"
668
- 1.0,1.0,1.0,666,"y=1,a=1"
669
- 1.0,1.0,1.0,667,"y=1,a=1"
670
- 1.0,1.0,1.0,668,"y=1,a=1"
671
- 1.0,1.0,1.0,669,"y=1,a=1"
672
- 1.0,1.0,1.0,670,"y=1,a=1"
673
- 1.0,1.0,1.0,671,"y=1,a=1"
674
- 1.0,1.0,1.0,672,"y=1,a=1"
675
- 1.0,1.0,1.0,673,"y=1,a=1"
676
- 1.0,1.0,1.0,674,"y=1,a=1"
677
- 1.0,1.0,1.0,675,"y=1,a=1"
678
- 1.0,1.0,1.0,676,"y=1,a=1"
679
- 1.0,1.0,1.0,677,"y=1,a=1"
680
- 1.0,1.0,1.0,678,"y=1,a=1"
681
- 1.0,1.0,1.0,679,"y=1,a=1"
682
- 1.0,1.0,1.0,680,"y=1,a=1"
683
- 1.0,1.0,1.0,681,"y=1,a=1"
684
- 1.0,1.0,1.0,682,"y=1,a=1"
685
- 1.0,1.0,1.0,683,"y=1,a=1"
686
- 1.0,1.0,1.0,684,"y=1,a=1"
687
- 1.0,1.0,1.0,685,"y=1,a=1"
688
- 1.0,1.0,1.0,686,"y=1,a=1"
689
- 1.0,1.0,1.0,687,"y=1,a=1"
690
- 1.0,1.0,1.0,688,"y=1,a=1"
691
- 1.0,1.0,1.0,689,"y=1,a=1"
692
- 1.0,1.0,1.0,690,"y=1,a=1"
693
- 1.0,1.0,1.0,691,"y=1,a=1"
694
- 1.0,1.0,1.0,692,"y=1,a=1"
695
- 1.0,1.0,1.0,693,"y=1,a=1"
696
- 1.0,1.0,1.0,694,"y=1,a=1"
697
- 1.0,1.0,1.0,695,"y=1,a=1"
698
- 1.0,1.0,1.0,696,"y=1,a=1"
699
- 1.0,1.0,1.0,697,"y=1,a=1"
700
- 1.0,1.0,1.0,698,"y=1,a=1"
701
- 1.0,1.0,1.0,699,"y=1,a=1"
702
- 1.0,1.0,1.0,700,"y=1,a=1"
703
- 1.0,1.0,1.0,701,"y=1,a=1"
704
- 1.0,1.0,1.0,702,"y=1,a=1"
705
- 1.0,1.0,1.0,703,"y=1,a=1"
706
- 1.0,1.0,1.0,704,"y=1,a=1"
707
- 1.0,1.0,1.0,705,"y=1,a=1"
708
- 1.0,1.0,1.0,706,"y=1,a=1"
709
- 1.0,1.0,1.0,707,"y=1,a=1"
710
- 1.0,1.0,1.0,708,"y=1,a=1"
711
- 1.0,1.0,1.0,709,"y=1,a=1"
712
- 1.0,1.0,1.0,710,"y=1,a=1"
713
- 1.0,1.0,1.0,711,"y=1,a=1"
714
- 1.0,1.0,1.0,712,"y=1,a=1"
715
- 1.0,1.0,1.0,713,"y=1,a=1"
716
- 1.0,1.0,1.0,714,"y=1,a=1"
717
- 1.0,1.0,1.0,715,"y=1,a=1"
718
- 1.0,1.0,1.0,716,"y=1,a=1"
719
- 1.0,1.0,1.0,717,"y=1,a=1"
720
- 1.0,1.0,1.0,718,"y=1,a=1"
721
- 1.0,1.0,1.0,719,"y=1,a=1"
722
- 1.0,1.0,1.0,720,"y=1,a=1"
723
- 1.0,1.0,1.0,721,"y=1,a=1"
724
- 1.0,1.0,1.0,722,"y=1,a=1"
725
- 1.0,1.0,1.0,723,"y=1,a=1"
726
- 1.0,1.0,1.0,724,"y=1,a=1"
727
- 1.0,1.0,1.0,725,"y=1,a=1"
728
- 1.0,1.0,1.0,726,"y=1,a=1"
729
- 1.0,1.0,1.0,727,"y=1,a=1"
730
- 1.0,1.0,1.0,728,"y=1,a=1"
731
- 1.0,1.0,1.0,729,"y=1,a=1"
732
- 1.0,1.0,1.0,730,"y=1,a=1"
733
- 1.0,1.0,1.0,731,"y=1,a=1"
734
- 1.0,1.0,1.0,732,"y=1,a=1"
735
- 1.0,1.0,1.0,733,"y=1,a=1"
736
- 1.0,1.0,1.0,734,"y=1,a=1"
737
- 1.0,1.0,1.0,735,"y=1,a=1"
738
- 1.0,1.0,1.0,736,"y=1,a=1"
739
- 1.0,1.0,1.0,737,"y=1,a=1"
740
- 1.0,1.0,1.0,738,"y=1,a=1"
741
- 1.0,1.0,1.0,739,"y=1,a=1"
742
- 1.0,1.0,1.0,740,"y=1,a=1"
743
- 1.0,1.0,1.0,741,"y=1,a=1"
744
- 1.0,1.0,1.0,742,"y=1,a=1"
745
- 1.0,1.0,1.0,743,"y=1,a=1"
746
- 1.0,1.0,1.0,744,"y=1,a=1"
747
- 1.0,1.0,1.0,745,"y=1,a=1"
748
- 1.0,1.0,1.0,746,"y=1,a=1"
749
- 1.0,1.0,1.0,747,"y=1,a=1"
750
- 1.0,1.0,1.0,748,"y=1,a=1"
751
- 1.0,1.0,1.0,749,"y=1,a=1"
752
- 1.0,1.0,1.0,750,"y=1,a=1"
753
- 1.0,1.0,1.0,751,"y=1,a=1"
754
- 1.0,1.0,1.0,752,"y=1,a=1"
755
- 1.0,1.0,1.0,753,"y=1,a=1"
756
- 1.0,1.0,1.0,754,"y=1,a=1"
757
- 1.0,1.0,1.0,755,"y=1,a=1"
758
- 1.0,1.0,1.0,756,"y=1,a=1"
759
- 1.0,1.0,1.0,757,"y=1,a=1"
760
- 1.0,1.0,1.0,758,"y=1,a=1"
761
- 1.0,1.0,1.0,759,"y=1,a=1"
762
- 1.0,1.0,1.0,760,"y=1,a=1"
763
- 1.0,1.0,1.0,761,"y=1,a=1"
764
- 1.0,1.0,1.0,762,"y=1,a=1"
765
- 1.0,1.0,1.0,763,"y=1,a=1"
766
- 1.0,1.0,1.0,764,"y=1,a=1"
767
- 1.0,1.0,1.0,765,"y=1,a=1"
768
- 1.0,1.0,1.0,766,"y=1,a=1"
769
- 1.0,1.0,1.0,767,"y=1,a=1"
770
- 1.0,1.0,1.0,768,"y=1,a=1"
771
- 1.0,1.0,1.0,769,"y=1,a=1"
772
- 1.0,1.0,1.0,770,"y=1,a=1"
773
- 1.0,1.0,1.0,771,"y=1,a=1"
774
- 1.0,1.0,1.0,772,"y=1,a=1"
775
- 1.0,1.0,1.0,773,"y=1,a=1"
776
- 1.0,1.0,1.0,774,"y=1,a=1"
777
- 1.0,1.0,1.0,775,"y=1,a=1"
778
- 1.0,1.0,1.0,776,"y=1,a=1"
779
- 1.0,1.0,1.0,777,"y=1,a=1"
780
- 1.0,1.0,1.0,778,"y=1,a=1"
781
- 1.0,1.0,1.0,779,"y=1,a=1"
782
- 1.0,1.0,1.0,780,"y=1,a=1"
783
- 1.0,1.0,1.0,781,"y=1,a=1"
784
- 1.0,1.0,1.0,782,"y=1,a=1"
785
- 1.0,1.0,1.0,783,"y=1,a=1"
786
- 1.0,1.0,1.0,784,"y=1,a=1"
787
- 1.0,1.0,1.0,785,"y=1,a=1"
788
- 1.0,1.0,1.0,786,"y=1,a=1"
789
- 1.0,1.0,1.0,787,"y=1,a=1"
790
- 1.0,1.0,1.0,788,"y=1,a=1"
791
- 0.0,0.0,0.0,789,"y=0,a=0"
792
- 0.0,0.0,0.0,790,"y=0,a=0"
793
- 0.0,0.0,0.0,791,"y=0,a=0"
794
- 0.0,0.0,0.0,792,"y=0,a=0"
795
- 0.0,0.0,0.0,793,"y=0,a=0"
796
- 0.0,0.0,0.0,794,"y=0,a=0"
797
- 0.0,0.0,0.0,795,"y=0,a=0"
798
- 0.0,0.0,0.0,796,"y=0,a=0"
799
- 0.0,0.0,0.0,797,"y=0,a=0"
800
- 0.0,0.0,0.0,798,"y=0,a=0"
801
- 0.0,0.0,0.0,799,"y=0,a=0"
802
- 0.0,0.0,0.0,800,"y=0,a=0"
803
- 0.0,0.0,0.0,801,"y=0,a=0"
804
- 0.0,0.0,0.0,802,"y=0,a=0"
805
- 0.0,0.0,0.0,803,"y=0,a=0"
806
- 0.0,0.0,0.0,804,"y=0,a=0"
807
- 0.0,0.0,0.0,805,"y=0,a=0"
808
- 0.0,0.0,0.0,806,"y=0,a=0"
809
- 0.0,0.0,0.0,807,"y=0,a=0"
810
- 0.0,0.0,0.0,808,"y=0,a=0"
811
- 0.0,0.0,0.0,809,"y=0,a=0"
812
- 0.0,0.0,0.0,810,"y=0,a=0"
813
- 0.0,0.0,0.0,811,"y=0,a=0"
814
- 0.0,0.0,0.0,812,"y=0,a=0"
815
- 0.0,0.0,0.0,813,"y=0,a=0"
816
- 0.0,0.0,0.0,814,"y=0,a=0"
817
- 0.0,0.0,0.0,815,"y=0,a=0"
818
- 0.0,0.0,0.0,816,"y=0,a=0"
819
- 0.0,0.0,0.0,817,"y=0,a=0"
820
- 0.0,0.0,0.0,818,"y=0,a=0"
821
- 0.0,0.0,0.0,819,"y=0,a=0"
822
- 0.0,0.0,0.0,820,"y=0,a=0"
823
- 0.0,0.0,0.0,821,"y=0,a=0"
824
- 0.0,0.0,0.0,822,"y=0,a=0"
825
- 0.0,0.0,0.0,823,"y=0,a=0"
826
- 0.0,0.0,0.0,824,"y=0,a=0"
827
- 0.0,0.0,0.0,825,"y=0,a=0"
828
- 0.0,0.0,0.0,826,"y=0,a=0"
829
- 0.0,0.0,0.0,827,"y=0,a=0"
830
- 0.0,0.0,0.0,828,"y=0,a=0"
831
- 0.0,0.0,0.0,829,"y=0,a=0"
832
- 0.0,0.0,0.0,830,"y=0,a=0"
833
- 0.0,0.0,0.0,831,"y=0,a=0"
834
- 0.0,0.0,0.0,832,"y=0,a=0"
835
- 0.0,0.0,0.0,833,"y=0,a=0"
836
- 0.0,0.0,0.0,834,"y=0,a=0"
837
- 0.0,0.0,0.0,835,"y=0,a=0"
838
- 0.0,0.0,0.0,836,"y=0,a=0"
839
- 0.0,0.0,0.0,837,"y=0,a=0"
840
- 0.0,0.0,0.0,838,"y=0,a=0"
841
- 0.0,0.0,0.0,839,"y=0,a=0"
842
- 0.0,0.0,0.0,840,"y=0,a=0"
843
- 0.0,0.0,0.0,841,"y=0,a=0"
844
- 0.0,0.0,0.0,842,"y=0,a=0"
845
- 0.0,0.0,0.0,843,"y=0,a=0"
846
- 0.0,0.0,0.0,844,"y=0,a=0"
847
- 0.0,0.0,0.0,845,"y=0,a=0"
848
- 0.0,0.0,0.0,846,"y=0,a=0"
849
- 0.0,0.0,0.0,847,"y=0,a=0"
850
- 0.0,0.0,0.0,848,"y=0,a=0"
851
- 0.0,0.0,0.0,849,"y=0,a=0"
852
- 0.0,0.0,0.0,850,"y=0,a=0"
853
- 0.0,0.0,0.0,851,"y=0,a=0"
854
- 0.0,0.0,0.0,852,"y=0,a=0"
855
- 0.0,0.0,0.0,853,"y=0,a=0"
856
- 0.0,0.0,0.0,854,"y=0,a=0"
857
- 0.0,0.0,0.0,855,"y=0,a=0"
858
- 0.0,0.0,0.0,856,"y=0,a=0"
859
- 0.0,0.0,0.0,857,"y=0,a=0"
860
- 0.0,0.0,0.0,858,"y=0,a=0"
861
- 0.0,0.0,0.0,859,"y=0,a=0"
862
- 0.0,0.0,0.0,860,"y=0,a=0"
863
- 0.0,0.0,0.0,861,"y=0,a=0"
864
- 0.0,0.0,0.0,862,"y=0,a=0"
865
- 0.0,0.0,0.0,863,"y=0,a=0"
866
- 0.0,0.0,0.0,864,"y=0,a=0"
867
- 0.0,0.0,0.0,865,"y=0,a=0"
868
- 0.0,0.0,0.0,866,"y=0,a=0"
869
- 0.0,0.0,0.0,867,"y=0,a=0"
870
- 0.0,0.0,0.0,868,"y=0,a=0"
871
- 0.0,0.0,0.0,869,"y=0,a=0"
872
- 0.0,0.0,0.0,870,"y=0,a=0"
873
- 0.0,0.0,0.0,871,"y=0,a=0"
874
- 0.0,0.0,0.0,872,"y=0,a=0"
875
- 0.0,0.0,0.0,873,"y=0,a=0"
876
- 0.0,0.0,0.0,874,"y=0,a=0"
877
- 0.0,0.0,0.0,875,"y=0,a=0"
878
- 0.0,0.0,0.0,876,"y=0,a=0"
879
- 0.0,0.0,0.0,877,"y=0,a=0"
880
- 0.0,0.0,0.0,878,"y=0,a=0"
881
- 0.0,0.0,0.0,879,"y=0,a=0"
882
- 0.0,0.0,0.0,880,"y=0,a=0"
883
- 0.0,0.0,0.0,881,"y=0,a=0"
884
- 0.0,0.0,0.0,882,"y=0,a=0"
885
- 0.0,0.0,0.0,883,"y=0,a=0"
886
- 0.0,0.0,0.0,884,"y=0,a=0"
887
- 0.0,0.0,0.0,885,"y=0,a=0"
888
- 0.0,0.0,0.0,886,"y=0,a=0"
889
- 0.0,0.0,0.0,887,"y=0,a=0"
890
- 0.0,0.0,0.0,888,"y=0,a=0"
891
- 0.0,0.0,0.0,889,"y=0,a=0"
892
- 0.0,0.0,0.0,890,"y=0,a=0"
893
- 0.0,0.0,0.0,891,"y=0,a=0"
894
- 0.0,0.0,0.0,892,"y=0,a=0"
895
- 0.0,0.0,0.0,893,"y=0,a=0"
896
- 0.0,0.0,0.0,894,"y=0,a=0"
897
- 0.0,0.0,0.0,895,"y=0,a=0"
898
- 0.0,0.0,0.0,896,"y=0,a=0"
899
- 0.0,0.0,0.0,897,"y=0,a=0"
900
- 0.0,0.0,0.0,898,"y=0,a=0"
901
- 0.0,0.0,0.0,899,"y=0,a=0"
902
- 0.0,0.0,0.0,900,"y=0,a=0"
903
- 0.0,0.0,0.0,901,"y=0,a=0"
904
- 0.0,0.0,0.0,902,"y=0,a=0"
905
- 0.0,0.0,0.0,903,"y=0,a=0"
906
- 0.0,0.0,0.0,904,"y=0,a=0"
907
- 0.0,0.0,0.0,905,"y=0,a=0"
908
- 0.0,0.0,0.0,906,"y=0,a=0"
909
- 0.0,0.0,0.0,907,"y=0,a=0"
910
- 0.0,0.0,0.0,908,"y=0,a=0"
911
- 0.0,0.0,0.0,909,"y=0,a=0"
912
- 0.0,0.0,0.0,910,"y=0,a=0"
913
- 0.0,0.0,0.0,911,"y=0,a=0"
914
- 0.0,0.0,0.0,912,"y=0,a=0"
915
- 0.0,0.0,0.0,913,"y=0,a=0"
916
- 0.0,0.0,0.0,914,"y=0,a=0"
917
- 0.0,0.0,0.0,915,"y=0,a=0"
918
- 0.0,0.0,0.0,916,"y=0,a=0"
919
- 0.0,0.0,0.0,917,"y=0,a=0"
920
- 0.0,0.0,0.0,918,"y=0,a=0"
921
- 0.0,0.0,0.0,919,"y=0,a=0"
922
- 0.0,0.0,0.0,920,"y=0,a=0"
923
- 0.0,0.0,0.0,921,"y=0,a=0"
924
- 0.0,0.0,0.0,922,"y=0,a=0"
925
- 0.0,0.0,0.0,923,"y=0,a=0"
926
- 0.0,0.0,0.0,924,"y=0,a=0"
927
- 0.0,0.0,0.0,925,"y=0,a=0"
928
- 0.0,0.0,0.0,926,"y=0,a=0"
929
- 0.0,0.0,0.0,927,"y=0,a=0"
930
- 0.0,0.0,0.0,928,"y=0,a=0"
931
- 0.0,0.0,0.0,929,"y=0,a=0"
932
- 0.0,0.0,0.0,930,"y=0,a=0"
933
- 0.0,0.0,0.0,931,"y=0,a=0"
934
- 0.0,0.0,0.0,932,"y=0,a=0"
935
- 0.0,0.0,0.0,933,"y=0,a=0"
936
- 0.0,0.0,0.0,934,"y=0,a=0"
937
- 0.0,0.0,0.0,935,"y=0,a=0"
938
- 0.0,0.0,0.0,936,"y=0,a=0"
939
- 0.0,0.0,0.0,937,"y=0,a=0"
940
- 0.0,0.0,0.0,938,"y=0,a=0"
941
- 0.0,0.0,0.0,939,"y=0,a=0"
942
- 0.0,0.0,0.0,940,"y=0,a=0"
943
- 0.0,0.0,0.0,941,"y=0,a=0"
944
- 0.0,0.0,0.0,942,"y=0,a=0"
945
- 0.0,0.0,0.0,943,"y=0,a=0"
946
- 0.0,0.0,0.0,944,"y=0,a=0"
947
- 0.0,0.0,0.0,945,"y=0,a=0"
948
- 0.0,0.0,0.0,946,"y=0,a=0"
949
- 0.0,0.0,0.0,947,"y=0,a=0"
950
- 0.0,0.0,0.0,948,"y=0,a=0"
951
- 0.0,0.0,0.0,949,"y=0,a=0"
952
- 0.0,0.0,0.0,950,"y=0,a=0"
953
- 0.0,0.0,0.0,951,"y=0,a=0"
954
- 0.0,0.0,0.0,952,"y=0,a=0"
955
- 0.0,0.0,0.0,953,"y=0,a=0"
956
- 0.0,0.0,0.0,954,"y=0,a=0"
957
- 0.0,0.0,0.0,955,"y=0,a=0"
958
- 0.0,0.0,0.0,956,"y=0,a=0"
959
- 0.0,0.0,0.0,957,"y=0,a=0"
960
- 0.0,0.0,0.0,958,"y=0,a=0"
961
- 0.0,0.0,0.0,959,"y=0,a=0"
962
- 0.0,0.0,0.0,960,"y=0,a=0"
963
- 0.0,0.0,0.0,961,"y=0,a=0"
964
- 0.0,0.0,0.0,962,"y=0,a=0"
965
- 0.0,0.0,0.0,963,"y=0,a=0"
966
- 0.0,0.0,0.0,964,"y=0,a=0"
967
- 0.0,0.0,0.0,965,"y=0,a=0"
968
- 0.0,0.0,0.0,966,"y=0,a=0"
969
- 0.0,0.0,0.0,967,"y=0,a=0"
970
- 0.0,0.0,0.0,968,"y=0,a=0"
971
- 0.0,0.0,0.0,969,"y=0,a=0"
972
- 0.0,0.0,0.0,970,"y=0,a=0"
973
- 0.0,0.0,0.0,971,"y=0,a=0"
974
- 0.0,0.0,0.0,972,"y=0,a=0"
975
- 0.0,0.0,0.0,973,"y=0,a=0"
976
- 0.0,0.0,0.0,974,"y=0,a=0"
977
- 0.0,0.0,0.0,975,"y=0,a=0"
978
- 0.0,0.0,0.0,976,"y=0,a=0"
979
- 0.0,0.0,0.0,977,"y=0,a=0"
980
- 0.0,0.0,0.0,978,"y=0,a=0"
981
- 0.0,0.0,0.0,979,"y=0,a=0"
982
- 0.0,0.0,0.0,980,"y=0,a=0"
983
- 0.0,0.0,0.0,981,"y=0,a=0"
984
- 0.0,0.0,0.0,982,"y=0,a=0"
985
- 0.0,0.0,0.0,983,"y=0,a=0"
986
- 0.0,0.0,0.0,984,"y=0,a=0"
987
- 0.0,0.0,0.0,985,"y=0,a=0"
988
- 0.0,0.0,0.0,986,"y=0,a=0"
989
- 0.0,0.0,0.0,987,"y=0,a=0"
990
- 0.0,0.0,0.0,988,"y=0,a=0"
991
- 0.0,0.0,0.0,989,"y=0,a=0"
992
- 0.0,0.0,0.0,990,"y=0,a=0"
993
- 0.0,0.0,0.0,991,"y=0,a=0"
994
- 0.0,0.0,0.0,992,"y=0,a=0"
995
- 0.0,0.0,0.0,993,"y=0,a=0"
996
- 0.0,0.0,0.0,994,"y=0,a=0"
997
- 0.0,0.0,0.0,995,"y=0,a=0"
998
- 0.0,0.0,0.0,996,"y=0,a=0"
999
- 0.0,0.0,0.0,997,"y=0,a=0"
1000
- 0.0,0.0,0.0,998,"y=0,a=0"
1001
- 0.0,0.0,0.0,999,"y=0,a=0"
1002
- 0.0,0.0,0.0,1000,"y=0,a=0"
1003
- 0.0,0.0,0.0,1001,"y=0,a=0"
1004
- 0.0,0.0,0.0,1002,"y=0,a=0"
1005
- 0.0,0.0,0.0,1003,"y=0,a=0"
1006
- 0.0,0.0,0.0,1004,"y=0,a=0"
1007
- 0.0,0.0,0.0,1005,"y=0,a=0"
1008
- 0.0,0.0,0.0,1006,"y=0,a=0"
1009
- 0.0,0.0,0.0,1007,"y=0,a=0"
1010
- 0.0,0.0,0.0,1008,"y=0,a=0"
1011
- 0.0,0.0,0.0,1009,"y=0,a=0"
1012
- 0.0,0.0,0.0,1010,"y=0,a=0"
1013
- 0.0,0.0,0.0,1011,"y=0,a=0"
1014
- 0.0,0.0,0.0,1012,"y=0,a=0"
1015
- 0.0,0.0,0.0,1013,"y=0,a=0"
1016
- 0.0,0.0,0.0,1014,"y=0,a=0"
1017
- 0.0,0.0,0.0,1015,"y=0,a=0"
1018
- 0.0,0.0,0.0,1016,"y=0,a=0"
1019
- 0.0,0.0,0.0,1017,"y=0,a=0"
1020
- 0.0,0.0,0.0,1018,"y=0,a=0"
1021
- 0.0,0.0,0.0,1019,"y=0,a=0"
1022
- 0.0,0.0,0.0,1020,"y=0,a=0"
1023
- 0.0,0.0,0.0,1021,"y=0,a=0"
1024
- 0.0,0.0,0.0,1022,"y=0,a=0"
1025
- 0.0,0.0,0.0,1023,"y=0,a=0"
1026
- 0.0,0.0,0.0,1024,"y=0,a=0"
1027
- 0.0,0.0,0.0,1025,"y=0,a=0"
1028
- 0.0,0.0,0.0,1026,"y=0,a=0"
1029
- 0.0,0.0,0.0,1027,"y=0,a=0"
1030
- 0.0,0.0,0.0,1028,"y=0,a=0"
1031
- 0.0,0.0,0.0,1029,"y=0,a=0"
1032
- 0.0,0.0,0.0,1030,"y=0,a=0"
1033
- 0.0,0.0,0.0,1031,"y=0,a=0"
1034
- 0.0,0.0,0.0,1032,"y=0,a=0"
1035
- 0.0,0.0,0.0,1033,"y=0,a=0"
1036
- 0.0,0.0,0.0,1034,"y=0,a=0"
1037
- 0.0,0.0,0.0,1035,"y=0,a=0"
1038
- 0.0,0.0,0.0,1036,"y=0,a=0"
1039
- 0.0,0.0,0.0,1037,"y=0,a=0"
1040
- 0.0,0.0,0.0,1038,"y=0,a=0"
1041
- 0.0,0.0,0.0,1039,"y=0,a=0"
1042
- 0.0,0.0,0.0,1040,"y=0,a=0"
1043
- 0.0,0.0,0.0,1041,"y=0,a=0"
1044
- 0.0,0.0,0.0,1042,"y=0,a=0"
1045
- 0.0,0.0,0.0,1043,"y=0,a=0"
1046
- 0.0,0.0,0.0,1044,"y=0,a=0"
1047
- 0.0,0.0,0.0,1045,"y=0,a=0"
1048
- 0.0,0.0,0.0,1046,"y=0,a=0"
1049
- 0.0,0.0,0.0,1047,"y=0,a=0"
1050
- 0.0,0.0,0.0,1048,"y=0,a=0"
1051
- 0.0,0.0,0.0,1049,"y=0,a=0"
1052
- 0.0,0.0,0.0,1050,"y=0,a=0"
1053
- 0.0,0.0,0.0,1051,"y=0,a=0"
1054
- 0.0,0.0,0.0,1052,"y=0,a=0"
1055
- 0.0,0.0,0.0,1053,"y=0,a=0"
1056
- 0.0,0.0,0.0,1054,"y=0,a=0"
1057
- 0.0,0.0,0.0,1055,"y=0,a=0"
1058
- 0.0,0.0,0.0,1056,"y=0,a=0"
1059
- 0.0,0.0,0.0,1057,"y=0,a=0"
1060
- 0.0,0.0,0.0,1058,"y=0,a=0"
1061
- 0.0,0.0,0.0,1059,"y=0,a=0"
1062
- 0.0,0.0,0.0,1060,"y=0,a=0"
1063
- 0.0,0.0,0.0,1061,"y=0,a=0"
1064
- 0.0,0.0,0.0,1062,"y=0,a=0"
1065
- 0.0,0.0,0.0,1063,"y=0,a=0"
1066
- 0.0,0.0,0.0,1064,"y=0,a=0"
1067
- 0.0,0.0,0.0,1065,"y=0,a=0"
1068
- 0.0,0.0,0.0,1066,"y=0,a=0"
1069
- 0.0,0.0,0.0,1067,"y=0,a=0"
1070
- 0.0,0.0,0.0,1068,"y=0,a=0"
1071
- 0.0,0.0,0.0,1069,"y=0,a=0"
1072
- 0.0,0.0,0.0,1070,"y=0,a=0"
1073
- 0.0,0.0,0.0,1071,"y=0,a=0"
1074
- 0.0,0.0,0.0,1072,"y=0,a=0"
1075
- 0.0,0.0,0.0,1073,"y=0,a=0"
1076
- 0.0,0.0,0.0,1074,"y=0,a=0"
1077
- 0.0,0.0,0.0,1075,"y=0,a=0"
1078
- 0.0,0.0,0.0,1076,"y=0,a=0"
1079
- 0.0,0.0,0.0,1077,"y=0,a=0"
1080
- 0.0,0.0,0.0,1078,"y=0,a=0"
1081
- 0.0,0.0,0.0,1079,"y=0,a=0"
1082
- 0.0,0.0,0.0,1080,"y=0,a=0"
1083
- 0.0,0.0,0.0,1081,"y=0,a=0"
1084
- 0.0,0.0,0.0,1082,"y=0,a=0"
1085
- 0.0,0.0,0.0,1083,"y=0,a=0"
1086
- 0.0,0.0,0.0,1084,"y=0,a=0"
1087
- 0.0,0.0,0.0,1085,"y=0,a=0"
1088
- 0.0,0.0,0.0,1086,"y=0,a=0"
1089
- 0.0,0.0,0.0,1087,"y=0,a=0"
1090
- 0.0,0.0,0.0,1088,"y=0,a=0"
1091
- 0.0,0.0,0.0,1089,"y=0,a=0"
1092
- 0.0,0.0,0.0,1090,"y=0,a=0"
1093
- 0.0,0.0,0.0,1091,"y=0,a=0"
1094
- 0.0,0.0,0.0,1092,"y=0,a=0"
1095
- 0.0,0.0,0.0,1093,"y=0,a=0"
1096
- 0.0,0.0,0.0,1094,"y=0,a=0"
1097
- 0.0,0.0,0.0,1095,"y=0,a=0"
1098
- 0.0,0.0,0.0,1096,"y=0,a=0"
1099
- 0.0,0.0,0.0,1097,"y=0,a=0"
1100
- 0.0,0.0,0.0,1098,"y=0,a=0"
1101
- 0.0,0.0,0.0,1099,"y=0,a=0"
1102
- 0.0,0.0,0.0,1100,"y=0,a=0"
1103
- 0.0,0.0,0.0,1101,"y=0,a=0"
1104
- 0.0,0.0,0.0,1102,"y=0,a=0"
1105
- 0.0,0.0,0.0,1103,"y=0,a=0"
1106
- 0.0,0.0,0.0,1104,"y=0,a=0"
1107
- 0.0,0.0,0.0,1105,"y=0,a=0"
1108
- 0.0,0.0,0.0,1106,"y=0,a=0"
1109
- 0.0,0.0,0.0,1107,"y=0,a=0"
1110
- 0.0,0.0,0.0,1108,"y=0,a=0"
1111
- 0.0,0.0,0.0,1109,"y=0,a=0"
1112
- 0.0,0.0,0.0,1110,"y=0,a=0"
1113
- 0.0,0.0,0.0,1111,"y=0,a=0"
1114
- 0.0,0.0,0.0,1112,"y=0,a=0"
1115
- 0.0,0.0,0.0,1113,"y=0,a=0"
1116
- 0.0,0.0,0.0,1114,"y=0,a=0"
1117
- 0.0,0.0,0.0,1115,"y=0,a=0"
1118
- 0.0,0.0,0.0,1116,"y=0,a=0"
1119
- 0.0,0.0,0.0,1117,"y=0,a=0"
1120
- 0.0,0.0,0.0,1118,"y=0,a=0"
1121
- 0.0,0.0,0.0,1119,"y=0,a=0"
1122
- 0.0,0.0,0.0,1120,"y=0,a=0"
1123
- 0.0,0.0,0.0,1121,"y=0,a=0"
1124
- 0.0,0.0,0.0,1122,"y=0,a=0"
1125
- 0.0,0.0,0.0,1123,"y=0,a=0"
1126
- 0.0,0.0,0.0,1124,"y=0,a=0"
1127
- 0.0,0.0,0.0,1125,"y=0,a=0"
1128
- 0.0,0.0,0.0,1126,"y=0,a=0"
1129
- 0.0,0.0,0.0,1127,"y=0,a=0"
1130
- 0.0,0.0,0.0,1128,"y=0,a=0"
1131
- 0.0,0.0,0.0,1129,"y=0,a=0"
1132
- 0.0,0.0,0.0,1130,"y=0,a=0"
1133
- 0.0,0.0,0.0,1131,"y=0,a=0"
1134
- 0.0,0.0,0.0,1132,"y=0,a=0"
1135
- 0.0,0.0,0.0,1133,"y=0,a=0"
1136
- 0.0,0.0,0.0,1134,"y=0,a=0"
1137
- 0.0,0.0,0.0,1135,"y=0,a=0"
1138
- 0.0,0.0,0.0,1136,"y=0,a=0"
1139
- 0.0,0.0,0.0,1137,"y=0,a=0"
1140
- 0.0,0.0,0.0,1138,"y=0,a=0"
1141
- 0.0,0.0,0.0,1139,"y=0,a=0"
1142
- 0.0,0.0,0.0,1140,"y=0,a=0"
1143
- 0.0,0.0,0.0,1141,"y=0,a=0"
1144
- 0.0,0.0,0.0,1142,"y=0,a=0"
1145
- 0.0,0.0,0.0,1143,"y=0,a=0"
1146
- 0.0,0.0,0.0,1144,"y=0,a=0"
1147
- 0.0,0.0,0.0,1145,"y=0,a=0"
1148
- 0.0,0.0,0.0,1146,"y=0,a=0"
1149
- 0.0,0.0,0.0,1147,"y=0,a=0"
1150
- 0.0,0.0,0.0,1148,"y=0,a=0"
1151
- 0.0,0.0,0.0,1149,"y=0,a=0"
1152
- 0.0,0.0,0.0,1150,"y=0,a=0"
1153
- 0.0,0.0,0.0,1151,"y=0,a=0"
1154
- 0.0,0.0,0.0,1152,"y=0,a=0"
1155
- 0.0,0.0,0.0,1153,"y=0,a=0"
1156
- 0.0,0.0,0.0,1154,"y=0,a=0"
1157
- 0.0,0.0,0.0,1155,"y=0,a=0"
1158
- 0.0,0.0,0.0,1156,"y=0,a=0"
1159
- 0.0,0.0,0.0,1157,"y=0,a=0"
1160
- 0.0,0.0,0.0,1158,"y=0,a=0"
1161
- 0.0,0.0,0.0,1159,"y=0,a=0"
1162
- 0.0,0.0,0.0,1160,"y=0,a=0"
1163
- 0.0,0.0,0.0,1161,"y=0,a=0"
1164
- 0.0,0.0,0.0,1162,"y=0,a=0"
1165
- 0.0,0.0,0.0,1163,"y=0,a=0"
1166
- 0.0,0.0,0.0,1164,"y=0,a=0"
1167
- 0.0,0.0,0.0,1165,"y=0,a=0"
1168
- 0.0,0.0,0.0,1166,"y=0,a=0"
1169
- 0.0,0.0,0.0,1167,"y=0,a=0"
1170
- 0.0,0.0,0.0,1168,"y=0,a=0"
1171
- 0.0,0.0,0.0,1169,"y=0,a=0"
1172
- 0.0,0.0,0.0,1170,"y=0,a=0"
1173
- 0.0,0.0,0.0,1171,"y=0,a=0"
1174
- 0.0,0.0,0.0,1172,"y=0,a=0"
1175
- 0.0,0.0,0.0,1173,"y=0,a=0"
1176
- 0.0,0.0,0.0,1174,"y=0,a=0"
1177
- 0.0,0.0,0.0,1175,"y=0,a=0"
1178
- 0.0,0.0,0.0,1176,"y=0,a=0"
1179
- 0.0,0.0,0.0,1177,"y=0,a=0"
1180
- 0.0,0.0,0.0,1178,"y=0,a=0"
1181
- 0.0,0.0,0.0,1179,"y=0,a=0"
1182
- 0.0,0.0,0.0,1180,"y=0,a=0"
1183
- 0.0,0.0,0.0,1181,"y=0,a=0"
1184
- 0.0,0.0,0.0,1182,"y=0,a=0"
1185
- 0.0,0.0,0.0,1183,"y=0,a=0"
1186
- 0.0,0.0,0.0,1184,"y=0,a=0"
1187
- 0.0,0.0,0.0,1185,"y=0,a=0"
1188
- 0.0,0.0,0.0,1186,"y=0,a=0"
1189
- 0.0,0.0,0.0,1187,"y=0,a=0"
1190
- 0.0,0.0,0.0,1188,"y=0,a=0"
1191
- 0.0,0.0,0.0,1189,"y=0,a=0"
1192
- 0.0,0.0,0.0,1190,"y=0,a=0"
1193
- 0.0,0.0,0.0,1191,"y=0,a=0"
1194
- 0.0,0.0,0.0,1192,"y=0,a=0"
1195
- 0.0,0.0,0.0,1193,"y=0,a=0"
1196
- 0.0,0.0,0.0,1194,"y=0,a=0"
1197
- 0.0,0.0,0.0,1195,"y=0,a=0"
1198
- 0.0,0.0,0.0,1196,"y=0,a=0"
1199
- 0.0,0.0,0.0,1197,"y=0,a=0"
1200
- 0.0,0.0,0.0,1198,"y=0,a=0"
1201
- 0.0,0.0,0.0,1199,"y=0,a=0"
1202
- 0.0,0.0,0.0,1200,"y=0,a=0"
1203
- 0.0,0.0,0.0,1201,"y=0,a=0"
1204
- 0.0,0.0,0.0,1202,"y=0,a=0"
1205
- 0.0,0.0,0.0,1203,"y=0,a=0"
1206
- 0.0,0.0,0.0,1204,"y=0,a=0"
1207
- 0.0,0.0,0.0,1205,"y=0,a=0"
1208
- 0.0,0.0,0.0,1206,"y=0,a=0"
1209
- 0.0,0.0,0.0,1207,"y=0,a=0"
1210
- 0.0,0.0,0.0,1208,"y=0,a=0"
1211
- 0.0,0.0,0.0,1209,"y=0,a=0"
1212
- 0.0,0.0,0.0,1210,"y=0,a=0"
1213
- 0.0,0.0,0.0,1211,"y=0,a=0"
1214
- 0.0,0.0,0.0,1212,"y=0,a=0"
1215
- 0.0,0.0,0.0,1213,"y=0,a=0"
1216
- 0.0,0.0,0.0,1214,"y=0,a=0"
1217
- 0.0,0.0,0.0,1215,"y=0,a=0"
1218
- 0.0,0.0,0.0,1216,"y=0,a=0"
1219
- 0.0,0.0,0.0,1217,"y=0,a=0"
1220
- 0.0,0.0,0.0,1218,"y=0,a=0"
1221
- 0.0,0.0,0.0,1219,"y=0,a=0"
1222
- 0.0,0.0,0.0,1220,"y=0,a=0"
1223
- 0.0,0.0,0.0,1221,"y=0,a=0"
1224
- 0.0,0.0,0.0,1222,"y=0,a=0"
1225
- 0.0,0.0,0.0,1223,"y=0,a=0"
1226
- 0.0,0.0,0.0,1224,"y=0,a=0"
1227
- 0.0,0.0,0.0,1225,"y=0,a=0"
1228
- 0.0,0.0,0.0,1226,"y=0,a=0"
1229
- 0.0,0.0,0.0,1227,"y=0,a=0"
1230
- 0.0,0.0,0.0,1228,"y=0,a=0"
1231
- 0.0,0.0,0.0,1229,"y=0,a=0"
1232
- 0.0,0.0,0.0,1230,"y=0,a=0"
1233
- 0.0,0.0,0.0,1231,"y=0,a=0"
1234
- 0.0,0.0,0.0,1232,"y=0,a=0"
1235
- 0.0,0.0,0.0,1233,"y=0,a=0"
1236
- 0.0,0.0,0.0,1234,"y=0,a=0"
1237
- 0.0,0.0,0.0,1235,"y=0,a=0"
1238
- 0.0,0.0,0.0,1236,"y=0,a=0"
1239
- 0.0,0.0,0.0,1237,"y=0,a=0"
1240
- 0.0,0.0,0.0,1238,"y=0,a=0"
1241
- 0.0,0.0,0.0,1239,"y=0,a=0"
1242
- 0.0,0.0,0.0,1240,"y=0,a=0"
1243
- 0.0,0.0,0.0,1241,"y=0,a=0"
1244
- 0.0,0.0,0.0,1242,"y=0,a=0"
1245
- 0.0,0.0,0.0,1243,"y=0,a=0"
1246
- 0.0,0.0,0.0,1244,"y=0,a=0"
1247
- 0.0,0.0,0.0,1245,"y=0,a=0"
1248
- 0.0,0.0,0.0,1246,"y=0,a=0"
1249
- 0.0,0.0,0.0,1247,"y=0,a=0"
1250
- 0.0,0.0,0.0,1248,"y=0,a=0"
1251
- 0.0,0.0,0.0,1249,"y=0,a=0"
1252
- 0.0,0.0,0.0,1250,"y=0,a=0"
1253
- 0.0,0.0,0.0,1251,"y=0,a=0"
1254
- 0.0,0.0,0.0,1252,"y=0,a=0"
1255
- 0.0,0.0,0.0,1253,"y=0,a=0"
1256
- 0.0,0.0,0.0,1254,"y=0,a=0"
1257
- 0.0,0.0,0.0,1255,"y=0,a=0"
1258
- 0.0,0.0,0.0,1256,"y=0,a=0"
1259
- 0.0,0.0,0.0,1257,"y=0,a=0"
1260
- 0.0,0.0,0.0,1258,"y=0,a=0"
1261
- 0.0,0.0,0.0,1259,"y=0,a=0"
1262
- 0.0,0.0,0.0,1260,"y=0,a=0"
1263
- 0.0,0.0,0.0,1261,"y=0,a=0"
1264
- 0.0,0.0,0.0,1262,"y=0,a=0"
1265
- 0.0,0.0,0.0,1263,"y=0,a=0"
1266
- 0.0,0.0,0.0,1264,"y=0,a=0"
1267
- 0.0,0.0,0.0,1265,"y=0,a=0"
1268
- 0.0,0.0,0.0,1266,"y=0,a=0"
1269
- 0.0,0.0,0.0,1267,"y=0,a=0"
1270
- 0.0,0.0,0.0,1268,"y=0,a=0"
1271
- 0.0,0.0,0.0,1269,"y=0,a=0"
1272
- 0.0,0.0,0.0,1270,"y=0,a=0"
1273
- 0.0,0.0,0.0,1271,"y=0,a=0"
1274
- 0.0,0.0,0.0,1272,"y=0,a=0"
1275
- 0.0,0.0,0.0,1273,"y=0,a=0"
1276
- 0.0,0.0,0.0,1274,"y=0,a=0"
1277
- 0.0,0.0,0.0,1275,"y=0,a=0"
1278
- 0.0,0.0,0.0,1276,"y=0,a=0"
1279
- 0.0,0.0,0.0,1277,"y=0,a=0"
1280
- 0.0,0.0,0.0,1278,"y=0,a=0"
1281
- 0.0,0.0,0.0,1279,"y=0,a=0"
1282
- 0.0,0.0,0.0,1280,"y=0,a=0"
1283
- 0.0,0.0,0.0,1281,"y=0,a=0"
1284
- 0.0,0.0,0.0,1282,"y=0,a=0"
1285
- 0.0,0.0,0.0,1283,"y=0,a=0"
1286
- 0.0,0.0,0.0,1284,"y=0,a=0"
1287
- 0.0,0.0,0.0,1285,"y=0,a=0"
1288
- 0.0,0.0,0.0,1286,"y=0,a=0"
1289
- 0.0,0.0,0.0,1287,"y=0,a=0"
1290
- 0.0,0.0,0.0,1288,"y=0,a=0"
1291
- 0.0,0.0,0.0,1289,"y=0,a=0"
1292
- 0.0,0.0,0.0,1290,"y=0,a=0"
1293
- 0.0,0.0,0.0,1291,"y=0,a=0"
1294
- 0.0,0.0,0.0,1292,"y=0,a=0"
1295
- 0.0,0.0,0.0,1293,"y=0,a=0"
1296
- 0.0,0.0,0.0,1294,"y=0,a=0"
1297
- 0.0,0.0,0.0,1295,"y=0,a=0"
1298
- 0.0,0.0,0.0,1296,"y=0,a=0"
1299
- 0.0,0.0,0.0,1297,"y=0,a=0"
1300
- 0.0,0.0,0.0,1298,"y=0,a=0"
1301
- 0.0,0.0,0.0,1299,"y=0,a=0"
1302
- 0.0,0.0,0.0,1300,"y=0,a=0"
1303
- 0.0,0.0,0.0,1301,"y=0,a=0"
1304
- 0.0,0.0,0.0,1302,"y=0,a=0"
1305
- 0.0,0.0,0.0,1303,"y=0,a=0"
1306
- 0.0,0.0,0.0,1304,"y=0,a=0"
1307
- 0.0,0.0,0.0,1305,"y=0,a=0"
1308
- 0.0,0.0,0.0,1306,"y=0,a=0"
1309
- 0.0,0.0,0.0,1307,"y=0,a=0"
1310
- 0.0,0.0,0.0,1308,"y=0,a=0"
1311
- 0.0,0.0,0.0,1309,"y=0,a=0"
1312
- 0.0,0.0,0.0,1310,"y=0,a=0"
1313
- 0.0,0.0,0.0,1311,"y=0,a=0"
1314
- 0.0,0.0,0.0,1312,"y=0,a=0"
1315
- 0.0,0.0,0.0,1313,"y=0,a=0"
1316
- 0.0,0.0,0.0,1314,"y=0,a=0"
1317
- 0.0,0.0,0.0,1315,"y=0,a=0"
1318
- 0.0,0.0,0.0,1316,"y=0,a=0"
1319
- 0.0,0.0,0.0,1317,"y=0,a=0"
1320
- 0.0,0.0,0.0,1318,"y=0,a=0"
1321
- 0.0,0.0,0.0,1319,"y=0,a=0"
1322
- 0.0,0.0,0.0,1320,"y=0,a=0"
1323
- 0.0,0.0,0.0,1321,"y=0,a=0"
1324
- 0.0,0.0,0.0,1322,"y=0,a=0"
1325
- 0.0,0.0,0.0,1323,"y=0,a=0"
1326
- 0.0,0.0,0.0,1324,"y=0,a=0"
1327
- 0.0,0.0,0.0,1325,"y=0,a=0"
1328
- 0.0,0.0,0.0,1326,"y=0,a=0"
1329
- 0.0,0.0,0.0,1327,"y=0,a=0"
1330
- 0.0,0.0,0.0,1328,"y=0,a=0"
1331
- 0.0,0.0,0.0,1329,"y=0,a=0"
1332
- 0.0,0.0,0.0,1330,"y=0,a=0"
1333
- 0.0,0.0,0.0,1331,"y=0,a=0"
1334
- 0.0,0.0,0.0,1332,"y=0,a=0"
1335
- 0.0,0.0,0.0,1333,"y=0,a=0"
1336
- 0.0,0.0,0.0,1334,"y=0,a=0"
1337
- 0.0,0.0,0.0,1335,"y=0,a=0"
1338
- 0.0,0.0,0.0,1336,"y=0,a=0"
1339
- 0.0,0.0,0.0,1337,"y=0,a=0"
1340
- 0.0,0.0,0.0,1338,"y=0,a=0"
1341
- 0.0,0.0,0.0,1339,"y=0,a=0"
1342
- 0.0,0.0,0.0,1340,"y=0,a=0"
1343
- 0.0,0.0,0.0,1341,"y=0,a=0"
1344
- 0.0,0.0,0.0,1342,"y=0,a=0"
1345
- 0.0,0.0,0.0,1343,"y=0,a=0"
1346
- 0.0,0.0,0.0,1344,"y=0,a=0"
1347
- 0.0,0.0,0.0,1345,"y=0,a=0"
1348
- 0.0,0.0,0.0,1346,"y=0,a=0"
1349
- 0.0,0.0,0.0,1347,"y=0,a=0"
1350
- 0.0,0.0,0.0,1348,"y=0,a=0"
1351
- 0.0,0.0,0.0,1349,"y=0,a=0"
1352
- 0.0,0.0,0.0,1350,"y=0,a=0"
1353
- 0.0,0.0,0.0,1351,"y=0,a=0"
1354
- 0.0,0.0,0.0,1352,"y=0,a=0"
1355
- 0.0,0.0,0.0,1353,"y=0,a=0"
1356
- 0.0,0.0,0.0,1354,"y=0,a=0"
1357
- 0.0,0.0,0.0,1355,"y=0,a=0"
1358
- 0.0,0.0,0.0,1356,"y=0,a=0"
1359
- 0.0,0.0,0.0,1357,"y=0,a=0"
1360
- 0.0,0.0,0.0,1358,"y=0,a=0"
1361
- 0.0,0.0,0.0,1359,"y=0,a=0"
1362
- 0.0,0.0,0.0,1360,"y=0,a=0"
1363
- 0.0,0.0,0.0,1361,"y=0,a=0"
1364
- 0.0,0.0,0.0,1362,"y=0,a=0"
1365
- 0.0,0.0,0.0,1363,"y=0,a=0"
1366
- 0.0,0.0,0.0,1364,"y=0,a=0"
1367
- 0.0,0.0,0.0,1365,"y=0,a=0"
1368
- 0.0,0.0,0.0,1366,"y=0,a=0"
1369
- 0.0,0.0,0.0,1367,"y=0,a=0"
1370
- 0.0,0.0,0.0,1368,"y=0,a=0"
1371
- 0.0,0.0,0.0,1369,"y=0,a=0"
1372
- 0.0,0.0,0.0,1370,"y=0,a=0"
1373
- 0.0,0.0,0.0,1371,"y=0,a=0"
1374
- 0.0,0.0,0.0,1372,"y=0,a=0"
1375
- 0.0,0.0,0.0,1373,"y=0,a=0"
1376
- 0.0,0.0,0.0,1374,"y=0,a=0"
1377
- 0.0,0.0,0.0,1375,"y=0,a=0"
1378
- 0.0,0.0,0.0,1376,"y=0,a=0"
1379
- 0.0,0.0,0.0,1377,"y=0,a=0"
1380
- 0.0,0.0,0.0,1378,"y=0,a=0"
1381
- 0.0,0.0,0.0,1379,"y=0,a=0"
1382
- 0.0,0.0,0.0,1380,"y=0,a=0"
1383
- 0.0,0.0,0.0,1381,"y=0,a=0"
1384
- 0.0,0.0,0.0,1382,"y=0,a=0"
1385
- 0.0,0.0,0.0,1383,"y=0,a=0"
1386
- 0.0,0.0,0.0,1384,"y=0,a=0"
1387
- 0.0,0.0,0.0,1385,"y=0,a=0"
1388
- 0.0,0.0,0.0,1386,"y=0,a=0"
1389
- 0.0,0.0,0.0,1387,"y=0,a=0"
1390
- 0.0,0.0,0.0,1388,"y=0,a=0"
1391
- 0.0,0.0,0.0,1389,"y=0,a=0"
1392
- 0.0,0.0,0.0,1390,"y=0,a=0"
1393
- 0.0,0.0,0.0,1391,"y=0,a=0"
1394
- 0.0,0.0,0.0,1392,"y=0,a=0"
1395
- 0.0,0.0,0.0,1393,"y=0,a=0"
1396
- 0.0,0.0,0.0,1394,"y=0,a=0"
1397
- 0.0,0.0,0.0,1395,"y=0,a=0"
1398
- 0.0,0.0,0.0,1396,"y=0,a=0"
1399
- 0.0,0.0,0.0,1397,"y=0,a=0"
1400
- 0.0,0.0,0.0,1398,"y=0,a=0"
1401
- 0.0,0.0,0.0,1399,"y=0,a=0"
1402
- 0.0,0.0,0.0,1400,"y=0,a=0"
1403
- 0.0,0.0,0.0,1401,"y=0,a=0"
1404
- 0.0,0.0,0.0,1402,"y=0,a=0"
1405
- 0.0,0.0,0.0,1403,"y=0,a=0"
1406
- 0.0,0.0,0.0,1404,"y=0,a=0"
1407
- 0.0,0.0,0.0,1405,"y=0,a=0"
1408
- 0.0,0.0,0.0,1406,"y=0,a=0"
1409
- 0.0,0.0,0.0,1407,"y=0,a=0"
1410
- 0.0,0.0,0.0,1408,"y=0,a=0"
1411
- 0.0,0.0,0.0,1409,"y=0,a=0"
1412
- 0.0,0.0,0.0,1410,"y=0,a=0"
1413
- 0.0,0.0,0.0,1411,"y=0,a=0"
1414
- 0.0,0.0,0.0,1412,"y=0,a=0"
1415
- 0.0,0.0,0.0,1413,"y=0,a=0"
1416
- 0.0,0.0,0.0,1414,"y=0,a=0"
1417
- 0.0,0.0,0.0,1415,"y=0,a=0"
1418
- 0.0,0.0,0.0,1416,"y=0,a=0"
1419
- 0.0,0.0,0.0,1417,"y=0,a=0"
1420
- 0.0,0.0,0.0,1418,"y=0,a=0"
1421
- 0.0,0.0,0.0,1419,"y=0,a=0"
1422
- 0.0,0.0,0.0,1420,"y=0,a=0"
1423
- 0.0,0.0,0.0,1421,"y=0,a=0"
1424
- 0.0,0.0,0.0,1422,"y=0,a=0"
1425
- 0.0,0.0,0.0,1423,"y=0,a=0"
1426
- 0.0,0.0,0.0,1424,"y=0,a=0"
1427
- 0.0,0.0,0.0,1425,"y=0,a=0"
1428
- 0.0,0.0,0.0,1426,"y=0,a=0"
1429
- 0.0,0.0,0.0,1427,"y=0,a=0"
1430
- 0.0,0.0,0.0,1428,"y=0,a=0"
1431
- 0.0,0.0,0.0,1429,"y=0,a=0"
1432
- 0.0,0.0,0.0,1430,"y=0,a=0"
1433
- 0.0,0.0,0.0,1431,"y=0,a=0"
1434
- 0.0,0.0,0.0,1432,"y=0,a=0"
1435
- 0.0,0.0,0.0,1433,"y=0,a=0"
1436
- 0.0,0.0,0.0,1434,"y=0,a=0"
1437
- 0.0,0.0,0.0,1435,"y=0,a=0"
1438
- 0.0,0.0,0.0,1436,"y=0,a=0"
1439
- 0.0,0.0,0.0,1437,"y=0,a=0"
1440
- 0.0,0.0,0.0,1438,"y=0,a=0"
1441
- 0.0,0.0,0.0,1439,"y=0,a=0"
1442
- 0.0,0.0,0.0,1440,"y=0,a=0"
1443
- 0.0,0.0,0.0,1441,"y=0,a=0"
1444
- 0.0,0.0,0.0,1442,"y=0,a=0"
1445
- 0.0,0.0,0.0,1443,"y=0,a=0"
1446
- 0.0,0.0,0.0,1444,"y=0,a=0"
1447
- 0.0,0.0,0.0,1445,"y=0,a=0"
1448
- 0.0,0.0,0.0,1446,"y=0,a=0"
1449
- 0.0,0.0,0.0,1447,"y=0,a=0"
1450
- 0.0,0.0,0.0,1448,"y=0,a=0"
1451
- 0.0,0.0,0.0,1449,"y=0,a=0"
1452
- 0.0,0.0,0.0,1450,"y=0,a=0"
1453
- 0.0,0.0,0.0,1451,"y=0,a=0"
1454
- 0.0,0.0,0.0,1452,"y=0,a=0"
1455
- 0.0,0.0,0.0,1453,"y=0,a=0"
1456
- 0.0,0.0,0.0,1454,"y=0,a=0"
1457
- 0.0,0.0,0.0,1455,"y=0,a=0"
1458
- 0.0,0.0,0.0,1456,"y=0,a=0"
1459
- 0.0,0.0,0.0,1457,"y=0,a=0"
1460
- 0.0,0.0,0.0,1458,"y=0,a=0"
1461
- 0.0,0.0,0.0,1459,"y=0,a=0"
1462
- 0.0,0.0,0.0,1460,"y=0,a=0"
1463
- 0.0,0.0,0.0,1461,"y=0,a=0"
1464
- 0.0,0.0,0.0,1462,"y=0,a=0"
1465
- 0.0,0.0,0.0,1463,"y=0,a=0"
1466
- 0.0,0.0,0.0,1464,"y=0,a=0"
1467
- 0.0,0.0,0.0,1465,"y=0,a=0"
1468
- 0.0,0.0,0.0,1466,"y=0,a=0"
1469
- 0.0,0.0,0.0,1467,"y=0,a=0"
1470
- 0.0,0.0,0.0,1468,"y=0,a=0"
1471
- 0.0,0.0,0.0,1469,"y=0,a=0"
1472
- 0.0,0.0,0.0,1470,"y=0,a=0"
1473
- 0.0,0.0,0.0,1471,"y=0,a=0"
1474
- 0.0,0.0,0.0,1472,"y=0,a=0"
1475
- 0.0,0.0,0.0,1473,"y=0,a=0"
1476
- 0.0,0.0,0.0,1474,"y=0,a=0"
1477
- 0.0,0.0,0.0,1475,"y=0,a=0"
1478
- 0.0,0.0,0.0,1476,"y=0,a=0"
1479
- 0.0,0.0,0.0,1477,"y=0,a=0"
1480
- 0.0,0.0,0.0,1478,"y=0,a=0"
1481
- 0.0,0.0,0.0,1479,"y=0,a=0"
1482
- 0.0,0.0,0.0,1480,"y=0,a=0"
1483
- 0.0,0.0,0.0,1481,"y=0,a=0"
1484
- 0.0,0.0,0.0,1482,"y=0,a=0"
1485
- 0.0,0.0,0.0,1483,"y=0,a=0"
1486
- 0.0,0.0,0.0,1484,"y=0,a=0"
1487
- 0.0,0.0,0.0,1485,"y=0,a=0"
1488
- 0.0,0.0,0.0,1486,"y=0,a=0"
1489
- 0.0,0.0,0.0,1487,"y=0,a=0"
