% function a=a % Best mean match so far is image #x true_pairs % First, X only %translation only, subsection % ICP 2, expressions, nearest, [200,300,250,400] region true_pairs=[7.2044 7.8305 9.5734 6.8193 8.1520 0.1101 9.3481 0.7542 7.7785 9.0118 8.1920 15.1300 7.7178 10.1092 8.2556 8.6169 8.2997 7.1118 10.2950 7.3796 14.1074 8.6471 9.9035 6.8874 16.3659 8.7164 11.2254 7.9359 6.3192 1.5379 8.0701 8.3570 7.8267 8.8238 11.7372 8.7046 8.9715 6.9430 5.9669 9.5834]; % 1:size(true_pairs) false_pairs=[7.5913 7.5199 7.5494 7.3867 0.6316 9.3821 4.2738 7.5044 8.1057 9.0951]; % ICP 1 % first record true pairs true_pairs=[8.8615 8.3914 7.0144 10.4179 8.1453 2.0450 9.1946 5.5180 5.7092 8.4541 4.5593 13.3195 9.7371 9.5879 8.2556 8.0915 7.2908 8.1788 10.3057 10.1137 14.7931 9.0851 9.4008 5.9653 14.0513 7.6024 9.4768 8.4722 8.0457 1.9632 7.5285 8.3570 3.0391 8.8212 10.3730 8.7047 8.4504 6.9430 7.0966 8.9488 5.2766 10.8728 8.5356 0.4031 8.9242 3.9503 6.4804 7.9015 8.2362 9.0038 3.7131 14.2417 6.0536 8.6682 7.9287 5.3351 7.5805 7.3010 5.5865 7.5379 10.9035 8.3801 18.4563 3.2807 7.5658 7.5658 8.9777 10.9290 7.6479 7.6251 9.4871 13.0863 8.2246 7.4505 2.2580 6.9697 8.2448 12.2569 5.2949 7.5566 8.7524 4.0575 7.1877 15.4280]; false_pairs=[8.7051 8.6178 3.7864 7.8927 2.4970 8.8925 9.9944 7.5029 9.7051 8.6034 10.6313 7.5931 9.9178 9.7948 8.0481 7.4107 7.8863 7.5284 9.1945 14.5815 20.0654 10.2768 7.7808 10.5277 17.5197 10.3346 10.4870 8.2278 2.5788 7.9670 7.5902 8.4291 11.8193 10.8504 8.1261 8.8684 7.9580 21.7229 8.7310 8.2645 5.0874 11.6228 15.8824 7.5588 10.4592 22.4227 5.4035 14.5948 8.3372 3.8284 9.6453 13.3153 7.0586 7.0563 13.2551 7.8330 4.3832 10.2203 17.0089 7.7889 7.8484 7.5035 10.4019 10.5273 8.2530 8.9350 8.9171 10.3024 7.8421 8.4803 22.7885 1.1581 7.5731 3.2406 6.6605 8.4381 9.2560 5.4430 8.0262 8.9199 9.6420 13.9999 2.3169]; % moving on to median of median of squared differences in a [200,300,250,400] region, as above (ICP1) true_pairs=[479.0203 459.5976 391.9718 368.6050 414.8397 253.0678 284.8616 186.7340 354.7825 392.9198 391.0110 201.8027 241.0536 261.9818 351.6228 450.5618 404.1912 414.4369 228.6442 381.0826 0 234.7591 245.8603 333.3419 218.3029 440.5916 210.4282 461.4350 416.9227 239.2611 419.0416 456.1053 437.7210 238.9154 192.4172 365.7828 347.7014 279.6367 436.8740 413.9462 303.0981 198.2466 454.1375 425.1440 341.5041 427.7647 273.2403 422.2864 94.8332 326.4552 239.5509 186.9505 370.8286 405.6231 367.6211 255.7581 338.4852 0 251.9443 280.6053 365.3685 447.1774 352.5366 442.0297 419.4356 419.4356 327.5410 238.4701 332.0857 347.1089 48.7899 256.7169 312.7740 426.9396 252.0577 363.1322 422.2014 188.6164 259.2830 433.7231 232.7031 243.7176 447.6993 89.5985]; false_pairs=[443.7114 378.3552 367.0134 429.5987 226.4747 413.6846 182.6089 