1490
- 0.0,0.0,0.0,1488,"y=0,a=0"
1491
- 0.0,0.0,0.0,1489,"y=0,a=0"
1492
- 0.0,0.0,0.0,1490,"y=0,a=0"
1493
- 0.0,0.0,0.0,1491,"y=0,a=0"
1494
- 0.0,0.0,0.0,1492,"y=0,a=0"
1495
- 0.0,0.0,0.0,1493,"y=0,a=0"
1496
- 0.0,0.0,0.0,1494,"y=0,a=0"
1497
- 0.0,0.0,0.0,1495,"y=0,a=0"
1498
- 0.0,0.0,0.0,1496,"y=0,a=0"
1499
- 0.0,0.0,0.0,1497,"y=0,a=0"
1500
- 0.0,0.0,0.0,1498,"y=0,a=0"
1501
- 0.0,0.0,0.0,1499,"y=0,a=0"
1502
- 0.0,0.0,0.0,1500,"y=0,a=0"
1503
- 0.0,0.0,0.0,1501,"y=0,a=0"
1504
- 0.0,0.0,0.0,1502,"y=0,a=0"
1505
- 0.0,0.0,0.0,1503,"y=0,a=0"
1506
- 0.0,0.0,0.0,1504,"y=0,a=0"
1507
- 0.0,0.0,0.0,1505,"y=0,a=0"
1508
- 0.0,0.0,0.0,1506,"y=0,a=0"
1509
- 0.0,0.0,0.0,1507,"y=0,a=0"
1510
- 0.0,0.0,0.0,1508,"y=0,a=0"
1511
- 0.0,0.0,0.0,1509,"y=0,a=0"
1512
- 0.0,0.0,0.0,1510,"y=0,a=0"
1513
- 0.0,0.0,0.0,1511,"y=0,a=0"
1514
- 0.0,0.0,0.0,1512,"y=0,a=0"
1515
- 0.0,0.0,0.0,1513,"y=0,a=0"
1516
- 0.0,0.0,0.0,1514,"y=0,a=0"
1517
- 0.0,0.0,0.0,1515,"y=0,a=0"
1518
- 0.0,0.0,0.0,1516,"y=0,a=0"
1519
- 0.0,0.0,0.0,1517,"y=0,a=0"
1520
- 0.0,0.0,0.0,1518,"y=0,a=0"
1521
- 0.0,0.0,0.0,1519,"y=0,a=0"
1522
- 0.0,0.0,0.0,1520,"y=0,a=0"
1523
- 0.0,0.0,0.0,1521,"y=0,a=0"
1524
- 0.0,0.0,0.0,1522,"y=0,a=0"
1525
- 0.0,0.0,0.0,1523,"y=0,a=0"
1526
- 0.0,0.0,0.0,1524,"y=0,a=0"
1527
- 0.0,0.0,0.0,1525,"y=0,a=0"
1528
- 0.0,0.0,0.0,1526,"y=0,a=0"
1529
- 0.0,0.0,0.0,1527,"y=0,a=0"
1530
- 0.0,0.0,0.0,1528,"y=0,a=0"
1531
- 0.0,0.0,0.0,1529,"y=0,a=0"
1532
- 0.0,0.0,0.0,1530,"y=0,a=0"
1533
- 0.0,0.0,0.0,1531,"y=0,a=0"
1534
- 0.0,0.0,0.0,1532,"y=0,a=0"
1535
- 0.0,0.0,0.0,1533,"y=0,a=0"
1536
- 0.0,0.0,0.0,1534,"y=0,a=0"
1537
- 0.0,0.0,0.0,1535,"y=0,a=0"
1538
- 0.0,0.0,0.0,1536,"y=0,a=0"
1539
- 0.0,0.0,0.0,1537,"y=0,a=0"
1540
- 0.0,0.0,0.0,1538,"y=0,a=0"
1541
- 0.0,0.0,0.0,1539,"y=0,a=0"
1542
- 0.0,0.0,0.0,1540,"y=0,a=0"
1543
- 0.0,0.0,0.0,1541,"y=0,a=0"
1544
- 0.0,0.0,0.0,1542,"y=0,a=0"
1545
- 0.0,0.0,0.0,1543,"y=0,a=0"
1546
- 0.0,0.0,0.0,1544,"y=0,a=0"
1547
- 0.0,0.0,0.0,1545,"y=0,a=0"
1548
- 0.0,0.0,0.0,1546,"y=0,a=0"
1549
- 0.0,0.0,0.0,1547,"y=0,a=0"
1550
- 0.0,0.0,0.0,1548,"y=0,a=0"
1551
- 0.0,0.0,0.0,1549,"y=0,a=0"
1552
- 0.0,0.0,0.0,1550,"y=0,a=0"
1553
- 0.0,0.0,0.0,1551,"y=0,a=0"
1554
- 0.0,0.0,0.0,1552,"y=0,a=0"
1555
- 0.0,0.0,0.0,1553,"y=0,a=0"
1556
- 0.0,0.0,0.0,1554,"y=0,a=0"
1557
- 0.0,0.0,0.0,1555,"y=0,a=0"
1558
- 0.0,0.0,0.0,1556,"y=0,a=0"
1559
- 0.0,0.0,0.0,1557,"y=0,a=0"
1560
- 0.0,0.0,0.0,1558,"y=0,a=0"
1561
- 0.0,0.0,0.0,1559,"y=0,a=0"
1562
- 0.0,0.0,0.0,1560,"y=0,a=0"
1563
- 0.0,0.0,0.0,1561,"y=0,a=0"
1564
- 0.0,0.0,0.0,1562,"y=0,a=0"
1565
- 0.0,0.0,0.0,1563,"y=0,a=0"
1566
- 0.0,0.0,0.0,1564,"y=0,a=0"
1567
- 0.0,0.0,0.0,1565,"y=0,a=0"
1568
- 0.0,0.0,0.0,1566,"y=0,a=0"
1569
- 0.0,0.0,0.0,1567,"y=0,a=0"
1570
- 0.0,0.0,0.0,1568,"y=0,a=0"
1571
- 0.0,0.0,0.0,1569,"y=0,a=0"
1572
- 0.0,0.0,0.0,1570,"y=0,a=0"
1573
- 0.0,0.0,0.0,1571,"y=0,a=0"
1574
- 0.0,0.0,0.0,1572,"y=0,a=0"
1575
- 1.0,1.0,0.0,1573,"y=1,a=0"
1576
- 1.0,1.0,0.0,1574,"y=1,a=0"
1577
- 1.0,1.0,0.0,1575,"y=1,a=0"
1578
- 1.0,1.0,0.0,1576,"y=1,a=0"
1579
- 1.0,1.0,0.0,1577,"y=1,a=0"
1580
- 1.0,1.0,0.0,1578,"y=1,a=0"
1581
- 1.0,1.0,0.0,1579,"y=1,a=0"
1582
- 1.0,1.0,0.0,1580,"y=1,a=0"
1583
- 1.0,1.0,0.0,1581,"y=1,a=0"
1584
- 1.0,1.0,0.0,1582,"y=1,a=0"
1585
- 1.0,1.0,0.0,1583,"y=1,a=0"
1586
- 1.0,1.0,0.0,1584,"y=1,a=0"
1587
- 1.0,1.0,0.0,1585,"y=1,a=0"
1588
- 1.0,1.0,0.0,1586,"y=1,a=0"
1589
- 1.0,1.0,0.0,1587,"y=1,a=0"
1590
- 1.0,1.0,0.0,1588,"y=1,a=0"
1591
- 1.0,1.0,0.0,1589,"y=1,a=0"
1592
- 1.0,1.0,0.0,1590,"y=1,a=0"
1593
- 1.0,1.0,0.0,1591,"y=1,a=0"
1594
- 1.0,1.0,0.0,1592,"y=1,a=0"
1595
- 1.0,1.0,0.0,1593,"y=1,a=0"
1596
- 1.0,1.0,0.0,1594,"y=1,a=0"
1597
- 1.0,1.0,0.0,1595,"y=1,a=0"
1598
- 1.0,1.0,0.0,1596,"y=1,a=0"
1599
- 1.0,1.0,0.0,1597,"y=1,a=0"
1600
- 1.0,1.0,0.0,1598,"y=1,a=0"
1601
- 1.0,1.0,0.0,1599,"y=1,a=0"
1602
- 1.0,1.0,0.0,1600,"y=1,a=0"
1603
- 1.0,1.0,0.0,1601,"y=1,a=0"
1604
- 1.0,1.0,0.0,1602,"y=1,a=0"
1605
- 1.0,1.0,0.0,1603,"y=1,a=0"
1606
- 1.0,1.0,0.0,1604,"y=1,a=0"
1607
- 1.0,1.0,0.0,1605,"y=1,a=0"
1608
- 1.0,1.0,0.0,1606,"y=1,a=0"
1609
- 1.0,1.0,0.0,1607,"y=1,a=0"
1610
- 1.0,1.0,0.0,1608,"y=1,a=0"
1611
- 1.0,1.0,0.0,1609,"y=1,a=0"
1612
- 1.0,1.0,0.0,1610,"y=1,a=0"
1613
- 1.0,1.0,0.0,1611,"y=1,a=0"
1614
- 1.0,1.0,0.0,1612,"y=1,a=0"
1615
- 1.0,1.0,0.0,1613,"y=1,a=0"
1616
- 1.0,1.0,0.0,1614,"y=1,a=0"
1617
- 1.0,1.0,0.0,1615,"y=1,a=0"
1618
- 1.0,1.0,0.0,1616,"y=1,a=0"
1619
- 1.0,1.0,0.0,1617,"y=1,a=0"
1620
- 1.0,1.0,0.0,1618,"y=1,a=0"
1621
- 1.0,1.0,0.0,1619,"y=1,a=0"
1622
- 1.0,1.0,0.0,1620,"y=1,a=0"
1623
- 1.0,1.0,0.0,1621,"y=1,a=0"
1624
- 1.0,1.0,0.0,1622,"y=1,a=0"
1625
- 1.0,1.0,0.0,1623,"y=1,a=0"
1626
- 1.0,1.0,0.0,1624,"y=1,a=0"
1627
- 1.0,1.0,0.0,1625,"y=1,a=0"
1628
- 1.0,1.0,0.0,1626,"y=1,a=0"
1629
- 1.0,1.0,0.0,1627,"y=1,a=0"
1630
- 1.0,1.0,0.0,1628,"y=1,a=0"
1631
- 1.0,1.0,0.0,1629,"y=1,a=0"
1632
- 1.0,1.0,0.0,1630,"y=1,a=0"
1633
- 1.0,1.0,0.0,1631,"y=1,a=0"
1634
- 1.0,1.0,0.0,1632,"y=1,a=0"
1635
- 1.0,1.0,0.0,1633,"y=1,a=0"
1636
- 1.0,1.0,0.0,1634,"y=1,a=0"
1637
- 1.0,1.0,0.0,1635,"y=1,a=0"
1638
- 1.0,1.0,0.0,1636,"y=1,a=0"
1639
- 1.0,1.0,0.0,1637,"y=1,a=0"
1640
- 1.0,1.0,0.0,1638,"y=1,a=0"
1641
- 1.0,1.0,0.0,1639,"y=1,a=0"
1642
- 1.0,1.0,0.0,1640,"y=1,a=0"
1643
- 1.0,1.0,0.0,1641,"y=1,a=0"
1644
- 1.0,1.0,0.0,1642,"y=1,a=0"
1645
- 1.0,1.0,0.0,1643,"y=1,a=0"
1646
- 1.0,1.0,0.0,1644,"y=1,a=0"
1647
- 1.0,1.0,0.0,1645,"y=1,a=0"
1648
- 1.0,1.0,0.0,1646,"y=1,a=0"
1649
- 1.0,1.0,0.0,1647,"y=1,a=0"
1650
- 1.0,1.0,0.0,1648,"y=1,a=0"
1651
- 1.0,1.0,0.0,1649,"y=1,a=0"
1652
- 1.0,1.0,0.0,1650,"y=1,a=0"
1653
- 1.0,1.0,0.0,1651,"y=1,a=0"
1654
- 1.0,1.0,0.0,1652,"y=1,a=0"
1655
- 1.0,1.0,0.0,1653,"y=1,a=0"
1656
- 1.0,1.0,0.0,1654,"y=1,a=0"
1657
- 1.0,1.0,0.0,1655,"y=1,a=0"
1658
- 1.0,1.0,0.0,1656,"y=1,a=0"
1659
- 1.0,1.0,0.0,1657,"y=1,a=0"
1660
- 1.0,1.0,0.0,1658,"y=1,a=0"
1661
- 1.0,1.0,0.0,1659,"y=1,a=0"
1662
- 1.0,1.0,0.0,1660,"y=1,a=0"
1663
- 1.0,1.0,0.0,1661,"y=1,a=0"
1664
- 1.0,1.0,0.0,1662,"y=1,a=0"
1665
- 1.0,1.0,0.0,1663,"y=1,a=0"
1666
- 1.0,1.0,0.0,1664,"y=1,a=0"
1667
- 1.0,1.0,0.0,1665,"y=1,a=0"
1668
- 1.0,1.0,0.0,1666,"y=1,a=0"
1669
- 1.0,1.0,0.0,1667,"y=1,a=0"
1670
- 1.0,1.0,0.0,1668,"y=1,a=0"
1671
- 1.0,1.0,0.0,1669,"y=1,a=0"
1672
- 1.0,1.0,0.0,1670,"y=1,a=0"
1673
- 1.0,1.0,0.0,1671,"y=1,a=0"
1674
- 1.0,1.0,0.0,1672,"y=1,a=0"
1675
- 1.0,1.0,0.0,1673,"y=1,a=0"
1676
- 1.0,1.0,0.0,1674,"y=1,a=0"
1677
- 1.0,1.0,0.0,1675,"y=1,a=0"
1678
- 1.0,1.0,0.0,1676,"y=1,a=0"
1679
- 1.0,1.0,0.0,1677,"y=1,a=0"
1680
- 1.0,1.0,0.0,1678,"y=1,a=0"
1681
- 1.0,1.0,0.0,1679,"y=1,a=0"
1682
- 1.0,1.0,0.0,1680,"y=1,a=0"
1683
- 1.0,1.0,0.0,1681,"y=1,a=0"
1684
- 1.0,1.0,0.0,1682,"y=1,a=0"
1685
- 1.0,1.0,0.0,1683,"y=1,a=0"
1686
- 1.0,1.0,0.0,1684,"y=1,a=0"
1687
- 1.0,1.0,0.0,1685,"y=1,a=0"
1688
- 1.0,1.0,0.0,1686,"y=1,a=0"
1689
- 1.0,1.0,0.0,1687,"y=1,a=0"
1690
- 1.0,1.0,0.0,1688,"y=1,a=0"
1691
- 1.0,1.0,0.0,1689,"y=1,a=0"
1692
- 1.0,1.0,0.0,1690,"y=1,a=0"
1693
- 1.0,1.0,0.0,1691,"y=1,a=0"
1694
- 1.0,1.0,0.0,1692,"y=1,a=0"
1695
- 1.0,1.0,0.0,1693,"y=1,a=0"
1696
- 1.0,1.0,0.0,1694,"y=1,a=0"
1697
- 1.0,1.0,0.0,1695,"y=1,a=0"
1698
- 1.0,1.0,0.0,1696,"y=1,a=0"
1699
- 1.0,1.0,0.0,1697,"y=1,a=0"
1700
- 1.0,1.0,0.0,1698,"y=1,a=0"
1701
- 1.0,1.0,0.0,1699,"y=1,a=0"
1702
- 1.0,1.0,0.0,1700,"y=1,a=0"
1703
- 1.0,1.0,0.0,1701,"y=1,a=0"
1704
- 1.0,1.0,0.0,1702,"y=1,a=0"
1705
- 1.0,1.0,0.0,1703,"y=1,a=0"
1706
- 1.0,1.0,0.0,1704,"y=1,a=0"
1707
- 1.0,1.0,0.0,1705,"y=1,a=0"
1708
- 1.0,1.0,0.0,1706,"y=1,a=0"
1709
- 1.0,1.0,0.0,1707,"y=1,a=0"
1710
- 1.0,1.0,0.0,1708,"y=1,a=0"
1711
- 1.0,1.0,0.0,1709,"y=1,a=0"
1712
- 1.0,1.0,0.0,1710,"y=1,a=0"
1713
- 1.0,1.0,0.0,1711,"y=1,a=0"
1714
- 1.0,1.0,0.0,1712,"y=1,a=0"
1715
- 1.0,1.0,0.0,1713,"y=1,a=0"
1716
- 1.0,1.0,0.0,1714,"y=1,a=0"
1717
- 1.0,1.0,0.0,1715,"y=1,a=0"
1718
- 1.0,1.0,0.0,1716,"y=1,a=0"
1719
- 1.0,1.0,0.0,1717,"y=1,a=0"
1720
- 1.0,1.0,0.0,1718,"y=1,a=0"
1721
- 1.0,1.0,0.0,1719,"y=1,a=0"
1722
- 1.0,1.0,0.0,1720,"y=1,a=0"
1723
- 1.0,1.0,0.0,1721,"y=1,a=0"
1724
- 1.0,1.0,0.0,1722,"y=1,a=0"
1725
- 1.0,1.0,0.0,1723,"y=1,a=0"
1726
- 1.0,1.0,0.0,1724,"y=1,a=0"
1727
- 1.0,1.0,0.0,1725,"y=1,a=0"
1728
- 1.0,1.0,0.0,1726,"y=1,a=0"
1729
- 1.0,1.0,0.0,1727,"y=1,a=0"
1730
- 1.0,1.0,0.0,1728,"y=1,a=0"
1731
- 1.0,1.0,0.0,1729,"y=1,a=0"
1732
- 1.0,1.0,0.0,1730,"y=1,a=0"
1733
- 1.0,1.0,0.0,1731,"y=1,a=0"
1734
- 1.0,1.0,0.0,1732,"y=1,a=0"
1735
- 1.0,1.0,0.0,1733,"y=1,a=0"
1736
- 1.0,1.0,0.0,1734,"y=1,a=0"
1737
- 1.0,1.0,0.0,1735,"y=1,a=0"
1738
- 1.0,1.0,0.0,1736,"y=1,a=0"
1739
- 1.0,1.0,0.0,1737,"y=1,a=0"
1740
- 1.0,1.0,0.0,1738,"y=1,a=0"
1741
- 1.0,1.0,0.0,1739,"y=1,a=0"
1742
- 1.0,1.0,0.0,1740,"y=1,a=0"
1743
- 1.0,1.0,0.0,1741,"y=1,a=0"
1744
- 1.0,1.0,0.0,1742,"y=1,a=0"
1745
- 1.0,1.0,0.0,1743,"y=1,a=0"
1746
- 1.0,1.0,0.0,1744,"y=1,a=0"
1747
- 1.0,1.0,0.0,1745,"y=1,a=0"
1748
- 1.0,1.0,0.0,1746,"y=1,a=0"
1749
- 1.0,1.0,0.0,1747,"y=1,a=0"
1750
- 1.0,1.0,0.0,1748,"y=1,a=0"
1751
- 1.0,1.0,0.0,1749,"y=1,a=0"
1752
- 1.0,1.0,0.0,1750,"y=1,a=0"
1753
- 1.0,1.0,0.0,1751,"y=1,a=0"
1754
- 1.0,1.0,0.0,1752,"y=1,a=0"
1755
- 1.0,1.0,0.0,1753,"y=1,a=0"
1756
- 1.0,1.0,0.0,1754,"y=1,a=0"
1757
- 1.0,1.0,0.0,1755,"y=1,a=0"
1758
- 1.0,1.0,0.0,1756,"y=1,a=0"
1759
- 1.0,1.0,0.0,1757,"y=1,a=0"
1760
- 1.0,1.0,0.0,1758,"y=1,a=0"
1761
- 1.0,1.0,0.0,1759,"y=1,a=0"
1762
- 1.0,1.0,0.0,1760,"y=1,a=0"
1763
- 1.0,1.0,0.0,1761,"y=1,a=0"
1764
- 1.0,1.0,0.0,1762,"y=1,a=0"
1765
- 1.0,1.0,0.0,1763,"y=1,a=0"
1766
- 1.0,1.0,0.0,1764,"y=1,a=0"
1767
- 1.0,1.0,0.0,1765,"y=1,a=0"
1768
- 1.0,1.0,0.0,1766,"y=1,a=0"
1769
- 1.0,1.0,0.0,1767,"y=1,a=0"
1770
- 1.0,1.0,0.0,1768,"y=1,a=0"
1771
- 0.0,0.0,1.0,1769,"y=0,a=1"
1772
- 0.0,0.0,1.0,1770,"y=0,a=1"
1773
- 0.0,0.0,1.0,1771,"y=0,a=1"
1774
- 0.0,0.0,1.0,1772,"y=0,a=1"
1775
- 0.0,0.0,1.0,1773,"y=0,a=1"
1776
- 0.0,0.0,1.0,1774,"y=0,a=1"
1777
- 0.0,0.0,1.0,1775,"y=0,a=1"
1778
- 0.0,0.0,1.0,1776,"y=0,a=1"
1779
- 0.0,0.0,1.0,1777,"y=0,a=1"
1780
- 0.0,0.0,1.0,1778,"y=0,a=1"
1781
- 0.0,0.0,1.0,1779,"y=0,a=1"
1782
- 0.0,0.0,1.0,1780,"y=0,a=1"
1783
- 0.0,0.0,1.0,1781,"y=0,a=1"
1784
- 0.0,0.0,1.0,1782,"y=0,a=1"
1785
- 0.0,0.0,1.0,1783,"y=0,a=1"
1786
- 0.0,0.0,1.0,1784,"y=0,a=1"
1787
- 0.0,0.0,1.0,1785,"y=0,a=1"
1788
- 0.0,0.0,1.0,1786,"y=0,a=1"
1789
- 0.0,0.0,1.0,1787,"y=0,a=1"
1790
- 0.0,0.0,1.0,1788,"y=0,a=1"
1791
- 0.0,0.0,1.0,1789,"y=0,a=1"
1792
- 0.0,0.0,1.0,1790,"y=0,a=1"
1793
- 0.0,0.0,1.0,1791,"y=0,a=1"
1794
- 0.0,0.0,1.0,1792,"y=0,a=1"
1795
- 0.0,0.0,1.0,1793,"y=0,a=1"
1796
- 0.0,0.0,1.0,1794,"y=0,a=1"
1797
- 0.0,0.0,1.0,1795,"y=0,a=1"
1798
- 0.0,0.0,1.0,1796,"y=0,a=1"
1799
- 0.0,0.0,1.0,1797,"y=0,a=1"
1800
- 0.0,0.0,1.0,1798,"y=0,a=1"
1801
- 0.0,0.0,1.0,1799,"y=0,a=1"
1802
- 0.0,0.0,1.0,1800,"y=0,a=1"
1803
- 0.0,0.0,1.0,1801,"y=0,a=1"
1804
- 0.0,0.0,1.0,1802,"y=0,a=1"
1805
- 0.0,0.0,1.0,1803,"y=0,a=1"
1806
- 0.0,0.0,1.0,1804,"y=0,a=1"
1807
- 0.0,0.0,1.0,1805,"y=0,a=1"
1808
- 0.0,0.0,1.0,1806,"y=0,a=1"
1809
- 0.0,0.0,1.0,1807,"y=0,a=1"
1810
- 0.0,0.0,1.0,1808,"y=0,a=1"
1811
- 0.0,0.0,1.0,1809,"y=0,a=1"
1812
- 0.0,0.0,1.0,1810,"y=0,a=1"
1813
- 0.0,0.0,1.0,1811,"y=0,a=1"
1814
- 0.0,0.0,1.0,1812,"y=0,a=1"
1815
- 0.0,0.0,1.0,1813,"y=0,a=1"
1816
- 0.0,0.0,1.0,1814,"y=0,a=1"
1817
- 0.0,0.0,1.0,1815,"y=0,a=1"
1818
- 0.0,0.0,1.0,1816,"y=0,a=1"
1819
- 0.0,0.0,1.0,1817,"y=0,a=1"
1820
- 0.0,0.0,1.0,1818,"y=0,a=1"
1821
- 0.0,0.0,1.0,1819,"y=0,a=1"
1822
- 0.0,0.0,1.0,1820,"y=0,a=1"
1823
- 0.0,0.0,1.0,1821,"y=0,a=1"
1824
- 0.0,0.0,1.0,1822,"y=0,a=1"
1825
- 0.0,0.0,1.0,1823,"y=0,a=1"
1826
- 0.0,0.0,1.0,1824,"y=0,a=1"
1827
- 0.0,0.0,1.0,1825,"y=0,a=1"
1828
- 0.0,0.0,1.0,1826,"y=0,a=1"
1829
- 0.0,0.0,1.0,1827,"y=0,a=1"
1830
- 0.0,0.0,1.0,1828,"y=0,a=1"
1831
- 0.0,0.0,1.0,1829,"y=0,a=1"
1832
- 0.0,0.0,1.0,1830,"y=0,a=1"
1833
- 0.0,0.0,1.0,1831,"y=0,a=1"
1834
- 0.0,0.0,1.0,1832,"y=0,a=1"
1835
- 0.0,0.0,1.0,1833,"y=0,a=1"
1836
- 0.0,0.0,1.0,1834,"y=0,a=1"
1837
- 0.0,0.0,1.0,1835,"y=0,a=1"
1838
- 0.0,0.0,1.0,1836,"y=0,a=1"
1839
- 0.0,0.0,1.0,1837,"y=0,a=1"
1840
- 0.0,0.0,1.0,1838,"y=0,a=1"
1841
- 0.0,0.0,1.0,1839,"y=0,a=1"
1842
- 0.0,0.0,1.0,1840,"y=0,a=1"
1843
- 0.0,0.0,1.0,1841,"y=0,a=1"
1844
- 0.0,0.0,1.0,1842,"y=0,a=1"
1845
- 0.0,0.0,1.0,1843,"y=0,a=1"
1846
- 0.0,0.0,1.0,1844,"y=0,a=1"
1847
- 0.0,0.0,1.0,1845,"y=0,a=1"
1848
- 0.0,0.0,1.0,1846,"y=0,a=1"
1849
- 0.0,0.0,1.0,1847,"y=0,a=1"
1850
- 0.0,0.0,1.0,1848,"y=0,a=1"
1851
- 0.0,0.0,1.0,1849,"y=0,a=1"
1852
- 0.0,0.0,1.0,1850,"y=0,a=1"
1853
- 0.0,0.0,1.0,1851,"y=0,a=1"
1854
- 0.0,0.0,1.0,1852,"y=0,a=1"
1855
- 0.0,0.0,1.0,1853,"y=0,a=1"
1856
- 0.0,0.0,1.0,1854,"y=0,a=1"
1857
- 0.0,0.0,1.0,1855,"y=0,a=1"
1858
- 0.0,0.0,1.0,1856,"y=0,a=1"
1859
- 0.0,0.0,1.0,1857,"y=0,a=1"
1860
- 0.0,0.0,1.0,1858,"y=0,a=1"
1861
- 0.0,0.0,1.0,1859,"y=0,a=1"
1862
- 0.0,0.0,1.0,1860,"y=0,a=1"
1863
- 0.0,0.0,1.0,1861,"y=0,a=1"
1864
- 0.0,0.0,1.0,1862,"y=0,a=1"
1865
- 0.0,0.0,1.0,1863,"y=0,a=1"
1866
- 0.0,0.0,1.0,1864,"y=0,a=1"
1867
- 0.0,0.0,1.0,1865,"y=0,a=1"
1868
- 0.0,0.0,1.0,1866,"y=0,a=1"
1869
- 0.0,0.0,1.0,1867,"y=0,a=1"
1870
- 0.0,0.0,1.0,1868,"y=0,a=1"
1871
- 0.0,0.0,1.0,1869,"y=0,a=1"
1872
- 0.0,0.0,1.0,1870,"y=0,a=1"
1873
- 0.0,0.0,1.0,1871,"y=0,a=1"
1874
- 0.0,0.0,1.0,1872,"y=0,a=1"
1875
- 0.0,0.0,1.0,1873,"y=0,a=1"
1876
- 0.0,0.0,1.0,1874,"y=0,a=1"
1877
- 0.0,0.0,1.0,1875,"y=0,a=1"
1878
- 0.0,0.0,1.0,1876,"y=0,a=1"
1879
- 0.0,0.0,1.0,1877,"y=0,a=1"
1880
- 0.0,0.0,1.0,1878,"y=0,a=1"
1881
- 0.0,0.0,1.0,1879,"y=0,a=1"
1882
- 0.0,0.0,1.0,1880,"y=0,a=1"
1883
- 0.0,0.0,1.0,1881,"y=0,a=1"
1884
- 0.0,0.0,1.0,1882,"y=0,a=1"
1885
- 0.0,0.0,1.0,1883,"y=0,a=1"
1886
- 0.0,0.0,1.0,1884,"y=0,a=1"
1887
- 0.0,0.0,1.0,1885,"y=0,a=1"
1888
- 0.0,0.0,1.0,1886,"y=0,a=1"
1889
- 0.0,0.0,1.0,1887,"y=0,a=1"
1890
- 0.0,0.0,1.0,1888,"y=0,a=1"
1891
- 0.0,0.0,1.0,1889,"y=0,a=1"
1892
- 0.0,0.0,1.0,1890,"y=0,a=1"
1893
- 0.0,0.0,1.0,1891,"y=0,a=1"
1894
- 0.0,0.0,1.0,1892,"y=0,a=1"
1895
- 0.0,0.0,1.0,1893,"y=0,a=1"
1896
- 0.0,0.0,1.0,1894,"y=0,a=1"
1897
- 0.0,0.0,1.0,1895,"y=0,a=1"
1898
- 0.0,0.0,1.0,1896,"y=0,a=1"
1899
- 0.0,0.0,1.0,1897,"y=0,a=1"
1900
- 0.0,0.0,1.0,1898,"y=0,a=1"
1901
- 0.0,0.0,1.0,1899,"y=0,a=1"
1902
- 0.0,0.0,1.0,1900,"y=0,a=1"
1903
- 0.0,0.0,1.0,1901,"y=0,a=1"
1904
- 0.0,0.0,1.0,1902,"y=0,a=1"
1905
- 0.0,0.0,1.0,1903,"y=0,a=1"
1906
- 0.0,0.0,1.0,1904,"y=0,a=1"
1907
- 0.0,0.0,1.0,1905,"y=0,a=1"
1908
- 0.0,0.0,1.0,1906,"y=0,a=1"
1909
- 0.0,0.0,1.0,1907,"y=0,a=1"
1910
- 0.0,0.0,1.0,1908,"y=0,a=1"
1911
- 0.0,0.0,1.0,1909,"y=0,a=1"
1912
- 0.0,0.0,1.0,1910,"y=0,a=1"
1913
- 0.0,0.0,1.0,1911,"y=0,a=1"
1914
- 0.0,0.0,1.0,1912,"y=0,a=1"
1915
- 0.0,0.0,1.0,1913,"y=0,a=1"
1916
- 0.0,0.0,1.0,1914,"y=0,a=1"
1917
- 0.0,0.0,1.0,1915,"y=0,a=1"
1918
- 0.0,0.0,1.0,1916,"y=0,a=1"
1919
- 0.0,0.0,1.0,1917,"y=0,a=1"
1920
- 0.0,0.0,1.0,1918,"y=0,a=1"
1921
- 0.0,0.0,1.0,1919,"y=0,a=1"
1922
- 0.0,0.0,1.0,1920,"y=0,a=1"
1923
- 0.0,0.0,1.0,1921,"y=0,a=1"
1924
- 0.0,0.0,1.0,1922,"y=0,a=1"
1925
- 0.0,0.0,1.0,1923,"y=0,a=1"
1926
- 0.0,0.0,1.0,1924,"y=0,a=1"
1927
- 0.0,0.0,1.0,1925,"y=0,a=1"
1928
- 0.0,0.0,1.0,1926,"y=0,a=1"
1929
- 0.0,0.0,1.0,1927,"y=0,a=1"
1930
- 0.0,0.0,1.0,1928,"y=0,a=1"
1931
- 0.0,0.0,1.0,1929,"y=0,a=1"
1932
- 0.0,0.0,1.0,1930,"y=0,a=1"
1933
- 0.0,0.0,1.0,1931,"y=0,a=1"
1934
- 0.0,0.0,1.0,1932,"y=0,a=1"
1935
- 0.0,0.0,1.0,1933,"y=0,a=1"
1936
- 0.0,0.0,1.0,1934,"y=0,a=1"
1937
- 0.0,0.0,1.0,1935,"y=0,a=1"
1938
- 0.0,0.0,1.0,1936,"y=0,a=1"
1939
- 0.0,0.0,1.0,1937,"y=0,a=1"
1940
- 0.0,0.0,1.0,1938,"y=0,a=1"
1941
- 0.0,0.0,1.0,1939,"y=0,a=1"
1942
- 0.0,0.0,1.0,1940,"y=0,a=1"
1943
- 0.0,0.0,1.0,1941,"y=0,a=1"
1944
- 0.0,0.0,1.0,1942,"y=0,a=1"
1945
- 0.0,0.0,1.0,1943,"y=0,a=1"
1946
- 0.0,0.0,1.0,1944,"y=0,a=1"
1947
- 0.0,0.0,1.0,1945,"y=0,a=1"
1948
- 0.0,0.0,1.0,1946,"y=0,a=1"
1949
- 0.0,0.0,1.0,1947,"y=0,a=1"
1950
- 0.0,0.0,1.0,1948,"y=0,a=1"
1951
- 0.0,0.0,1.0,1949,"y=0,a=1"
1952
- 0.0,0.0,1.0,1950,"y=0,a=1"
1953
- 0.0,0.0,1.0,1951,"y=0,a=1"
1954
- 0.0,0.0,1.0,1952,"y=0,a=1"
1955
- 0.0,0.0,1.0,1953,"y=0,a=1"
1956
- 0.0,0.0,1.0,1954,"y=0,a=1"
1957
- 0.0,0.0,1.0,1955,"y=0,a=1"
1958
- 0.0,0.0,1.0,1956,"y=0,a=1"
1959
- 0.0,0.0,1.0,1957,"y=0,a=1"
1960
- 0.0,0.0,1.0,1958,"y=0,a=1"
1961
- 0.0,0.0,1.0,1959,"y=0,a=1"
1962
- 0.0,0.0,1.0,1960,"y=0,a=1"
1963
- 0.0,0.0,1.0,1961,"y=0,a=1"
1964
- 0.0,0.0,1.0,1962,"y=0,a=1"
1965
- 0.0,0.0,1.0,1963,"y=0,a=1"
1966
- 0.0,0.0,1.0,1964,"y=0,a=1"
1967
- 0.0,0.0,1.0,1965,"y=0,a=1"
1968
- 0.0,0.0,1.0,1966,"y=0,a=1"
1969
- 0.0,0.0,1.0,1967,"y=0,a=1"
1970
- 0.0,0.0,1.0,1968,"y=0,a=1"
1971
- 0.0,0.0,1.0,1969,"y=0,a=1"
1972
- 0.0,0.0,1.0,1970,"y=0,a=1"
1973
- 0.0,0.0,1.0,1971,"y=0,a=1"
1974
- 0.0,0.0,1.0,1972,"y=0,a=1"
1975
- 0.0,0.0,1.0,1973,"y=0,a=1"
1976
- 0.0,0.0,1.0,1974,"y=0,a=1"
1977
- 0.0,0.0,1.0,1975,"y=0,a=1"
1978
- 0.0,0.0,1.0,1976,"y=0,a=1"
1979
- 0.0,0.0,1.0,1977,"y=0,a=1"
1980
- 0.0,0.0,1.0,1978,"y=0,a=1"
1981
- 0.0,0.0,1.0,1979,"y=0,a=1"
1982
- 0.0,0.0,1.0,1980,"y=0,a=1"
1983
- 0.0,0.0,1.0,1981,"y=0,a=1"
1984
- 0.0,0.0,1.0,1982,"y=0,a=1"
1985
- 0.0,0.0,1.0,1983,"y=0,a=1"
1986
- 0.0,0.0,1.0,1984,"y=0,a=1"
1987
- 0.0,0.0,1.0,1985,"y=0,a=1"
1988
- 0.0,0.0,1.0,1986,"y=0,a=1"
1989
- 0.0,0.0,1.0,1987,"y=0,a=1"
1990
- 0.0,0.0,1.0,1988,"y=0,a=1"
1991
- 0.0,0.0,1.0,1989,"y=0,a=1"
1992
- 0.0,0.0,1.0,1990,"y=0,a=1"
1993
- 0.0,0.0,1.0,1991,"y=0,a=1"
1994
- 0.0,0.0,1.0,1992,"y=0,a=1"
1995
- 0.0,0.0,1.0,1993,"y=0,a=1"
1996
- 0.0,0.0,1.0,1994,"y=0,a=1"
1997
- 0.0,0.0,1.0,1995,"y=0,a=1"
1998
- 0.0,0.0,1.0,1996,"y=0,a=1"
1999
- 0.0,0.0,1.0,1997,"y=0,a=1"
2000
- 0.0,0.0,1.0,1998,"y=0,a=1"
2001
- 0.0,0.0,1.0,1999,"y=0,a=1"
2002
- 0.0,0.0,1.0,2000,"y=0,a=1"
2003
- 0.0,0.0,1.0,2001,"y=0,a=1"
2004
- 0.0,0.0,1.0,2002,"y=0,a=1"
2005
- 0.0,0.0,1.0,2003,"y=0,a=1"
2006
- 0.0,0.0,1.0,2004,"y=0,a=1"
2007
- 0.0,0.0,1.0,2005,"y=0,a=1"
2008
- 0.0,0.0,1.0,2006,"y=0,a=1"
2009
- 0.0,0.0,1.0,2007,"y=0,a=1"
2010
- 0.0,0.0,1.0,2008,"y=0,a=1"
2011
- 0.0,0.0,1.0,2009,"y=0,a=1"
2012
- 0.0,0.0,1.0,2010,"y=0,a=1"
2013
- 0.0,0.0,1.0,2011,"y=0,a=1"
2014
- 0.0,0.0,1.0,2012,"y=0,a=1"
2015
- 0.0,0.0,1.0,2013,"y=0,a=1"
2016
- 0.0,0.0,1.0,2014,"y=0,a=1"
2017
- 0.0,0.0,1.0,2015,"y=0,a=1"
2018
- 0.0,0.0,1.0,2016,"y=0,a=1"
2019
- 0.0,0.0,1.0,2017,"y=0,a=1"
2020
- 0.0,0.0,1.0,2018,"y=0,a=1"
2021
- 0.0,0.0,1.0,2019,"y=0,a=1"
2022
- 0.0,0.0,1.0,2020,"y=0,a=1"
2023
- 0.0,0.0,1.0,2021,"y=0,a=1"
2024
- 0.0,0.0,1.0,2022,"y=0,a=1"
2025
- 0.0,0.0,1.0,2023,"y=0,a=1"
2026
- 0.0,0.0,1.0,2024,"y=0,a=1"
2027
- 0.0,0.0,1.0,2025,"y=0,a=1"
2028
- 0.0,0.0,1.0,2026,"y=0,a=1"
2029
- 0.0,0.0,1.0,2027,"y=0,a=1"
2030
- 0.0,0.0,1.0,2028,"y=0,a=1"
2031
- 0.0,0.0,1.0,2029,"y=0,a=1"
2032
- 0.0,0.0,1.0,2030,"y=0,a=1"
2033
- 0.0,0.0,1.0,2031,"y=0,a=1"
2034
- 0.0,0.0,1.0,2032,"y=0,a=1"
2035
- 0.0,0.0,1.0,2033,"y=0,a=1"
2036
- 0.0,0.0,1.0,2034,"y=0,a=1"
2037
- 0.0,0.0,1.0,2035,"y=0,a=1"
2038
- 0.0,0.0,1.0,2036,"y=0,a=1"
2039
- 0.0,0.0,1.0,2037,"y=0,a=1"
2040
- 0.0,0.0,1.0,2038,"y=0,a=1"
2041
- 0.0,0.0,1.0,2039,"y=0,a=1"
2042
- 0.0,0.0,1.0,2040,"y=0,a=1"
2043
- 0.0,0.0,1.0,2041,"y=0,a=1"
2044
- 0.0,0.0,1.0,2042,"y=0,a=1"
2045
- 0.0,0.0,1.0,2043,"y=0,a=1"
2046
- 0.0,0.0,1.0,2044,"y=0,a=1"
2047
- 0.0,0.0,1.0,2045,"y=0,a=1"
2048
- 0.0,0.0,1.0,2046,"y=0,a=1"
2049
- 0.0,0.0,1.0,2047,"y=0,a=1"
2050
- 0.0,0.0,1.0,2048,"y=0,a=1"
2051
- 0.0,0.0,1.0,2049,"y=0,a=1"
2052
- 0.0,0.0,1.0,2050,"y=0,a=1"
2053
- 0.0,0.0,1.0,2051,"y=0,a=1"
2054
- 0.0,0.0,1.0,2052,"y=0,a=1"
2055
- 0.0,0.0,1.0,2053,"y=0,a=1"
2056
- 0.0,0.0,1.0,2054,"y=0,a=1"
2057
- 0.0,0.0,1.0,2055,"y=0,a=1"
2058
- 0.0,0.0,1.0,2056,"y=0,a=1"
2059
- 0.0,0.0,1.0,2057,"y=0,a=1"
2060
- 0.0,0.0,1.0,2058,"y=0,a=1"
2061
- 0.0,0.0,1.0,2059,"y=0,a=1"
2062
- 0.0,0.0,1.0,2060,"y=0,a=1"
2063
- 0.0,0.0,1.0,2061,"y=0,a=1"
2064
- 0.0,0.0,1.0,2062,"y=0,a=1"
2065
- 0.0,0.0,1.0,2063,"y=0,a=1"
2066
- 0.0,0.0,1.0,2064,"y=0,a=1"
2067
- 0.0,0.0,1.0,2065,"y=0,a=1"
2068
- 0.0,0.0,1.0,2066,"y=0,a=1"
2069
- 0.0,0.0,1.0,2067,"y=0,a=1"
2070
- 0.0,0.0,1.0,2068,"y=0,a=1"
2071
- 0.0,0.0,1.0,2069,"y=0,a=1"
2072
- 0.0,0.0,1.0,2070,"y=0,a=1"
2073
- 0.0,0.0,1.0,2071,"y=0,a=1"
2074
- 0.0,0.0,1.0,2072,"y=0,a=1"
2075
- 0.0,0.0,1.0,2073,"y=0,a=1"
2076
- 0.0,0.0,1.0,2074,"y=0,a=1"
2077
- 0.0,0.0,1.0,2075,"y=0,a=1"
2078
- 0.0,0.0,1.0,2076,"y=0,a=1"
2079
- 0.0,0.0,1.0,2077,"y=0,a=1"
2080
- 0.0,0.0,1.0,2078,"y=0,a=1"
2081
- 0.0,0.0,1.0,2079,"y=0,a=1"
2082
- 0.0,0.0,1.0,2080,"y=0,a=1"
2083
- 0.0,0.0,1.0,2081,"y=0,a=1"
2084
- 0.0,0.0,1.0,2082,"y=0,a=1"
2085
- 0.0,0.0,1.0,2083,"y=0,a=1"
2086
- 0.0,0.0,1.0,2084,"y=0,a=1"
2087
- 0.0,0.0,1.0,2085,"y=0,a=1"
2088
- 0.0,0.0,1.0,2086,"y=0,a=1"
2089
- 0.0,0.0,1.0,2087,"y=0,a=1"
2090
- 0.0,0.0,1.0,2088,"y=0,a=1"
2091
- 0.0,0.0,1.0,2089,"y=0,a=1"
2092
- 0.0,0.0,1.0,2090,"y=0,a=1"
2093
- 0.0,0.0,1.0,2091,"y=0,a=1"
2094
- 0.0,0.0,1.0,2092,"y=0,a=1"
2095
- 0.0,0.0,1.0,2093,"y=0,a=1"
2096
- 0.0,0.0,1.0,2094,"y=0,a=1"
2097
- 0.0,0.0,1.0,2095,"y=0,a=1"
2098
- 0.0,0.0,1.0,2096,"y=0,a=1"
2099
- 0.0,0.0,1.0,2097,"y=0,a=1"
2100
- 0.0,0.0,1.0,2098,"y=0,a=1"
2101
- 0.0,0.0,1.0,2099,"y=0,a=1"
2102
- 0.0,0.0,1.0,2100,"y=0,a=1"
2103
- 0.0,0.0,1.0,2101,"y=0,a=1"
2104
- 0.0,0.0,1.0,2102,"y=0,a=1"
2105
- 0.0,0.0,1.0,2103,"y=0,a=1"
2106
- 0.0,0.0,1.0,2104,"y=0,a=1"
2107
- 0.0,0.0,1.0,2105,"y=0,a=1"
2108
- 0.0,0.0,1.0,2106,"y=0,a=1"
2109
- 0.0,0.0,1.0,2107,"y=0,a=1"
2110
- 0.0,0.0,1.0,2108,"y=0,a=1"
2111
- 0.0,0.0,1.0,2109,"y=0,a=1"
2112
- 0.0,0.0,1.0,2110,"y=0,a=1"
2113
- 0.0,0.0,1.0,2111,"y=0,a=1"
2114
- 0.0,0.0,1.0,2112,"y=0,a=1"
2115
- 0.0,0.0,1.0,2113,"y=0,a=1"
2116
- 0.0,0.0,1.0,2114,"y=0,a=1"
2117
- 0.0,0.0,1.0,2115,"y=0,a=1"
2118
- 0.0,0.0,1.0,2116,"y=0,a=1"
2119
- 0.0,0.0,1.0,2117,"y=0,a=1"
2120
- 0.0,0.0,1.0,2118,"y=0,a=1"
2121
- 0.0,0.0,1.0,2119,"y=0,a=1"
2122
- 0.0,0.0,1.0,2120,"y=0,a=1"
2123
- 0.0,0.0,1.0,2121,"y=0,a=1"
2124
- 0.0,0.0,1.0,2122,"y=0,a=1"
2125
- 0.0,0.0,1.0,2123,"y=0,a=1"
2126
- 0.0,0.0,1.0,2124,"y=0,a=1"
2127
- 0.0,0.0,1.0,2125,"y=0,a=1"
2128
- 0.0,0.0,1.0,2126,"y=0,a=1"
2129
- 0.0,0.0,1.0,2127,"y=0,a=1"
2130
- 0.0,0.0,1.0,2128,"y=0,a=1"
2131
- 0.0,0.0,1.0,2129,"y=0,a=1"
2132
- 0.0,0.0,1.0,2130,"y=0,a=1"
2133
- 0.0,0.0,1.0,2131,"y=0,a=1"
2134
- 0.0,0.0,1.0,2132,"y=0,a=1"
2135
- 0.0,0.0,1.0,2133,"y=0,a=1"
2136
- 0.0,0.0,1.0,2134,"y=0,a=1"
2137
- 0.0,0.0,1.0,2135,"y=0,a=1"
2138
- 0.0,0.0,1.0,2136,"y=0,a=1"
2139
- 0.0,0.0,1.0,2137,"y=0,a=1"
2140
- 0.0,0.0,1.0,2138,"y=0,a=1"
2141
- 0.0,0.0,1.0,2139,"y=0,a=1"
2142
- 0.0,0.0,1.0,2140,"y=0,a=1"
2143
- 0.0,0.0,1.0,2141,"y=0,a=1"
2144
- 0.0,0.0,1.0,2142,"y=0,a=1"
2145
- 0.0,0.0,1.0,2143,"y=0,a=1"
2146
- 0.0,0.0,1.0,2144,"y=0,a=1"
2147
- 0.0,0.0,1.0,2145,"y=0,a=1"
2148
- 0.0,0.0,1.0,2146,"y=0,a=1"
2149
- 0.0,0.0,1.0,2147,"y=0,a=1"
2150
- 0.0,0.0,1.0,2148,"y=0,a=1"
2151
- 0.0,0.0,1.0,2149,"y=0,a=1"
2152
- 0.0,0.0,1.0,2150,"y=0,a=1"
2153
- 0.0,0.0,1.0,2151,"y=0,a=1"
2154
- 0.0,0.0,1.0,2152,"y=0,a=1"
2155
- 0.0,0.0,1.0,2153,"y=0,a=1"
2156
- 0.0,0.0,1.0,2154,"y=0,a=1"
2157
- 0.0,0.0,1.0,2155,"y=0,a=1"
2158
- 0.0,0.0,1.0,2156,"y=0,a=1"
2159
- 0.0,0.0,1.0,2157,"y=0,a=1"
2160
- 0.0,0.0,1.0,2158,"y=0,a=1"
2161
- 0.0,0.0,1.0,2159,"y=0,a=1"
2162
- 0.0,0.0,1.0,2160,"y=0,a=1"
2163
- 0.0,0.0,1.0,2161,"y=0,a=1"
2164
- 0.0,0.0,1.0,2162,"y=0,a=1"
2165
- 0.0,0.0,1.0,2163,"y=0,a=1"
2166
- 0.0,0.0,1.0,2164,"y=0,a=1"
2167
- 0.0,0.0,1.0,2165,"y=0,a=1"
2168
- 0.0,0.0,1.0,2166,"y=0,a=1"
2169
- 0.0,0.0,1.0,2167,"y=0,a=1"
2170
- 0.0,0.0,1.0,2168,"y=0,a=1"
2171
- 0.0,0.0,1.0,2169,"y=0,a=1"
2172
- 0.0,0.0,1.0,2170,"y=0,a=1"
2173
- 0.0,0.0,1.0,2171,"y=0,a=1"
2174
- 0.0,0.0,1.0,2172,"y=0,a=1"
2175
- 0.0,0.0,1.0,2173,"y=0,a=1"
2176
- 0.0,0.0,1.0,2174,"y=0,a=1"
2177
- 0.0,0.0,1.0,2175,"y=0,a=1"
2178
- 0.0,0.0,1.0,2176,"y=0,a=1"
2179
- 0.0,0.0,1.0,2177,"y=0,a=1"
2180
- 0.0,0.0,1.0,2178,"y=0,a=1"
2181
- 0.0,0.0,1.0,2179,"y=0,a=1"
2182
- 0.0,0.0,1.0,2180,"y=0,a=1"
2183
- 0.0,0.0,1.0,2181,"y=0,a=1"
2184
- 0.0,0.0,1.0,2182,"y=0,a=1"
2185
- 0.0,0.0,1.0,2183,"y=0,a=1"
2186
- 0.0,0.0,1.0,2184,"y=0,a=1"
2187
- 0.0,0.0,1.0,2185,"y=0,a=1"
2188
- 0.0,0.0,1.0,2186,"y=0,a=1"
2189
- 0.0,0.0,1.0,2187,"y=0,a=1"
2190
- 0.0,0.0,1.0,2188,"y=0,a=1"
2191
- 0.0,0.0,1.0,2189,"y=0,a=1"
2192
- 0.0,0.0,1.0,2190,"y=0,a=1"
2193
- 0.0,0.0,1.0,2191,"y=0,a=1"
2194
- 0.0,0.0,1.0,2192,"y=0,a=1"
2195
- 0.0,0.0,1.0,2193,"y=0,a=1"
2196
- 0.0,0.0,1.0,2194,"y=0,a=1"
2197
- 0.0,0.0,1.0,2195,"y=0,a=1"
2198
- 0.0,0.0,1.0,2196,"y=0,a=1"
2199
- 0.0,0.0,1.0,2197,"y=0,a=1"
2200
- 0.0,0.0,1.0,2198,"y=0,a=1"
2201
- 0.0,0.0,1.0,2199,"y=0,a=1"
2202
- 0.0,0.0,1.0,2200,"y=0,a=1"
2203
- 0.0,0.0,1.0,2201,"y=0,a=1"
2204
- 0.0,0.0,1.0,2202,"y=0,a=1"
2205
- 0.0,0.0,1.0,2203,"y=0,a=1"
2206
- 0.0,0.0,1.0,2204,"y=0,a=1"
2207
- 0.0,0.0,1.0,2205,"y=0,a=1"
2208
- 0.0,0.0,1.0,2206,"y=0,a=1"
2209
- 0.0,0.0,1.0,2207,"y=0,a=1"
2210
- 0.0,0.0,1.0,2208,"y=0,a=1"
2211
- 0.0,0.0,1.0,2209,"y=0,a=1"
2212
- 0.0,0.0,1.0,2210,"y=0,a=1"
2213
- 0.0,0.0,1.0,2211,"y=0,a=1"
2214
- 0.0,0.0,1.0,2212,"y=0,a=1"
2215
- 0.0,0.0,1.0,2213,"y=0,a=1"
2216
- 0.0,0.0,1.0,2214,"y=0,a=1"
2217
- 0.0,0.0,1.0,2215,"y=0,a=1"
2218
- 0.0,0.0,1.0,2216,"y=0,a=1"
2219
- 0.0,0.0,1.0,2217,"y=0,a=1"
2220
- 0.0,0.0,1.0,2218,"y=0,a=1"
2221
- 0.0,0.0,1.0,2219,"y=0,a=1"
2222
- 0.0,0.0,1.0,2220,"y=0,a=1"
2223
- 0.0,0.0,1.0,2221,"y=0,a=1"
2224
- 0.0,0.0,1.0,2222,"y=0,a=1"
2225
- 0.0,0.0,1.0,2223,"y=0,a=1"
2226
- 0.0,0.0,1.0,2224,"y=0,a=1"
2227
- 0.0,0.0,1.0,2225,"y=0,a=1"
2228
- 0.0,0.0,1.0,2226,"y=0,a=1"
2229
- 0.0,0.0,1.0,2227,"y=0,a=1"
2230
- 0.0,0.0,1.0,2228,"y=0,a=1"
2231
- 0.0,0.0,1.0,2229,"y=0,a=1"
2232
- 0.0,0.0,1.0,2230,"y=0,a=1"
2233
- 0.0,0.0,1.0,2231,"y=0,a=1"
2234
- 0.0,0.0,1.0,2232,"y=0,a=1"
2235
- 0.0,0.0,1.0,2233,"y=0,a=1"
2236
- 0.0,0.0,1.0,2234,"y=0,a=1"
2237
- 0.0,0.0,1.0,2235,"y=0,a=1"
2238
- 0.0,0.0,1.0,2236,"y=0,a=1"
2239
- 0.0,0.0,1.0,2237,"y=0,a=1"
2240
- 0.0,0.0,1.0,2238,"y=0,a=1"
2241
- 0.0,0.0,1.0,2239,"y=0,a=1"
2242
- 0.0,0.0,1.0,2240,"y=0,a=1"
2243
- 0.0,0.0,1.0,2241,"y=0,a=1"
2244
- 0.0,0.0,1.0,2242,"y=0,a=1"
2245
- 0.0,0.0,1.0,2243,"y=0,a=1"
2246
- 0.0,0.0,1.0,2244,"y=0,a=1"
2247
- 0.0,0.0,1.0,2245,"y=0,a=1"
2248
- 0.0,0.0,1.0,2246,"y=0,a=1"
2249
- 0.0,0.0,1.0,2247,"y=0,a=1"
2250
- 0.0,0.0,1.0,2248,"y=0,a=1"
2251
- 0.0,0.0,1.0,2249,"y=0,a=1"
2252
- 0.0,0.0,1.0,2250,"y=0,a=1"
2253
- 0.0,0.0,1.0,2251,"y=0,a=1"
2254
- 0.0,0.0,1.0,2252,"y=0,a=1"
2255
- 0.0,0.0,1.0,2253,"y=0,a=1"
2256
- 0.0,0.0,1.0,2254,"y=0,a=1"
2257
- 0.0,0.0,1.0,2255,"y=0,a=1"
2258
- 0.0,0.0,1.0,2256,"y=0,a=1"
2259
- 0.0,0.0,1.0,2257,"y=0,a=1"
2260
- 0.0,0.0,1.0,2258,"y=0,a=1"
2261
- 0.0,0.0,1.0,2259,"y=0,a=1"
2262
- 0.0,0.0,1.0,2260,"y=0,a=1"
2263
- 0.0,0.0,1.0,2261,"y=0,a=1"
2264
- 0.0,0.0,1.0,2262,"y=0,a=1"
2265
- 0.0,0.0,1.0,2263,"y=0,a=1"
2266
- 0.0,0.0,1.0,2264,"y=0,a=1"
2267
- 0.0,0.0,1.0,2265,"y=0,a=1"
2268
- 0.0,0.0,1.0,2266,"y=0,a=1"
2269
- 0.0,0.0,1.0,2267,"y=0,a=1"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_additional_info.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:614ce643d5d645c2e15a168460a6ea78d4792bcaf6f883b04cfd2b288ee93cb6
3
- size 75142
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_cat_dataframe_mitigation.csv DELETED
The diff for this file is too large to render. See raw diff
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_classifier_embeddings.npy DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:3be01994c300a71aabca8aa39d0c9e92d2193bb7c721937028ca205323e11926
3
- size 18579584
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_clip_embeddings.npy DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:fc058a850ea594c65910132ce14247dd224de4445dbc32709cdbde541c023a7e
3
- size 2322560
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/train_dog_dataframe_mitigation.csv DELETED
The diff for this file is too large to render. See raw diff
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_additional_info.csv DELETED
@@ -1,350 +0,0 @@
1
- out_put_GT,out_put_predict,attribute_bg_predict,idx,gs
2
- 1.0,1.0,1.0,0,"y=1,a=1"
3
- 1.0,1.0,1.0,1,"y=1,a=1"
4
- 1.0,1.0,1.0,2,"y=1,a=1"
5
- 1.0,1.0,1.0,3,"y=1,a=1"
6
- 1.0,1.0,1.0,4,"y=1,a=1"
7
- 1.0,0.0,1.0,5,"y=1,a=1"
8
- 1.0,1.0,1.0,6,"y=1,a=1"
9
- 1.0,1.0,1.0,7,"y=1,a=1"
10
- 1.0,1.0,1.0,8,"y=1,a=1"
11
- 1.0,1.0,1.0,9,"y=1,a=1"
12
- 1.0,1.0,1.0,10,"y=1,a=1"
13
- 1.0,1.0,1.0,11,"y=1,a=1"
14
- 1.0,1.0,1.0,12,"y=1,a=1"
15
- 1.0,1.0,1.0,13,"y=1,a=1"
16
- 1.0,0.0,1.0,14,"y=1,a=1"
17
- 1.0,1.0,1.0,15,"y=1,a=1"
18
- 1.0,1.0,1.0,16,"y=1,a=1"
19
- 1.0,1.0,1.0,17,"y=1,a=1"
20
- 1.0,1.0,1.0,18,"y=1,a=1"
21
- 1.0,1.0,1.0,19,"y=1,a=1"
22
- 1.0,1.0,1.0,20,"y=1,a=1"
23
- 1.0,1.0,1.0,21,"y=1,a=1"
24
- 1.0,1.0,1.0,22,"y=1,a=1"
25
- 1.0,1.0,1.0,23,"y=1,a=1"
26
- 1.0,1.0,1.0,24,"y=1,a=1"
27
- 1.0,1.0,1.0,25,"y=1,a=1"
28
- 1.0,1.0,1.0,26,"y=1,a=1"
29
- 1.0,1.0,1.0,27,"y=1,a=1"
30
- 1.0,1.0,1.0,28,"y=1,a=1"
31
- 1.0,1.0,1.0,29,"y=1,a=1"
32
- 1.0,1.0,1.0,30,"y=1,a=1"
33
- 1.0,1.0,1.0,31,"y=1,a=1"
34
- 1.0,1.0,1.0,32,"y=1,a=1"
35
- 1.0,1.0,1.0,33,"y=1,a=1"
36
- 1.0,1.0,1.0,34,"y=1,a=1"
37
- 1.0,0.0,1.0,35,"y=1,a=1"
38
- 1.0,1.0,1.0,36,"y=1,a=1"
39
- 1.0,1.0,1.0,37,"y=1,a=1"
40
- 1.0,1.0,1.0,38,"y=1,a=1"
41
- 1.0,1.0,1.0,39,"y=1,a=1"
42
- 1.0,1.0,1.0,40,"y=1,a=1"
43
- 1.0,1.0,1.0,41,"y=1,a=1"
44
- 1.0,1.0,1.0,42,"y=1,a=1"
45
- 1.0,1.0,1.0,43,"y=1,a=1"
46
- 1.0,1.0,1.0,44,"y=1,a=1"
47
- 1.0,1.0,1.0,45,"y=1,a=1"
48
- 1.0,1.0,1.0,46,"y=1,a=1"
49
- 1.0,1.0,1.0,47,"y=1,a=1"
50
- 1.0,1.0,1.0,48,"y=1,a=1"
51
- 1.0,1.0,1.0,49,"y=1,a=1"
52
- 1.0,1.0,1.0,50,"y=1,a=1"
53
- 1.0,1.0,1.0,51,"y=1,a=1"
54
- 1.0,1.0,1.0,52,"y=1,a=1"
55
- 1.0,1.0,1.0,53,"y=1,a=1"
56
- 1.0,1.0,1.0,54,"y=1,a=1"
57
- 1.0,1.0,1.0,55,"y=1,a=1"
58
- 1.0,1.0,1.0,56,"y=1,a=1"
59
- 1.0,1.0,1.0,57,"y=1,a=1"
60
- 1.0,1.0,1.0,58,"y=1,a=1"
61
- 1.0,1.0,1.0,59,"y=1,a=1"
62
- 1.0,1.0,1.0,60,"y=1,a=1"
63
- 1.0,1.0,1.0,61,"y=1,a=1"
64
- 1.0,0.0,1.0,62,"y=1,a=1"
65
- 1.0,1.0,1.0,63,"y=1,a=1"
66
- 1.0,1.0,1.0,64,"y=1,a=1"
67
- 1.0,1.0,1.0,65,"y=1,a=1"
68
- 1.0,1.0,1.0,66,"y=1,a=1"
69
- 1.0,1.0,1.0,67,"y=1,a=1"
70
- 1.0,1.0,1.0,68,"y=1,a=1"
71
- 1.0,0.0,1.0,69,"y=1,a=1"
72
- 1.0,1.0,1.0,70,"y=1,a=1"
73
- 1.0,1.0,1.0,71,"y=1,a=1"
74
- 1.0,1.0,1.0,72,"y=1,a=1"
75
- 1.0,1.0,1.0,73,"y=1,a=1"
76
- 1.0,1.0,1.0,74,"y=1,a=1"
77
- 1.0,1.0,1.0,75,"y=1,a=1"
78
- 1.0,1.0,1.0,76,"y=1,a=1"
79
- 1.0,1.0,1.0,77,"y=1,a=1"
80
- 1.0,1.0,1.0,78,"y=1,a=1"
81
- 1.0,1.0,1.0,79,"y=1,a=1"
82
- 1.0,1.0,1.0,80,"y=1,a=1"
83
- 1.0,1.0,1.0,81,"y=1,a=1"
84
- 1.0,1.0,1.0,82,"y=1,a=1"
85
- 1.0,1.0,1.0,83,"y=1,a=1"
86
- 1.0,1.0,1.0,84,"y=1,a=1"
87
- 1.0,0.0,1.0,85,"y=1,a=1"
88
- 1.0,1.0,1.0,86,"y=1,a=1"
89
- 1.0,1.0,1.0,87,"y=1,a=1"
90
- 1.0,1.0,1.0,88,"y=1,a=1"
91
- 1.0,1.0,1.0,89,"y=1,a=1"
92
- 1.0,1.0,1.0,90,"y=1,a=1"
93
- 1.0,1.0,1.0,91,"y=1,a=1"
94
- 1.0,1.0,1.0,92,"y=1,a=1"
95
- 1.0,1.0,1.0,93,"y=1,a=1"
96
- 1.0,1.0,1.0,94,"y=1,a=1"
97
- 1.0,1.0,1.0,95,"y=1,a=1"
98
- 1.0,1.0,1.0,96,"y=1,a=1"
99
- 1.0,1.0,1.0,97,"y=1,a=1"
100
- 1.0,1.0,1.0,98,"y=1,a=1"
101
- 1.0,1.0,1.0,99,"y=1,a=1"
102
- 1.0,1.0,1.0,100,"y=1,a=1"
103
- 1.0,1.0,1.0,101,"y=1,a=1"
104
- 1.0,1.0,1.0,102,"y=1,a=1"
105
- 1.0,1.0,1.0,103,"y=1,a=1"
106
- 1.0,1.0,1.0,104,"y=1,a=1"
107
- 1.0,1.0,1.0,105,"y=1,a=1"
108
- 1.0,1.0,1.0,106,"y=1,a=1"
109
- 1.0,1.0,1.0,107,"y=1,a=1"
110
- 1.0,1.0,1.0,108,"y=1,a=1"
111
- 1.0,1.0,1.0,109,"y=1,a=1"
112
- 1.0,1.0,1.0,110,"y=1,a=1"
113
- 1.0,1.0,1.0,111,"y=1,a=1"
114
- 1.0,1.0,1.0,112,"y=1,a=1"
115
- 1.0,1.0,1.0,113,"y=1,a=1"
116
- 0.0,0.0,0.0,114,"y=0,a=0"
117
- 0.0,0.0,0.0,115,"y=0,a=0"
118
- 0.0,0.0,0.0,116,"y=0,a=0"
119
- 0.0,0.0,0.0,117,"y=0,a=0"
120
- 0.0,0.0,0.0,118,"y=0,a=0"
121
- 0.0,0.0,0.0,119,"y=0,a=0"
122
- 0.0,0.0,0.0,120,"y=0,a=0"
123
- 0.0,0.0,0.0,121,"y=0,a=0"
124
- 0.0,0.0,0.0,122,"y=0,a=0"
125
- 0.0,0.0,0.0,123,"y=0,a=0"
126
- 0.0,0.0,0.0,124,"y=0,a=0"
127
- 0.0,0.0,0.0,125,"y=0,a=0"
128
- 0.0,0.0,0.0,126,"y=0,a=0"
129
- 0.0,0.0,0.0,127,"y=0,a=0"
130
- 0.0,0.0,0.0,128,"y=0,a=0"
131
- 0.0,0.0,0.0,129,"y=0,a=0"
132
- 0.0,0.0,0.0,130,"y=0,a=0"
133
- 0.0,0.0,0.0,131,"y=0,a=0"
134
- 0.0,0.0,0.0,132,"y=0,a=0"
135
- 0.0,0.0,0.0,133,"y=0,a=0"
136
- 0.0,0.0,0.0,134,"y=0,a=0"
137
- 0.0,0.0,0.0,135,"y=0,a=0"
138
- 0.0,0.0,0.0,136,"y=0,a=0"
139
- 0.0,0.0,0.0,137,"y=0,a=0"
140
- 0.0,0.0,0.0,138,"y=0,a=0"
141
- 0.0,0.0,0.0,139,"y=0,a=0"
142
- 0.0,0.0,0.0,140,"y=0,a=0"
143
- 0.0,0.0,0.0,141,"y=0,a=0"
144
- 0.0,0.0,0.0,142,"y=0,a=0"
145
- 0.0,0.0,0.0,143,"y=0,a=0"
146
- 0.0,0.0,0.0,144,"y=0,a=0"
147
- 0.0,0.0,0.0,145,"y=0,a=0"
148
- 0.0,0.0,0.0,146,"y=0,a=0"
149
- 0.0,0.0,0.0,147,"y=0,a=0"
150
- 0.0,0.0,0.0,148,"y=0,a=0"
151
- 0.0,0.0,0.0,149,"y=0,a=0"
152
- 0.0,0.0,0.0,150,"y=0,a=0"
153
- 0.0,0.0,0.0,151,"y=0,a=0"
154
- 0.0,0.0,0.0,152,"y=0,a=0"
155
- 0.0,0.0,0.0,153,"y=0,a=0"
156
- 0.0,0.0,0.0,154,"y=0,a=0"
157
- 0.0,1.0,0.0,155,"y=0,a=0"
158
- 0.0,0.0,0.0,156,"y=0,a=0"
159
- 0.0,0.0,0.0,157,"y=0,a=0"
160
- 0.0,0.0,0.0,158,"y=0,a=0"
161
- 0.0,0.0,0.0,159,"y=0,a=0"
162
- 0.0,0.0,0.0,160,"y=0,a=0"
163
- 0.0,0.0,0.0,161,"y=0,a=0"
164
- 0.0,0.0,0.0,162,"y=0,a=0"
165
- 0.0,0.0,0.0,163,"y=0,a=0"
166
- 0.0,0.0,0.0,164,"y=0,a=0"
167
- 0.0,0.0,0.0,165,"y=0,a=0"
168
- 0.0,0.0,0.0,166,"y=0,a=0"
169
- 0.0,0.0,0.0,167,"y=0,a=0"
170
- 0.0,0.0,0.0,168,"y=0,a=0"
171
- 0.0,0.0,0.0,169,"y=0,a=0"
172
- 0.0,0.0,0.0,170,"y=0,a=0"
173
- 0.0,0.0,0.0,171,"y=0,a=0"
174
- 0.0,0.0,0.0,172,"y=0,a=0"
175
- 0.0,0.0,0.0,173,"y=0,a=0"
176
- 0.0,0.0,0.0,174,"y=0,a=0"
177
- 0.0,0.0,0.0,175,"y=0,a=0"
178
- 0.0,0.0,0.0,176,"y=0,a=0"
179
- 0.0,0.0,0.0,177,"y=0,a=0"
180
- 0.0,0.0,0.0,178,"y=0,a=0"
181
- 0.0,0.0,0.0,179,"y=0,a=0"
182
- 0.0,0.0,0.0,180,"y=0,a=0"
183
- 0.0,0.0,0.0,181,"y=0,a=0"
184
- 0.0,0.0,0.0,182,"y=0,a=0"
185
- 0.0,0.0,0.0,183,"y=0,a=0"
186
- 0.0,0.0,0.0,184,"y=0,a=0"
187
- 0.0,0.0,0.0,185,"y=0,a=0"
188
- 0.0,0.0,0.0,186,"y=0,a=0"
189
- 0.0,0.0,0.0,187,"y=0,a=0"
190
- 0.0,0.0,0.0,188,"y=0,a=0"
191
- 0.0,0.0,0.0,189,"y=0,a=0"
192
- 0.0,0.0,0.0,190,"y=0,a=0"
193
- 0.0,0.0,0.0,191,"y=0,a=0"
194
- 0.0,0.0,0.0,192,"y=0,a=0"
195
- 0.0,0.0,0.0,193,"y=0,a=0"
196
- 0.0,0.0,0.0,194,"y=0,a=0"
197
- 0.0,0.0,0.0,195,"y=0,a=0"
198
- 0.0,0.0,0.0,196,"y=0,a=0"
199
- 0.0,1.0,0.0,197,"y=0,a=0"
200
- 0.0,0.0,0.0,198,"y=0,a=0"
201
- 0.0,0.0,0.0,199,"y=0,a=0"
202
- 0.0,0.0,0.0,200,"y=0,a=0"
203
- 0.0,0.0,0.0,201,"y=0,a=0"
204
- 0.0,0.0,0.0,202,"y=0,a=0"
205
- 0.0,0.0,0.0,203,"y=0,a=0"
206
- 0.0,0.0,0.0,204,"y=0,a=0"
207
- 0.0,0.0,0.0,205,"y=0,a=0"
208
- 0.0,0.0,0.0,206,"y=0,a=0"
209
- 0.0,0.0,0.0,207,"y=0,a=0"
210
- 0.0,0.0,0.0,208,"y=0,a=0"
211
- 0.0,0.0,0.0,209,"y=0,a=0"
212
- 0.0,0.0,0.0,210,"y=0,a=0"
213
- 0.0,0.0,0.0,211,"y=0,a=0"
214
- 0.0,0.0,0.0,212,"y=0,a=0"
215
- 0.0,0.0,0.0,213,"y=0,a=0"
216
- 0.0,0.0,0.0,214,"y=0,a=0"
217
- 0.0,0.0,0.0,215,"y=0,a=0"
218
- 0.0,0.0,0.0,216,"y=0,a=0"
219
- 0.0,0.0,0.0,217,"y=0,a=0"
220
- 0.0,0.0,0.0,218,"y=0,a=0"
221
- 0.0,0.0,0.0,219,"y=0,a=0"
222
- 0.0,1.0,0.0,220,"y=0,a=0"
223
- 0.0,0.0,0.0,221,"y=0,a=0"
224
- 0.0,0.0,0.0,222,"y=0,a=0"
225
- 0.0,0.0,0.0,223,"y=0,a=0"
226
- 0.0,0.0,0.0,224,"y=0,a=0"
227
- 0.0,0.0,0.0,225,"y=0,a=0"
228
- 0.0,0.0,0.0,226,"y=0,a=0"
229
- 0.0,0.0,0.0,227,"y=0,a=0"
230
- 0.0,0.0,0.0,228,"y=0,a=0"
231
- 0.0,0.0,0.0,229,"y=0,a=0"
232
- 0.0,0.0,0.0,230,"y=0,a=0"
233
- 0.0,0.0,0.0,231,"y=0,a=0"
234
- 0.0,0.0,0.0,232,"y=0,a=0"
235
- 0.0,0.0,0.0,233,"y=0,a=0"
236
- 0.0,0.0,0.0,234,"y=0,a=0"
237
- 0.0,0.0,0.0,235,"y=0,a=0"
238
- 0.0,0.0,0.0,236,"y=0,a=0"
239
- 0.0,0.0,0.0,237,"y=0,a=0"
240
- 0.0,0.0,0.0,238,"y=0,a=0"
241
- 0.0,0.0,0.0,239,"y=0,a=0"
242
- 0.0,0.0,0.0,240,"y=0,a=0"
243
- 1.0,1.0,0.0,241,"y=1,a=0"
244
- 1.0,1.0,0.0,242,"y=1,a=0"
245
- 1.0,1.0,0.0,243,"y=1,a=0"
246
- 1.0,1.0,0.0,244,"y=1,a=0"
247
- 1.0,1.0,0.0,245,"y=1,a=0"
248
- 1.0,1.0,0.0,246,"y=1,a=0"
249
- 1.0,0.0,0.0,247,"y=1,a=0"
250
- 1.0,1.0,0.0,248,"y=1,a=0"
251
- 1.0,1.0,0.0,249,"y=1,a=0"
252
- 1.0,0.0,0.0,250,"y=1,a=0"
253
- 1.0,1.0,0.0,251,"y=1,a=0"
254
- 1.0,0.0,0.0,252,"y=1,a=0"
255
- 1.0,0.0,0.0,253,"y=1,a=0"
256
- 1.0,1.0,0.0,254,"y=1,a=0"
257
- 1.0,0.0,0.0,255,"y=1,a=0"
258
- 1.0,1.0,0.0,256,"y=1,a=0"
259
- 1.0,0.0,0.0,257,"y=1,a=0"
260
- 1.0,1.0,0.0,258,"y=1,a=0"
261
- 1.0,0.0,0.0,259,"y=1,a=0"
262
- 1.0,1.0,0.0,260,"y=1,a=0"
263
- 1.0,0.0,0.0,261,"y=1,a=0"
264
- 1.0,1.0,0.0,262,"y=1,a=0"
265
- 1.0,1.0,0.0,263,"y=1,a=0"
266
- 1.0,1.0,0.0,264,"y=1,a=0"
267
- 1.0,0.0,0.0,265,"y=1,a=0"
268
- 1.0,1.0,0.0,266,"y=1,a=0"
269
- 1.0,0.0,0.0,267,"y=1,a=0"
270
- 1.0,1.0,0.0,268,"y=1,a=0"
271
- 1.0,1.0,0.0,269,"y=1,a=0"
272
- 1.0,1.0,0.0,270,"y=1,a=0"
273
- 1.0,1.0,0.0,271,"y=1,a=0"
274
- 1.0,1.0,0.0,272,"y=1,a=0"
275
- 1.0,1.0,0.0,273,"y=1,a=0"
276
- 0.0,0.0,1.0,274,"y=0,a=1"
277
- 0.0,1.0,1.0,275,"y=0,a=1"
278
- 0.0,1.0,1.0,276,"y=0,a=1"
279
- 0.0,0.0,1.0,277,"y=0,a=1"
280
- 0.0,0.0,1.0,278,"y=0,a=1"
281
- 0.0,0.0,1.0,279,"y=0,a=1"
282
- 0.0,0.0,1.0,280,"y=0,a=1"
283
- 0.0,0.0,1.0,281,"y=0,a=1"
284
- 0.0,0.0,1.0,282,"y=0,a=1"
285
- 0.0,0.0,1.0,283,"y=0,a=1"
286
- 0.0,0.0,1.0,284,"y=0,a=1"
287
- 0.0,0.0,1.0,285,"y=0,a=1"
288
- 0.0,0.0,1.0,286,"y=0,a=1"
289
- 0.0,0.0,1.0,287,"y=0,a=1"
290
- 0.0,0.0,1.0,288,"y=0,a=1"
291
- 0.0,0.0,1.0,289,"y=0,a=1"
292
- 0.0,0.0,1.0,290,"y=0,a=1"
293
- 0.0,0.0,1.0,291,"y=0,a=1"
294
- 0.0,0.0,1.0,292,"y=0,a=1"
295
- 0.0,0.0,1.0,293,"y=0,a=1"
296
- 0.0,0.0,1.0,294,"y=0,a=1"
297
- 0.0,0.0,1.0,295,"y=0,a=1"
298
- 0.0,0.0,1.0,296,"y=0,a=1"
299
- 0.0,1.0,1.0,297,"y=0,a=1"
300
- 0.0,0.0,1.0,298,"y=0,a=1"
301
- 0.0,0.0,1.0,299,"y=0,a=1"
302
- 0.0,0.0,1.0,300,"y=0,a=1"
303
- 0.0,0.0,1.0,301,"y=0,a=1"
304
- 0.0,0.0,1.0,302,"y=0,a=1"
305
- 0.0,0.0,1.0,303,"y=0,a=1"
306
- 0.0,0.0,1.0,304,"y=0,a=1"
307
- 0.0,0.0,1.0,305,"y=0,a=1"
308
- 0.0,1.0,1.0,306,"y=0,a=1"
309
- 0.0,0.0,1.0,307,"y=0,a=1"
310
- 0.0,0.0,1.0,308,"y=0,a=1"
311
- 0.0,0.0,1.0,309,"y=0,a=1"
312
- 0.0,0.0,1.0,310,"y=0,a=1"
313
- 0.0,0.0,1.0,311,"y=0,a=1"
314
- 0.0,1.0,1.0,312,"y=0,a=1"
315
- 0.0,0.0,1.0,313,"y=0,a=1"
316
- 0.0,0.0,1.0,314,"y=0,a=1"
317
- 0.0,0.0,1.0,315,"y=0,a=1"
318
- 0.0,0.0,1.0,316,"y=0,a=1"
319
- 0.0,0.0,1.0,317,"y=0,a=1"
320
- 0.0,0.0,1.0,318,"y=0,a=1"
321
- 0.0,1.0,1.0,319,"y=0,a=1"
322
- 0.0,0.0,1.0,320,"y=0,a=1"
323
- 0.0,0.0,1.0,321,"y=0,a=1"
324
- 0.0,0.0,1.0,322,"y=0,a=1"
325
- 0.0,0.0,1.0,323,"y=0,a=1"
326
- 0.0,0.0,1.0,324,"y=0,a=1"
327
- 0.0,0.0,1.0,325,"y=0,a=1"
328
- 0.0,0.0,1.0,326,"y=0,a=1"
329
- 0.0,0.0,1.0,327,"y=0,a=1"
330
- 0.0,0.0,1.0,328,"y=0,a=1"
331
- 0.0,0.0,1.0,329,"y=0,a=1"
332
- 0.0,0.0,1.0,330,"y=0,a=1"
333
- 0.0,0.0,1.0,331,"y=0,a=1"
334
- 0.0,0.0,1.0,332,"y=0,a=1"
335
- 0.0,0.0,1.0,333,"y=0,a=1"
336
- 0.0,0.0,1.0,334,"y=0,a=1"
337
- 0.0,0.0,1.0,335,"y=0,a=1"
338
- 0.0,0.0,1.0,336,"y=0,a=1"
339
- 0.0,0.0,1.0,337,"y=0,a=1"
340
- 0.0,0.0,1.0,338,"y=0,a=1"
341
- 0.0,0.0,1.0,339,"y=0,a=1"
342
- 0.0,0.0,1.0,340,"y=0,a=1"
343
- 0.0,0.0,1.0,341,"y=0,a=1"
344
- 0.0,0.0,1.0,342,"y=0,a=1"
345
- 0.0,0.0,1.0,343,"y=0,a=1"
346
- 0.0,0.0,1.0,344,"y=0,a=1"
347
- 0.0,1.0,1.0,345,"y=0,a=1"
348
- 0.0,0.0,1.0,346,"y=0,a=1"
349
- 0.0,0.0,1.0,347,"y=0,a=1"
350
- 0.0,1.0,1.0,348,"y=0,a=1"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_additional_info.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:36c37d856824a023c9b24bf5accb0cb28185aa89da9c0b3b8aa412daae96d5db
3
- size 12074
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_cat_dataframe_mitigation.csv DELETED
@@ -1,350 +0,0 @@
1
- out_put_GT,out_put_predict,attribute_bg_predict,idx,gs,Predictions_bin,H1_desk,H1_desk_bin,H2_laptop,H2_laptop_bin,H3_monitor,H3_monitor_bin,H4_keyboard,H4_keyboard_bin,H5_bookshelf,H5_bookshelf_bin,H6_cushion,H6_cushion_bin,H7_bed,H7_bed_bin,H8_suitcase,H8_suitcase_bin,H9_plant,H9_plant_bin,H10_mouse,H10_mouse_bin
2
- 1.0,1.0,1.0,0,"y=1,a=1",1,0.14918202,0,0.135185,0,0.14954567,0,0.14612508,0,0.14674985,0,0.15579918,0,0.14804476,0,0.16124207,0,0.14829572,0,0.17646569,0
3
- 1.0,1.0,1.0,1,"y=1,a=1",1,0.18808864,0,0.17224611,0,0.20011097,0,0.17465769,0,0.1949326,1,0.19043794,0,0.18474624,0,0.1804874,0,0.20158121,0,0.19801566,0
4
- 1.0,1.0,1.0,2,"y=1,a=1",1,0.2720383,1,0.2805897,1,0.27898145,1,0.26854035,1,0.22733384,1,0.26252192,1,0.25786558,1,0.22550228,1,0.21713573,1,0.27492908,1
5
- 1.0,1.0,1.0,3,"y=1,a=1",1,0.26460347,1,0.26456037,1,0.26948994,1,0.26351473,1,0.21943356,1,0.21879192,1,0.21634685,1,0.19949515,1,0.19904058,0,0.27151975,1
6
- 1.0,1.0,1.0,4,"y=1,a=1",1,0.16506594,0,0.14243713,0,0.16374901,0,0.16299322,0,0.16455445,0,0.16884916,0,0.15892398,0,0.14553447,0,0.17886934,0,0.19293545,0
7
- 1.0,0.0,1.0,5,"y=1,a=1",0,0.16592945,0,0.15628847,0,0.1576602,0,0.1593551,0,0.13070565,0,0.16227855,0,0.13926007,0,0.1491936,0,0.16476151,0,0.17979649,0
8
- 1.0,1.0,1.0,6,"y=1,a=1",1,0.20982656,1,0.20586646,1,0.21819139,1,0.21016301,1,0.17721508,0,0.20507683,1,0.19284746,0,0.18542558,0,0.18030347,0,0.23721105,1
9
- 1.0,1.0,1.0,7,"y=1,a=1",1,0.24500331,1,0.2312396,1,0.24709454,1,0.23379774,1,0.2126905,1,0.18814798,0,0.19757402,0,0.19467533,1,0.1918699,0,0.23883878,1
10
- 1.0,1.0,1.0,8,"y=1,a=1",1,0.23310202,1,0.21956152,1,0.23195104,1,0.22205101,1,0.21695907,1,0.23419756,1,0.22876611,1,0.21685758,1,0.20523106,1,0.24942546,1
11
- 1.0,1.0,1.0,9,"y=1,a=1",1,0.23082379,1,0.22133048,1,0.24231601,1,0.22178163,1,0.19207586,0,0.20327051,1,0.20408893,1,0.2015633,1,0.18194555,0,0.2337424,1
12
- 1.0,1.0,1.0,10,"y=1,a=1",1,0.25280207,1,0.2633658,1,0.2684375,1,0.26176217,1,0.20767707,1,0.2530205,1,0.23824166,1,0.20546576,1,0.19746658,0,0.28100348,1
13
- 1.0,1.0,1.0,11,"y=1,a=1",1,0.2515317,1,0.2506656,1,0.2667894,1,0.26093367,1,0.22594781,1,0.25163648,1,0.23127358,1,0.22136545,1,0.22495213,1,0.2832945,1
14
- 1.0,1.0,1.0,12,"y=1,a=1",1,0.20723383,1,0.18627001,0,0.21074636,0,0.18671913,0,0.20880644,1,0.17960477,0,0.19098195,0,0.20450802,1,0.19553421,0,0.20925522,0
15
- 1.0,1.0,1.0,13,"y=1,a=1",1,0.19467133,0,0.17954384,0,0.1954554,0,0.190851,0,0.18664913,0,0.18784189,0,0.17784183,0,0.18953146,0,0.19380994,0,0.21623807,0
16
- 1.0,0.0,1.0,14,"y=1,a=1",0,0.23302566,1,0.21306497,1,0.23878558,1,0.19823462,0,0.22078156,1,0.2038839,1,0.20805298,1,0.19040868,0,0.19705418,0,0.22773379,1
17
- 1.0,1.0,1.0,15,"y=1,a=1",1,0.23603897,1,0.2209853,1,0.2286572,1,0.22776073,1,0.22782582,1,0.2582558,1,0.25403312,1,0.2177338,1,0.2192626,1,0.24736309,1
18
- 1.0,1.0,1.0,16,"y=1,a=1",1,0.20084918,0,0.19642638,1,0.22053342,1,0.20797901,1,0.19498403,1,0.21031626,1,0.2199545,1,0.20381947,1,0.19795328,0,0.2341182,1
19
- 1.0,1.0,1.0,17,"y=1,a=1",1,0.24032396,1,0.21314566,1,0.24737127,1,0.20142125,1,0.2381964,1,0.19577157,0,0.19841875,0,0.18415247,0,0.19512875,0,0.22132961,0
20
- 1.0,1.0,1.0,18,"y=1,a=1",1,0.27044612,1,0.26103505,1,0.26542506,1,0.24525109,1,0.198214,1,0.1850347,0,0.18345311,0,0.18294251,0,0.18088228,0,0.25096124,1
21
- 1.0,1.0,1.0,19,"y=1,a=1",1,0.24081142,1,0.2282868,1,0.24047627,1,0.23509589,1,0.21630974,1,0.22657879,1,0.21789834,1,0.21246164,1,0.20560437,1,0.24956565,1
22
- 1.0,1.0,1.0,20,"y=1,a=1",1,0.250499,1,0.2325734,1,0.26529396,1,0.23085849,1,0.2353075,1,0.19849584,0,0.19833717,0,0.19679596,1,0.21242519,1,0.24295884,1
23
- 1.0,1.0,1.0,21,"y=1,a=1",1,0.29385033,1,0.29363868,1,0.30531794,1,0.2925731,1,0.24283266,1,0.22276992,1,0.21915226,1,0.20549062,1,0.22012651,1,0.29922324,1
24
- 1.0,1.0,1.0,22,"y=1,a=1",1,0.2661484,1,0.24825007,1,0.27139705,1,0.2581948,1,0.23888707,1,0.20162618,1,0.19930974,1,0.2021133,1,0.22034535,1,0.2691849,1
25
- 1.0,1.0,1.0,23,"y=1,a=1",1,0.22938056,1,0.23251891,1,0.24030852,1,0.22993752,1,0.20673613,1,0.24235441,1,0.235421,1,0.22662312,1,0.20569701,1,0.25615445,1
26
- 1.0,1.0,1.0,24,"y=1,a=1",1,0.20357741,1,0.19473058,1,0.20518285,0,0.21250638,1,0.1826023,0,0.21527916,1,0.2050335,1,0.16834708,0,0.19579925,0,0.24032888,1
27
- 1.0,1.0,1.0,25,"y=1,a=1",1,0.19561073,0,0.19071166,0,0.19807649,0,0.19030929,0,0.17578481,0,0.21423607,1,0.21494494,1,0.18257155,0,0.16841842,0,0.21747893,0
28
- 1.0,1.0,1.0,26,"y=1,a=1",1,0.24666329,1,0.22966036,1,0.25909862,1,0.23965073,1,0.24256153,1,0.23264417,1,0.23837239,1,0.2186695,1,0.22907533,1,0.26110274,1
29
- 1.0,1.0,1.0,27,"y=1,a=1",1,0.24682452,1,0.236043,1,0.2516072,1,0.24583125,1,0.23221248,1,0.23232372,1,0.2278402,1,0.21393633,1,0.21929967,1,0.26153925,1
30
- 1.0,1.0,1.0,28,"y=1,a=1",1,0.23396204,1,0.22718894,1,0.24283323,1,0.23318522,1,0.22062887,1,0.24030188,1,0.22905603,1,0.22469872,1,0.22128749,1,0.25807613,1
31
- 1.0,1.0,1.0,29,"y=1,a=1",1,0.21113068,1,0.20732369,1,0.21353424,0,0.20579994,1,0.18806444,0,0.21769235,1,0.21200266,1,0.19139552,0,0.18248735,0,0.21955985,0
32
- 1.0,1.0,1.0,30,"y=1,a=1",1,0.21581821,1,0.20661552,1,0.2396784,1,0.2132668,1,0.19233255,0,0.18108256,0,0.17404552,0,0.1664504,0,0.20106016,0,0.2307864,1
33
- 1.0,1.0,1.0,31,"y=1,a=1",1,0.24960198,1,0.22839212,1,0.26137465,1,0.24222508,1,0.24809477,1,0.24038856,1,0.24047056,1,0.23982878,1,0.2577575,1,0.2652057,1
34
- 1.0,1.0,1.0,32,"y=1,a=1",1,0.24214858,1,0.23669687,1,0.24491887,1,0.23395792,1,0.21840268,1,0.26362914,1,0.25771272,1,0.21358423,1,0.20889358,1,0.25089854,1
35
- 1.0,1.0,1.0,33,"y=1,a=1",1,0.21145305,1,0.20611179,1,0.21254526,0,0.20306325,1,0.19298603,1,0.20828584,1,0.20274167,1,0.21860635,1,0.19308886,0,0.22172143,0
36
- 1.0,1.0,1.0,34,"y=1,a=1",1,0.21149358,1,0.18740949,0,0.21407275,0,0.20450029,1,0.2041204,1,0.1876463,0,0.18943837,0,0.20153339,1,0.20661128,1,0.23527117,1
37
- 1.0,0.0,1.0,35,"y=1,a=1",0,0.2065194,1,0.19282573,1,0.20551118,0,0.196793,0,0.1834541,0,0.1865526,0,0.18770143,0,0.17654756,0,0.1854583,0,0.23121221,1
38
- 1.0,1.0,1.0,36,"y=1,a=1",1,0.300301,1,0.28806198,1,0.29359245,1,0.28983203,1,0.2301674,1,0.22641353,1,0.21582848,1,0.20282653,1,0.21795844,1,0.28849375,1
39
- 1.0,1.0,1.0,37,"y=1,a=1",1,0.18824852,0,0.18694536,0,0.19601533,0,0.19084322,0,0.17501795,0,0.20562208,1,0.20641515,1,0.18759719,0,0.18000512,0,0.21351737,0
40
- 1.0,1.0,1.0,38,"y=1,a=1",1,0.22508633,1,0.21701105,1,0.23042928,1,0.22120623,1,0.22274482,1,0.22234832,1,0.2193227,1,0.21574499,1,0.20986862,1,0.2480872,1
41
- 1.0,1.0,1.0,39,"y=1,a=1",1,0.25539845,1,0.24324428,1,0.26052257,1,0.25559217,1,0.23580028,1,0.24946216,1,0.23863249,1,0.21270932,1,0.23053221,1,0.27488884,1
42
- 1.0,1.0,1.0,40,"y=1,a=1",1,0.21633007,1,0.20946714,1,0.23368041,1,0.19638835,0,0.20962436,1,0.22015509,1,0.21845828,1,0.23339967,1,0.21132998,1,0.24280094,1
43
- 1.0,1.0,1.0,41,"y=1,a=1",1,0.20399967,1,0.19766936,1,0.20451583,0,0.21182607,1,0.19553584,1,0.24390097,1,0.23612015,1,0.21415462,1,0.21131973,1,0.229774,1
44
- 1.0,1.0,1.0,42,"y=1,a=1",1,0.20011559,0,0.18911944,0,0.20231062,0,0.19727509,0,0.19572692,1,0.22962789,1,0.22037384,1,0.21052672,1,0.19788837,0,0.22432329,1
45
- 1.0,1.0,1.0,43,"y=1,a=1",1,0.27114812,1,0.26288837,1,0.2667561,1,0.25822324,1,0.23317352,1,0.2269912,1,0.2223634,1,0.20191379,1,0.21575892,1,0.2650475,1
46
- 1.0,1.0,1.0,44,"y=1,a=1",1,0.24382146,1,0.23713419,1,0.24097006,1,0.23534271,1,0.21185756,1,0.26186463,1,0.25484222,1,0.204803,1,0.21871823,1,0.25098354,1
47
- 1.0,1.0,1.0,45,"y=1,a=1",1,0.27862087,1,0.27932557,1,0.29518002,1,0.28173575,1,0.23396477,1,0.21744041,1,0.21163239,1,0.20973828,1,0.21862303,1,0.2942211,1
48
- 1.0,1.0,1.0,46,"y=1,a=1",1,0.23341517,1,0.22057045,1,0.24168515,1,0.22613393,1,0.21921806,1,0.22604501,1,0.22165819,1,0.21471274,1,0.21670061,1,0.24939333,1
49
- 1.0,1.0,1.0,47,"y=1,a=1",1,0.23603357,1,0.21192858,1,0.24234086,1,0.22802086,1,0.23573016,1,0.2434946,1,0.24580956,1,0.21453081,1,0.22771452,1,0.24229299,1
50
- 1.0,1.0,1.0,48,"y=1,a=1",1,0.20115514,0,0.19188881,0,0.20256549,0,0.19086042,0,0.1944264,1,0.21958555,1,0.2186017,1,0.20917855,1,0.19220795,0,0.21743368,0
51
- 1.0,1.0,1.0,49,"y=1,a=1",1,0.24096982,1,0.2169454,1,0.2539254,1,0.23026218,1,0.23812486,1,0.2137183,1,0.22435264,1,0.21374944,1,0.23400803,1,0.25362468,1
52
- 1.0,1.0,1.0,50,"y=1,a=1",1,0.24474998,1,0.2252487,1,0.24990885,1,0.22999899,1,0.23933916,1,0.2437178,1,0.23669846,1,0.22654785,1,0.2317289,1,0.2562089,1
53
- 1.0,1.0,1.0,51,"y=1,a=1",1,0.27259207,1,0.27095538,1,0.26539287,1,0.2722704,1,0.23738554,1,0.2685707,1,0.25718492,1,0.22803417,1,0.23248065,1,0.2770716,1
54
- 1.0,1.0,1.0,52,"y=1,a=1",1,0.2079595,1,0.17286925,0,0.20556262,0,0.19446576,0,0.19328822,1,0.18607813,0,0.18665986,0,0.1774859,0,0.20739178,1,0.23109439,1
55
- 1.0,1.0,1.0,53,"y=1,a=1",1,0.21132603,1,0.20660368,1,0.2186373,1,0.21453416,1,0.19533314,1,0.20189255,1,0.19584984,0,0.19296972,1,0.20467366,1,0.23302071,1
56
- 1.0,1.0,1.0,54,"y=1,a=1",1,0.21970744,1,0.20503329,1,0.22873442,1,0.2184555,1,0.19682267,1,0.2181955,1,0.21227764,1,0.19763544,1,0.21724437,1,0.24327421,1
57
- 1.0,1.0,1.0,55,"y=1,a=1",1,0.21370523,1,0.21573366,1,0.2304947,1,0.21289814,1,0.19174184,0,0.17563874,0,0.17448263,0,0.1905395,0,0.17632669,0,0.21958165,0
58
- 1.0,1.0,1.0,56,"y=1,a=1",1,0.2268465,1,0.21189335,1,0.23011823,1,0.22422586,1,0.22376159,1,0.21954939,1,0.2199489,1,0.19825898,1,0.21570373,1,0.23890984,1
59
- 1.0,1.0,1.0,57,"y=1,a=1",1,0.30054322,1,0.30965555,1,0.31972823,1,0.29817548,1,0.23985136,1,0.235685,1,0.23087278,1,0.1939974,1,0.22706214,1,0.30048457,1
60
- 1.0,1.0,1.0,58,"y=1,a=1",1,0.22702281,1,0.20719473,1,0.23783098,1,0.212955,1,0.2303693,1,0.2120716,1,0.21160926,1,0.21443878,1,0.23163456,1,0.23491809,1
61
- 1.0,1.0,1.0,59,"y=1,a=1",1,0.25080025,1,0.24712873,1,0.27197617,1,0.2536027,1,0.22339877,1,0.22819364,1,0.22051343,1,0.22738926,1,0.22325589,1,0.2725179,1
62
- 1.0,1.0,1.0,60,"y=1,a=1",1,0.26431873,1,0.25846735,1,0.26745987,1,0.2587122,1,0.24812974,1,0.26875934,1,0.2612535,1,0.23846349,1,0.24205846,1,0.27479196,1
63
- 1.0,1.0,1.0,61,"y=1,a=1",1,0.20738158,1,0.19304647,1,0.22491542,1,0.19575691,0,0.20773113,1,0.20553067,1,0.20413405,1,0.21071696,1,0.20623499,1,0.23466302,1
64
- 1.0,0.0,1.0,62,"y=1,a=1",0,0.21235356,1,0.20541279,1,0.21532276,0,0.196767,0,0.20000143,1,0.25012276,1,0.26160038,1,0.21447413,1,0.20855765,1,0.22879986,1
65
- 1.0,1.0,1.0,63,"y=1,a=1",1,0.21557851,1,0.20136511,1,0.23277429,1,0.22333789,1,0.21342948,1,0.21725927,1,0.21998535,1,0.20254517,1,0.23024118,1,0.25221202,1
66
- 1.0,1.0,1.0,64,"y=1,a=1",1,0.27582422,1,0.2665368,1,0.2741963,1,0.25651366,1,0.24914466,1,0.26855195,1,0.27212495,1,0.26755327,1,0.23926339,1,0.2711308,1
67
- 1.0,1.0,1.0,65,"y=1,a=1",1,0.28146237,1,0.27459276,1,0.3072545,1,0.27850848,1,0.25308087,1,0.24796163,1,0.2404381,1,0.24706483,1,0.24127264,1,0.29914653,1
68
- 1.0,1.0,1.0,66,"y=1,a=1",1,0.21057858,1,0.20424949,1,0.22382703,1,0.21612972,1,0.18898746,0,0.21453975,1,0.21450162,1,0.20373228,1,0.20923033,1,0.24003193,1
69
- 1.0,1.0,1.0,67,"y=1,a=1",1,0.22897613,1,0.22687429,1,0.24145937,1,0.21496494,1,0.22795801,1,0.22806959,1,0.22619496,1,0.1900905,0,0.19453755,0,0.23350862,1
70
- 1.0,1.0,1.0,68,"y=1,a=1",1,0.2671824,1,0.26197812,1,0.27327707,1,0.2699127,1,0.25715214,1,0.29378614,1,0.2896779,1,0.25931427,1,0.24780183,1,0.29412398,1
71
- 1.0,0.0,1.0,69,"y=1,a=1",0,0.19328229,0,0.16973694,0,0.19719815,0,0.1894738,0,0.1987227,1,0.19300176,0,0.19783422,0,0.18307213,0,0.2097322,1,0.21363078,0
72
- 1.0,1.0,1.0,70,"y=1,a=1",1,0.24239616,1,0.25566277,1,0.2664009,1,0.25761846,1,0.21123688,1,0.21323788,1,0.2122109,1,0.21083051,1,0.21647312,1,0.27574927,1
73
- 1.0,1.0,1.0,71,"y=1,a=1",1,0.25860846,1,0.22462703,1,0.2631388,1,0.23614617,1,0.25393403,1,0.2393616,1,0.24599552,1,0.22769952,1,0.27129218,1,0.2710734,1
74
- 1.0,1.0,1.0,72,"y=1,a=1",1,0.22528952,1,0.21948119,1,0.2266803,1,0.21254005,1,0.21196912,1,0.23736124,1,0.24301685,1,0.23365664,1,0.19852522,0,0.24856852,1
75
- 1.0,1.0,1.0,73,"y=1,a=1",1,0.24583587,1,0.22093992,1,0.24975267,1,0.23023234,1,0.23835748,1,0.21956638,1,0.21994911,1,0.20242722,1,0.22400573,1,0.25438726,1
76
- 1.0,1.0,1.0,74,"y=1,a=1",1,0.2868168,1,0.29905638,1,0.3066819,1,0.29066125,1,0.24560347,1,0.24417624,1,0.23061575,1,0.2090091,1,0.23975688,1,0.2953739,1
77
- 1.0,1.0,1.0,75,"y=1,a=1",1,0.16416055,0,0.13990116,0,0.16182974,0,0.15106788,0,0.17215678,0,0.1834992,0,0.18388988,0,0.16966341,0,0.18992107,0,0.18227124,0
78
- 1.0,1.0,1.0,76,"y=1,a=1",1,0.19770266,0,0.19712193,1,0.20103633,0,0.1892699,0,0.19021522,0,0.2401312,1,0.2459865,1,0.1712602,0,0.17645222,0,0.21494842,0
79
- 1.0,1.0,1.0,77,"y=1,a=1",1,0.23028697,1,0.21576563,1,0.23385496,1,0.20976256,1,0.24270284,1,0.20343448,1,0.20130545,1,0.21646304,1,0.20995831,1,0.23629849,1
80
- 1.0,1.0,1.0,78,"y=1,a=1",1,0.24965575,1,0.22042216,1,0.23700695,1,0.23041646,1,0.24067922,1,0.23980218,1,0.22305728,1,0.19514152,1,0.24539709,1,0.23606226,1
81
- 1.0,1.0,1.0,79,"y=1,a=1",1,0.21365401,1,0.20848349,1,0.21549228,1,0.22096445,1,0.21337457,1,0.23801509,1,0.24719162,1,0.21991308,1,0.24018788,1,0.24428746,1
82
- 1.0,1.0,1.0,80,"y=1,a=1",1,0.25227845,1,0.2164045,1,0.2538119,1,0.2144523,1,0.25385007,1,0.22110038,1,0.22606632,1,0.19832894,1,0.22166155,1,0.251443,1
83
- 1.0,1.0,1.0,81,"y=1,a=1",1,0.21126987,1,0.19769005,1,0.21379298,0,0.21231951,1,0.2016337,1,0.21433905,1,0.21166894,1,0.1988535,1,0.2086812,1,0.2368025,1
84
- 1.0,1.0,1.0,82,"y=1,a=1",1,0.11789042,0,0.118957974,0,0.11534204,0,0.13542433,0,0.115532294,0,0.14387101,0,0.14294225,0,0.097139776,0,0.13975339,0,0.14095141,0
85
- 1.0,1.0,1.0,83,"y=1,a=1",1,0.24033137,1,0.21363065,1,0.23672712,1,0.23182514,1,0.21793857,1,0.22313763,1,0.23347037,1,0.20051986,1,0.22138074,1,0.26121873,1
86
- 1.0,1.0,1.0,84,"y=1,a=1",1,0.25923657,1,0.24760947,1,0.25419697,1,0.22787572,1,0.2532716,1,0.25406703,1,0.27350742,1,0.2335024,1,0.23825246,1,0.26422894,1
87
- 1.0,0.0,1.0,85,"y=1,a=1",0,0.16963407,0,0.16665287,0,0.18355778,0,0.17631964,0,0.18006432,0,0.21489199,1,0.23296426,1,0.17669848,0,0.18535851,0,0.19624281,0
88
- 1.0,1.0,1.0,86,"y=1,a=1",1,0.23854518,1,0.23091361,1,0.25231874,1,0.23289096,1,0.22850074,1,0.23721875,1,0.22899489,1,0.22010028,1,0.2222333,1,0.255959,1
89
- 1.0,1.0,1.0,87,"y=1,a=1",1,0.21865474,1,0.18506564,0,0.222112,1,0.19915216,1,0.20785916,1,0.20096396,0,0.20505497,1,0.18610162,0,0.2181858,1,0.23658007,1
90
- 1.0,1.0,1.0,88,"y=1,a=1",1,0.26229066,1,0.2341337,1,0.26551032,1,0.23854938,1,0.24851564,1,0.20722963,1,0.2078548,1,0.20581,1,0.22691,1,0.25868186,1
91
- 1.0,1.0,1.0,89,"y=1,a=1",1,0.21027935,1,0.19741616,1,0.215046,0,0.20575303,1,0.19838864,1,0.20911634,1,0.20086989,1,0.184738,0,0.19582482,0,0.22793819,1
92
- 1.0,1.0,1.0,90,"y=1,a=1",1,0.22002263,1,0.21393897,1,0.22615868,1,0.22222517,1,0.20920216,1,0.22436018,1,0.21773545,1,0.19901596,1,0.21043843,1,0.24080983,1
93
- 1.0,1.0,1.0,91,"y=1,a=1",1,0.24667545,1,0.22864215,1,0.24795413,1,0.23020476,1,0.21877313,1,0.21979472,1,0.21382092,1,0.22397198,1,0.2073956,1,0.2536378,1
94
- 1.0,1.0,1.0,92,"y=1,a=1",1,0.2411433,1,0.23253492,1,0.25128493,1,0.2245112,1,0.2409982,1,0.21842864,1,0.21605322,1,0.21676412,1,0.23073165,1,0.2563819,1
95
- 1.0,1.0,1.0,93,"y=1,a=1",1,0.24005789,1,0.21759517,1,0.24053556,1,0.22789584,1,0.23411246,1,0.25160038,1,0.24472626,1,0.22506978,1,0.22569965,1,0.24103218,1
96
- 1.0,1.0,1.0,94,"y=1,a=1",1,0.24086896,1,0.22613066,1,0.2408175,1,0.22912724,1,0.22638181,1,0.2407335,1,0.2343653,1,0.2159186,1,0.20938244,1,0.25630695,1
97
- 1.0,1.0,1.0,95,"y=1,a=1",1,0.21287285,1,0.20718798,1,0.23659985,1,0.20771392,1,0.21358655,1,0.21442199,1,0.22273722,1,0.20475037,1,0.21248087,1,0.23839916,1