387.5414 432.9117 413.3011 213.1505 340.3165 371.1417 235.1430 366.5908 412.7987 403.4489 421.3227 222.0700 0 312.0900 224.9486 205.5436 244.3713 220.7240 196.3953 246.6617 393.2380 209.6361 418.4768 448.1069 388.4698 359.5118 185.4908 423.6771 265.8992 305.1387 468.8819 501.4328 330.5981 288.6674 192.5100 436.1394 399.0268 187.9079 487.1727 330.1206 191.7901 379.4472 238.3824 312.4556 215.3234 416.2775 379.1920 198.3165 360.2415 0 227.1598 292.0474 307.8898 411.9628 201.0130 357.9499 440.8739 460.0052 327.5510 346.6789 232.2088 307.8189 322.5861 21.0424 340.7599 351.8917 264.9386 358.3653 387.5426 188.7837 289.8035 421.2968 354.6012 187.6291 347.3525 206.5657]; plot_roc(true_pairs, false_pairs) % Now turning to Z data (bug fixed) true_pairs=[29.6999 1.9816 3.4424 13.8747 19.2698 3.7487 40.5630 5.9221 13.6765 18.1760 6.7455 15.8246 4.1506 3.4963 16.4790 3.9074 10.3109 28.2469 5.3874 10.3109 0.9489 99.5379 6.8055 8.5617 4.0031 3.0023 5.9479 3.3768 3.3181 15.9689 5.4148 18.5465 108.5684 2.7545 3.4962 4.3019 19.4962]; false_pairs=[1.7584 6.1576 50.0383 64.2259 24.2618 194.3738 29.6814 63.0677 100.0257 61.4326 61.3474 121.2156 114.2389 42.6115 54.7280 79.2807 615.9247 19.4672 28.9181 23.4528 73.2516 49.4543 5.3430 132.4220 109.1671 40.7286 11.1020 14.4567 112.1783 54.5518 138.9912 167.4128 7.0782 19.8342 9.2277 5.7812 37.4015 16.0254 76.8248 31.7174 76.7833 96.8428 69.6524 0.3074 12.4917 222.9118 23.6601 305.5663 22.8412 77.8988 258.7514 14.4192 59.5874 168.8031 8.7378 24.6837 24.3293 133.4608 90.2081 97.6897 0.0500 12.8456 11.5853 61.5001 30.6030 70.8295 31.8089 198.3855 10.3393 58.3405 8.1785 91.5236 13.2426 21.3431 20.9153 37.5858 41.2864 11.6600]; % ICP2 GIP geometric, after bug removal, still buggy % true 17.7391 21.6725 4.2704 4.4116 44.8342 131.0604 false 48.5728 0 0 18.1753 25.4006 10.2361 25.9964 % ICP1 GIP geometric, after bug removal, ICP totally disabled, median with quadratic differences % true true_pairs=[ 1.5584 1.9816 1.4798 1.7311 9.9941 3.7487 12.2194 1.1667 3.2571 1.7644 1.2850 15.8246 4.1506 1.7299 5.0242 3.9074 3.5520 3.0142 1.8411 4.5566 0.9489 21.1945 0.9734 3.8925 2.4888 5.9479 3.3768 0.9318 0.2596 5.4148 31.2267 20.0829 2.7545 3.4962 4.3019 13.9013 90.6004 8.0753 21.4459 11.9854 1.3645 5.1522]; false_pairs=[ 7.0993 6.1576 7.0190 4.4495 24.2618 10.4042 10.9654 2.2658 18.2364 10.7171 4.0334 37.3594 14.8109 12.8808 7.2274 79.2807 94.4621 4.1613 3.9078 23.4528 52.2572 98.7199 23.0216 5.5611 14.1218 11.1020 12.6487 12.8983 18.5059 28.6944 18.4296 6.6138 19.8342 9.2277 22.3020 57.4437 16.0254 4.4656 31.7174 23.5861 101.9907 26.7642]; % same as above, but bigger face mask, ICP1 (GIP), and average of quadratic differences true_pairs=[ 17.8887 7.6314 6.8036 5.2082 28.7511 19.8877 21.8621 5.6501 19.4367 14.4905 9.1436 