98
- 1.0,1.0,1.0,96,"y=1,a=1",1,0.20995277,1,0.19032313,0,0.22427326,1,0.19362262,0,0.23286106,1,0.20620649,1,0.21449421,1,0.19822688,1,0.22969685,1,0.22772598,1
99
- 1.0,1.0,1.0,97,"y=1,a=1",1,0.21337968,1,0.1899211,0,0.21313281,0,0.18982916,0,0.21727689,1,0.19908637,0,0.19756916,0,0.17646512,0,0.22149679,1,0.21632801,0
100
- 1.0,1.0,1.0,98,"y=1,a=1",1,0.24263307,1,0.2287541,1,0.25070146,1,0.22671038,1,0.22454673,1,0.21518223,1,0.21025583,1,0.20306349,1,0.20725833,1,0.24109253,1
101
- 1.0,1.0,1.0,99,"y=1,a=1",1,0.19478111,0,0.17738697,0,0.19590129,0,0.18484269,0,0.1717388,0,0.18708298,0,0.1817043,0,0.16630681,0,0.18868712,0,0.20437674,0
102
- 1.0,1.0,1.0,100,"y=1,a=1",1,0.24383126,1,0.23776114,1,0.25038245,1,0.2486227,1,0.23123938,1,0.25016525,1,0.24757917,1,0.21495253,1,0.22384706,1,0.26573914,1
103
- 1.0,1.0,1.0,101,"y=1,a=1",1,0.21835512,1,0.21283922,1,0.22775626,1,0.21257089,1,0.2011495,1,0.21478795,1,0.20941602,1,0.19066107,0,0.19771564,0,0.23191391,1
104
- 1.0,1.0,1.0,102,"y=1,a=1",1,0.16390581,0,0.14307225,0,0.15205048,0,0.1512447,0,0.16140726,0,0.17974158,0,0.17872608,0,0.15279473,0,0.15746447,0,0.15953116,0
105
- 1.0,1.0,1.0,103,"y=1,a=1",1,0.2787304,1,0.2632091,1,0.28117037,1,0.26711044,1,0.24895124,1,0.24570987,1,0.23812883,1,0.2318672,1,0.22557168,1,0.28122663,1
106
- 1.0,1.0,1.0,104,"y=1,a=1",1,0.2542118,1,0.24304147,1,0.2623701,1,0.25170037,1,0.21679054,1,0.20762305,1,0.1990801,0,0.19518888,1,0.20494537,1,0.2703149,1
107
- 1.0,1.0,1.0,105,"y=1,a=1",1,0.23939346,1,0.23248099,1,0.24927552,1,0.23511127,1,0.22268662,1,0.21727103,1,0.21540509,1,0.1967893,1,0.21771888,1,0.24602841,1
108
- 1.0,1.0,1.0,106,"y=1,a=1",1,0.2918334,1,0.28780392,1,0.3063287,1,0.29703838,1,0.23414893,1,0.25174326,1,0.23460741,1,0.2426706,1,0.24279027,1,0.3273438,1
109
- 1.0,1.0,1.0,107,"y=1,a=1",1,0.23201141,1,0.21638742,1,0.25051987,1,0.20377405,1,0.22764929,1,0.19904968,0,0.20308506,1,0.18206193,0,0.19676177,0,0.21826725,0
110
- 1.0,1.0,1.0,108,"y=1,a=1",1,0.24011551,1,0.22746643,1,0.24325733,1,0.23562396,1,0.22604123,1,0.23471719,1,0.23691335,1,0.21124576,1,0.22164342,1,0.25850734,1
111
- 1.0,1.0,1.0,109,"y=1,a=1",1,0.26079035,1,0.25195643,1,0.2706794,1,0.25326172,1,0.24264541,1,0.26214102,1,0.25310415,1,0.22316492,1,0.23401895,1,0.29071853,1
112
- 1.0,1.0,1.0,110,"y=1,a=1",1,0.25823933,1,0.2553587,1,0.2680014,1,0.26522392,1,0.22503063,1,0.2336448,1,0.22707403,1,0.22219849,1,0.22423263,1,0.27788416,1
113
- 1.0,1.0,1.0,111,"y=1,a=1",1,0.2300313,1,0.22645755,1,0.2276172,1,0.24440189,1,0.20669311,1,0.27265224,1,0.27359352,1,0.19514194,1,0.20408942,1,0.25836244,1
114
- 1.0,1.0,1.0,112,"y=1,a=1",1,0.23886847,1,0.22699434,1,0.24264985,1,0.2289337,1,0.22644013,1,0.25177082,1,0.24448004,1,0.23258449,1,0.21634328,1,0.25316274,1
115
- 1.0,1.0,1.0,113,"y=1,a=1",1,0.27109453,1,0.26769415,1,0.28277114,1,0.2725088,1,0.24565782,1,0.24817805,1,0.23568477,1,0.22240762,1,0.23157471,1,0.28980908,1
116
- 0.0,0.0,0.0,114,"y=0,a=0",0,0.18029778,0,0.1686227,0,0.1968581,0,0.17786089,0,0.18207276,0,0.16569497,0,0.1727903,0,0.17595223,0,0.18423238,0,0.19868502,0
117
- 0.0,0.0,0.0,115,"y=0,a=0",0,0.17085375,0,0.14770105,0,0.17498846,0,0.16678797,0,0.16991785,0,0.14718477,0,0.1496817,0,0.1723823,0,0.1729165,0,0.1782358,0
118
- 0.0,0.0,0.0,116,"y=0,a=0",0,0.1582394,0,0.14983448,0,0.1647575,0,0.15131207,0,0.13398683,0,0.15271847,0,0.16883396,0,0.1676948,0,0.16509452,0,0.16373585,0
119
- 0.0,0.0,0.0,117,"y=0,a=0",0,0.12865524,0,0.1243246,0,0.15455103,0,0.13519818,0,0.13776326,0,0.14620261,0,0.14147411,0,0.1470412,0,0.15547852,0,0.16257875,0
120
- 0.0,0.0,0.0,118,"y=0,a=0",0,0.14533116,0,0.14249414,0,0.17006578,0,0.1507309,0,0.15166104,0,0.15473214,0,0.13904095,0,0.15879089,0,0.1644054,0,0.18674755,0
121
- 0.0,0.0,0.0,119,"y=0,a=0",0,0.14079754,0,0.13436547,0,0.14415456,0,0.14466818,0,0.13949102,0,0.15000679,0,0.13019684,0,0.15086599,0,0.15921913,0,0.16411251,0
122
- 0.0,0.0,0.0,120,"y=0,a=0",0,0.16715546,0,0.16879377,0,0.19742265,0,0.17058331,0,0.16135709,0,0.16491887,0,0.1550736,0,0.17306228,0,0.18987003,0,0.19768251,0
123
- 0.0,0.0,0.0,121,"y=0,a=0",0,0.15928881,0,0.15465198,0,0.19086497,0,0.17010337,0,0.15236677,0,0.15933208,0,0.15575162,0,0.14472067,0,0.17849614,0,0.20014791,0
124
- 0.0,0.0,0.0,122,"y=0,a=0",0,0.1705028,0,0.15747894,0,0.18929388,0,0.1771321,0,0.16603193,0,0.15294354,0,0.15015757,0,0.17766996,0,0.1794345,0,0.18042223,0
125
- 0.0,0.0,0.0,123,"y=0,a=0",0,0.23984762,1,0.23974137,1,0.2547934,1,0.2322835,1,0.23145202,1,0.22637883,1,0.22848225,1,0.27435228,1,0.23380613,1,0.25193426,1
126
- 0.0,0.0,0.0,124,"y=0,a=0",0,0.17181708,0,0.16473892,0,0.20738238,0,0.1868539,0,0.16817166,0,0.17254953,0,0.15907699,0,0.17232957,0,0.2005466,0,0.22335683,0
127
- 0.0,0.0,0.0,125,"y=0,a=0",0,0.1551536,0,0.14666802,0,0.18843788,0,0.16593067,0,0.17204596,0,0.16763026,0,0.15259057,0,0.14526953,0,0.19772854,0,0.21546604,0
128
- 0.0,0.0,0.0,126,"y=0,a=0",0,0.14391132,0,0.1344748,0,0.1653603,0,0.15071845,0,0.14487517,0,0.15002812,0,0.13560353,0,0.1338609,0,0.17041834,0,0.18402705,0
129
- 0.0,0.0,0.0,127,"y=0,a=0",0,0.1299178,0,0.12771279,0,0.16180456,0,0.13787675,0,0.11466526,0,0.13625878,0,0.13666883,0,0.1338309,0,0.15875442,0,0.15695399,0
130
- 0.0,0.0,0.0,128,"y=0,a=0",0,0.16409463,0,0.1627358,0,0.20999582,0,0.17959684,0,0.17033483,0,0.17131406,0,0.1782588,0,0.1706297,0,0.20512053,1,0.22960348,1
131
- 0.0,0.0,0.0,129,"y=0,a=0",0,0.1322562,0,0.13010424,0,0.15081072,0,0.13209276,0,0.102794275,0,0.13583407,0,0.14340545,0,0.13050605,0,0.17388259,0,0.17068376,0
132
- 0.0,0.0,0.0,130,"y=0,a=0",0,0.16256581,0,0.15216778,0,0.18497717,0,0.16255444,0,0.15424001,0,0.15476768,0,0.14367591,0,0.15543857,0,0.17935692,0,0.19857977,0
133
- 0.0,0.0,0.0,131,"y=0,a=0",0,0.13028656,0,0.13299674,0,0.15085977,0,0.1374497,0,0.13217463,0,0.15656301,0,0.14264144,0,0.13723896,0,0.16146258,0,0.17847137,0
134
- 0.0,0.0,0.0,132,"y=0,a=0",0,0.17410244,0,0.15233405,0,0.19843824,0,0.16456032,0,0.17107333,0,0.15761204,0,0.15888816,0,0.15164071,0,0.19665328,0,0.20943367,0
135
- 0.0,0.0,0.0,133,"y=0,a=0",0,0.14377448,0,0.13862504,0,0.16830431,0,0.15629491,0,0.144408,0,0.15043062,0,0.14869097,0,0.1451426,0,0.18034387,0,0.18573011,0
136
- 0.0,0.0,0.0,134,"y=0,a=0",0,0.13441439,0,0.12484734,0,0.15960665,0,0.1450944,0,0.14126854,0,0.1530692,0,0.1526475,0,0.14794935,0,0.15393089,0,0.17498383,0
137
- 0.0,0.0,0.0,135,"y=0,a=0",0,0.18564044,0,0.17271306,0,0.19117707,0,0.18355367,0,0.19280678,0,0.18519007,0,0.18715066,0,0.2045647,1,0.19122936,0,0.19176093,0
138
- 0.0,0.0,0.0,136,"y=0,a=0",0,0.2055357,1,0.1883031,0,0.2146354,0,0.19576186,0,0.19040601,0,0.19177487,0,0.19280374,0,0.21236634,1,0.212333,1,0.21356758,0
139
- 0.0,0.0,0.0,137,"y=0,a=0",0,0.17645587,0,0.17679186,0,0.21501453,0,0.1941291,0,0.17542252,0,0.18392502,0,0.1542891,0,0.17463577,0,0.19783865,0,0.24994515,1
140
- 0.0,0.0,0.0,138,"y=0,a=0",0,0.16462052,0,0.15787894,0,0.18927614,0,0.1579563,0,0.1541054,0,0.1712566,0,0.16652673,0,0.14785068,0,0.17993565,0,0.19217128,0
141
- 0.0,0.0,0.0,139,"y=0,a=0",0,0.15095825,0,0.13802989,0,0.16359276,0,0.14693962,0,0.12466343,0,0.13407105,0,0.14345728,0,0.17253597,0,0.13485834,0,0.16565982,0
142
- 0.0,0.0,0.0,140,"y=0,a=0",0,0.1850136,0,0.17701386,0,0.2013675,0,0.19117506,0,0.16754651,0,0.18373199,0,0.18593541,0,0.18953976,0,0.18438,0,0.20893389,0
143
- 0.0,0.0,0.0,141,"y=0,a=0",0,0.18437472,0,0.17877966,0,0.20655304,0,0.19163083,0,0.18005355,0,0.19504353,0,0.18140826,0,0.17910548,0,0.2045552,1,0.2195337,0
144
- 0.0,0.0,0.0,142,"y=0,a=0",0,0.13093753,0,0.122555375,0,0.16956286,0,0.12547195,0,0.110481985,0,0.11805771,0,0.12030352,0,0.11027442,0,0.14483085,0,0.1636478,0
145
- 0.0,0.0,0.0,143,"y=0,a=0",0,0.17871957,0,0.17733037,0,0.21830758,1,0.20035525,1,0.17414969,0,0.2136273,1,0.19542722,0,0.18729849,0,0.24223192,1,0.24503446,1
146
- 0.0,0.0,0.0,144,"y=0,a=0",0,0.18885651,0,0.18624596,0,0.19434267,0,0.17961113,0,0.1641296,0,0.1715104,0,0.1704928,0,0.18391967,0,0.17811641,0,0.2120338,0
147
- 0.0,0.0,0.0,145,"y=0,a=0",0,0.18014428,0,0.16254738,0,0.19552575,0,0.18032688,0,0.1802712,0,0.16632602,0,0.16908129,0,0.18040143,0,0.18395628,0,0.20554139,0
148
- 0.0,0.0,0.0,146,"y=0,a=0",0,0.19383457,0,0.1907825,0,0.21981159,1,0.20102808,1,0.17602843,0,0.17648491,0,0.16757858,0,0.18713798,0,0.18692459,0,0.21404421,0
149
- 0.0,0.0,0.0,147,"y=0,a=0",0,0.18945122,0,0.18415385,0,0.20750982,0,0.18804342,0,0.169033,0,0.19144323,0,0.19482143,0,0.17617567,0,0.1897883,0,0.20364831,0
150
- 0.0,0.0,0.0,148,"y=0,a=0",0,0.1995509,0,0.19989948,1,0.21401699,0,0.1937878,0,0.19188912,0,0.21135476,1,0.20536697,1,0.22006714,1,0.2295777,1,0.22774227,1
151
- 0.0,0.0,0.0,149,"y=0,a=0",0,0.14903484,0,0.15959144,0,0.18608198,0,0.16295078,0,0.15673842,0,0.17927143,0,0.15346716,0,0.14926283,0,0.18988852,0,0.20944689,0
152
- 0.0,0.0,0.0,150,"y=0,a=0",0,0.14206313,0,0.12909815,0,0.17142928,0,0.14126633,0,0.13297054,0,0.14118767,0,0.1346981,0,0.14284045,0,0.15748906,0,0.1707296,0
153
- 0.0,0.0,0.0,151,"y=0,a=0",0,0.17773996,0,0.17108785,0,0.2154563,0,0.18499894,0,0.18292446,0,0.19277272,0,0.18064466,0,0.17050608,0,0.21493287,1,0.23249924,1
154
- 0.0,0.0,0.0,152,"y=0,a=0",0,0.165511,0,0.1625717,0,0.18537922,0,0.17557195,0,0.16459653,0,0.1793123,0,0.17069793,0,0.16611978,0,0.19568682,0,0.19895132,0
155
- 0.0,0.0,0.0,153,"y=0,a=0",0,0.16929272,0,0.16113445,0,0.21176265,0,0.18504857,0,0.16226847,0,0.16367869,0,0.14939238,0,0.16641165,0,0.2033841,1,0.21897009,0
156
- 0.0,0.0,0.0,154,"y=0,a=0",0,0.16743894,0,0.15611264,0,0.19085221,0,0.17252734,0,0.15879208,0,0.17078452,0,0.16409303,0,0.17574379,0,0.19056349,0,0.20746754,0
157
- 0.0,1.0,0.0,155,"y=0,a=0",1,0.15788938,0,0.14032277,0,0.17826,0,0.15018602,0,0.15119094,0,0.15825716,0,0.16314267,0,0.15816286,0,0.17818308,0,0.18160515,0
158
- 0.0,0.0,0.0,156,"y=0,a=0",0,0.16048162,0,0.1474172,0,0.18079297,0,0.17111978,0,0.17660525,0,0.1705663,0,0.1719448,0,0.14278309,0,0.19334075,0,0.19698168,0
159
- 0.0,0.0,0.0,157,"y=0,a=0",0,0.18033099,0,0.18107533,0,0.20926842,0,0.18023677,0,0.1704732,0,0.17835945,0,0.17849427,0,0.2183076,1,0.19285767,0,0.21187566,0
160
- 0.0,0.0,0.0,158,"y=0,a=0",0,0.1635563,0,0.15952337,0,0.20176479,0,0.17415582,0,0.16357562,0,0.17242031,0,0.16175066,0,0.16770498,0,0.20164683,0,0.2100245,0
161
- 0.0,0.0,0.0,159,"y=0,a=0",0,0.17322949,0,0.15530996,0,0.19875066,0,0.17092492,0,0.15942867,0,0.14205915,0,0.1551916,0,0.16578433,0,0.1850591,0,0.19583587,0
162
- 0.0,0.0,0.0,160,"y=0,a=0",0,0.2005436,0,0.19225574,1,0.2183684,1,0.1975069,0,0.1820534,0,0.19014277,0,0.19638106,0,0.1919303,1,0.19275354,0,0.2212899,0
163
- 0.0,0.0,0.0,161,"y=0,a=0",0,0.16757104,0,0.15214093,0,0.16844037,0,0.16518627,0,0.15271282,0,0.15782985,0,0.15086044,0,0.16942456,0,0.15827815,0,0.16963813,0
164
- 0.0,0.0,0.0,162,"y=0,a=0",0,0.1621121,0,0.14637263,0,0.19842146,0,0.16856238,0,0.1657964,0,0.14823988,0,0.15244587,0,0.16172951,0,0.19473723,0,0.19250455,0
165
- 0.0,0.0,0.0,163,"y=0,a=0",0,0.15121078,0,0.14961429,0,0.19234456,0,0.17699526,0,0.15743805,0,0.17302968,0,0.16829537,0,0.1555303,0,0.18848646,0,0.20009524,0
166
- 0.0,0.0,0.0,164,"y=0,a=0",0,0.18958935,0,0.16763395,0,0.19085482,0,0.18419273,0,0.1753192,0,0.17576613,0,0.17994624,0,0.20416896,1,0.17323051,0,0.19241548,0
167
- 0.0,0.0,0.0,165,"y=0,a=0",0,0.1779791,0,0.16440256,0,0.18855885,0,0.17548034,0,0.17188077,0,0.15506168,0,0.16682933,0,0.18593973,0,0.17114337,0,0.18473214,0
168
- 0.0,0.0,0.0,166,"y=0,a=0",0,0.19465667,0,0.1891763,0,0.20267615,0,0.1777944,0,0.17262182,0,0.19199328,0,0.19358683,0,0.20294902,1,0.19026968,0,0.20537783,0
169
- 0.0,0.0,0.0,167,"y=0,a=0",0,0.1694218,0,0.16453932,0,0.20969793,0,0.18565902,0,0.18391602,0,0.17090371,0,0.17306438,0,0.17086285,0,0.229222,1,0.21638101,0
170
- 0.0,0.0,0.0,168,"y=0,a=0",0,0.20216766,0,0.18038554,0,0.20511515,0,0.19299239,0,0.19250056,0,0.20041524,0,0.19515961,0,0.22095233,1,0.19587162,0,0.20701401,0
171
- 0.0,0.0,0.0,169,"y=0,a=0",0,0.187594,0,0.16130765,0,0.19706963,0,0.18338612,0,0.18214823,0,0.17700967,0,0.18579505,0,0.16399647,0,0.18830433,0,0.19580838,0
172
- 0.0,0.0,0.0,170,"y=0,a=0",0,0.17776303,0,0.17145285,0,0.17970783,0,0.17851001,0,0.16985562,0,0.20979527,1,0.20485047,1,0.19026655,0,0.19621633,0,0.19206712,0
173
- 0.0,0.0,0.0,171,"y=0,a=0",0,0.14824237,0,0.14982171,0,0.19343247,0,0.17185125,0,0.14749295,0,0.17424484,0,0.16781741,0,0.13803025,0,0.17989275,0,0.20925789,0
174
- 0.0,0.0,0.0,172,"y=0,a=0",0,0.17134011,0,0.16556206,0,0.20389368,0,0.19205463,0,0.17004123,0,0.18278737,0,0.16054344,0,0.15152599,0,0.19504695,0,0.22866867,1
175
- 0.0,0.0,0.0,173,"y=0,a=0",0,0.19637133,0,0.18438359,0,0.20114544,0,0.1745365,0,0.16614126,0,0.17250983,0,0.17064522,0,0.20135437,1,0.17791092,0,0.20175372,0
176
- 0.0,0.0,0.0,174,"y=0,a=0",0,0.16181415,0,0.15551457,0,0.17950048,0,0.17112643,0,0.15923409,0,0.15431865,0,0.15582071,0,0.16320986,0,0.15529206,0,0.18607622,0
177
- 0.0,0.0,0.0,175,"y=0,a=0",0,0.155802,0,0.15391754,0,0.18946782,0,0.1674502,0,0.15974292,0,0.16356076,0,0.16154775,0,0.1815384,0,0.17419882,0,0.19933566,0
178
- 0.0,0.0,0.0,176,"y=0,a=0",0,0.15333079,0,0.15511888,0,0.19893482,0,0.16921715,0,0.15177006,0,0.15210913,0,0.15037799,0,0.14729989,0,0.17435753,0,0.19350821,0
179
- 0.0,0.0,0.0,177,"y=0,a=0",0,0.15622537,0,0.16028509,0,0.18962315,0,0.1774514,0,0.15750779,0,0.182536,0,0.17105275,0,0.16804555,0,0.20155808,0,0.202267,0
180
- 0.0,0.0,0.0,178,"y=0,a=0",0,0.1909429,0,0.18205689,0,0.1996407,0,0.1830991,0,0.17883834,0,0.17671867,0,0.17834091,0,0.19330496,1,0.18708998,0,0.20046787,0
181
- 0.0,0.0,0.0,179,"y=0,a=0",0,0.13409728,0,0.14163385,0,0.174089,0,0.15159932,0,0.12004204,0,0.13092442,0,0.12467622,0,0.13984081,0,0.15518287,0,0.18097177,0
182
- 0.0,0.0,0.0,180,"y=0,a=0",0,0.23138076,1,0.22382224,1,0.2536354,1,0.23526193,1,0.21117076,1,0.2252605,1,0.22066347,1,0.2014027,1,0.23057531,1,0.25262147,1
183
- 0.0,0.0,0.0,181,"y=0,a=0",0,0.16241981,0,0.15664646,0,0.19213799,0,0.17753573,0,0.1607194,0,0.15754163,0,0.1573823,0,0.15611404,0,0.17523772,0,0.19635218,0
184
- 0.0,0.0,0.0,182,"y=0,a=0",0,0.1671074,0,0.16356447,0,0.19625337,0,0.18003921,0,0.16189429,0,0.17115562,0,0.16677354,0,0.1831835,0,0.17705345,0,0.200269,0
185
- 0.0,0.0,0.0,183,"y=0,a=0",0,0.15558733,0,0.1615348,0,0.19407377,0,0.16577871,0,0.14778827,0,0.15862438,0,0.14932403,0,0.15861551,0,0.18464981,0,0.19873372,0
186
- 0.0,0.0,0.0,184,"y=0,a=0",0,0.20242296,0,0.19724262,1,0.22088157,1,0.1918309,0,0.20251024,1,0.16566215,0,0.17687301,0,0.1849733,0,0.19264732,0,0.21395399,0
187
- 0.0,0.0,0.0,185,"y=0,a=0",0,0.1421357,0,0.13117588,0,0.16605154,0,0.14999713,0,0.1403702,0,0.16214205,0,0.16299035,0,0.12708977,0,0.1698506,0,0.1718569,0
188
- 0.0,0.0,0.0,186,"y=0,a=0",0,0.10331964,0,0.09553324,0,0.14091966,0,0.10421714,0,0.10442194,0,0.10374982,0,0.106529474,0,0.115942396,0,0.14882423,0,0.14967316,0
189
- 0.0,0.0,0.0,187,"y=0,a=0",0,0.18463437,0,0.17150286,0,0.19700988,0,0.18998808,0,0.15560538,0,0.15590584,0,0.15794368,0,0.16887037,0,0.16385342,0,0.1928471,0
190
- 0.0,0.0,0.0,188,"y=0,a=0",0,0.18312456,0,0.17638199,0,0.20763516,0,0.19302426,0,0.19550587,1,0.20274816,1,0.19916505,0,0.18740425,0,0.22126271,1,0.21572003,0
191
- 0.0,0.0,0.0,189,"y=0,a=0",0,0.20578597,1,0.2011208,1,0.21193027,0,0.20530222,1,0.18261068,0,0.21402025,1,0.2169659,1,0.21684454,1,0.21085739,1,0.21559358,0
192
- 0.0,0.0,0.0,190,"y=0,a=0",0,0.14702383,0,0.1407689,0,0.17234121,0,0.15425341,0,0.15426287,0,0.16904315,0,0.16310489,0,0.14530557,0,0.17510243,0,0.18815136,0
193
- 0.0,0.0,0.0,191,"y=0,a=0",0,0.18526214,0,0.18448976,0,0.18001832,0,0.18390822,0,0.15702543,0,0.19004925,0,0.18092719,0,0.17657037,0,0.16682343,0,0.17867276,0
194
- 0.0,0.0,0.0,192,"y=0,a=0",0,0.13303283,0,0.1285344,0,0.16515803,0,0.14575413,0,0.13684769,0,0.14103292,0,0.13657907,0,0.124251075,0,0.16068688,0,0.16979146,0
195
- 0.0,0.0,0.0,193,"y=0,a=0",0,0.16700819,0,0.16258429,0,0.19738217,0,0.17237934,0,0.16924766,0,0.1764861,0,0.17349225,0,0.17815457,0,0.20613316,1,0.20567384,0
196
- 0.0,0.0,0.0,194,"y=0,a=0",0,0.16576768,0,0.15299504,0,0.18103789,0,0.16410165,0,0.16079885,0,0.15940899,0,0.14926775,0,0.1757925,0,0.17567919,0,0.18319541,0
197
- 0.0,0.0,0.0,195,"y=0,a=0",0,0.14900292,0,0.14665754,0,0.1754466,0,0.16981083,0,0.14908877,0,0.1494838,0,0.13460393,0,0.17100495,0,0.16085462,0,0.18899581,0
198
- 0.0,0.0,0.0,196,"y=0,a=0",0,0.20045997,0,0.19574799,1,0.24562079,1,0.20129584,1,0.21048571,1,0.19792864,0,0.19765873,0,0.22312908,1,0.22844225,1,0.23244248,1
199
- 0.0,1.0,0.0,197,"y=0,a=0",1,0.18413447,0,0.17614025,0,0.21231872,0,0.18073224,0,0.18740995,0,0.19751552,0,0.20201889,1,0.18657905,0,0.20325828,0,0.22677135,1
200
- 0.0,0.0,0.0,198,"y=0,a=0",0,0.1709119,0,0.16842139,0,0.20236576,0,0.18563613,0,0.16861787,0,0.18619804,0,0.18237644,0,0.18117233,0,0.21078126,1,0.21506724,0
201
- 0.0,0.0,0.0,199,"y=0,a=0",0,0.17767738,0,0.16478157,0,0.19531688,0,0.18646921,0,0.1794866,0,0.17220385,0,0.17085436,0,0.18669784,0,0.19198595,0,0.20617461,0
202
- 0.0,0.0,0.0,200,"y=0,a=0",0,0.19358245,0,0.18968904,0,0.20596337,0,0.18890654,0,0.192863,1,0.19888689,0,0.20090526,1,0.18728378,0,0.19559672,0,0.21813825,0
203
- 0.0,0.0,0.0,201,"y=0,a=0",0,0.18786119,0,0.17412603,0,0.18725117,0,0.17093273,0,0.17843091,0,0.16193867,0,0.17276222,0,0.20070855,1,0.18431363,0,0.1825579,0
204
- 0.0,0.0,0.0,202,"y=0,a=0",0,0.1713545,0,0.17546494,0,0.17421581,0,0.1595448,0,0.14197843,0,0.18793061,0,0.18742876,0,0.1815537,0,0.16107318,0,0.19526608,0
205
- 0.0,0.0,0.0,203,"y=0,a=0",0,0.19417325,0,0.18216078,0,0.22019021,1,0.19517009,0,0.18900134,0,0.18393698,0,0.17524911,0,0.19504945,1,0.21270446,1,0.22716755,1
206
- 0.0,0.0,0.0,204,"y=0,a=0",0,0.18824841,0,0.18648688,0,0.22204225,1,0.19276859,0,0.17252909,0,0.17519829,0,0.17027906,0,0.18126275,0,0.19882776,0,0.23948596,1
207
- 0.0,0.0,0.0,205,"y=0,a=0",0,0.1566661,0,0.1543474,0,0.18823174,0,0.16070054,0,0.15872963,0,0.1657341,0,0.15907037,0,0.15123796,0,0.18004157,0,0.19102798,0
208
- 0.0,0.0,0.0,206,"y=0,a=0",0,0.21374312,1,0.20822936,1,0.21968965,1,0.21625787,1,0.18225047,0,0.22312358,1,0.2009094,1,0.21122816,1,0.20578013,1,0.22949794,1
209
- 0.0,0.0,0.0,207,"y=0,a=0",0,0.16143614,0,0.15616035,0,0.19117713,0,0.1757717,0,0.16027533,0,0.1568342,0,0.15702777,0,0.17252512,0,0.17994635,0,0.20479359,0
210
- 0.0,0.0,0.0,208,"y=0,a=0",0,0.10286345,0,0.09615747,0,0.12942359,0,0.11327766,0,0.10869159,0,0.11491886,0,0.103001274,0,0.108186424,0,0.1356143,0,0.14129306,0
211
- 0.0,0.0,0.0,209,"y=0,a=0",0,0.14938492,0,0.14103709,0,0.18224925,0,0.14936198,0,0.13653867,0,0.14758068,0,0.16337219,0,0.15497147,0,0.1878085,0,0.19491373,0
212
- 0.0,0.0,0.0,210,"y=0,a=0",0,0.19099674,0,0.18639924,0,0.23121335,1,0.19799134,0,0.18235792,0,0.20752673,1,0.19243526,0,0.1773318,0,0.21135582,1,0.25249907,1
213
- 0.0,0.0,0.0,211,"y=0,a=0",0,0.20056693,0,0.15986979,0,0.21965536,1,0.20338096,1,0.19977748,1,0.19974814,0,0.21095006,1,0.21225709,1,0.23643851,1,0.24058416,1
214
- 0.0,0.0,0.0,212,"y=0,a=0",0,0.13553318,0,0.12414019,0,0.16332129,0,0.13388398,0,0.13364181,0,0.13739523,0,0.12526721,0,0.1348299,0,0.1582558,0,0.16722631,0
215
- 0.0,0.0,0.0,213,"y=0,a=0",0,0.22478892,1,0.20954038,1,0.23459284,1,0.21762459,1,0.18074833,0,0.1714295,0,0.16684923,0,0.20610902,1,0.22019836,1,0.23977327,1
216
- 0.0,0.0,0.0,214,"y=0,a=0",0,0.20353512,1,0.19716185,1,0.20157804,0,0.18836062,0,0.17931373,0,0.19305219,0,0.19975545,1,0.22039965,1,0.1864368,0,0.2035147,0
217
- 0.0,0.0,0.0,215,"y=0,a=0",0,0.17955787,0,0.18460837,0,0.20145063,0,0.18093398,0,0.1644731,0,0.18152392,0,0.1715769,0,0.17202024,0,0.18968213,0,0.20006305,0
218
- 0.0,0.0,0.0,216,"y=0,a=0",0,0.18724075,0,0.18168592,0,0.2059739,0,0.19323209,0,0.16811037,0,0.19843954,0,0.20040107,1,0.1831988,0,0.20143795,0,0.2238625,0
219
- 0.0,0.0,0.0,217,"y=0,a=0",0,0.11870327,0,0.115139075,0,0.15201017,0,0.12848109,0,0.12649058,0,0.13655801,0,0.13349691,0,0.120842695,0,0.16761434,0,0.15366241,0
220
- 0.0,0.0,0.0,218,"y=0,a=0",0,0.20197429,0,0.1804537,0,0.20341574,0,0.19235301,0,0.16811843,0,0.1758517,0,0.17304361,0,0.19118147,0,0.16591807,0,0.20272249,0
221
- 0.0,0.0,0.0,219,"y=0,a=0",0,0.17197856,0,0.16197662,0,0.19407463,0,0.17553762,0,0.1729521,0,0.16983365,0,0.17426218,0,0.17077899,0,0.19437619,0,0.21006925,0
222
- 0.0,1.0,0.0,220,"y=0,a=0",1,0.2232407,1,0.20709021,1,0.22577277,1,0.21117367,1,0.22034506,1,0.20399383,1,0.20067492,1,0.19289598,1,0.22058007,1,0.21561618,0
223
- 0.0,0.0,0.0,221,"y=0,a=0",0,0.20409033,1,0.1928379,1,0.20987377,0,0.20165966,1,0.190871,0,0.21311656,1,0.20735562,1,0.20023446,1,0.2183468,1,0.21663515,0
224
- 0.0,0.0,0.0,222,"y=0,a=0",0,0.19772397,0,0.18862548,0,0.21520689,0,0.21211691,1,0.19634573,1,0.22593254,1,0.20374167,1,0.21375765,1,0.23510264,1,0.23463607,1
225
- 0.0,0.0,0.0,223,"y=0,a=0",0,0.20305647,0,0.17684881,0,0.22525477,1,0.19354515,0,0.19918416,1,0.18420964,0,0.1791771,0,0.18421206,0,0.2149554,1,0.22169615,0
226
- 0.0,0.0,0.0,224,"y=0,a=0",0,0.1541933,0,0.14398645,0,0.18121704,0,0.15899044,0,0.16476855,0,0.16575949,0,0.15632652,0,0.1511771,0,0.18645073,0,0.18091854,0
227
- 0.0,0.0,0.0,225,"y=0,a=0",0,0.12900697,0,0.11992986,0,0.16273388,0,0.14338718,0,0.14032263,0,0.13518131,0,0.1337693,0,0.14213589,0,0.14042981,0,0.16806932,0
228
- 0.0,0.0,0.0,226,"y=0,a=0",0,0.15364635,0,0.15652622,0,0.19160223,0,0.16493519,0,0.15461604,0,0.175078,0,0.16080528,0,0.17327407,0,0.18050882,0,0.19697326,0
229
- 0.0,0.0,0.0,227,"y=0,a=0",0,0.12579437,0,0.103185795,0,0.1464992,0,0.11623773,0,0.13856676,0,0.1128924,0,0.10560492,0,0.120260164,0,0.14325048,0,0.1502481,0
230
- 0.0,0.0,0.0,228,"y=0,a=0",0,0.1768043,0,0.16868652,0,0.22080056,1,0.19180936,0,0.17947763,0,0.17463645,0,0.17476423,0,0.14949253,0,0.20430711,1,0.22376354,0
231
- 0.0,0.0,0.0,229,"y=0,a=0",0,0.16314465,0,0.16191022,0,0.20683251,0,0.1779429,0,0.15743844,0,0.16617604,0,0.15326507,0,0.1643446,0,0.19133401,0,0.20558688,0
232
- 0.0,0.0,0.0,230,"y=0,a=0",0,0.13602896,0,0.12555009,0,0.17057958,0,0.15748557,0,0.13823983,0,0.16778392,0,0.16498591,0,0.12496173,0,0.16313288,0,0.18511583,0
233
- 0.0,0.0,0.0,231,"y=0,a=0",0,0.1645793,0,0.16531493,0,0.19516248,0,0.1701683,0,0.16829012,0,0.1752734,0,0.16308819,0,0.16291092,0,0.20838843,1,0.2145241,0
234
- 0.0,0.0,0.0,232,"y=0,a=0",0,0.17366491,0,0.16333838,0,0.19699873,0,0.18628469,0,0.16632532,0,0.18128236,0,0.18111368,0,0.15430635,0,0.19131912,0,0.20733741,0
235
- 0.0,0.0,0.0,233,"y=0,a=0",0,0.16735621,0,0.16563083,0,0.19982453,0,0.18159592,0,0.16926551,0,0.17008601,0,0.15957575,0,0.16306145,0,0.19321306,0,0.21690711,0
236
- 0.0,0.0,0.0,234,"y=0,a=0",0,0.16123061,0,0.14589235,0,0.18911257,0,0.16486631,0,0.16670468,0,0.15970464,0,0.16187626,0,0.17698655,0,0.18767731,0,0.18963765,0
237
- 0.0,0.0,0.0,235,"y=0,a=0",0,0.18736579,0,0.17791197,0,0.1992026,0,0.19394006,0,0.16380972,0,0.20237273,1,0.18950474,0,0.16743378,0,0.19088227,0,0.22537805,1
238
- 0.0,0.0,0.0,236,"y=0,a=0",0,0.14350933,0,0.14849947,0,0.16895482,0,0.14799842,0,0.13096942,0,0.158757,0,0.1413828,0,0.15759493,0,0.16447923,0,0.19672003,0
239
- 0.0,0.0,0.0,237,"y=0,a=0",0,0.17240088,0,0.15954302,0,0.20344149,0,0.17593239,0,0.17724748,0,0.17883494,0,0.17593908,0,0.16610256,0,0.19711746,0,0.18920599,0
240
- 0.0,0.0,0.0,238,"y=0,a=0",0,0.20370333,1,0.19386831,1,0.23136501,1,0.20041046,1,0.21420059,1,0.20521036,1,0.2173301,1,0.20018339,1,0.23019123,1,0.23681813,1
241
- 0.0,0.0,0.0,239,"y=0,a=0",0,0.1755447,0,0.16460209,0,0.18178995,0,0.17053846,0,0.16286738,0,0.17890897,0,0.16446081,0,0.16683213,0,0.17515309,0,0.20318948,0
242
- 0.0,0.0,0.0,240,"y=0,a=0",0,0.1866325,0,0.18116792,0,0.19975397,0,0.18526612,0,0.17914349,0,0.1911787,0,0.18237804,0,0.18233821,0,0.19372211,0,0.21201855,0
243
- 1.0,1.0,0.0,241,"y=1,a=0",1,0.19772999,0,0.18554172,0,0.21207334,0,0.19394347,0,0.20878433,1,0.20499018,1,0.2036996,1,0.1916503,1,0.24530925,1,0.22088656,0
244
- 1.0,1.0,0.0,242,"y=1,a=0",1,0.2495996,1,0.25036162,1,0.27629164,1,0.2557612,1,0.22514606,1,0.2484995,1,0.24925399,1,0.23814641,1,0.22180781,1,0.27682018,1
245
- 1.0,1.0,0.0,243,"y=1,a=0",1,0.14663886,0,0.14138463,0,0.18902937,0,0.15114596,0,0.14780308,0,0.1490947,0,0.15499136,0,0.15144311,0,0.21423255,1,0.19537994,0
246
- 1.0,1.0,0.0,244,"y=1,a=0",1,0.21153246,1,0.20050155,1,0.22775759,1,0.20905374,1,0.19112654,0,0.18813094,0,0.19364084,0,0.18880169,0,0.21162784,1,0.2451642,1
247
- 1.0,1.0,0.0,245,"y=1,a=0",1,0.2053377,1,0.20323709,1,0.23107383,1,0.21217239,1,0.19935428,1,0.21381314,1,0.20410816,1,0.19546567,1,0.22402962,1,0.25053284,1
248
- 1.0,1.0,0.0,246,"y=1,a=0",1,0.23113385,1,0.23198533,1,0.2388632,1,0.23407146,1,0.20693536,1,0.23807265,1,0.23201984,1,0.23500131,1,0.2132462,1,0.24786344,1
249
- 1.0,0.0,0.0,247,"y=1,a=0",0,0.18010357,0,0.15281752,0,0.18881294,0,0.17714961,0,0.1587961,0,0.1455677,0,0.1468761,0,0.18436645,0,0.1960872,0,0.19112599,0
250
- 1.0,1.0,0.0,248,"y=1,a=0",1,0.19258592,0,0.1747521,0,0.19719517,0,0.19788381,0,0.16880886,0,0.1963003,0,0.18482125,0,0.17311426,0,0.20031282,0,0.23047628,1
251
- 1.0,1.0,0.0,249,"y=1,a=0",1,0.24242802,1,0.23646352,1,0.26510742,1,0.25733817,1,0.24404012,1,0.2502831,1,0.23848097,1,0.22052555,1,0.25164565,1,0.27749738,1
252
- 1.0,0.0,0.0,250,"y=1,a=0",0,0.24361776,1,0.21337569,1,0.23021086,1,0.21967207,1,0.22887999,1,0.22907414,1,0.23530409,1,0.2075505,1,0.20864093,1,0.23665324,1
253
- 1.0,1.0,0.0,251,"y=1,a=0",1,0.22806597,1,0.22717085,1,0.23127669,1,0.23314957,1,0.20576347,1,0.21770988,1,0.21492344,1,0.1815516,0,0.20427303,1,0.24178132,1
254
- 1.0,0.0,0.0,252,"y=1,a=0",0,0.21571197,1,0.19726953,1,0.2346426,1,0.21019995,1,0.22681479,1,0.1944615,0,0.20316677,1,0.21461093,1,0.23898283,1,0.24214661,1
255
- 1.0,0.0,0.0,253,"y=1,a=0",0,0.17836247,0,0.1736558,0,0.18820122,0,0.17313175,0,0.17124644,0,0.17588544,0,0.18729103,0,0.17857142,0,0.1971966,0,0.20813437,0
256
- 1.0,1.0,0.0,254,"y=1,a=0",1,0.20589739,1,0.19236909,1,0.21777178,1,0.19842999,1,0.20541278,1,0.18958233,0,0.18459213,0,0.19441694,1,0.21210767,1,0.2211222,0
257
- 1.0,0.0,0.0,255,"y=1,a=0",0,0.18264093,0,0.17222047,0,0.19559903,0,0.19111094,0,0.16770686,0,0.18596494,0,0.18903835,0,0.19793561,1,0.20248759,0,0.19202693,0
258
- 1.0,1.0,0.0,256,"y=1,a=0",1,0.23681757,1,0.22702649,1,0.24866538,1,0.24443801,1,0.23416752,1,0.22375531,1,0.22966205,1,0.2310961,1,0.24231426,1,0.2537614,1
259
- 1.0,0.0,0.0,257,"y=1,a=0",0,0.18349496,0,0.17393182,0,0.20088309,0,0.1726081,0,0.17968166,0,0.17393282,0,0.18400924,0,0.19191334,1,0.2070278,1,0.21480596,0
260
- 1.0,1.0,0.0,258,"y=1,a=0",1,0.21561073,1,0.19652234,1,0.22616722,1,0.20544945,1,0.20138262,1,0.20539092,1,0.20686609,1,0.21100241,1,0.24476227,1,0.23629673,1
261
- 1.0,0.0,0.0,259,"y=1,a=0",0,0.15586439,0,0.17390095,0,0.20037211,0,0.16844657,0,0.15270372,0,0.15692799,0,0.17615819,0,0.17453757,0,0.19042505,0,0.20780016,0
262
- 1.0,1.0,0.0,260,"y=1,a=0",1,0.21890438,1,0.21929358,1,0.23495536,1,0.22374244,1,0.21180825,1,0.22823597,1,0.2139606,1,0.22624083,1,0.2203691,1,0.24685778,1
263
- 1.0,0.0,0.0,261,"y=1,a=0",0,0.17892566,0,0.16049999,0,0.18530537,0,0.15825358,0,0.17291139,0,0.1612122,0,0.1731141,0,0.18522471,0,0.19506359,0,0.19787125,0
264
- 1.0,1.0,0.0,262,"y=1,a=0",1,0.22955778,1,0.21162154,1,0.24652153,1,0.22134882,1,0.22747329,1,0.20388137,1,0.20429003,1,0.2207915,1,0.23447871,1,0.24823338,1
265
- 1.0,1.0,0.0,263,"y=1,a=0",1,0.21646215,1,0.18238921,0,0.21741337,1,0.19619174,0,0.20669954,1,0.17589262,0,0.17480558,0,0.17587663,0,0.20148045,0,0.22787532,1
266
- 1.0,1.0,0.0,264,"y=1,a=0",1,0.20562139,1,0.18513328,0,0.21542047,0,0.19013837,0,0.20287889,1,0.1814993,0,0.18511982,0,0.19959068,1,0.20405237,1,0.203105,0
267
- 1.0,0.0,0.0,265,"y=1,a=0",0,0.14048041,0,0.13567825,0,0.15409908,0,0.14072257,0,0.14379103,0,0.14731535,0,0.13921301,0,0.15352902,0,0.15079662,0,0.16713853,0
268
- 1.0,1.0,0.0,266,"y=1,a=0",1,0.20920864,1,0.21258605,1,0.2376458,1,0.21050718,1,0.19802059,1,0.21771981,1,0.20704773,1,0.20854554,1,0.2088407,1,0.24317968,1
269
- 1.0,0.0,0.0,267,"y=1,a=0",0,0.15526368,0,0.15188862,0,0.16957757,0,0.1591613,0,0.1420954,0,0.15517296,0,0.1533228,0,0.14912368,0,0.17990239,0,0.19011499,0
270
- 1.0,1.0,0.0,268,"y=1,a=0",1,0.2081187,1,0.20016286,1,0.22878157,1,0.21092497,1,0.2069242,1,0.20653833,1,0.2046176,1,0.20672764,1,0.23426722,1,0.23824926,1
271
- 1.0,1.0,0.0,269,"y=1,a=0",1,0.19315024,0,0.19789104,1,0.2151047,0,0.20590402,1,0.1860925,0,0.20650984,1,0.19723038,0,0.17385754,0,0.18812743,0,0.23326205,1
272
- 1.0,1.0,0.0,270,"y=1,a=0",1,0.19792756,0,0.19242935,1,0.21857971,1,0.19310082,0,0.18848056,0,0.18509251,0,0.19262655,0,0.19627434,1,0.21222384,1,0.23359725,1
273
- 1.0,1.0,0.0,271,"y=1,a=0",1,0.23228696,1,0.21258256,1,0.24202281,1,0.21802586,1,0.2415061,1,0.21819967,1,0.21359913,1,0.20898409,1,0.22661291,1,0.23531699,1
274
- 1.0,1.0,0.0,272,"y=1,a=0",1,0.1997595,0,0.18913122,0,0.21029477,0,0.19467501,0,0.19960795,1,0.20611319,1,0.20830373,1,0.20282379,1,0.2284281,1,0.22663996,1
275
- 1.0,1.0,0.0,273,"y=1,a=0",1,0.19698532,0,0.18413009,0,0.21983458,1,0.19984208,1,0.19331332,1,0.209149,1,0.20408385,1,0.17693381,0,0.21012187,1,0.2371477,1
276
- 0.0,0.0,1.0,274,"y=0,a=1",0,0.21848139,1,0.20534082,1,0.22087625,1,0.20376143,1,0.19390893,1,0.1952817,0,0.20602357,1,0.18531246,0,0.1888013,0,0.22602248,1
277
- 0.0,1.0,1.0,275,"y=0,a=1",1,0.23718525,1,0.23884197,1,0.25716344,1,0.23347639,1,0.23269325,1,0.26484004,1,0.26436657,1,0.2364746,1,0.23702016,1,0.2733954,1
278
- 0.0,1.0,1.0,276,"y=0,a=1",1,0.22532725,1,0.20723979,1,0.21037677,0,0.19956951,1,0.2269903,1,0.2420525,1,0.24765019,1,0.19682707,1,0.19972758,0,0.20095024,0
279
- 0.0,0.0,1.0,277,"y=0,a=1",0,0.17697562,0,0.17119017,0,0.2013584,0,0.1743695,0,0.1659483,0,0.19018091,0,0.17763428,0,0.16174722,0,0.17510845,0,0.20066999,0
280
- 0.0,0.0,1.0,278,"y=0,a=1",0,0.20452811,1,0.20669578,1,0.23089638,1,0.20769845,1,0.19692644,1,0.20987073,1,0.21673493,1,0.17469327,0,0.19209802,0,0.22220083,0
281
- 0.0,0.0,1.0,279,"y=0,a=1",0,0.15372984,0,0.1356732,0,0.16195402,0,0.15672716,0,0.13801903,0,0.1511061,0,0.1551472,0,0.17048253,0,0.13885221,0,0.16307111,0
282
- 0.0,0.0,1.0,280,"y=0,a=1",0,0.18558452,0,0.17830971,0,0.2121205,0,0.18751575,0,0.18639013,0,0.18859243,0,0.18976368,0,0.16673173,0,0.21168903,1,0.214639,0
283
- 0.0,0.0,1.0,281,"y=0,a=1",0,0.21035232,1,0.1997398,1,0.2176728,1,0.21031623,1,0.20306009,1,0.22994018,1,0.22526217,1,0.19873045,1,0.21480714,1,0.24550611,1
284
- 0.0,0.0,1.0,282,"y=0,a=1",0,0.19059864,0,0.18607274,0,0.18378896,0,0.17943886,0,0.1814818,0,0.21125336,1,0.22214663,1,0.17937997,0,0.16545235,0,0.18839361,0
285
- 0.0,0.0,1.0,283,"y=0,a=1",0,0.20893098,1,0.19569713,1,0.21972635,1,0.19757551,0,0.18151216,0,0.1679617,0,0.16688922,0,0.19849038,1,0.19451639,0,0.2205397,0
286
- 0.0,0.0,1.0,284,"y=0,a=1",0,0.2031817,0,0.19548035,1,0.2065647,0,0.20324361,1,0.19908398,1,0.23879656,1,0.23276034,1,0.1929115,1,0.2080366,1,0.21420674,0
287
- 0.0,0.0,1.0,285,"y=0,a=1",0,0.19030717,0,0.18328506,0,0.20867886,0,0.18476588,0,0.17440222,0,0.19031322,0,0.17489755,0,0.17120837,0,0.19284672,0,0.22429405,0
288
- 0.0,0.0,1.0,286,"y=0,a=1",0,0.17736758,0,0.16640814,0,0.19879629,0,0.17172705,0,0.17489943,0,0.17854406,0,0.17558672,0,0.19239466,1,0.2264143,1,0.21499702,0
289
- 0.0,0.0,1.0,287,"y=0,a=1",0,0.19545841,0,0.1870042,0,0.20073044,0,0.20068303,1,0.18487796,0,0.20693685,1,0.21696448,1,0.21501088,1,0.19447066,0,0.21711381,0
290
- 0.0,0.0,1.0,288,"y=0,a=1",0,0.19715346,0,0.18491031,0,0.21506575,0,0.19831409,1,0.19096941,0,0.20573378,1,0.18810341,0,0.19724765,1,0.21798033,1,0.2075684,0
291
- 0.0,0.0,1.0,289,"y=0,a=1",0,0.20087096,0,0.18472514,0,0.21281081,0,0.19113033,0,0.1913178,0,0.1964039,0,0.19356316,0,0.1777666,0,0.19557253,0,0.20848662,0
292
- 0.0,0.0,1.0,290,"y=0,a=1",0,0.2232982,1,0.19131295,0,0.22088102,1,0.208907,1,0.2139192,1,0.22246246,1,0.214023,1,0.19093946,0,0.20124061,0,0.2158545,0
293
- 0.0,0.0,1.0,291,"y=0,a=1",0,0.23581547,1,0.21218257,1,0.23860325,1,0.21733716,1,0.22433142,1,0.21477942,1,0.21332006,1,0.2037639,1,0.22588277,1,0.23702973,1
294
- 0.0,0.0,1.0,292,"y=0,a=1",0,0.16020526,0,0.14153095,0,0.1702929,0,0.15704541,0,0.16369924,0,0.15903191,0,0.14619616,0,0.16160093,0,0.17233557,0,0.1839081,0
295
- 0.0,0.0,1.0,293,"y=0,a=1",0,0.20154768,0,0.18522516,0,0.21530056,0,0.18772131,0,0.20301127,1,0.22139126,1,0.22354473,1,0.18198681,0,0.19799761,0,0.21688567,0
296
- 0.0,0.0,1.0,294,"y=0,a=1",0,0.22475329,1,0.21101461,1,0.22864911,1,0.21544656,1,0.22390169,1,0.2132365,1,0.2034997,1,0.20482837,1,0.20487694,1,0.23649962,1
297
- 0.0,0.0,1.0,295,"y=0,a=1",0,0.18162684,0,0.15977156,0,0.1783822,0,0.16536611,0,0.16808747,0,0.18906546,0,0.17863642,0,0.17307518,0,0.18005754,0,0.2014465,0
298
- 0.0,0.0,1.0,296,"y=0,a=1",0,0.22589359,1,0.21431655,1,0.23039848,1,0.22789091,1,0.20510176,1,0.23648354,1,0.22514789,1,0.19369327,1,0.22220643,1,0.24164313,1
299
- 0.0,1.0,1.0,297,"y=0,a=1",1,0.23402146,1,0.2195513,1,0.23481265,1,0.22145776,1,0.22239546,1,0.24497786,1,0.2412157,1,0.19761594,1,0.2238866,1,0.23060668,1
300
- 0.0,0.0,1.0,298,"y=0,a=1",0,0.18533756,0,0.18190254,0,0.20024924,0,0.18987426,0,0.17440887,0,0.21356896,1,0.19961214,1,0.1662753,0,0.18502122,0,0.19716392,0
301
- 0.0,0.0,1.0,299,"y=0,a=1",0,0.25535753,1,0.25658414,1,0.27949014,1,0.27223158,1,0.21904811,1,0.2581438,1,0.25776953,1,0.22951543,1,0.23139295,1,0.28996274,1
302
- 0.0,0.0,1.0,300,"y=0,a=1",0,0.21911122,1,0.20456156,1,0.22276667,1,0.20469858,1,0.21698317,1,0.22709382,1,0.22971627,1,0.2050434,1,0.20898171,1,0.21792255,0
303
- 0.0,0.0,1.0,301,"y=0,a=1",0,0.18189713,0,0.18075305,0,0.21331468,0,0.19372556,0,0.17487282,0,0.18099122,0,0.17577964,0,0.16457514,0,0.18995215,0,0.23194149,1
304
- 0.0,0.0,1.0,302,"y=0,a=1",0,0.18605468,0,0.16241379,0,0.19295695,0,0.17614403,0,0.17712688,0,0.17069095,0,0.16371031,0,0.15907557,0,0.18930309,0,0.190566,0
305
- 0.0,0.0,1.0,303,"y=0,a=1",0,0.18459536,0,0.17795262,0,0.20497674,0,0.17082235,0,0.17300741,0,0.15693328,0,0.17445342,0,0.2089021,1,0.18560271,0,0.20206681,0
306
- 0.0,0.0,1.0,304,"y=0,a=1",0,0.2167127,1,0.19161364,0,0.21111576,0,0.19637692,0,0.19777957,1,0.2135154,1,0.21263987,1,0.1908909,0,0.19696112,0,0.21736771,0
307
- 0.0,0.0,1.0,305,"y=0,a=1",0,0.16532786,0,0.15122603,0,0.19007953,0,0.17379883,0,0.16092278,0,0.16482459,0,0.14794631,0,0.16411436,0,0.2041865,1,0.21486585,0
308
- 0.0,1.0,1.0,306,"y=0,a=1",1,0.23055547,1,0.21783623,1,0.25364977,1,0.21297987,1,0.21716328,1,0.18289575,0,0.19805117,0,0.18875144,0,0.19989277,0,0.2405514,1
309
- 0.0,0.0,1.0,307,"y=0,a=1",0,0.21222456,1,0.20432097,1,0.23533486,1,0.21793814,1,0.20501886,1,0.23850448,1,0.23043987,1,0.18861876,0,0.21607952,1,0.24646798,1
310
- 0.0,0.0,1.0,308,"y=0,a=1",0,0.24025077,1,0.20808148,1,0.2202887,1,0.20768659,1,0.21668275,1,0.22749206,1,0.22356153,1,0.18984134,0,0.21377891,1,0.21660791,0
311
- 0.0,0.0,1.0,309,"y=0,a=1",0,0.2026076,0,0.1976221,1,0.20293023,0,0.18624169,0,0.1880043,0,0.22470719,1,0.23078191,1,0.16852798,0,0.17233557,0,0.20735483,0
312
- 0.0,0.0,1.0,310,"y=0,a=1",0,0.18706973,0,0.16797493,0,0.20270291,0,0.18066375,0,0.1942603,1,0.18273619,0,0.18784396,0,0.17415263,0,0.21607958,1,0.20286198,0
313
- 0.0,0.0,1.0,311,"y=0,a=1",0,0.20207712,0,0.1868444,0,0.21801913,1,0.18902281,0,0.1896137,0,0.1810322,0,0.17259361,0,0.18091911,0,0.1956467,0,0.21960802,0
314
- 0.0,1.0,1.0,312,"y=0,a=1",1,0.25365737,1,0.2476521,1,0.26870954,1,0.23771656,1,0.19414209,1,0.17552973,0,0.17151335,0,0.15109329,0,0.17432883,0,0.24575813,1
315
- 0.0,0.0,1.0,313,"y=0,a=1",0,0.13991144,0,0.14708439,0,0.14600195,0,0.145918,0,0.11959734,0,0.13914625,0,0.13401212,0,0.119212806,0,0.13306399,0,0.16572292,0
316
- 0.0,0.0,1.0,314,"y=0,a=1",0,0.20823206,1,0.1929914,1,0.21906774,1,0.20416039,1,0.19402774,1,0.20214523,1,0.19441491,0,0.17558639,0,0.18426515,0,0.21629985,0
317
- 0.0,0.0,1.0,315,"y=0,a=1",0,0.19799808,0,0.17516787,0,0.19859932,0,0.17722355,0,0.18511964,0,0.18399216,0,0.17869943,0,0.19428064,1,0.18846108,0,0.20961937,0
318
- 0.0,0.0,1.0,316,"y=0,a=1",0,0.19943553,0,0.18185233,0,0.21980749,1,0.2013961,1,0.18831733,0,0.1811257,0,0.18311273,0,0.18733697,0,0.20591694,1,0.21837643,0
319
- 0.0,0.0,1.0,317,"y=0,a=1",0,0.22289301,1,0.1954033,1,0.2263815,1,0.20299575,1,0.22277549,1,0.20436548,1,0.19910865,0,0.20136246,1,0.20568043,1,0.22243889,0
320
- 0.0,0.0,1.0,318,"y=0,a=1",0,0.19148779,0,0.17677006,0,0.22635318,1,0.19602819,0,0.18839711,0,0.18572623,0,0.19109869,0,0.19597252,1,0.20838203,1,0.22701952,1
321
- 0.0,1.0,1.0,319,"y=0,a=1",1,0.2181359,1,0.19097309,0,0.21925105,1,0.19426872,0,0.20935725,1,0.20822597,1,0.21447366,1,0.18845806,0,0.2074419,1,0.21624258,0
322
- 0.0,0.0,1.0,320,"y=0,a=1",0,0.18922755,0,0.19865133,1,0.21065538,0,0.20450617,1,0.16919912,0,0.23251122,1,0.22993952,1,0.17885286,0,0.17249978,0,0.2224932,0
323
- 0.0,0.0,1.0,321,"y=0,a=1",0,0.18577105,0,0.18397868,0,0.18737087,0,0.18357916,0,0.17276372,0,0.22132307,1,0.22046734,1,0.1798982,0,0.18707219,0,0.20828405,0
324
- 0.0,0.0,1.0,322,"y=0,a=1",0,0.13269737,0,0.116946265,0,0.12042096,0,0.14538874,0,0.116431035,0,0.13562222,0,0.13973965,0,0.12034461,0,0.09547332,0,0.13540766,0
325
- 0.0,0.0,1.0,323,"y=0,a=1",0,0.21647198,1,0.21164858,1,0.23443738,1,0.22813721,1,0.19131239,0,0.22926193,1,0.21381654,1,0.19930165,1,0.21252503,1,0.25445086,1
326
- 0.0,0.0,1.0,324,"y=0,a=1",0,0.19954558,0,0.19720419,1,0.21032256,0,0.20568669,1,0.18351242,0,0.21227872,1,0.20505893,1,0.18486491,0,0.19066304,0,0.23060402,1
327
- 0.0,0.0,1.0,325,"y=0,a=1",0,0.19339043,0,0.17691281,0,0.2197487,1,0.17955317,0,0.1919763,0,0.18624513,0,0.18525115,0,0.20839079,1,0.20916536,1,0.2076879,0
328
- 0.0,0.0,1.0,326,"y=0,a=1",0,0.20772615,1,0.20346501,1,0.22057885,1,0.21529526,1,0.19905809,1,0.23622642,1,0.23643032,1,0.19139521,0,0.19624566,0,0.23381026,1
329
- 0.0,0.0,1.0,327,"y=0,a=1",0,0.17268847,0,0.17375764,0,0.18513271,0,0.17333844,0,0.15580273,0,0.19605698,0,0.20738442,1,0.17806609,0,0.16710553,0,0.20041159,0
330
- 0.0,0.0,1.0,328,"y=0,a=1",0,0.14620644,0,0.13086419,0,0.15182438,0,0.14449297,0,0.1326148,0,0.13568701,0,0.12859145,0,0.1349421,0,0.14378151,0,0.15537246,0
331
- 0.0,0.0,1.0,329,"y=0,a=1",0,0.20241146,0,0.18369803,0,0.21347152,0,0.20017107,1,0.1959262,1,0.18808182,0,0.1834945,0,0.21689263,1,0.23123744,1,0.23187247,1
332
- 0.0,0.0,1.0,330,"y=0,a=1",0,0.16605356,0,0.15470272,0,0.17940193,0,0.16406445,0,0.15379505,0,0.15628147,0,0.14800096,0,0.17093407,0,0.16875497,0,0.17820375,0
333
- 0.0,0.0,1.0,331,"y=0,a=1",0,0.17221603,0,0.16269173,0,0.189356,0,0.16662835,0,0.17228498,0,0.16396427,0,0.16602227,0,0.15599222,0,0.17471643,0,0.18236637,0
334
- 0.0,0.0,1.0,332,"y=0,a=1",0,0.16326521,0,0.15880111,0,0.18470365,0,0.16946997,0,0.15895209,0,0.16921338,0,0.16341259,0,0.1741824,0,0.17848927,0,0.18570991,0
335
- 0.0,0.0,1.0,333,"y=0,a=1",0,0.18717116,0,0.18663418,0,0.20911306,0,0.18066756,0,0.17478538,0,0.19558923,0,0.20128681,1,0.20370747,1,0.19760594,0,0.22665004,1
336
- 0.0,0.0,1.0,334,"y=0,a=1",0,0.17738049,0,0.16015145,0,0.19238767,0,0.1680558,0,0.17507811,0,0.17766832,0,0.17438059,0,0.16172116,0,0.17654245,0,0.19533232,0
337
- 0.0,0.0,1.0,335,"y=0,a=1",0,0.17614669,0,0.16520037,0,0.17818035,0,0.16789441,0,0.16729796,0,0.15521726,0,0.15675344,0,0.16129869,0,0.18332542,0,0.18947132,0
338
- 0.0,0.0,1.0,336,"y=0,a=1",0,0.2474843,1,0.2340048,1,0.26390693,1,0.2388826,1,0.21037886,1,0.19526145,0,0.19587496,0,0.19823128,1,0.20864819,1,0.26065767,1
339
- 0.0,0.0,1.0,337,"y=0,a=1",0,0.14936572,0,0.14494173,0,0.17836,0,0.1522361,0,0.16160755,0,0.15931198,0,0.14689864,0,0.15455124,0,0.19451298,0,0.18236418,0
340
- 0.0,0.0,1.0,338,"y=0,a=1",0,0.09237595,0,0.09049153,0,0.111261904,0,0.0982149,0,0.092711635,0,0.121866114,0,0.109754205,0,0.082466595,0,0.10442813,0,0.12522417,0
341
- 0.0,0.0,1.0,339,"y=0,a=1",0,0.22860081,1,0.20945537,1,0.22582681,1,0.21504064,1,0.19405437,1,0.20364949,1,0.20231766,1,0.20662352,1,0.19789974,0,0.23341608,1
342
- 0.0,0.0,1.0,340,"y=0,a=1",0,0.20921457,1,0.18439814,0,0.22021651,1,0.20140725,1,0.20432606,1,0.183159,0,0.18914077,0,0.18436134,0,0.21917138,1,0.22174992,0
343
- 0.0,0.0,1.0,341,"y=0,a=1",0,0.21628004,1,0.19822998,1,0.23022313,1,0.20556949,1,0.18387213,0,0.19123177,0,0.19617431,0,0.18364848,0,0.19300704,0,0.2357738,1
344
- 0.0,0.0,1.0,342,"y=0,a=1",0,0.21025671,1,0.20249218,1,0.23648487,1,0.20273305,1,0.18787017,0,0.23079403,1,0.22501484,1,0.19602633,1,0.21701875,1,0.23678096,1
345
- 0.0,0.0,1.0,343,"y=0,a=1",0,0.22687523,1,0.21205105,1,0.23910221,1,0.22351508,1,0.22323346,1,0.23382038,1,0.22812055,1,0.20748575,1,0.22124915,1,0.24544,1
346
- 0.0,0.0,1.0,344,"y=0,a=1",0,0.22327232,1,0.20221874,1,0.23255366,1,0.1995567,1,0.20900725,1,0.18824993,0,0.19923946,1,0.18051824,0,0.18528354,0,0.2149125,0
347
- 0.0,1.0,1.0,345,"y=0,a=1",1,0.20927426,1,0.20044847,1,0.21748435,1,0.2141206,1,0.19940221,1,0.22796038,1,0.21453853,1,0.1790605,0,0.2053612,1,0.25431675,1
348
- 0.0,0.0,1.0,346,"y=0,a=1",0,0.18991387,0,0.18074237,0,0.21032716,0,0.19067661,0,0.18996127,0,0.19394916,0,0.19371554,0,0.20045856,1,0.19816665,0,0.21188311,0
349
- 0.0,0.0,1.0,347,"y=0,a=1",0,0.19997975,0,0.19965269,1,0.217353,1,0.20226909,1,0.17777583,0,0.20461386,1,0.19394751,0,0.21839029,1,0.19719027,0,0.21660113,0
350
- 0.0,1.0,1.0,348,"y=0,a=1",1,0.22311638,1,0.22060972,1,0.23566572,1,0.22814637,1,0.21513581,1,0.23640697,1,0.22508626,1,0.22194727,1,0.22864869,1,0.26722664,1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_classifier_embeddings.npy DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:4a8c102d8da2468c4a714e0783b6b2d309bc36a1a7ce5ff4cabd13f1db8515ca
3
- size 2859136
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_clip_embeddings.npy DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:984e464e8170dc4c40c0250b2e6bce7942c7304ece72405d870d2e53c7fd9d9a
3
- size 357504
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/clip_img_encoder_ViT-B/32/valid_dog_dataframe_mitigation.csv DELETED
@@ -1,350 +0,0 @@
1
- out_put_GT,out_put_predict,attribute_bg_predict,idx,gs,Predictions_bin,H1_frisbee,H1_frisbee_bin,H2_grass,H2_grass_bin,H3_beach,H3_beach_bin,H4_water,H4_water_bin,H5_park,H5_park_bin,H6_ball,H6_ball_bin,H7_leash,H7_leash_bin,H8_person,H8_person_bin,H9_jumping,H9_jumping_bin,H10_running,H10_running_bin
2
- 1.0,1.0,1.0,0,"y=1,a=1",1,0.168473,0,0.14535885,0,0.13266435,0,0.14735563,0,0.15369758,0,0.16461085,0,0.17121619,0,0.17579815,0,0.17349447,0,0.16248782,0
3
- 1.0,1.0,1.0,1,"y=1,a=1",1,0.12738979,0,0.14154433,0,0.11722441,0,0.14708982,0,0.14066507,0,0.15168156,0,0.17133781,0,0.16129531,0,0.16993232,0,0.14132753,0
4
- 1.0,1.0,1.0,2,"y=1,a=1",1,0.1488204,0,0.1650556,0,0.14529684,0,0.17646627,0,0.16241184,0,0.18986979,0,0.20329532,0,0.22238722,0,0.20592241,0,0.18286751,0
5
- 1.0,1.0,1.0,3,"y=1,a=1",1,0.113790505,0,0.1285048,0,0.1089577,0,0.14778255,0,0.12476107,0,0.15067133,0,0.15982401,0,0.16502503,0,0.16319117,0,0.13661411,0
6
- 1.0,1.0,1.0,4,"y=1,a=1",1,0.18629639,0,0.14070487,0,0.13282418,0,0.15560085,0,0.15207973,0,0.18384106,0,0.16514462,0,0.16663355,0,0.18246208,0,0.16191949,0
7
- 1.0,0.0,1.0,5,"y=1,a=1",0,0.18487023,0,0.1778955,0,0.122889385,0,0.14629741,0,0.1915666,0,0.19551687,0,0.21327166,0,0.24315307,1,0.17978545,0,0.18388253,0
8
- 1.0,1.0,1.0,6,"y=1,a=1",1,0.12968354,0,0.12873952,0,0.1100219,0,0.1437349,0,0.1368618,0,0.1551494,0,0.16560063,0,0.16571128,0,0.15357675,0,0.13635401,0
9
- 1.0,1.0,1.0,7,"y=1,a=1",1,0.12405328,0,0.13577747,0,0.11470317,0,0.15801597,0,0.14177212,0,0.16095886,0,0.17175357,0,0.17582889,0,0.17848983,0,0.14920196,0
10
- 1.0,1.0,1.0,8,"y=1,a=1",1,0.1421234,0,0.14337958,0,0.12102511,0,0.16182473,0,0.1460876,0,0.17521544,0,0.18415087,0,0.19287163,0,0.18485287,0,0.15529841,0
11
- 1.0,1.0,1.0,9,"y=1,a=1",1,0.14019817,0,0.15160269,0,0.13301712,0,0.16305277,0,0.15827778,0,0.16721153,0,0.19432038,0,0.20165822,0,0.18033308,0,0.16515583,0
12
- 1.0,1.0,1.0,10,"y=1,a=1",1,0.13775535,0,0.15683521,0,0.13452363,0,0.15952566,0,0.15683204,0,0.17935142,0,0.18629684,0,0.19161153,0,0.19089375,0,0.1626939,0
13
- 1.0,1.0,1.0,11,"y=1,a=1",1,0.1429556,0,0.15615593,0,0.13108987,0,0.16889337,0,0.16077104,0,0.18498148,0,0.1872416,0,0.1943343,0,0.18524157,0,0.16022232,0
14
- 1.0,1.0,1.0,12,"y=1,a=1",1,0.123259544,0,0.1261137,0,0.10902279,0,0.15008025,0,0.12827495,0,0.15557882,0,0.17162186,0,0.1664964,0,0.18289596,0,0.14815196,0
15
- 1.0,1.0,1.0,13,"y=1,a=1",1,0.19575472,0,0.16404018,0,0.15329486,0,0.18734272,0,0.17527679,0,0.21565422,1,0.20879659,0,0.21542636,0,0.21754432,0,0.19227742,0
16
- 1.0,0.0,1.0,14,"y=1,a=1",0,0.14252263,0,0.1568192,0,0.13842145,0,0.15955284,0,0.15493445,0,0.17976467,0,0.18638332,0,0.18878067,0,0.20399496,0,0.17633095,0
17
- 1.0,1.0,1.0,15,"y=1,a=1",1,0.1721721,0,0.17918561,0,0.1611705,0,0.17397408,0,0.18153675,0,0.2003836,0,0.20283246,0,0.2087083,0,0.21097942,0,0.18477668,0
18
- 1.0,1.0,1.0,16,"y=1,a=1",1,0.14341518,0,0.14958695,0,0.14158525,0,0.18091159,0,0.15800531,0,0.17711702,0,0.18316662,0,0.18490745,0,0.19188714,0,0.16607009,0
19
- 1.0,1.0,1.0,17,"y=1,a=1",1,0.14375274,0,0.13789515,0,0.13484028,0,0.15220368,0,0.14248785,0,0.17929985,0,0.1726326,0,0.18264769,0,0.2070635,0,0.172958,0
20
- 1.0,1.0,1.0,18,"y=1,a=1",1,0.12320319,0,0.135,0,0.13032359,0,0.146567,0,0.13794903,0,0.14687707,0,0.16137788,0,0.16968924,0,0.17240332,0,0.15378314,0
21
- 1.0,1.0,1.0,19,"y=1,a=1",1,0.12988843,0,0.14419939,0,0.1206677,0,0.14682604,0,0.15327434,0,0.16675715,0,0.18621948,0,0.1870428,0,0.17460275,0,0.1543307,0
22
- 1.0,1.0,1.0,20,"y=1,a=1",1,0.11361327,0,0.12294378,0,0.10592536,0,0.14003834,0,0.124909684,0,0.15246767,0,0.1673094,0,0.16420662,0,0.18172382,0,0.14260975,0
23
- 1.0,1.0,1.0,21,"y=1,a=1",1,0.12120511,0,0.14389971,0,0.11853251,0,0.15079503,0,0.14177223,0,0.16580115,0,0.18037607,0,0.17473646,0,0.1764227,0,0.15230438,0
24
- 1.0,1.0,1.0,22,"y=1,a=1",1,0.14649732,0,0.15046985,0,0.13230726,0,0.17900549,0,0.1554253,0,0.17740738,0,0.18590766,0,0.19302492,0,0.20007536,0,0.1695886,0
25
- 1.0,1.0,1.0,23,"y=1,a=1",1,0.123829484,0,0.14268921,0,0.114363596,0,0.1549293,0,0.15058112,0,0.16000329,0,0.18295474,0,0.18218414,0,0.17243284,0,0.1488292,0
26
- 1.0,1.0,1.0,24,"y=1,a=1",1,0.14646359,0,0.14001915,0,0.11987685,0,0.165338,0,0.14143245,0,0.17419668,0,0.16543372,0,0.16876017,0,0.17299196,0,0.15104738,0
27
- 1.0,1.0,1.0,25,"y=1,a=1",1,0.13465022,0,0.13674155,0,0.12567776,0,0.15982069,0,0.14818336,0,0.18413493,0,0.18980014,0,0.21776533,0,0.1725971,0,0.15138078,0
28
- 1.0,1.0,1.0,26,"y=1,a=1",1,0.13912815,0,0.15908661,0,0.13181756,0,0.1837086,0,0.16224153,0,0.18286929,0,0.19208507,0,0.18822108,0,0.20205225,0,0.17229274,0
29
- 1.0,1.0,1.0,27,"y=1,a=1",1,0.12710634,0,0.14819735,0,0.13033661,0,0.1633701,0,0.14727618,0,0.17061289,0,0.18175524,0,0.17710914,0,0.18493763,0,0.15419035,0
30
- 1.0,1.0,1.0,28,"y=1,a=1",1,0.12685466,0,0.1567368,0,0.13559486,0,0.16055734,0,0.16114064,0,0.17074403,0,0.1857261,0,0.18541132,0,0.17571747,0,0.15247998,0
31
- 1.0,1.0,1.0,29,"y=1,a=1",1,0.11501087,0,0.13172121,0,0.1171766,0,0.14073858,0,0.13168247,0,0.14981446,0,0.16198292,0,0.16511586,0,0.1566481,0,0.13190147,0
32
- 1.0,1.0,1.0,30,"y=1,a=1",1,0.16682996,0,0.17553926,0,0.14147606,0,0.17506981,0,0.17966586,0,0.18651223,0,0.19148111,0,0.19137096,0,0.20805933,0,0.1878171,0
33
- 1.0,1.0,1.0,31,"y=1,a=1",1,0.16722292,0,0.1705039,0,0.14035037,0,0.2077425,1,0.17959304,0,0.20176734,0,0.20537817,0,0.20180358,0,0.21287122,0,0.18088734,0
34
- 1.0,1.0,1.0,32,"y=1,a=1",1,0.13882238,0,0.15679923,0,0.12674898,0,0.16553092,0,0.15842491,0,0.18022278,0,0.1814161,0,0.19687814,0,0.18654303,0,0.159559,0
35
- 1.0,1.0,1.0,33,"y=1,a=1",1,0.123314165,0,0.1400847,0,0.10700226,0,0.14687607,0,0.14961521,0,0.15795027,0,0.18214935,0,0.16676001,0,0.16289498,0,0.13371591,0
36
- 1.0,1.0,1.0,34,"y=1,a=1",1,0.18857586,0,0.15934011,0,0.1487703,0,0.19712168,1,0.16803421,0,0.19806625,0,0.20813143,0,0.20772558,0,0.21247213,0,0.1847407,0
37
- 1.0,0.0,1.0,35,"y=1,a=1",0,0.19745785,1,0.1605089,0,0.1400475,0,0.18225563,0,0.17034547,0,0.20876506,0,0.20560521,0,0.21877764,0,0.21209775,0,0.19302425,0
38
- 1.0,1.0,1.0,36,"y=1,a=1",1,0.15399013,0,0.17333877,0,0.1502444,0,0.16757509,0,0.18020044,0,0.19190444,0,0.20245068,0,0.20490567,0,0.20770913,0,0.18828449,0
39
- 1.0,1.0,1.0,37,"y=1,a=1",1,0.15300405,0,0.15515244,0,0.13908194,0,0.15964547,0,0.16729154,0,0.1710501,0,0.18544675,0,0.18330276,0,0.18781754,0,0.17351587,0
40
- 1.0,1.0,1.0,38,"y=1,a=1",1,0.13881625,0,0.15390052,0,0.124390446,0,0.1646899,0,0.15618089,0,0.17321984,0,0.17303939,0,0.1697249,0,0.18123274,0,0.15039508,0
41
- 1.0,1.0,1.0,39,"y=1,a=1",1,0.14436266,0,0.16041522,0,0.14477164,0,0.17638493,0,0.16177852,0,0.18467182,0,0.19018626,0,0.19619758,0,0.19413827,0,0.17035325,0
42
- 1.0,1.0,1.0,40,"y=1,a=1",1,0.15616204,0,0.16005789,0,0.15650271,0,0.18143958,0,0.16776182,0,0.19472894,0,0.21372293,0,0.20342016,0,0.20522192,0,0.17843406,0
43
- 1.0,1.0,1.0,41,"y=1,a=1",1,0.17170905,0,0.18558288,0,0.15514232,0,0.1867644,0,0.18016769,0,0.20706213,0,0.19753456,0,0.19658928,0,0.21371217,0,0.18445726,0
44
- 1.0,1.0,1.0,42,"y=1,a=1",1,0.15691145,0,0.16422094,0,0.1446877,0,0.17105687,0,0.16436918,0,0.17970815,0,0.19255492,0,0.19329289,0,0.18672384,0,0.16313182,0
45
- 1.0,1.0,1.0,43,"y=1,a=1",1,0.14856425,0,0.15910749,0,0.13135679,0,0.16224283,0,0.1602044,0,0.18741351,0,0.18099564,0,0.1799451,0,0.19931668,0,0.17194155,0
46
- 1.0,1.0,1.0,44,"y=1,a=1",1,0.17873845,0,0.19135125,0,0.16133536,0,0.19779566,1,0.20030871,0,0.21118926,0,0.20823672,0,0.23537715,1,0.21279237,0,0.18789361,0
47
- 1.0,1.0,1.0,45,"y=1,a=1",1,0.13754228,0,0.14194769,0,0.1280386,0,0.170529,0,0.14489755,0,0.16944464,0,0.18099889,0,0.18239655,0,0.17801374,0,0.15136202,0
48
- 1.0,1.0,1.0,46,"y=1,a=1",1,0.13592309,0,0.1476283,0,0.13285817,0,0.17597987,0,0.1581929,0,0.17194676,0,0.19831897,0,0.1942862,0,0.18608207,0,0.15917605,0
49
- 1.0,1.0,1.0,47,"y=1,a=1",1,0.18010047,0,0.18705074,0,0.15744081,0,0.20090911,1,0.19375016,0,0.21729876,1,0.22446582,1,0.23234715,1,0.23271805,1,0.20759128,0
50
- 1.0,1.0,1.0,48,"y=1,a=1",1,0.1719654,0,0.1601832,0,0.17015676,0,0.18286178,0,0.172767,0,0.19451372,0,0.20277755,0,0.20658843,0,0.19645874,0,0.17038855,0
51
- 1.0,1.0,1.0,49,"y=1,a=1",1,0.1497303,0,0.15568495,0,0.12789258,0,0.20793709,1,0.16221167,0,0.1814513,0,0.20029663,0,0.19393665,0,0.20512556,0,0.16946964,0
52
- 1.0,1.0,1.0,50,"y=1,a=1",1,0.15764576,0,0.17202307,0,0.14421293,0,0.18639497,0,0.17509572,0,0.19138147,0,0.2046246,0,0.1987683,0,0.20351996,0,0.17177856,0
53
- 1.0,1.0,1.0,51,"y=1,a=1",1,0.1715982,0,0.1771918,0,0.1539806,0,0.181045,0,0.18232195,0,0.19787656,0,0.20349805,0,0.20703681,0,0.20456825,0,0.17479618,0
54
- 1.0,1.0,1.0,52,"y=1,a=1",1,0.17696847,0,0.15605752,0,0.1284968,0,0.19006708,0,0.16433583,0,0.18705693,0,0.18338792,0,0.18360478,0,0.19952038,0,0.17354,0
55
- 1.0,1.0,1.0,53,"y=1,a=1",1,0.14965387,0,0.16745928,0,0.14246035,0,0.16686855,0,0.17176057,0,0.16651565,0,0.18408684,0,0.18222319,0,0.17610088,0,0.16348024,0
56
- 1.0,1.0,1.0,54,"y=1,a=1",1,0.17431936,0,0.18274166,0,0.17344123,0,0.19229527,0,0.19993399,0,0.20890449,0,0.2189903,0,0.22121944,0,0.20981519,0,0.19680923,0
57
- 1.0,1.0,1.0,55,"y=1,a=1",1,0.12352788,0,0.13581215,0,0.117907725,0,0.14515246,0,0.14971985,0,0.16015661,0,0.17383581,0,0.17417826,0,0.17248774,0,0.15365866,0
58
- 1.0,1.0,1.0,56,"y=1,a=1",1,0.14562036,0,0.14755623,0,0.12650284,0,0.16823342,0,0.15317366,0,0.1804505,0,0.18077974,0,0.18845633,0,0.194457,0,0.1648097,0
59
- 1.0,1.0,1.0,57,"y=1,a=1",1,0.15216343,0,0.17220329,0,0.14656095,0,0.17034005,0,0.16900949,0,0.19492456,0,0.18912314,0,0.19992028,0,0.20303297,0,0.1784592,0
60
- 1.0,1.0,1.0,58,"y=1,a=1",1,0.15307795,0,0.17184143,0,0.13453898,0,0.17628802,0,0.17603672,0,0.18564378,0,0.20373547,0,0.19411916,0,0.20574632,0,0.18318287,0
61
- 1.0,1.0,1.0,59,"y=1,a=1",1,0.14875461,0,0.15452321,0,0.13244584,0,0.17560135,0,0.15827528,0,0.18483472,0,0.19070897,0,0.18223679,0,0.19302465,0,0.16229579,0
62
- 1.0,1.0,1.0,60,"y=1,a=1",1,0.1484309,0,0.16782632,0,0.14928694,0,0.17912762,0,0.17104353,0,0.19356962,0,0.20503335,0,0.20661084,0,0.20366552,0,0.17101556,0
63
- 1.0,1.0,1.0,61,"y=1,a=1",1,0.18390784,0,0.16874734,0,0.1710532,0,0.19232284,0,0.17643864,0,0.19649264,0,0.2093916,0,0.21105064,0,0.22046433,0,0.19162512,0
64
- 1.0,0.0,1.0,62,"y=1,a=1",0,0.19086605,0,0.1847265,0,0.14556314,0,0.19764093,1,0.18449509,0,0.21152566,1,0.20602112,0,0.21890281,0,0.21758562,0,0.19456987,0
65
- 1.0,1.0,1.0,63,"y=1,a=1",1,0.18139187,0,0.16569924,0,0.14882296,0,0.20883802,1,0.167167,0,0.20255557,0,0.19811723,0,0.19739376,0,0.20594202,0,0.18246569,0
66
- 1.0,1.0,1.0,64,"y=1,a=1",1,0.13222651,0,0.1638175,0,0.13865185,0,0.17228217,0,0.16906269,0,0.1846076,0,0.20789662,0,0.20643918,0,0.20420182,0,0.17014281,0
67
- 1.0,1.0,1.0,65,"y=1,a=1",1,0.1657155,0,0.1713299,0,0.1425851,0,0.1876305,0,0.18267784,0,0.2060972,0,0.22529759,1,0.22499445,1,0.22572714,1,0.19395587,0
68
- 1.0,1.0,1.0,66,"y=1,a=1",1,0.14890246,0,0.17102064,0,0.13991058,0,0.1875532,0,0.18070418,0,0.19369039,0,0.20048764,0,0.19808544,0,0.19173953,0,0.17872111,0
69
- 1.0,1.0,1.0,67,"y=1,a=1",1,0.18089959,0,0.16014846,0,0.15253557,0,0.17800528,0,0.16050816,0,0.20234586,0,0.18378061,0,0.19632678,0,0.21535818,0,0.17989221,0
70
- 1.0,1.0,1.0,68,"y=1,a=1",1,0.13583237,0,0.16197485,0,0.13079141,0,0.17989083,0,0.1676509,0,0.18960834,0,0.20284858,0,0.21183133,0,0.2099034,0,0.17323832,0
71
- 1.0,0.0,1.0,69,"y=1,a=1",0,0.19401717,0,0.18588282,0,0.16822165,0,0.19748382,1,0.1916812,0,0.21581563,1,0.21063001,0,0.21420006,0,0.22111224,1,0.20423506,0
72
- 1.0,1.0,1.0,70,"y=1,a=1",1,0.14156377,0,0.1589834,0,0.1340146,0,0.17356774,0,0.16064331,0,0.1684381,0,0.18272942,0,0.1788121,0,0.18018876,0,0.15891522,0
73
- 1.0,1.0,1.0,71,"y=1,a=1",1,0.14841984,0,0.15551507,0,0.12916599,0,0.19680882,1,0.15993878,0,0.18884514,0,0.20120412,0,0.19968913,0,0.20790854,0,0.16914098,0
74
- 1.0,1.0,1.0,72,"y=1,a=1",1,0.18323676,0,0.16287842,0,0.14114527,0,0.18732975,0,0.17567009,0,0.20162387,0,0.20415431,0,0.20640218,0,0.21885161,0,0.1805359,0
75
- 1.0,1.0,1.0,73,"y=1,a=1",1,0.15390545,0,0.15745606,0,0.13131139,0,0.17749503,0,0.15962946,0,0.18944067,0,0.18302935,0,0.18430832,0,0.19737238,0,0.1610164,0
76
- 1.0,1.0,1.0,74,"y=1,a=1",1,0.13705343,0,0.16639002,0,0.1445444,0,0.16311407,0,0.16331744,0,0.18304281,0,0.1907334,0,0.19411168,0,0.1941024,0,0.17026135,0
77
- 1.0,1.0,1.0,75,"y=1,a=1",1,0.1645426,0,0.15380427,0,0.1278367,0,0.17437619,0,0.16585825,0,0.18127581,0,0.1841548,0,0.18163463,0,0.1926568,0,0.16537292,0
78
- 1.0,1.0,1.0,76,"y=1,a=1",1,0.1633048,0,0.15517996,0,0.13807094,0,0.15665792,0,0.14437549,0,0.19143566,0,0.18077481,0,0.20951517,0,0.19858506,0,0.17358021,0
79
- 1.0,1.0,1.0,77,"y=1,a=1",1,0.17263663,0,0.19063444,0,0.14869788,0,0.18632309,0,0.18413226,0,0.1965637,0,0.1978199,0,0.19923398,0,0.21956372,0,0.19339769,0
80