30.1486 9.9716 11.7395 12.8643 15.0446 11.5903 8.9287 9.9965 11.0788 3.4027 46.7972 54.0217 4.7770 8.2134 9.2816 17.8282 7.6560 7.2524 2.8268 16.0472 62.4334 37.0656 8.9518 6.6378 15.4014 26.3681 118.1935 18.7305 57.2736 19.4108 17.6948 10.6423 26.5321 34.5647 30.6458 44.6402 35.7931 8.2301 9.5655 12.5430 147.2713 8.7558 33.2801 23.5576 31.8368 7.3877 16.5008 10.1315 52.1627 11.4245 12.1679 15.1850 64.6503 64.6503 27.6126 18.2393 15.1099 12.2879 26.6976 4.4497 8.3362 25.3933 5.6474 11.8664 4.9861 35.2724 16.3034 8.9302 11.4712 13.5607 19.1210 33.6135]; false_pairs=[18.1304 31.8950 31.2130 33.4273 57.1792 40.4022 23.2319 13.2745 45.6002 34.5522 20.1910 62.6310 29.9558 37.1952 31.1099 71.0402 105.3787 14.2065 15.6359 34.2633 97.5960 106.5168 50.9759 81.7235 36.1976 25.6158 39.9071 27.5792 42.1784 31.1420 110.0640 70.0933 15.5060 28.2125 18.1168 29.5183 100.9936 43.7384 24.0054 164.0885 61.4299 93.6805 58.3761 106.2305 15.0460 30.3318 37.3718 92.4152 41.0181 79.3460 33.3871 129.6056 45.9428 15.8098 119.3539 86.3937 29.6439 41.8525 61.6026 47.2753 28.1696 36.5617 35.8129 64.6503 32.7772 32.9385 58.7183 33.9650 49.8904 16.3954 18.2783 51.3479 17.0681 44.7262 18.1650 27.1695 34.7193 38.1104 28.4361 42.7632 30.7158 67.0285]; % downsampled by a factor of 10 true_pairs=[ 17.7401 5.8574 5.5522 5.8619 29.1256 21.5621 21.4076 4.6142 17.4169 12.4050 7.0138 26.2527 8.4583 10.9007 14.0934 15.3592 11.3741 9.2007 13.0398 10.9238 4.2138 45.4112 52.9785 7.6471 8.0349 7.0201 17.4331 8.1787 6.0300 3.2867 15.1630 62.6182 33.9595 9.3444 7.6881 15.0176 26.1249 116.4471 17.2428 58.2090 18.3922 18.0144 10.6778 25.8909 30.8010 31.9597 43.7702 35.1675 10.2321 8.9440 12.2096 137.8193 11.0212 32.6693 23.1938 30.0153 7.6797 15.5081 10.9109 49.4216 10.9547 10.6991 14.9826 64.2652 64.2652 29.8185 18.6953 11.7591 11.5106 23.9783 5.0067 6.4707 25.8207 5.4859 12.8856 5.4864 33.5320 17.7923 7.0396 10.9659 12.1812 19.3814 35.2608]; 7 false_pairs=[ 17.1605 29.5283 30.5833 34.5914 53.2520 41.9130 22.6977 11.3684 43.2397 32.8834 18.4035 60.7474 29.9202 37.7945 31.8019 68.1332 106.5647 15.1105 17.4448 32.4396 100.1002 106.7513 50.8670 81.6325 37.5620 24.1096 37.4577 26.6161 40.1219 29.8385 105.3872 70.0092 12.4147 29.3577 18.5921 28.4899 98.5580 40.6500 25.1834 160.1829 59.4736 94.4936 55.6457 104.1057 21.0418 29.6781 40.4924 93.1080 41.2180 77.1020 30.9409 124.0173 48.3730 15.3118 117.7847 85.9715 27.3770 42.6077 63.6314 41.4214 27.6064 35.1016 34.9131 64.2652 31.2406 34.7715 56.2675 28.8168 48.1338 14.8216 18.0334 52.1921 16.2967 42.3175 18.5369 28.8699 35.0387 38.0166 25.3147 38.6850 29.6486 69.3799]; % model-based, similar to the above true match_score_concat = Columns 1 through 7 true_pairs=[ 0.0078 0.0078 0.0086 0.0085 0.0102 0.0096 0.0090 0.0094 0.0104 0.0094 0.0077 