- 1.0,1.0,1.0,78,"y=1,a=1",1,0.21853359,1,0.19760615,1,0.15788856,0,0.20226017,1,0.1965237,0,0.2349244,1,0.21772705,0,0.23773706,1,0.25176975,1,0.2109399,1
81
- 1.0,1.0,1.0,79,"y=1,a=1",1,0.190481,0,0.20904298,1,0.1810733,1,0.2110296,1,0.22634123,1,0.21624433,1,0.23330788,1,0.22328444,0,0.22031677,0,0.2080004,0
82
- 1.0,1.0,1.0,80,"y=1,a=1",1,0.19105637,0,0.16486546,0,0.16332771,0,0.1886486,0,0.16935842,0,0.21156543,1,0.2053306,0,0.21516272,0,0.24023306,1,0.20616958,0
83
- 1.0,1.0,1.0,81,"y=1,a=1",1,0.1525147,0,0.16207223,0,0.12800616,0,0.17251416,0,0.16961682,0,0.17477955,0,0.19234669,0,0.18228081,0,0.18870944,0,0.1688231,0
84
- 1.0,1.0,1.0,82,"y=1,a=1",1,0.091945164,0,0.12323455,0,0.08742946,0,0.109301865,0,0.12025843,0,0.12622851,0,0.10198266,0,0.11205821,0,0.12250861,0,0.11797039,0
85
- 1.0,1.0,1.0,83,"y=1,a=1",1,0.14271408,0,0.13601065,0,0.09605827,0,0.18568371,0,0.14397536,0,0.16818489,0,0.17244469,0,0.17251028,0,0.1780762,0,0.14019737,0
86
- 1.0,1.0,1.0,84,"y=1,a=1",1,0.20113255,1,0.18215007,0,0.15712124,0,0.2006804,1,0.18518631,0,0.2271398,1,0.22398525,1,0.2415721,1,0.24320522,1,0.20600802,0
87
- 1.0,0.0,1.0,85,"y=1,a=1",0,0.13831763,0,0.16831304,0,0.16505301,0,0.17107692,0,0.17307417,0,0.17650752,0,0.2099851,0,0.20355916,0,0.1951156,0,0.18917775,0
88
- 1.0,1.0,1.0,86,"y=1,a=1",1,0.13891445,0,0.1625505,0,0.14098102,0,0.1752822,0,0.16130894,0,0.17377187,0,0.19026457,0,0.18909818,0,0.18604311,0,0.15783262,0
89
- 1.0,1.0,1.0,87,"y=1,a=1",1,0.15898873,0,0.1448936,0,0.113484934,0,0.1918351,0,0.1552264,0,0.1807553,0,0.1897181,0,0.18521377,0,0.18637176,0,0.15559734,0
90
- 1.0,1.0,1.0,88,"y=1,a=1",1,0.14332165,0,0.13876212,0,0.12895454,0,0.17734088,0,0.14662053,0,0.1778874,0,0.19243571,0,0.19957863,0,0.20165212,0,0.1563459,0
91
- 1.0,1.0,1.0,89,"y=1,a=1",1,0.12377915,0,0.134884,0,0.10795848,0,0.14254524,0,0.1356338,0,0.15833044,0,0.17151734,0,0.16401836,0,0.16746135,0,0.14214353,0
92
- 1.0,1.0,1.0,90,"y=1,a=1",1,0.117094085,0,0.14488903,0,0.10975431,0,0.14635167,0,0.14737324,0,0.15325952,0,0.16944006,0,0.16222814,0,0.1586585,0,0.1320547,0
93
- 1.0,1.0,1.0,91,"y=1,a=1",1,0.1214293,0,0.13364069,0,0.10626869,0,0.15054251,0,0.14059201,0,0.1544341,0,0.18239,0,0.17724389,0,0.16957562,0,0.14027366,0
94
- 1.0,1.0,1.0,92,"y=1,a=1",1,0.14400768,0,0.1587994,0,0.1401,0,0.17458734,0,0.15953253,0,0.17860942,0,0.20662801,0,0.19549313,0,0.19668409,0,0.16135284,0
95
- 1.0,1.0,1.0,93,"y=1,a=1",1,0.15514536,0,0.17407155,0,0.14914604,0,0.18553478,0,0.1817788,0,0.19772537,0,0.2079267,0,0.20619267,0,0.21031502,0,0.17644104,0
96
- 1.0,1.0,1.0,94,"y=1,a=1",1,0.14327364,0,0.14989805,0,0.13029474,0,0.1602505,0,0.15474615,0,0.17810276,0,0.19217801,0,0.18614659,0,0.18634443,0,0.15764153,0
97
- 1.0,1.0,1.0,95,"y=1,a=1",1,0.18632965,0,0.18312062,0,0.16076182,0,0.20150152,1,0.18573518,0,0.2128924,1,0.20850426,0,0.21346131,0,0.22739206,1,0.2107685,1
98
- 1.0,1.0,1.0,96,"y=1,a=1",1,0.1472536,0,0.14557208,0,0.12666345,0,0.1749201,0,0.14396735,0,0.18348216,0,0.17950083,0,0.17368396,0,0.19621117,0,0.15972707,0
99
- 1.0,1.0,1.0,97,"y=1,a=1",1,0.14708629,0,0.14739223,0,0.123407245,0,0.1523625,0,0.14834812,0,0.1787064,0,0.17501923,0,0.1754873,0,0.1899544,0,0.16834338,0
100
- 1.0,1.0,1.0,98,"y=1,a=1",1,0.14343245,0,0.1482803,0,0.12838398,0,0.17404889,0,0.1502923,0,0.17460258,0,0.18259732,0,0.19844213,0,0.18968965,0,0.15962453,0
101
- 1.0,1.0,1.0,99,"y=1,a=1",1,0.14066991,0,0.1424987,0,0.123590186,0,0.16426753,0,0.15667903,0,0.16950639,0,0.16748568,0,0.17215846,0,0.17550889,0,0.14984211,0
102
- 1.0,1.0,1.0,100,"y=1,a=1",1,0.13001989,0,0.16256,0,0.126862,0,0.16861056,0,0.16016434,0,0.17793535,0,0.18308154,0,0.1782236,0,0.18978272,0,0.1618554,0
103
- 1.0,1.0,1.0,101,"y=1,a=1",1,0.10945015,0,0.13751549,0,0.11814305,0,0.14249918,0,0.13484922,0,0.14792655,0,0.16635866,0,0.16511737,0,0.15957987,0,0.13601874,0
104
- 1.0,1.0,1.0,102,"y=1,a=1",1,0.11750871,0,0.12600687,0,0.11428928,0,0.12787864,0,0.13457498,0,0.14476998,0,0.15483855,0,0.16179232,0,0.15321602,0,0.13903803,0
105
- 1.0,1.0,1.0,103,"y=1,a=1",1,0.1326475,0,0.1439247,0,0.1284362,0,0.1720101,0,0.15397155,0,0.17767775,0,0.18770088,0,0.18316966,0,0.18771933,0,0.15689993,0
106
- 1.0,1.0,1.0,104,"y=1,a=1",1,0.12811248,0,0.13445708,0,0.11564976,0,0.15604475,0,0.1388439,0,0.16411999,0,0.17181115,0,0.16622195,0,0.17156643,0,0.14507815,0
107
- 1.0,1.0,1.0,105,"y=1,a=1",1,0.14436024,0,0.15809222,0,0.12911436,0,0.17486401,0,0.1680576,0,0.18187265,0,0.18602598,0,0.20043924,0,0.1971273,0,0.17757675,0
108
- 1.0,1.0,1.0,106,"y=1,a=1",1,0.16227606,0,0.16766275,0,0.14883368,0,0.18441854,0,0.17929311,0,0.20337275,0,0.20923862,0,0.2018477,0,0.20004311,0,0.17560755,0
109
- 1.0,1.0,1.0,107,"y=1,a=1",1,0.15334493,0,0.1430109,0,0.13691479,0,0.15845755,0,0.1486562,0,0.1781835,0,0.17819513,0,0.1840935,0,0.20656823,0,0.1662186,0
110
- 1.0,1.0,1.0,108,"y=1,a=1",1,0.14345995,0,0.15888928,0,0.1478824,0,0.1796146,0,0.17027752,0,0.18594062,0,0.19874847,0,0.20400631,0,0.19881491,0,0.17213646,0
111
- 1.0,1.0,1.0,109,"y=1,a=1",1,0.1541302,0,0.15847498,0,0.12535857,0,0.16034637,0,0.15883322,0,0.19423412,0,0.1961309,0,0.19463694,0,0.19846737,0,0.16751374,0
112
- 1.0,1.0,1.0,110,"y=1,a=1",1,0.1525761,0,0.1636606,0,0.14983411,0,0.18261886,0,0.17574072,0,0.19375306,0,0.19794029,0,0.20781812,0,0.19833067,0,0.1743542,0
113
- 1.0,1.0,1.0,111,"y=1,a=1",1,0.17207086,0,0.17792985,0,0.14989834,0,0.18266763,0,0.17691006,0,0.20889606,0,0.18449071,0,0.20900688,0,0.20759971,0,0.19038263,0
114
- 1.0,1.0,1.0,112,"y=1,a=1",1,0.13977616,0,0.15481547,0,0.13760696,0,0.16761199,0,0.16574432,0,0.17980468,0,0.19782215,0,0.19447999,0,0.18900083,0,0.16038182,0
115
- 1.0,1.0,1.0,113,"y=1,a=1",1,0.13768691,0,0.16005413,0,0.12927802,0,0.1610788,0,0.16672085,0,0.1804656,0,0.19191007,0,0.18682544,0,0.18408775,0,0.1600454,0
116
- 0.0,0.0,0.0,114,"y=0,a=0",0,0.21629956,1,0.19502085,1,0.18161944,1,0.1970709,1,0.21680143,1,0.22099246,1,0.25191927,1,0.25746343,1,0.22817212,1,0.23973352,1
117
- 0.0,0.0,0.0,115,"y=0,a=0",0,0.20373677,1,0.17104894,0,0.1715973,0,0.18102422,0,0.2045608,1,0.20834577,0,0.2462081,1,0.26103884,1,0.21337304,0,0.21313083,1
118
- 0.0,0.0,0.0,116,"y=0,a=0",0,0.14561327,0,0.17825732,0,0.14655517,0,0.15851271,0,0.19581327,0,0.16594557,0,0.21683206,0,0.20602374,0,0.18022679,0,0.19348283,0
119
- 0.0,0.0,0.0,117,"y=0,a=0",0,0.19888748,1,0.18042342,0,0.17747243,1,0.17108308,0,0.2031946,0,0.19166578,0,0.2379144,1,0.23602206,1,0.20021465,0,0.21815784,1
120
- 0.0,0.0,0.0,118,"y=0,a=0",0,0.2140958,1,0.2122949,1,0.18489678,1,0.19367363,0,0.21222784,1,0.21562949,1,0.22073296,0,0.21419479,0,0.22190404,1,0.22085562,1
121
- 0.0,0.0,0.0,119,"y=0,a=0",0,0.218953,1,0.19635825,1,0.16438636,0,0.17296287,0,0.2130866,1,0.21149413,0,0.22761635,1,0.25642404,1,0.21145822,0,0.21206616,1
122
- 0.0,0.0,0.0,120,"y=0,a=0",0,0.23516372,1,0.2225155,1,0.21182913,1,0.22854784,1,0.23320532,1,0.23634423,1,0.25623882,1,0.25961834,1,0.24648662,1,0.23816217,1
123
- 0.0,0.0,0.0,121,"y=0,a=0",0,0.23214318,1,0.21949787,1,0.1839395,1,0.20385863,1,0.22703049,1,0.23493706,1,0.22986428,1,0.22935934,1,0.24971467,1,0.24684589,1
124
- 0.0,0.0,0.0,122,"y=0,a=0",0,0.23290876,1,0.22216387,1,0.21598223,1,0.22287862,1,0.25175002,1,0.23658784,1,0.28372967,1,0.29013565,1,0.24121045,1,0.2488722,1
125
- 0.0,0.0,0.0,123,"y=0,a=0",0,0.22104344,1,0.2253988,1,0.20302819,1,0.22717239,1,0.2535634,1,0.24014424,1,0.27316776,1,0.27300632,1,0.26458478,1,0.2454344,1
126
- 0.0,0.0,0.0,124,"y=0,a=0",0,0.2912934,1,0.2556405,1,0.2354738,1,0.24183689,1,0.27839053,1,0.28138766,1,0.28967023,1,0.28253806,1,0.28382322,1,0.28533405,1
127
- 0.0,0.0,0.0,125,"y=0,a=0",0,0.27502146,1,0.26284122,1,0.21121967,1,0.2215158,1,0.27148134,1,0.27797183,1,0.26472178,1,0.25730821,1,0.29156038,1,0.2878151,1
128
- 0.0,0.0,0.0,126,"y=0,a=0",0,0.25565237,1,0.21149512,1,0.21923767,1,0.22270498,1,0.21953097,1,0.23591736,1,0.22151981,0,0.21187149,0,0.24027278,1,0.23575346,1
129
- 0.0,0.0,0.0,127,"y=0,a=0",0,0.19917871,1,0.19289087,1,0.16517395,0,0.19570328,1,0.2110863,1,0.19845173,0,0.23095247,1,0.221578,0,0.2119535,0,0.22226082,1
130
- 0.0,0.0,0.0,128,"y=0,a=0",0,0.31448007,1,0.26418847,1,0.28541306,1,0.2744318,1,0.2632333,1,0.2845582,1,0.27153718,1,0.27058944,1,0.30319074,1,0.30288166,1
131
- 0.0,0.0,0.0,129,"y=0,a=0",0,0.16484453,0,0.19631769,1,0.13354276,0,0.14672178,0,0.22065702,1,0.18332753,0,0.22069651,0,0.2088991,0,0.17627029,0,0.20310105,0
132
- 0.0,0.0,0.0,130,"y=0,a=0",0,0.2442046,1,0.22005297,1,0.19610228,1,0.2109937,1,0.23661515,1,0.24072827,1,0.2415021,1,0.24742095,1,0.24533257,1,0.24449895,1
133
- 0.0,0.0,0.0,131,"y=0,a=0",0,0.20537642,1,0.1811161,0,0.15250538,0,0.16765824,0,0.18527488,0,0.2061983,0,0.19409195,0,0.19965103,0,0.19881643,0,0.19574745,0
134
- 0.0,0.0,0.0,132,"y=0,a=0",0,0.22496285,1,0.2135807,1,0.21117483,1,0.20822401,1,0.21611689,1,0.23704219,1,0.24898085,1,0.24274257,1,0.24138911,1,0.2476287,1
135
- 0.0,0.0,0.0,133,"y=0,a=0",0,0.23561443,1,0.20928955,1,0.16284303,0,0.17080007,0,0.22017908,1,0.22295153,1,0.23831202,1,0.23783913,1,0.23278035,1,0.23553962,1
136
- 0.0,0.0,0.0,134,"y=0,a=0",0,0.2025066,1,0.17399184,0,0.19995397,1,0.20089392,1,0.19854882,0,0.20213503,0,0.22430365,1,0.22551602,1,0.21544594,0,0.22056738,1
137
- 0.0,0.0,0.0,135,"y=0,a=0",0,0.18957372,0,0.18514684,0,0.17393161,1,0.18156625,0,0.21165241,1,0.19927871,0,0.26260495,1,0.2673721,1,0.20362297,0,0.21589461,1
138
- 0.0,0.0,0.0,136,"y=0,a=0",0,0.21258034,1,0.20959607,1,0.18719526,1,0.21530502,1,0.23126294,1,0.22665884,1,0.24952729,1,0.24709885,1,0.23582236,1,0.234424,1
139
- 0.0,0.0,0.0,137,"y=0,a=0",0,0.32169738,1,0.27482897,1,0.23940212,1,0.24543403,1,0.2811145,1,0.29308948,1,0.26325628,1,0.272486,1,0.3035895,1,0.30090222,1
140
- 0.0,0.0,0.0,138,"y=0,a=0",0,0.21456146,1,0.22650397,1,0.19664958,1,0.192451,0,0.22628762,1,0.22772564,1,0.23576681,1,0.23801509,1,0.23277885,1,0.23941761,1
141
- 0.0,0.0,0.0,139,"y=0,a=0",0,0.15981878,0,0.14024648,0,0.2005361,1,0.20819703,1,0.16472198,0,0.16505125,0,0.1948408,0,0.2142679,0,0.17974393,0,0.1700003,0
142
- 0.0,0.0,0.0,140,"y=0,a=0",0,0.22810706,1,0.21680629,1,0.22314571,1,0.2241421,1,0.24369025,1,0.23661256,1,0.27321497,1,0.2803747,1,0.24615455,1,0.25539336,1
143
- 0.0,0.0,0.0,141,"y=0,a=0",0,0.2301,1,0.20891814,1,0.20083284,1,0.2175107,1,0.2236452,1,0.23934731,1,0.23209593,1,0.24597466,1,0.23860484,1,0.23154658,1
144
- 0.0,0.0,0.0,142,"y=0,a=0",0,0.16852516,0,0.16939113,0,0.15138349,0,0.1646085,0,0.16913226,0,0.17984252,0,0.20915264,0,0.19539025,0,0.18686783,0,0.20731235,0
145
- 0.0,0.0,0.0,143,"y=0,a=0",0,0.30314684,1,0.2866239,1,0.2533533,1,0.2632583,1,0.29672834,1,0.2945869,1,0.27519578,1,0.27682963,1,0.27411878,1,0.2952486,1
146
- 0.0,0.0,0.0,144,"y=0,a=0",0,0.16677696,0,0.16723591,0,0.13768567,0,0.1682342,0,0.17615378,0,0.18696676,0,0.20347056,0,0.21208458,0,0.1963465,0,0.1917766,0
147
- 0.0,0.0,0.0,145,"y=0,a=0",0,0.18749206,0,0.18077157,0,0.18636268,1,0.18881966,0,0.21005616,1,0.2114338,0,0.25653186,1,0.2476237,1,0.23787864,1,0.23430152,1
148
- 0.0,0.0,0.0,146,"y=0,a=0",0,0.21335763,1,0.19352087,1,0.19445308,1,0.2103263,1,0.21526074,1,0.23100486,1,0.23790868,1,0.23137888,1,0.23180407,1,0.21501175,1
149
- 0.0,0.0,0.0,147,"y=0,a=0",0,0.20003031,1,0.20952931,1,0.1829402,1,0.2049587,1,0.2197213,1,0.22288536,1,0.23420712,1,0.23333815,1,0.21602894,0,0.21633556,1
150
- 0.0,0.0,0.0,148,"y=0,a=0",0,0.24129343,1,0.24729958,1,0.20349193,1,0.21125305,1,0.25725636,1,0.2505853,1,0.26472607,1,0.2708717,1,0.2662022,1,0.26850894,1
151
- 0.0,0.0,0.0,149,"y=0,a=0",0,0.28448558,1,0.24803948,1,0.19221511,1,0.21236362,1,0.24887307,1,0.26611382,1,0.23234902,1,0.23339842,1,0.2566731,1,0.25177315,1
152
- 0.0,0.0,0.0,150,"y=0,a=0",0,0.22612208,1,0.1837663,0,0.17387533,1,0.19408184,0,0.21778569,1,0.20656893,0,0.23641928,1,0.24897379,1,0.23462485,1,0.23590052,1
153
- 0.0,0.0,0.0,151,"y=0,a=0",0,0.2925464,1,0.27941924,1,0.2645083,1,0.26494604,1,0.2912935,1,0.29494172,1,0.2924189,1,0.2771371,1,0.2890394,1,0.28854316,1
154
- 0.0,0.0,0.0,152,"y=0,a=0",0,0.23609947,1,0.22891739,1,0.18901241,1,0.19892745,1,0.24134274,1,0.23208232,1,0.23893097,1,0.23692636,1,0.23078223,1,0.23466298,1
155
- 0.0,0.0,0.0,153,"y=0,a=0",0,0.31985348,1,0.27244428,1,0.3119165,1,0.30027628,1,0.29036343,1,0.29357013,1,0.29854798,1,0.29302508,1,0.29939014,1,0.31521794,1
156
- 0.0,0.0,0.0,154,"y=0,a=0",0,0.21224323,1,0.22053578,1,0.21208304,1,0.19812931,1,0.23775162,1,0.22802089,1,0.25269696,1,0.24773309,1,0.23770687,1,0.255573,1
157
- 0.0,1.0,0.0,155,"y=0,a=0",1,0.17206214,0,0.16328365,0,0.1598063,0,0.19209553,0,0.17138651,0,0.18391578,0,0.19105129,0,0.18806146,0,0.19338576,0,0.1874248,0
158
- 0.0,0.0,0.0,156,"y=0,a=0",0,0.17085424,0,0.18980238,0,0.18197776,1,0.17881283,0,0.20477112,1,0.19770822,0,0.20508522,0,0.20000537,0,0.19573519,0,0.20845383,0
159
- 0.0,0.0,0.0,157,"y=0,a=0",0,0.23284508,1,0.1869626,0,0.20610423,1,0.2387685,1,0.2170435,1,0.23588294,1,0.2564756,1,0.24894775,1,0.24163687,1,0.21974562,1
160
- 0.0,0.0,0.0,158,"y=0,a=0",0,0.28844965,1,0.23848553,1,0.25746766,1,0.27938432,1,0.2592623,1,0.27334738,1,0.2509387,1,0.26393548,1,0.28369138,1,0.27884173,1
161
- 0.0,0.0,0.0,159,"y=0,a=0",0,0.22786354,1,0.21425496,1,0.23821446,1,0.23392887,1,0.2351839,1,0.23248175,1,0.26652214,1,0.25839835,1,0.2548886,1,0.2655325,1
162
- 0.0,0.0,0.0,160,"y=0,a=0",0,0.19386661,0,0.19815555,1,0.18591613,1,0.20349869,1,0.19989231,0,0.21784168,1,0.23608558,1,0.24985714,1,0.21973585,0,0.22014417,1
163
- 0.0,0.0,0.0,161,"y=0,a=0",0,0.15330206,0,0.14854272,0,0.12823813,0,0.16545646,0,0.18846102,0,0.17684111,0,0.22908205,1,0.24301316,1,0.19135259,0,0.19463651,0
164
- 0.0,0.0,0.0,162,"y=0,a=0",0,0.23059389,1,0.20348401,1,0.21154402,1,0.24646446,1,0.23137687,1,0.23219305,1,0.22989158,1,0.23956063,1,0.24651413,1,0.24177426,1
165
- 0.0,0.0,0.0,163,"y=0,a=0",0,0.2622468,1,0.25614005,1,0.24799164,1,0.26216137,1,0.26265445,1,0.2534338,1,0.26255333,1,0.25026754,1,0.27152318,1,0.2729322,1
166
- 0.0,0.0,0.0,164,"y=0,a=0",0,0.21135986,1,0.19091465,0,0.20744087,1,0.20740505,1,0.2220819,1,0.22807766,1,0.2811546,1,0.29234123,1,0.24752346,1,0.25643557,1
167
- 0.0,0.0,0.0,165,"y=0,a=0",0,0.16879244,0,0.1572513,0,0.15207331,0,0.181776,0,0.16746461,0,0.18681383,0,0.19406697,0,0.19419222,0,0.21110106,0,0.17872968,0
168
- 0.0,0.0,0.0,166,"y=0,a=0",0,0.15423098,0,0.17692415,0,0.15408017,0,0.16727218,0,0.1882566,0,0.18844599,0,0.22269353,1,0.21927144,0,0.19387552,0,0.18287477,0
169
- 0.0,0.0,0.0,167,"y=0,a=0",0,0.2549107,1,0.25376242,1,0.25808552,1,0.2607341,1,0.25381675,1,0.24651274,1,0.2453848,1,0.24476527,1,0.26549453,1,0.28409472,1
170
- 0.0,0.0,0.0,168,"y=0,a=0",0,0.23481452,1,0.21773267,1,0.20418544,1,0.21662375,1,0.24933773,1,0.24538557,1,0.27938887,1,0.2968815,1,0.25733355,1,0.24896894,1
171
- 0.0,0.0,0.0,169,"y=0,a=0",0,0.20319656,1,0.19939989,1,0.2060978,1,0.19552281,1,0.19843806,0,0.20807067,0,0.21813893,0,0.21431714,0,0.22718455,1,0.21502644,1
172
- 0.0,0.0,0.0,170,"y=0,a=0",0,0.19950864,1,0.2172359,1,0.20818372,1,0.2010315,1,0.23610987,1,0.21068895,0,0.2477224,1,0.24035892,1,0.22574595,1,0.22504038,1
173
- 0.0,0.0,0.0,171,"y=0,a=0",0,0.20406577,1,0.2391728,1,0.1670636,0,0.18132998,0,0.22413091,1,0.21908765,1,0.2273693,1,0.21544716,0,0.23357345,1,0.25408784,1
174
- 0.0,0.0,0.0,172,"y=0,a=0",0,0.29560846,1,0.25379673,1,0.2126526,1,0.23723492,1,0.26007006,1,0.27810472,1,0.24411352,1,0.24718428,1,0.28267694,1,0.27619013,1
175
- 0.0,0.0,0.0,173,"y=0,a=0",0,0.20402531,1,0.16705966,0,0.15941648,0,0.18591814,0,0.18590535,0,0.20996296,0,0.23320648,1,0.24023713,1,0.21538247,0,0.19797726,0
176
- 0.0,0.0,0.0,174,"y=0,a=0",0,0.18958841,0,0.16933335,0,0.18283533,1,0.18451813,0,0.18831237,0,0.1916644,0,0.21824616,0,0.23215371,1,0.19852223,0,0.20953193,0
177
- 0.0,0.0,0.0,175,"y=0,a=0",0,0.268441,1,0.22047198,1,0.25287405,1,0.25208837,1,0.2460345,1,0.26222655,1,0.2680952,1,0.2714164,1,0.27537957,1,0.2712622,1
178
- 0.0,0.0,0.0,176,"y=0,a=0",0,0.2843113,1,0.24434961,1,0.2480993,1,0.26810458,1,0.26790512,1,0.27672178,1,0.2736643,1,0.27890322,1,0.29405957,1,0.29807168,1
179
- 0.0,0.0,0.0,177,"y=0,a=0",0,0.28265357,1,0.2528501,1,0.213369,1,0.24079694,1,0.26418674,1,0.269131,1,0.26058006,1,0.2588181,1,0.2533689,1,0.2545793,1
180
- 0.0,0.0,0.0,178,"y=0,a=0",0,0.20542608,1,0.20167263,1,0.18901601,1,0.19862598,1,0.21660021,1,0.22179449,1,0.25101894,1,0.26096252,1,0.22537692,1,0.22015163,1
181
- 0.0,0.0,0.0,179,"y=0,a=0",0,0.23357715,1,0.20221975,1,0.21909353,1,0.23609838,1,0.22409108,1,0.22853467,1,0.23524955,1,0.23689812,1,0.2398624,1,0.24643768,1
182
- 0.0,0.0,0.0,180,"y=0,a=0",0,0.28076285,1,0.27910197,1,0.25229108,1,0.26419,1,0.29148746,1,0.29495186,1,0.29652125,1,0.29843313,1,0.30044496,1,0.29391554,1
183
- 0.0,0.0,0.0,181,"y=0,a=0",0,0.20729743,1,0.1829473,0,0.20294519,1,0.23637491,1,0.19588315,0,0.2146337,1,0.20508458,0,0.21674475,0,0.21656528,0,0.21035583,1
184
- 0.0,0.0,0.0,182,"y=0,a=0",0,0.2517862,1,0.21484187,1,0.23965485,1,0.26187205,1,0.23130158,1,0.24296303,1,0.24557392,1,0.24199909,1,0.247927,1,0.24450612,1
185
- 0.0,0.0,0.0,183,"y=0,a=0",0,0.25783062,1,0.24382694,1,0.20403251,1,0.21144985,1,0.25555688,1,0.25131536,1,0.2663087,1,0.2663301,1,0.24748535,1,0.25408342,1
186
- 0.0,0.0,0.0,184,"y=0,a=0",0,0.17844366,0,0.1724326,0,0.15518755,0,0.17632,0,0.19039401,0,0.1967407,0,0.23111588,1,0.23057148,1,0.20898932,0,0.20652053,0
187
- 0.0,0.0,0.0,185,"y=0,a=0",0,0.1677241,0,0.23196885,1,0.16306645,0,0.1792868,0,0.21269274,1,0.19367774,0,0.21760541,0,0.2125915,0,0.19482416,0,0.2227113,1
188
- 0.0,0.0,0.0,186,"y=0,a=0",0,0.18948488,0,0.18995544,0,0.16338828,0,0.1606218,0,0.19621146,0,0.19174053,0,0.21310678,0,0.19984348,0,0.19213337,0,0.22210403,1
189
- 0.0,0.0,0.0,187,"y=0,a=0",0,0.21735223,1,0.18445195,0,0.23855418,1,0.2602321,1,0.20388734,1,0.21067585,0,0.22809163,1,0.25402647,1,0.22281773,1,0.22068454,1
190
- 0.0,0.0,0.0,188,"y=0,a=0",0,0.22123262,1,0.23926373,1,0.20945041,1,0.23188646,1,0.2521487,1,0.24152152,1,0.25243852,1,0.2475077,1,0.24857543,1,0.2453662,1
191
- 0.0,0.0,0.0,189,"y=0,a=0",0,0.17009729,0,0.20316552,1,0.17605366,1,0.18706942,0,0.23663089,1,0.19699714,0,0.23261072,1,0.21938439,0,0.20253538,0,0.21510676,1
192
- 0.0,0.0,0.0,190,"y=0,a=0",0,0.17006601,0,0.16700102,0,0.14669426,0,0.18419802,0,0.17659315,0,0.19214354,0,0.17532341,0,0.17749189,0,0.18968184,0,0.18245308,0
193
- 0.0,0.0,0.0,191,"y=0,a=0",0,0.14008634,0,0.16535099,0,0.14299414,0,0.15148005,0,0.2010998,0,0.17151603,0,0.21086735,0,0.2397389,1,0.17843303,0,0.17960261,0
194
- 0.0,0.0,0.0,192,"y=0,a=0",0,0.2091137,1,0.19850035,1,0.20114557,1,0.20737454,1,0.20557173,1,0.20946483,0,0.20794424,0,0.20940235,0,0.21299437,0,0.21406375,1
195
- 0.0,0.0,0.0,193,"y=0,a=0",0,0.25352073,1,0.2632533,1,0.22773683,1,0.2277535,1,0.274835,1,0.2553297,1,0.28693074,1,0.28220308,1,0.25877896,1,0.28076303,1
196
- 0.0,0.0,0.0,194,"y=0,a=0",0,0.20184462,1,0.1994361,1,0.16574751,0,0.1936948,0,0.21374469,1,0.21498935,1,0.24419317,1,0.24352345,1,0.23042251,1,0.21962863,1
197
- 0.0,0.0,0.0,195,"y=0,a=0",0,0.24109352,1,0.1934802,1,0.2319635,1,0.23354708,1,0.21253034,1,0.22468254,1,0.23310211,1,0.25058892,1,0.23367698,1,0.23022185,1
198
- 0.0,0.0,0.0,196,"y=0,a=0",0,0.24413054,1,0.23028845,1,0.2166117,1,0.22513074,1,0.25882488,1,0.25571114,1,0.2521101,1,0.2611572,1,0.2650238,1,0.24669987,1
199
- 0.0,1.0,0.0,197,"y=0,a=0",1,0.21566533,1,0.20134467,1,0.20133708,1,0.23698838,1,0.21300705,1,0.22918321,1,0.25070015,1,0.25678498,1,0.25115117,1,0.2369642,1
200
- 0.0,0.0,0.0,198,"y=0,a=0",0,0.29364935,1,0.2679889,1,0.22294113,1,0.25373748,1,0.28787526,1,0.2818233,1,0.28333685,1,0.28021967,1,0.29341143,1,0.29194015,1
201
- 0.0,0.0,0.0,199,"y=0,a=0",0,0.2266373,1,0.20313895,1,0.22486074,1,0.23628527,1,0.21953505,1,0.22889793,1,0.2347579,1,0.2281052,1,0.25825578,1,0.23843017,1
202
- 0.0,0.0,0.0,200,"y=0,a=0",0,0.22769018,1,0.21127938,1,0.20660923,1,0.215718,1,0.221961,1,0.23830351,1,0.25159732,1,0.25003976,1,0.23426203,1,0.22437982,1
203
- 0.0,0.0,0.0,201,"y=0,a=0",0,0.16584875,0,0.18493684,0,0.14104292,0,0.1517926,0,0.20335507,1,0.1809374,0,0.2256738,1,0.22627783,1,0.19280146,0,0.20030874,0
204
- 0.0,0.0,0.0,202,"y=0,a=0",0,0.12695505,0,0.15220204,0,0.113902286,0,0.12785915,0,0.16797797,0,0.16680172,0,0.1912041,0,0.21238163,0,0.17740928,0,0.17314403,0
205
- 0.0,0.0,0.0,203,"y=0,a=0",0,0.23899457,1,0.22071286,1,0.20873387,1,0.22449706,1,0.24235405,1,0.25713754,1,0.2656874,1,0.27030945,1,0.25068966,1,0.24123792,1
206
- 0.0,0.0,0.0,204,"y=0,a=0",0,0.2886801,1,0.24552442,1,0.23213863,1,0.25016716,1,0.2594601,1,0.28408334,1,0.28063342,1,0.27151614,1,0.28184098,1,0.27264795,1
207
- 0.0,0.0,0.0,205,"y=0,a=0",0,0.25252998,1,0.22975953,1,0.22115922,1,0.23017691,1,0.2420262,1,0.2487741,1,0.24409305,1,0.24200432,1,0.2498957,1,0.24047926,1
208
- 0.0,0.0,0.0,206,"y=0,a=0",0,0.20079766,1,0.19982108,1,0.18150482,1,0.19900826,1,0.21181178,1,0.22380723,1,0.24105556,1,0.25907767,1,0.2291995,1,0.216905,1
209
- 0.0,0.0,0.0,207,"y=0,a=0",0,0.23642625,1,0.20767161,1,0.22604807,1,0.22577444,1,0.22970642,1,0.22978619,1,0.25716138,1,0.25598398,1,0.23618846,1,0.2506718,1
210
- 0.0,0.0,0.0,208,"y=0,a=0",0,0.21929546,1,0.18724711,0,0.17180155,0,0.17810732,0,0.18422444,0,0.20406261,0,0.20073183,0,0.20886286,0,0.2158861,0,0.2087399,0
211
- 0.0,0.0,0.0,209,"y=0,a=0",0,0.17935906,0,0.21372955,1,0.17692159,1,0.18204105,0,0.21356684,1,0.19930516,0,0.23060726,1,0.2105602,0,0.19585393,0,0.22706008,1
212
- 0.0,0.0,0.0,210,"y=0,a=0",0,0.3166798,1,0.26933578,1,0.26377356,1,0.26463467,1,0.27948895,1,0.30627394,1,0.28486025,1,0.2802874,1,0.3044695,1,0.30290216,1
213
- 0.0,0.0,0.0,211,"y=0,a=0",0,0.23337746,1,0.22874755,1,0.23093103,1,0.26244056,1,0.25569025,1,0.26840484,1,0.27775627,1,0.279691,1,0.27802828,1,0.27116546,1
214
- 0.0,0.0,0.0,212,"y=0,a=0",0,0.21178947,1,0.19036078,0,0.17382373,1,0.19525054,0,0.21411815,1,0.22326855,1,0.2232317,1,0.23549637,1,0.24309291,1,0.23349911,1
215
- 0.0,0.0,0.0,213,"y=0,a=0",0,0.22773713,1,0.21611793,1,0.18774924,1,0.22708164,1,0.25040522,1,0.24911353,1,0.29331946,1,0.2912908,1,0.25349125,1,0.25576717,1
216
- 0.0,0.0,0.0,214,"y=0,a=0",0,0.13947293,0,0.16429278,0,0.1379107,0,0.14864808,0,0.18685088,0,0.18624856,0,0.22041799,0,0.23328473,1,0.19379921,0,0.19116533,0
217
- 0.0,0.0,0.0,215,"y=0,a=0",0,0.22978464,1,0.22717,1,0.21136901,1,0.21371886,1,0.23405235,1,0.23359501,1,0.24807,1,0.25876504,1,0.23560134,1,0.23692045,1
218
- 0.0,0.0,0.0,216,"y=0,a=0",0,0.21776736,1,0.21560603,1,0.19631433,1,0.22191156,1,0.21508881,1,0.23112263,1,0.22687812,1,0.22083446,0,0.23079611,1,0.23356268,1
219
- 0.0,0.0,0.0,217,"y=0,a=0",0,0.17896779,0,0.19187793,1,0.1457917,0,0.17547773,0,0.21258047,1,0.1839966,0,0.20253313,0,0.21042036,0,0.19692647,0,0.21836044,1
220
- 0.0,0.0,0.0,218,"y=0,a=0",0,0.16325332,0,0.15591475,0,0.14208479,0,0.16283303,0,0.19026855,0,0.19199215,0,0.21578987,0,0.23742324,1,0.20475172,0,0.20093888,0
221
- 0.0,0.0,0.0,219,"y=0,a=0",0,0.23028114,1,0.21446303,1,0.18868937,1,0.20571032,1,0.22793756,1,0.22849755,1,0.2322729,1,0.23309378,1,0.23951285,1,0.25246274,1
222
- 0.0,1.0,0.0,220,"y=0,a=0",1,0.16641276,0,0.17429328,0,0.14059351,0,0.17198388,0,0.1763443,0,0.19800356,0,0.20417276,0,0.20977543,0,0.2099058,0,0.18135841,0
223
- 0.0,0.0,0.0,221,"y=0,a=0",0,0.2103267,1,0.21962301,1,0.19889604,1,0.21092755,1,0.24949032,1,0.23153736,1,0.2457409,1,0.23937817,1,0.22704524,1,0.22564198,1
224
- 0.0,0.0,0.0,222,"y=0,a=0",0,0.2842101,1,0.26317623,1,0.24808271,1,0.2624087,1,0.280448,1,0.28986725,1,0.28966036,1,0.2898888,1,0.2935936,1,0.2710144,1
225
- 0.0,0.0,0.0,223,"y=0,a=0",0,0.22123778,1,0.21625565,1,0.18215413,1,0.21967964,1,0.23170047,1,0.24066888,1,0.23844282,1,0.24092263,1,0.25062767,1,0.23548278,1
226
- 0.0,0.0,0.0,224,"y=0,a=0",0,0.20566696,1,0.2137607,1,0.21000236,1,0.20894638,1,0.22774553,1,0.22618112,1,0.22720385,1,0.22612607,1,0.21610597,0,0.21229994,1
227
- 0.0,0.0,0.0,225,"y=0,a=0",0,0.23512106,1,0.179679,0,0.2520198,1,0.23118253,1,0.19173616,0,0.21029252,0,0.21806663,0,0.22891021,1,0.22153209,1,0.22744562,1
228
- 0.0,0.0,0.0,226,"y=0,a=0",0,0.23524895,1,0.22567162,1,0.23538168,1,0.24507698,1,0.24362467,1,0.24576628,1,0.24733862,1,0.24346489,1,0.25319314,1,0.23733819,1
229
- 0.0,0.0,0.0,227,"y=0,a=0",0,0.20860867,1,0.17529035,0,0.1741857,1,0.16641888,0,0.18170416,0,0.19612658,0,0.20198075,0,0.21082135,0,0.21986982,0,0.21279086,1
230
- 0.0,0.0,0.0,228,"y=0,a=0",0,0.24253614,1,0.24025595,1,0.23621815,1,0.23319927,1,0.23726484,1,0.25158182,1,0.25464228,1,0.2591834,1,0.2571461,1,0.27436477,1
231
- 0.0,0.0,0.0,229,"y=0,a=0",0,0.28839484,1,0.2569612,1,0.26959002,1,0.2660921,1,0.26784682,1,0.27270743,1,0.26279,1,0.25919908,1,0.27425689,1,0.27319223,1
232
- 0.0,0.0,0.0,230,"y=0,a=0",0,0.20041944,1,0.22440276,1,0.21330556,1,0.21045257,1,0.22209994,1,0.21711914,1,0.22592175,1,0.22145651,0,0.22235492,1,0.2505273,1
233
- 0.0,0.0,0.0,231,"y=0,a=0",0,0.27019045,1,0.2451126,1,0.20195282,1,0.21667615,1,0.2460243,1,0.25713694,1,0.23482977,1,0.23829746,1,0.25937772,1,0.26290444,1
234
- 0.0,0.0,0.0,232,"y=0,a=0",0,0.2236806,1,0.20663135,1,0.23684818,1,0.24303634,1,0.20630963,1,0.22758475,1,0.2178103,0,0.22354726,0,0.23059414,1,0.22275674,1
235
- 0.0,0.0,0.0,233,"y=0,a=0",0,0.28241095,1,0.24043825,1,0.24422449,1,0.24917611,1,0.25039378,1,0.2667216,1,0.2484856,1,0.24711587,1,0.26455078,1,0.26782253,1
236
- 0.0,0.0,0.0,234,"y=0,a=0",0,0.252633,1,0.21890767,1,0.27482915,1,0.26445168,1,0.22477964,1,0.23809151,1,0.24524437,1,0.2475714,1,0.26222822,1,0.2533566,1
237
- 0.0,0.0,0.0,235,"y=0,a=0",0,0.22404334,1,0.2095977,1,0.1868104,1,0.205958,1,0.2123902,1,0.23034692,1,0.22797553,1,0.24222012,1,0.2243821,1,0.21936446,1
238
- 0.0,0.0,0.0,236,"y=0,a=0",0,0.24672501,1,0.22418459,1,0.16806728,0,0.17840654,0,0.23933062,1,0.24312513,1,0.24202488,1,0.26490426,1,0.24608666,1,0.24930163,1
239
- 0.0,0.0,0.0,237,"y=0,a=0",0,0.24095097,1,0.23467234,1,0.22968426,1,0.24310125,1,0.24306346,1,0.24854523,1,0.23887363,1,0.2488089,1,0.24751289,1,0.2391269,1
240
- 0.0,0.0,0.0,238,"y=0,a=0",0,0.23446895,1,0.23565847,1,0.21523207,1,0.22527646,1,0.23532683,1,0.25196365,1,0.25030857,1,0.25694314,1,0.2571471,1,0.25273997,1
241
- 0.0,0.0,0.0,239,"y=0,a=0",0,0.1874672,0,0.19137335,1,0.1807559,1,0.17952919,0,0.20582397,1,0.20376882,0,0.24297562,1,0.2563334,1,0.19819812,0,0.20446822,0
242
- 0.0,0.0,0.0,240,"y=0,a=0",0,0.23727746,1,0.22551553,1,0.21359491,1,0.22084968,1,0.2324767,1,0.24176045,1,0.24445117,1,0.24195665,1,0.2383373,1,0.23472777,1
243
- 1.0,1.0,0.0,241,"y=1,a=0",1,0.20452067,1,0.21111736,1,0.15552549,0,0.19058427,0,0.22104186,1,0.22302283,1,0.21935251,0,0.21185724,0,0.23208015,1,0.21684842,1
244
- 1.0,1.0,0.0,242,"y=1,a=0",1,0.15733221,0,0.17842025,0,0.16113433,0,0.1936348,0,0.18324094,0,0.21234788,1,0.21555184,0,0.20777558,0,0.22076884,1,0.19654536,0
245
- 1.0,1.0,0.0,243,"y=1,a=0",1,0.18644103,0,0.21210834,1,0.17192537,0,0.19644156,1,0.21296668,1,0.21041998,0,0.2210106,0,0.19975267,0,0.2084313,0,0.2239123,1
246
- 1.0,1.0,0.0,244,"y=1,a=0",1,0.16265246,0,0.16328613,0,0.16157296,0,0.20056346,1,0.17290497,0,0.19236496,0,0.2015057,0,0.19277176,0,0.20070332,0,0.188672,0
247
- 1.0,1.0,0.0,245,"y=1,a=0",1,0.15894023,0,0.190171,0,0.1388928,0,0.18009037,0,0.19030781,0,0.19182199,0,0.21246384,0,0.20190133,0,0.19981486,0,0.19270188,0
248
- 1.0,1.0,0.0,246,"y=1,a=0",1,0.15135571,0,0.17062551,0,0.1533219,0,0.18256244,0,0.1867607,0,0.18655488,0,0.2124808,0,0.22664258,1,0.19589522,0,0.17626329,0
249
- 1.0,0.0,0.0,247,"y=1,a=0",0,0.2133703,1,0.20811066,1,0.19593681,1,0.21934567,1,0.23809959,1,0.2156653,1,0.24656056,1,0.24153079,1,0.23738812,1,0.23927502,1
250
- 1.0,1.0,0.0,248,"y=1,a=0",1,0.20171773,1,0.19148222,1,0.16424762,0,0.19479841,0,0.20323254,0,0.21254373,1,0.19915926,0,0.20419005,0,0.21219109,0,0.20630959,0
251
- 1.0,1.0,0.0,249,"y=1,a=0",1,0.18709172,0,0.20002064,1,0.18916766,1,0.21564586,1,0.20353833,1,0.21104954,0,0.21499981,0,0.2129989,0,0.23113815,1,0.20934305,0
252
- 1.0,0.0,0.0,250,"y=1,a=0",0,0.18886843,0,0.18043806,0,0.15734847,0,0.19974397,1,0.18400183,0,0.21226545,1,0.20489293,0,0.21909517,0,0.23240262,1,0.2010367,0
253
- 1.0,1.0,0.0,251,"y=1,a=0",1,0.16571727,0,0.1798864,0,0.14590275,0,0.18256336,0,0.18201253,0,0.1960197,0,0.19001633,0,0.18647535,0,0.19755813,0,0.18146457,0
254
- 1.0,0.0,0.0,252,"y=1,a=0",0,0.2411273,1,0.21889615,1,0.20554303,1,0.23209485,1,0.2376653,1,0.25544477,1,0.28212136,1,0.27042514,1,0.2651294,1,0.26254365,1
255
- 1.0,0.0,0.0,253,"y=1,a=0",0,0.18269475,0,0.19423397,1,0.16265938,0,0.16971007,0,0.20788582,1,0.20810279,0,0.21570314,0,0.21674262,0,0.20639098,0,0.2027626,0
256
- 1.0,1.0,0.0,254,"y=1,a=0",1,0.17732568,0,0.17769574,0,0.1601289,0,0.18051778,0,0.19076088,0,0.18934776,0,0.20391351,0,0.20473425,0,0.20449467,0,0.1901151,0
257
- 1.0,0.0,0.0,255,"y=1,a=0",0,0.22199115,1,0.20837611,1,0.21539418,1,0.24717885,1,0.24789557,1,0.22605355,1,0.25255975,1,0.26291806,1,0.23402596,1,0.22557512,1
258
- 1.0,1.0,0.0,256,"y=1,a=0",1,0.19097683,0,0.20001349,1,0.17747572,1,0.21810088,1,0.21278803,1,0.22080283,1,0.22997661,1,0.2252729,1,0.24089487,1,0.21110949,1
259
- 1.0,0.0,0.0,257,"y=1,a=0",0,0.19589497,0,0.2083071,1,0.16222744,0,0.1877472,0,0.21968962,1,0.22346255,1,0.2348529,1,0.23145187,1,0.22538146,1,0.22240336,1
260
- 1.0,1.0,0.0,258,"y=1,a=0",1,0.1754934,0,0.1961267,1,0.17337036,0,0.19392362,0,0.22221544,1,0.20319447,0,0.22919239,1,0.21811505,0,0.21493106,0,0.2018818,0
261
- 1.0,0.0,0.0,259,"y=1,a=0",0,0.17653726,0,0.18875489,0,0.18398783,1,0.19627304,1,0.20110823,0,0.20043172,0,0.21211506,0,0.20534448,0,0.20220853,0,0.21033259,1
262
- 1.0,1.0,0.0,260,"y=1,a=0",1,0.14644095,0,0.17241094,0,0.14251393,0,0.16697317,0,0.18243162,0,0.18059352,0,0.2040591,0,0.19706696,0,0.18571739,0,0.1681581,0
263
- 1.0,0.0,0.0,261,"y=1,a=0",0,0.20301831,1,0.19913623,1,0.16923213,0,0.19026563,0,0.20804936,1,0.21913224,1,0.2246898,1,0.22678487,1,0.22255671,1,0.21252163,1
264
- 1.0,1.0,0.0,262,"y=1,a=0",1,0.14790666,0,0.17075881,0,0.14903045,0,0.18886465,0,0.18086088,0,0.19154394,0,0.21348073,0,0.1944013,0,0.2137134,0,0.18571801,0
265
- 1.0,1.0,0.0,263,"y=1,a=0",1,0.1543652,0,0.14264843,0,0.13460319,0,0.17362326,0,0.15707275,0,0.18408868,0,0.18043272,0,0.18743555,0,0.18802434,0,0.1684494,0
266
- 1.0,1.0,0.0,264,"y=1,a=0",1,0.14267789,0,0.1615772,0,0.12888831,0,0.17152666,0,0.18518612,0,0.1768903,0,0.21555336,0,0.21660587,0,0.19578883,0,0.18132035,0
267
- 1.0,0.0,0.0,265,"y=1,a=0",0,0.19292013,0,0.16753767,0,0.16631147,0,0.16867022,0,0.18473856,0,0.18187177,0,0.21238275,0,0.2186609,0,0.1975561,0,0.2030888,0
268
- 1.0,1.0,0.0,266,"y=1,a=0",1,0.17580684,0,0.17555466,0,0.16262561,0,0.19065213,0,0.19281618,0,0.20731771,0,0.22651523,1,0.22473629,0,0.21028228,0,0.18882643,0
269
- 1.0,0.0,0.0,267,"y=1,a=0",0,0.19628623,1,0.18980901,0,0.1729628,0,0.18700847,0,0.20100082,0,0.2027044,0,0.223571,1,0.227723,1,0.204899,0,0.20959547,0
270
- 1.0,1.0,0.0,268,"y=1,a=0",1,0.18254939,0,0.19592458,1,0.1749912,1,0.20354809,1,0.201583,0,0.20198978,0,0.2218996,0,0.20874512,0,0.2208232,1,0.20008308,0
271
- 1.0,1.0,0.0,269,"y=1,a=0",1,0.13963856,0,0.15541586,0,0.13573304,0,0.1510693,0,0.15163168,0,0.17166005,0,0.17028788,0,0.17568983,0,0.17331938,0,0.16728818,0
272
- 1.0,1.0,0.0,270,"y=1,a=0",1,0.19104649,0,0.20060787,1,0.17353632,1,0.19969128,1,0.20123301,0,0.21337102,1,0.22960585,1,0.217978,0,0.22352356,1,0.2148416,1
273
- 1.0,1.0,0.0,271,"y=1,a=0",1,0.123302534,0,0.13235818,0,0.12149988,0,0.14533232,0,0.13544087,0,0.1572121,0,0.17376052,0,0.16825137,0,0.17897634,0,0.147367,0
274
- 1.0,1.0,0.0,272,"y=1,a=0",1,0.18857212,0,0.19452149,1,0.1580871,0,0.19491251,0,0.20892155,1,0.2145855,1,0.22104578,0,0.22010237,0,0.22433752,1,0.20780015,0
275
- 1.0,1.0,0.0,273,"y=1,a=0",1,0.1789613,0,0.19278713,1,0.17687695,1,0.20068642,1,0.18420956,0,0.21465142,1,0.22755677,1,0.21240808,0,0.21244878,0,0.20448695,0
276
- 0.0,0.0,1.0,274,"y=0,a=1",0,0.18768796,0,0.1684503,0,0.16154538,0,0.193944,0,0.17780913,0,0.20010082,0,0.21436317,0,0.23401617,1,0.20928583,0,0.19521073,0
277
- 0.0,1.0,1.0,275,"y=0,a=1",1,0.21067397,1,0.20575356,1,0.18114,1,0.2218295,1,0.20704126,1,0.24108644,1,0.2222005,1,0.22351784,0,0.2483402,1,0.22210635,1
278
- 0.0,1.0,1.0,276,"y=0,a=1",1,0.15692672,0,0.16064724,0,0.13358133,0,0.15681237,0,0.16369559,0,0.18207215,0,0.18003458,0,0.20996669,0,0.20679213,0,0.17537759,0
279
- 0.0,0.0,1.0,277,"y=0,a=1",0,0.19092551,0,0.1932053,1,0.17847627,1,0.19239096,0,0.20213373,0,0.2125665,1,0.21032868,0,0.21703091,0,0.21187796,0,0.19755122,0
280
- 0.0,0.0,1.0,278,"y=0,a=1",0,0.20598732,1,0.2142972,1,0.18785344,1,0.21575506,1,0.21417792,1,0.23238055,1,0.23499212,1,0.23994868,1,0.23768966,1,0.22621594,1
281
- 0.0,0.0,1.0,279,"y=0,a=1",0,0.19802625,1,0.17272943,0,0.17577189,1,0.17717475,0,0.21493974,1,0.20631893,0,0.23802109,1,0.24710336,1,0.2353845,1,0.23128717,1
282
- 0.0,0.0,1.0,280,"y=0,a=1",0,0.24197099,1,0.24228983,1,0.22845596,1,0.24790013,1,0.25338095,1,0.24972673,1,0.2674324,1,0.2571945,1,0.2632547,1,0.26479018,1
283
- 0.0,0.0,1.0,281,"y=0,a=1",0,0.22963949,1,0.21703808,1,0.20492116,1,0.23268044,1,0.21905445,1,0.24699047,1,0.25432965,1,0.2512226,1,0.24274519,1,0.23304358,1
284
- 0.0,0.0,1.0,282,"y=0,a=1",0,0.1365051,0,0.14834264,0,0.12206918,0,0.14500552,0,0.15972595,0,0.16791627,0,0.18742703,0,0.206421,0,0.17360768,0,0.15761465,0
285
- 0.0,0.0,1.0,283,"y=0,a=1",0,0.16290581,0,0.17082068,0,0.15105909,0,0.17667791,0,0.18857785,0,0.19501732,0,0.20896961,0,0.19891483,0,0.20445247,0,0.19732736,0
286
- 0.0,0.0,1.0,284,"y=0,a=1",0,0.20686191,1,0.20082827,1,0.18902387,1,0.20268923,1,0.20577635,1,0.223628,1,0.21153738,0,0.22064184,0,0.2205974,1,0.20173755,0
287
- 0.0,0.0,1.0,285,"y=0,a=1",0,0.2561799,1,0.20433582,1,0.20019011,1,0.21505481,1,0.21179181,1,0.25151995,1,0.22391152,1,0.2270054,1,0.23601973,1,0.21825933,1
288
- 0.0,0.0,1.0,286,"y=0,a=1",0,0.24340051,1,0.2355254,1,0.19372782,1,0.21818061,1,0.24824095,1,0.24331456,1,0.25574175,1,0.24300538,1,0.23282468,1,0.236048,1
289
- 0.0,0.0,1.0,287,"y=0,a=1",0,0.19211385,0,0.1978869,1,0.17812842,1,0.19124657,0,0.21938258,1,0.21897921,1,0.23204623,1,0.23842868,1,0.22891,1,0.2285037,1
290
- 0.0,0.0,1.0,288,"y=0,a=1",0,0.24139443,1,0.23011588,1,0.2199077,1,0.23373078,1,0.25088504,1,0.24322297,1,0.2669912,1,0.2644924,1,0.2541938,1,0.24632965,1
291
- 0.0,0.0,1.0,289,"y=0,a=1",0,0.19908577,1,0.19653833,1,0.19142292,1,0.21152179,1,0.20430298,1,0.22385493,1,0.22403221,1,0.22676517,1,0.22410363,1,0.20945977,0
292
- 0.0,0.0,1.0,290,"y=0,a=1",0,0.1791116,0,0.18681708,0,0.1607036,0,0.20958026,1,0.1948501,0,0.20514302,0,0.21901482,0,0.2468999,1,0.22101736,1,0.19431736,0
293
- 0.0,0.0,1.0,291,"y=0,a=1",0,0.20324266,1,0.19970475,1,0.1722624,0,0.21550982,1,0.21673816,1,0.22466408,1,0.2444848,1,0.25119185,1,0.22767808,1,0.21621902,1
294
- 0.0,0.0,1.0,292,"y=0,a=1",0,0.21725191,1,0.18655008,0,0.18675445,1,0.19399849,0,0.2000428,0,0.21675898,1,0.22109362,0,0.21043995,0,0.21821061,0,0.20250832,0
295
- 0.0,0.0,1.0,293,"y=0,a=1",0,0.23502374,1,0.22101092,1,0.20427474,1,0.21952702,1,0.219014,1,0.24259882,1,0.24117145,1,0.24890566,1,0.25285324,1,0.2439149,1
296
- 0.0,0.0,1.0,294,"y=0,a=1",0,0.22077525,1,0.19177137,1,0.1791459,1,0.19375437,0,0.2001324,0,0.22478709,1,0.22224513,1,0.22641294,1,0.2342313,1,0.20913723,0
297
- 0.0,0.0,1.0,295,"y=0,a=1",0,0.20929895,1,0.17569047,0,0.16094615,0,0.18797727,0,0.1910247,0,0.20613107,0,0.20681323,0,0.2114887,0,0.21835972,0,0.19343685,0
298
- 0.0,0.0,1.0,296,"y=0,a=1",0,0.22427572,1,0.21536471,1,0.18689175,1,0.22620764,1,0.21933024,1,0.23297974,1,0.24025728,1,0.24672295,1,0.23432176,1,0.2277227,1
299
- 0.0,1.0,1.0,297,"y=0,a=1",1,0.18522026,0,0.19617607,1,0.14568636,0,0.17576559,0,0.19354154,0,0.2150575,1,0.21349859,0,0.23069565,1,0.22113079,1,0.20298804,0
300
- 0.0,0.0,1.0,298,"y=0,a=1",0,0.21347149,1,0.22466746,1,0.21252209,1,0.22781043,1,0.2341241,1,0.23126018,1,0.22529003,1,0.2360097,1,0.22538604,1,0.21534975,1
301
- 0.0,0.0,1.0,299,"y=0,a=1",0,0.25164354,1,0.25500643,1,0.2150827,1,0.2442034,1,0.2695667,1,0.280463,1,0.28338572,1,0.29445276,1,0.27313736,1,0.26576117,1
302
- 0.0,0.0,1.0,300,"y=0,a=1",0,0.20222877,1,0.19375385,1,0.1855876,1,0.20324434,1,0.19492856,0,0.22202833,1,0.21933642,0,0.22673142,1,0.23313047,1,0.20958939,0
303
- 0.0,0.0,1.0,301,"y=0,a=1",0,0.24592853,1,0.21392295,1,0.20390593,1,0.21034318,1,0.22838098,1,0.24630058,1,0.2282815,1,0.22721018,1,0.23837882,1,0.24070948,1
304
- 0.0,0.0,1.0,302,"y=0,a=1",0,0.20315918,1,0.18264957,0,0.17389305,1,0.20361227,1,0.19147006,0,0.2079876,0,0.21288282,0,0.20901397,0,0.21494094,0,0.19788776,0
305
- 0.0,0.0,1.0,303,"y=0,a=1",0,0.18637282,0,0.16396344,0,0.17588846,1,0.19435169,0,0.19336207,0,0.20713659,0,0.22852114,1,0.2313208,1,0.21821962,0,0.19855258,0
306
- 0.0,0.0,1.0,304,"y=0,a=1",0,0.2082397,1,0.19895037,1,0.18430774,1,0.20043705,1,0.20477307,1,0.21999504,1,0.243669,1,0.24668448,1,0.2310547,1,0.23024125,1
307
- 0.0,0.0,1.0,305,"y=0,a=1",0,0.26368874,1,0.24443796,1,0.22278364,1,0.22659686,1,0.25105467,1,0.26025626,1,0.24075693,1,0.2403913,1,0.27007145,1,0.26285625,1
308
- 0.0,1.0,1.0,306,"y=0,a=1",1,0.17703581,0,0.17341362,0,0.17007296,0,0.18653668,0,0.17465241,0,0.1973994,0,0.2084682,0,0.21131042,0,0.22388689,1,0.20800221,0
309
- 0.0,0.0,1.0,307,"y=0,a=1",0,0.22091392,1,0.23028226,1,0.20599693,1,0.23215914,1,0.22495882,1,0.23275994,1,0.24017374,1,0.22776154,1,0.2452296,1,0.22919558,1
310
- 0.0,0.0,1.0,308,"y=0,a=1",0,0.17976922,0,0.18790817,0,0.15712385,0,0.17633817,0,0.1953176,0,0.20205072,0,0.21068141,0,0.22057551,0,0.21617085,0,0.20008786,0
311
- 0.0,0.0,1.0,309,"y=0,a=1",0,0.15621074,0,0.15079054,0,0.14903931,0,0.16427948,0,0.14287327,0,0.19071937,0,0.18973581,0,0.22505765,1,0.18839927,0,0.16893321,0
312
- 0.0,0.0,1.0,310,"y=0,a=1",0,0.19058737,0,0.20033975,1,0.16036524,0,0.19592568,1,0.20882806,1,0.21107979,0,0.21077561,0,0.20991762,0,0.22851512,1,0.21235126,1
313
- 0.0,0.0,1.0,311,"y=0,a=1",0,0.2616855,1,0.2278416,1,0.20459288,1,0.2350299,1,0.23456293,1,0.26083732,1,0.26274145,1,0.25141937,1,0.26670486,1,0.25365245,1
314
- 0.0,1.0,1.0,312,"y=0,a=1",1,0.14144187,0,0.1340307,0,0.13280594,0,0.147326,0,0.13765192,0,0.16351037,0,0.16064821,0,0.1719367,0,0.18239214,0,0.16308367,0
315
- 0.0,0.0,1.0,313,"y=0,a=1",0,0.14111374,0,0.13461015,0,0.11381829,0,0.11968499,0,0.13873881,0,0.14789005,0,0.16658804,0,0.19232626,0,0.13667491,0,0.13856226,0
316
- 0.0,0.0,1.0,314,"y=0,a=1",0,0.21056516,1,0.20370631,1,0.18680361,1,0.20917898,1,0.21836284,1,0.23410411,1,0.23018517,1,0.25430828,1,0.25085568,1,0.23383503,1
317
- 0.0,0.0,1.0,315,"y=0,a=1",0,0.21127331,1,0.18803369,0,0.16908088,0,0.186321,0,0.21200067,1,0.22449325,1,0.23995505,1,0.24693257,1,0.22549114,1,0.21496162,1
318
- 0.0,0.0,1.0,316,"y=0,a=1",0,0.22699693,1,0.23286527,1,0.21354471,1,0.22360747,1,0.25353378,1,0.2542901,1,0.26783568,1,0.26670188,1,0.26337108,1,0.25876322,1
319
- 0.0,0.0,1.0,317,"y=0,a=1",0,0.17904602,0,0.18252489,0,0.17398752,1,0.199907,1,0.18854004,0,0.21427654,1,0.2260088,1,0.24210183,1,0.23067497,1,0.20377773,0
320
- 0.0,0.0,1.0,318,"y=0,a=1",0,0.29098943,1,0.25483027,1,0.31168416,1,0.28245455,1,0.27055538,1,0.28433666,1,0.3012747,1,0.28993067,1,0.28119627,1,0.29803103,1
321
- 0.0,1.0,1.0,319,"y=0,a=1",1,0.18567078,0,0.17793746,0,0.1506409,0,0.18194073,0,0.18111837,0,0.21510144,1,0.20828411,0,0.22306386,0,0.2320146,1,0.21044274,1
322
- 0.0,0.0,1.0,320,"y=0,a=1",0,0.19752356,1,0.20745127,1,0.18683329,1,0.21002169,1,0.2073097,1,0.22789946,1,0.22841789,1,0.24522313,1,0.21866876,0,0.20910159,0
323
- 0.0,0.0,1.0,321,"y=0,a=1",0,0.16187337,0,0.17458549,0,0.134996,0,0.15756324,0,0.17444707,0,0.19215013,0,0.1947665,0,0.20698187,0,0.18803108,0,0.17639482,0
324
- 0.0,0.0,1.0,322,"y=0,a=1",0,0.10519049,0,0.1270915,0,0.12846343,0,0.12506104,0,0.13021936,0,0.12551259,0,0.14159423,0,0.15215924,0,0.14914566,0,0.14743136,0
325
- 0.0,0.0,1.0,323,"y=0,a=1",0,0.24742116,1,0.2272611,1,0.19854335,1,0.23527761,1,0.23204902,1,0.25466424,1,0.24805693,1,0.25013065,1,0.25403062,1,0.24010117,1
326
- 0.0,0.0,1.0,324,"y=0,a=1",0,0.21501602,1,0.1991332,1,0.17137842,0,0.19789848,1,0.20582052,1,0.22454068,1,0.21601996,0,0.22070923,0,0.22228892,1,0.21238129,1
327
- 0.0,0.0,1.0,325,"y=0,a=1",0,0.22143015,1,0.20793274,1,0.18585904,1,0.22231802,1,0.2480672,1,0.2478809,1,0.25852504,1,0.2643324,1,0.26137117,1,0.23598339,1
328
- 0.0,0.0,1.0,326,"y=0,a=1",0,0.20164955,1,0.20447442,1,0.17261286,0,0.19950998,1,0.20366864,1,0.22225258,1,0.21544893,0,0.22087626,0,0.22775438,1,0.21432571,1
329
- 0.0,0.0,1.0,327,"y=0,a=1",0,0.17755665,0,0.18480621,0,0.17681406,1,0.19309722,0,0.18710215,0,0.20449847,0,0.20860237,0,0.21994588,0,0.20657094,0,0.19524235,0
330
- 0.0,0.0,1.0,328,"y=0,a=1",0,0.16971262,0,0.16090491,0,0.14127831,0,0.1608388,0,0.17465343,0,0.18907759,0,0.19915974,0,0.21437095,0,0.17700368,0,0.17483497,0
331
- 0.0,0.0,1.0,329,"y=0,a=1",0,0.271898,1,0.24239706,1,0.24552996,1,0.2434045,1,0.2691062,1,0.27311882,1,0.33109862,1,0.3312306,1,0.26391977,1,0.27052063,1
332
- 0.0,0.0,1.0,330,"y=0,a=1",0,0.2145814,1,0.20942722,1,0.18417647,1,0.1966857,1,0.23130886,1,0.22029549,1,0.2687577,1,0.27691007,1,0.23137192,1,0.23811151,1
333
- 0.0,0.0,1.0,331,"y=0,a=1",0,0.19843672,1,0.1941073,1,0.18539304,1,0.19571407,1,0.19408505,0,0.20881389,0,0.20736717,0,0.20486319,0,0.21785498,0,0.20563155,0
334
- 0.0,0.0,1.0,332,"y=0,a=1",0,0.18894643,0,0.1922554,1,0.18210673,1,0.18949808,0,0.21060154,1,0.19625023,0,0.23230442,1,0.22784293,1,0.20695692,0,0.2059202,0
335
- 0.0,0.0,1.0,333,"y=0,a=1",0,0.2213955,1,0.20117031,1,0.1865102,1,0.21220957,1,0.21683514,1,0.2364006,1,0.24916264,1,0.23646666,1,0.23053846,1,0.22242638,1
336
- 0.0,0.0,1.0,334,"y=0,a=1",0,0.22847202,1,0.20102899,1,0.20641702,1,0.21737643,1,0.20493712,1,0.22901507,1,0.22280532,1,0.2204652,0,0.22362801,1,0.21380237,1
337
- 0.0,0.0,1.0,335,"y=0,a=1",0,0.16441986,0,0.15896307,0,0.1321816,0,0.15527412,0,0.16879402,0,0.18480885,0,0.20366602,0,0.21650864,0,0.17833272,0,0.17569773,0
338
- 0.0,0.0,1.0,336,"y=0,a=1",0,0.20489675,1,0.20278408,1,0.18005273,1,0.2087082,1,0.21362735,1,0.2303075,1,0.24575412,1,0.23863684,1,0.24609475,1,0.22624514,1
339
- 0.0,0.0,1.0,337,"y=0,a=1",0,0.24326889,1,0.21643788,1,0.18943612,1,0.20244277,1,0.2089567,1,0.22333142,1,0.2117493,0,0.21209218,0,0.23942195,1,0.22973026,1
340
- 0.0,0.0,1.0,338,"y=0,a=1",0,0.17391317,0,0.15815875,0,0.1105669,0,0.119904995,0,0.1514945,0,0.16943128,0,0.16035938,0,0.17988071,0,0.1828063,0,0.17824803,0
341
- 0.0,0.0,1.0,339,"y=0,a=1",0,0.17601813,0,0.16153307,0,0.1388076,0,0.1751116,0,0.17056365,0,0.2054114,0,0.206908,0,0.2205061,0,0.21554737,0,0.19605742,0
342
- 0.0,0.0,1.0,340,"y=0,a=1",0,0.21627554,1,0.21085185,1,0.19511853,1,0.22568104,1,0.22726578,1,0.23525079,1,0.25497398,1,0.25212374,1,0.26022917,1,0.2534678,1
343
- 0.0,0.0,1.0,341,"y=0,a=1",0,0.21174192,1,0.18241715,0,0.17793235,1,0.22430702,1,0.18877845,0,0.2234418,1,0.22275092,1,0.2311245,1,0.22300237,1,0.20418893,0
344
- 0.0,0.0,1.0,342,"y=0,a=1",0,0.23266742,1,0.23948349,1,0.2187053,1,0.2376914,1,0.24095047,1,0.25995994,1,0.2602179,1,0.25877634,1,0.2546354,1,0.24898408,1
345
- 0.0,0.0,1.0,343,"y=0,a=1",0,0.21660379,1,0.2237122,1,0.18171325,1,0.21198367,1,0.22538786,1,0.24069092,1,0.24861251,1,0.2516553,1,0.25858516,1,0.24748905,1
346
- 0.0,0.0,1.0,344,"y=0,a=1",0,0.14393432,0,0.13918811,0,0.13370924,0,0.159444,0,0.16206053,0,0.17499113,0,0.18572144,0,0.19711898,0,0.19072397,0,0.1685637,0
347
- 0.0,1.0,1.0,345,"y=0,a=1",1,0.18739362,0,0.18093583,0,0.1725677,0,0.17728885,0,0.18858293,0,0.22536483,1,0.19325086,0,0.20089982,0,0.20627509,0,0.19063568,0
348
- 0.0,0.0,1.0,346,"y=0,a=1",0,0.2540075,1,0.23666354,1,0.23247011,1,0.24491917,1,0.25958875,1,0.25743863,1,0.29488266,1,0.29036,1,0.26886883,1,0.27230254,1
349
- 0.0,0.0,1.0,347,"y=0,a=1",0,0.22879533,1,0.21065862,1,0.19552872,1,0.21990448,1,0.23415542,1,0.23917405,1,0.25263095,1,0.25331798,1,0.24535254,1,0.22904144,1
350
- 0.0,1.0,1.0,348,"y=0,a=1",1,0.22623606,1,0.2165104,1,0.18967223,1,0.20150398,1,0.22110872,1,0.23969856,1,0.24782234,1,0.24761929,1,0.24282156,1,0.22634216,1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/done DELETED
@@ -1 +0,0 @@
1
- done
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/err.txt DELETED
@@ -1,390 +0,0 @@
1
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
2
- warnings.warn(_create_warning_msg(
3
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
4
- warnings.warn(_create_warning_msg(
5
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
6
- warnings.warn(
7
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
8
- warnings.warn(msg)
9
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
10
- warnings.warn(
11
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
12
- warnings.warn(
13
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
14
- warnings.warn(
15
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
16
- warnings.warn(
17
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
18
- warnings.warn(
19
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
20
- warnings.warn(
21
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
22
- warnings.warn(
23
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
24
- warnings.warn(
25
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
26
- warnings.warn(
27
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
28
- warnings.warn(
29
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
30
- warnings.warn(
31
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
32
- warnings.warn(
33
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
34
- warnings.warn(
35
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
36
- warnings.warn(
37
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
38
- warnings.warn(
39
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
40
- warnings.warn(
41
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
42
- warnings.warn(
43
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
44
- warnings.warn(
45
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
46
- warnings.warn(
47
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
48
- warnings.warn(
49
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
50
- warnings.warn(
51
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
52
- warnings.warn(
53
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
54
- warnings.warn(
55
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
56
- warnings.warn(
57
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
58
- warnings.warn(
59
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
60
- warnings.warn(
61
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
62
- warnings.warn(
63
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
64
- warnings.warn(
65
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
66
- warnings.warn(
67
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
68
- warnings.warn(
69
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
70
- warnings.warn(
71
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
72
- warnings.warn(
73
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
74
- warnings.warn(
75
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
76
- warnings.warn(
77
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
78
- warnings.warn(
79
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
80
- warnings.warn(
81
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
82
- warnings.warn(
83
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
84
- warnings.warn(
85
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
86
- warnings.warn(
87
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
88
- warnings.warn(
89
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
90
- warnings.warn(
91
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
92
- warnings.warn(
93
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
94
- warnings.warn(
95
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
96
- warnings.warn(
97
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
98
- warnings.warn(
99
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
100
- warnings.warn(
101
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
102
- warnings.warn(
103
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
104
- warnings.warn(
105
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
106
- warnings.warn(
107
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
108
- warnings.warn(
109
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
110
- warnings.warn(
111
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
112
- warnings.warn(
113
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
114
- warnings.warn(
115
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
116
- warnings.warn(
117
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
118
- warnings.warn(
119
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
120
- warnings.warn(
121
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
122
- warnings.warn(
123
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
124
- warnings.warn(
125
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
126
- warnings.warn(
127
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
128
- warnings.warn(
129
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
130
- warnings.warn(
131
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
132
- warnings.warn(
133
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
134
- warnings.warn(
135
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
136
- warnings.warn(
137
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
138
- warnings.warn(
139
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
140
- warnings.warn(
141
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
142
- warnings.warn(
143
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
144
- warnings.warn(
145
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
146
- warnings.warn(
147
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
148
- warnings.warn(
149
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
150
- warnings.warn(
151
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
152
- warnings.warn(
153
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
154
- warnings.warn(_create_warning_msg(
155
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
156
- warnings.warn(_create_warning_msg(
157
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
158
- warnings.warn(
159
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
160
- warnings.warn(msg)
161
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
162
- warnings.warn(
163
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
164
- warnings.warn(
165
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
166
- warnings.warn(
167
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
168
- warnings.warn(
169
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
170
- warnings.warn(
171
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
172
- warnings.warn(
173
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
174
- warnings.warn(
175
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
176
- warnings.warn(
177
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
178
- warnings.warn(
179
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
180
- warnings.warn(
181
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
182
- warnings.warn(
183
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
184
- warnings.warn(
185
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
186
- warnings.warn(
187
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
188
- warnings.warn(
189
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
190
- warnings.warn(
191
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
192
- warnings.warn(
193
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
194
- warnings.warn(
195
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
196
- warnings.warn(
197
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
198
- warnings.warn(
199
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
200
- warnings.warn(
201
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
202
- warnings.warn(
203
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
204
- warnings.warn(
205
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
206
- warnings.warn(
207
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
208
- warnings.warn(
209
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
210
- warnings.warn(
211
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
212
- warnings.warn(
213
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
214
- warnings.warn(
215
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
216
- warnings.warn(
217
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
218
- warnings.warn(
219
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
220
- warnings.warn(
221
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
222
- warnings.warn(
223
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
224
- warnings.warn(
225
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
226
- warnings.warn(
227
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
228
- warnings.warn(
229
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
230
- warnings.warn(
231
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
232
- warnings.warn(
233
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
234
- warnings.warn(
235
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
236
- warnings.warn(
237
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
238
- warnings.warn(
239
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
240
- warnings.warn(
241
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
242
- warnings.warn(
243
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
244
- warnings.warn(
245
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
246
- warnings.warn(
247
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
248
- warnings.warn(
249
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
250
- warnings.warn(
251
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
252
- warnings.warn(
253
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
254
- warnings.warn(
255
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
256
- warnings.warn(
257
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
258
- warnings.warn(
259
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
260
- warnings.warn(
261
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
262
- warnings.warn(
263
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
264
- warnings.warn(
265
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
266
- warnings.warn(
267
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
268
- warnings.warn(
269
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
270
- warnings.warn(
271
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
272
- warnings.warn(
273
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
274
- warnings.warn(
275
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
276
- warnings.warn(
277
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
278
- warnings.warn(
279
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
280
- warnings.warn(
281
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
282
- warnings.warn(
283
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
284
- warnings.warn(
285
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
286
- warnings.warn(
287
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
288
- warnings.warn(
289
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
290
- warnings.warn(
291
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
292
- warnings.warn(
293
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
294
- warnings.warn(
295
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
296
- warnings.warn(
297
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
298
- warnings.warn(
299
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
300
- warnings.warn(
301
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
302
- warnings.warn(
303
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
304
- warnings.warn(
305
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
306
- warnings.warn(
307
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
308
- warnings.warn(
309
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
310
- warnings.warn(
311
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
312
- warnings.warn(
313
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
314
- warnings.warn(
315
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
316
- warnings.warn(
317
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
318
- warnings.warn(
319
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
320
- warnings.warn(
321
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
322
- warnings.warn(
323
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
324
- warnings.warn(
325
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
326
- warnings.warn(
327
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
328
- warnings.warn(
329
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
330
- warnings.warn(
331
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
332
- warnings.warn(
333
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
334
- warnings.warn(
335
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
336
- warnings.warn(
337
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
338
- warnings.warn(
339
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
340
- warnings.warn(
341
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
342
- warnings.warn(
343
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
344
- warnings.warn(
345
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
346
- warnings.warn(
347
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
348
- warnings.warn(
349
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
350
- warnings.warn(
351
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
352
- warnings.warn(
353
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
354
- warnings.warn(
355
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
356
- warnings.warn(
357
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
358
- warnings.warn(
359
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
360
- warnings.warn(
361
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
362
- warnings.warn(
363
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
364
- warnings.warn(
365
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
366
- warnings.warn(
367
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
368
- warnings.warn(
369
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
370
- warnings.warn(
371
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
372
- warnings.warn(
373
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
374
- warnings.warn(
375
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
376
- warnings.warn(
377
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
378
- warnings.warn(_create_warning_msg(
379
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
380
- warnings.warn(
381
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
382
- warnings.warn(
383
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
384
- warnings.warn(
385
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
386
- warnings.warn(
387
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
388
- warnings.warn(
389
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
390
- warnings.warn(
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/events.out.tfevents.1712698653.dv004.ib.bridges2.psc.edu DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:9b00c159914bc1b2709f54792436b2052eb35f34890009813baf626b5f5fc592
3
- size 3562
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/events.out.tfevents.1712700841.dv004.ib.bridges2.psc.edu DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:d449d0f71a9fb2c932c72b84b224e8cf55e984d9a0f62827201b2ba6ed24adad
3
- size 5332
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/final_results.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:da584f98944b8fe1920182e2b86b175da5996942dbdb8b22a9b34bf1fe5c6153
3
- size 5261
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/model.pkl DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:b49e9cfbc2ad87b54e772b375313d4ddd9de4e53c139c72f21dfc82c90d318ef
3
- size 94409258
 
 
 
 
MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0/out.txt DELETED
@@ -1,555 +0,0 @@
1
- Environment:
2
- Python: 3.8.18
3
- PyTorch: 2.2.2+cu121
4
- Torchvision: 0.14.1
5
- CUDA: 12.1
6
- CUDNN: 8902
7
- NumPy: 1.24.3
8
- PIL: 10.0.1
9
- Args:
10
- algorithm: ERM
11
- checkpoint_freq: None
12
- cmnist_attr_prob: 0.5
13
- cmnist_flip_prob: 0.25
14
- cmnist_label_prob: 0.5
15
- cmnist_spur_prob: 0.2
16
- data_dir: /ocean/projects/asc170022p/shg121/PhD/Multimodal-mistakes-debug/data
17
- dataset: MetaShift
18
- es_metric: min_group:accuracy
19
- es_patience: 5
20
- es_strategy: metric
21
- hparams: None
22
- hparams_seed: 0
23
- image_arch: resnet_sup_in1k
24
- output_dir: /ocean/projects/asc170022p/shg121/PhD/Multimodal-mistakes-debug/out/MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0
25
- output_folder_name: resnet_sup_in1k_attrNo
26
- pretrained:
27
- resume:
28
- seed: 0
29
- skip_model_save: False
30
- stage1_algo: ERM
31
- stage1_folder: vanilla
32
- steps: None
33
- store_name: MetaShift_ERM_hparams0_seed0
34
- tb_log_all: False
35
- text_arch: bert-base-uncased
36
- train_attr: no
37
- use_es: False
38
- HParams:
39
- batch_size: 108
40
- group_balanced: False
41
- image_arch: resnet_sup_in1k
42
- last_layer_dropout: 0.0
43
- lr: 0.001
44
- nonlinear_classifier: False
45
- optimizer: sgd
46
- pretrained: True
47
- resnet18: False
48
- text_arch: bert-base-uncased
49
- weight_decay: 0.0001
50
- Dataset:
51
- [train] 2276 (without attributes)
52
- [val] 349
53
- [test] 874
54
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
55
- warnings.warn(_create_warning_msg(
56
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
57
- warnings.warn(_create_warning_msg(
58
-
59
-
60
- =======>>>> hparams <<<===========
61
- <class 'dict'>
62
- {'resnet18': False, 'nonlinear_classifier': False, 'group_balanced': False, 'pretrained': True, 'lr': 0.001, 'weight_decay': 0.0001, 'optimizer': 'sgd', 'last_layer_dropout': 0.0, 'batch_size': 108, 'image_arch': 'resnet_sup_in1k', 'text_arch': 'bert-base-uncased', 'steps': 5001}
63
- ==================================
64
-
65
-
66
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
67
- warnings.warn(
68
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
69
- warnings.warn(msg)
70
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
71
- warnings.warn(
72
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
73
- warnings.warn(
74
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
75
- warnings.warn(
76
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
77
- warnings.warn(
78
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
79
- warnings.warn(
80
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
81
- warnings.warn(
82
-
83
-
84
- step epoch loss te_avg_acc te_worst_acc va_avg_acc va_worst_acc
85
- 0 0.0000 0.7292 0.4691 0.2145 0.5387 0.2632
86
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
87
- warnings.warn(
88
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
89
- warnings.warn(
90
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
91
- warnings.warn(
92
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
93
- warnings.warn(
94
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
95
- warnings.warn(
96
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
97
- warnings.warn(
98
- 300 14.2355 0.0710 0.9142 0.7692 0.9198 0.6970
99
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
100
- warnings.warn(
101
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
102
- warnings.warn(
103
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
104
- warnings.warn(
105
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
106
- warnings.warn(
107
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
108
- warnings.warn(
109
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
110
- warnings.warn(
111
- 600 28.4710 0.0003 0.9153 0.7846 0.9198 0.6970
112
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
113
- warnings.warn(
114
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
115
- warnings.warn(
116
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
117
- warnings.warn(
118
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
119
- warnings.warn(
120
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
121
- warnings.warn(
122
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
123
- warnings.warn(
124
- 900 42.7065 0.0001 0.9142 0.8000 0.9198 0.6970
125
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
126
- warnings.warn(
127
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
128
- warnings.warn(
129
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
130
- warnings.warn(
131
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
132
- warnings.warn(
133
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
134
- warnings.warn(
135
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
136
- warnings.warn(
137
- 1200 56.9420 0.0000 0.9130 0.8000 0.9226 0.6970
138
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
139
- warnings.warn(
140
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
141
- warnings.warn(
142
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
143
- warnings.warn(
144
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
145
- warnings.warn(
146
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
147
- warnings.warn(
148
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
149
- warnings.warn(
150
- 1500 71.1775 0.0000 0.9130 0.8000 0.9226 0.6970
151
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
152
- warnings.warn(
153
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
154
- warnings.warn(
155
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
156
- warnings.warn(
157
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
158
- warnings.warn(
159
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
160
- warnings.warn(
161
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
162
- warnings.warn(
163
- 1800 85.4130 0.0000 0.9130 0.8000 0.9226 0.6970
164
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
165
- warnings.warn(
166
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
167
- warnings.warn(
168
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
169
- warnings.warn(
170
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
171
- warnings.warn(
172
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
173
- warnings.warn(
174
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
175
- warnings.warn(
176
- 2100 99.6485 0.0000 0.9130 0.8000 0.9226 0.6970
177
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
178
- warnings.warn(
179
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
180
- warnings.warn(
181
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
182
- warnings.warn(
183
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
184
- warnings.warn(
185
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
186
- warnings.warn(
187
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
188
- warnings.warn(
189
- 2400 113.8840 0.0000 0.9130 0.8000 0.9198 0.6970
190
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
191
- warnings.warn(
192
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
193
- warnings.warn(
194
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
195
- warnings.warn(
196
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
197
- warnings.warn(
198
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
199
- warnings.warn(
200
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
201
- warnings.warn(
202
- 2700 128.1195 0.0000 0.9130 0.8000 0.9226 0.6970
203
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
204
- warnings.warn(
205
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
206
- warnings.warn(
207
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
208
- warnings.warn(
209
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
210
- warnings.warn(
211
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
212
- warnings.warn(
213
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
214
- warnings.warn(
215
- 3000 142.3550 0.0000 0.9130 0.8000 0.9198 0.6970
216
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
217
- warnings.warn(
218
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
219
- warnings.warn(
220
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
221
- warnings.warn(
222
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
223
- warnings.warn(
224
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
225
- warnings.warn(
226
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
227
- warnings.warn(
228
- 3300 156.5905 0.0000 0.9130 0.8000 0.9198 0.6970
229
- Environment:
230
- Python: 3.8.18
231
- PyTorch: 2.2.2+cu121
232
- Torchvision: 0.14.1
233
- CUDA: 12.1
234
- CUDNN: 8902
235
- NumPy: 1.24.3
236
- PIL: 10.0.1
237
- Args:
238
- algorithm: ERM
239
- checkpoint_freq: None
240
- cmnist_attr_prob: 0.5
241
- cmnist_flip_prob: 0.25
242
- cmnist_label_prob: 0.5
243
- cmnist_spur_prob: 0.2
244
- data_dir: /ocean/projects/asc170022p/shg121/PhD/Multimodal-mistakes-debug/data
245
- dataset: MetaShift
246
- es_metric: min_group:accuracy
247
- es_patience: 5
248
- es_strategy: metric
249
- hparams: None
250
- hparams_seed: 0
251
- image_arch: resnet_sup_in1k
252
- output_dir: /ocean/projects/asc170022p/shg121/PhD/Multimodal-mistakes-debug/out/MetaShift/resnet_sup_in1k_attrNo/MetaShift_ERM_hparams0_seed0
253
- output_folder_name: resnet_sup_in1k_attrNo
254
- pretrained:
255
- resume:
256
- seed: 0
257
- skip_model_save: False
258
- stage1_algo: ERM
259
- stage1_folder: vanilla
260
- steps: None
261
- store_name: MetaShift_ERM_hparams0_seed0
262
- tb_log_all: False
263
- text_arch: bert-base-uncased
264
- train_attr: no
265
- use_es: False
266
- HParams:
267
- batch_size: 108
268
- group_balanced: False
269
- image_arch: resnet_sup_in1k
270
- last_layer_dropout: 0.0
271
- lr: 0.001
272
- nonlinear_classifier: False
273
- optimizer: sgd
274
- pretrained: True
275
- resnet18: False
276
- text_arch: bert-base-uncased
277
- weight_decay: 0.0001
278
- Dataset:
279
- [train] 2276 (without attributes)
280
- [val] 349
281
- [test] 874
282
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
283
- warnings.warn(_create_warning_msg(
284
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
285
- warnings.warn(_create_warning_msg(
286
-
287
-
288
- =======>>>> hparams <<<===========
289
- <class 'dict'>
290
- {'resnet18': False, 'nonlinear_classifier': False, 'group_balanced': False, 'pretrained': True, 'lr': 0.001, 'weight_decay': 0.0001, 'optimizer': 'sgd', 'last_layer_dropout': 0.0, 'batch_size': 108, 'image_arch': 'resnet_sup_in1k', 'text_arch': 'bert-base-uncased', 'steps': 5001}
291
- ==================================
292
-
293
-
294
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
295
- warnings.warn(
296
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
297
- warnings.warn(msg)
298
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
299
- warnings.warn(
300
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
301
- warnings.warn(
302
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
303
- warnings.warn(
304
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
305
- warnings.warn(
306
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
307
- warnings.warn(
308
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
309
- warnings.warn(
310
-
311
-
312
- step epoch loss te_avg_acc te_worst_acc va_avg_acc va_worst_acc
313
- 0 0.0000 0.7292 0.4691 0.2145 0.5387 0.2632
314
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
315
- warnings.warn(
316
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
317
- warnings.warn(
318
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
319
- warnings.warn(
320
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
321
- warnings.warn(
322
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
323
- warnings.warn(
324
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
325
- warnings.warn(
326
- 300 14.2355 0.0710 0.9142 0.7692 0.9198 0.6970
327
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
328
- warnings.warn(
329
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
330
- warnings.warn(
331
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
332
- warnings.warn(
333
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
334
- warnings.warn(
335
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
336
- warnings.warn(
337
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
338
- warnings.warn(
339
- 600 28.4710 0.0003 0.9153 0.7846 0.9198 0.6970
340
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
341
- warnings.warn(
342
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
343
- warnings.warn(
344
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
345
- warnings.warn(
346
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
347
- warnings.warn(
348
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
349
- warnings.warn(
350
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
351
- warnings.warn(
352
- 900 42.7065 0.0001 0.9142 0.8000 0.9198 0.6970
353
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
354
- warnings.warn(
355
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
356
- warnings.warn(
357
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
358
- warnings.warn(
359
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
360
- warnings.warn(
361
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
362
- warnings.warn(
363
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
364
- warnings.warn(
365
- 1200 56.9420 0.0000 0.9130 0.8000 0.9226 0.6970
366
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
367
- warnings.warn(
368
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
369
- warnings.warn(
370
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
371
- warnings.warn(
372
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
373
- warnings.warn(
374
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
375
- warnings.warn(
376
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
377
- warnings.warn(
378
- 1500 71.1775 0.0000 0.9130 0.8000 0.9226 0.6970
379
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
380
- warnings.warn(
381
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
382
- warnings.warn(
383
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
384
- warnings.warn(
385
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
386
- warnings.warn(
387
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
388
- warnings.warn(
389
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
390
- warnings.warn(
391
- 1800 85.4130 0.0000 0.9130 0.8000 0.9226 0.6970
392
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
393
- warnings.warn(
394
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
395
- warnings.warn(
396
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
397
- warnings.warn(
398
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
399
- warnings.warn(
400
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
401
- warnings.warn(
402
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
403
- warnings.warn(
404
- 2100 99.6485 0.0000 0.9130 0.8000 0.9226 0.6970
405
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
406
- warnings.warn(
407
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
408
- warnings.warn(
409
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
410
- warnings.warn(
411
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
412
- warnings.warn(
413
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
414
- warnings.warn(
415
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
416
- warnings.warn(
417
- 2400 113.8840 0.0000 0.9130 0.8000 0.9198 0.6970
418
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
419
- warnings.warn(
420
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
421
- warnings.warn(
422
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
423
- warnings.warn(
424
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
425
- warnings.warn(
426
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
427
- warnings.warn(
428
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
429
- warnings.warn(
430
- 2700 128.1195 0.0000 0.9130 0.8000 0.9226 0.6970
431
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
432
- warnings.warn(
433
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
434
- warnings.warn(
435
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
436
- warnings.warn(
437
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
438
- warnings.warn(
439
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
440
- warnings.warn(
441
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
442
- warnings.warn(
443
- 3000 142.3550 0.0000 0.9130 0.8000 0.9198 0.6970
444
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
445
- warnings.warn(
446
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
447
- warnings.warn(
448
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
449
- warnings.warn(
450
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
451
- warnings.warn(
452
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
453
- warnings.warn(
454
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
455
- warnings.warn(
456
- 3300 156.5905 0.0000 0.9130 0.8000 0.9198 0.6970
457
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
458
- warnings.warn(
459
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
460
- warnings.warn(
461
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
462
- warnings.warn(
463
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
464
- warnings.warn(
465
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
466
- warnings.warn(
467
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
468
- warnings.warn(
469
- 3600 170.8260 0.0000 0.9130 0.8000 0.9226 0.6970
470
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
471
- warnings.warn(
472
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
473
- warnings.warn(
474
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
475
- warnings.warn(
476
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
477
- warnings.warn(
478
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
479
- warnings.warn(
480
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
481
- warnings.warn(
482
- 3900 185.0615 0.0000 0.9130 0.8000 0.9226 0.6970
483
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
484
- warnings.warn(
485
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
486
- warnings.warn(
487
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
488
- warnings.warn(
489
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
490
- warnings.warn(
491
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
492
- warnings.warn(
493
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
494
- warnings.warn(
495
- 4200 199.2970 0.0000 0.9130 0.8000 0.9226 0.6970
496
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
497
- warnings.warn(
498
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
499
- warnings.warn(
500
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
501
- warnings.warn(
502
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
503
- warnings.warn(
504
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
505
- warnings.warn(
506
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
507
- warnings.warn(
508
- 4500 213.5325 0.0000 0.9119 0.8000 0.9226 0.6970
509
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
510
- warnings.warn(
511
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
512
- warnings.warn(
513
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
514
- warnings.warn(
515
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
516
- warnings.warn(
517
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
518
- warnings.warn(
519
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
520
- warnings.warn(
521
- 4800 227.7680 0.0000 0.9119 0.8000 0.9226 0.6970
522
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
523
- warnings.warn(
524
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
525
- warnings.warn(
526
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
527
- warnings.warn(
528
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
529
- warnings.warn(
530
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
531
- warnings.warn(
532
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
533
- warnings.warn(
534
- 5000 237.2583 0.0000 0.9119 0.8000 0.9226 0.6970
535
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
536
- warnings.warn(_create_warning_msg(
537
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
538
- warnings.warn(
539
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
540
- warnings.warn(
541
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
542
- warnings.warn(
543
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
544
- warnings.warn(
545
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
546
- warnings.warn(
547
- /ocean/projects/asc170022p/shg121/anaconda3/envs/breast_clip_rtx_6000/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2851: FutureWarning: Setting the eps parameter is deprecated and will be removed in 1.5. Instead eps will always havea default value of `np.finfo(y_pred.dtype).eps`.
548
- warnings.warn(
549
-
550
- Test accuracy (best validation checkpoint):
551
- mean: [0.912]
552
- worst: [0.800]
553
- Group-wise accuracy:
554
- [va] group-wise [0.976, 0.893, 0.697, 0.947]
555
- [te] group-wise [0.982, 0.822, 0.800, 0.928]