0.0083 0.0089 0.0103 0.0087 0.0092 0.0097 0.0106 0.0088 0.0087 0.0081 0.0096 0.0090 0.0091 0.0101 0.0093 0.0111 0.0104 0.0108 0.0075 0.0097 0.0085 0.0102 0.0094 0.0081 0.0091 0.0122 0.0137 0.0097 0.0146 0.0088 0.0104 0.0085 0.0079 0.0088 0.0081 0.0077 0.0150 0.0099 0.0095 0.0104 0.0078 0.0095 0.0132 0.0114 0.0095 0.0099 0.0136 0.0106 0.0179 0.0112 0.0095 0.0114 0.0075 0.0075 0.0104 0.0094 0.0113 0.0093 0.0106 0.0103 0.0096 0.0089 0.0087 0.0094 0.0094 0.0100 0.0103 0.0091 0.0104 0.0097 0.0097 0.0156]; Best concatenated match so far is image #65 match_score_mean = Columns 1 through 7 true_pairs=[ 15.4972 6.3390 5.9740 4.3541 26.9932 23.2056 20.9543 5.3403 15.8589 15.1653 15.6050 28.1425 9.3649 12.8546 13.3934 13.1810 12.7242 7.9601 11.8368 12.0609 3.7903 41.7898 55.1229 6.7562 7.2959 6.9997 15.2240 7.0646 7.6492 2.7793 16.9028 59.9196 33.2067 7.4270 5.4960 14.3981 25.2923 107.1039 15.8064 57.7336 19.1129 16.2658 8.5249 26.3786 31.4000 32.8639 45.2726 32.0852 7.7332 8.3936 11.1429 144.6750 12.6699 31.2620 21.2290 32.1917 5.7662 15.2217 12.9243 51.0294 10.2388 11.2088 14.7423 57.5682 57.5682 25.5248 20.5552 10.9545 10.5853 22.3255 5.5546 6.4215 22.9935 5.0617 13.2493 6.1670 34.8247 16.4587 6.7126 11.7898 13.1164 20.2207 29.5036]; match_score_concat = Columns 1 through 7 false_pairs=[ 0.0120 0.0152 0.0147 0.0120 0.0124 0.0146 0.0117 0.0113 0.0128 0.0107 0.0140 0.0131 0.0120 0.0148 0.0120 0.0154 0.0143 0.0122 0.0133 0.0151 0.0160 0.0140 0.0134 0.0115 0.0118 0.0137 0.0127 0.0149 0.0143 0.0131 0.0150 0.0145 0.0122 0.0111 0.0129 0.0132 0.0155 0.0128 0.0145 0.0165 0.0142 0.0141 0.0134 0.0155 0.0153 0.0160 0.0144 0.0143 0.0121 0.0124 0.0135 0.0143 0.0140 0.0100 0.0142 0.0152 0.0156 0.0164 0.0133 0.0124 0.0124 0.0135 0.0122 0.0088 0.0149 0.0128 0.0119 0.0107 0.0152 0.0126 0.0139 0.0122 0.0147 0.0162 0.0119 0.0119 0.0143 0.0153 0.0108 0.0141 0.0133 0.0157]; Best concatenated match so far is image #64 match_score_mean = Columns 1 through 7 false_pairs=[ 16.5153 27.3597 26.6447 33.2703 50.2098 39.6762 22.1358 11.8377 41.1139 33.7400 24.0615 58.7539 28.0980 37.0964 30.4686 66.7417 98.2551 14.7014 16.5909 32.0560 90.5240 100.9942 51.0385 79.4628 36.1130 23.3699 34.2286 26.0393 42.9069 30.3378 103.7891 69.2382 11.5409 28.3234 16.6158 28.2284 93.4303 38.6181 21.4936 157.6100 59.5723 89.6989 54.6078 101.1369 14.7367 26.5498 36.1164 84.5214 35.7184 70.4121 31.1818 120.7461 47.8723 14.6749 117.5860 80.6815 28.0563 41.1391 61.2883 43.0189 26.1516 34.0714 33.6393 57.5682 31.7731 30.9760 55.5841 27.8128 47.7709 12.2780 19.0796 47.8239 17.7918 42.2447 19.8072 29.1093 30.0576 35.8735 23.6053 40.4675 29.1388 64.8052]; median of quad true_pairs=[ 0.0018 0.0019 0.0017 0.0017 0.0030 0.0025 0.0021 0.0029 0.0049 0.0028 0.0019 0.0038 0.0023 0.0027 0.0043 0.0021 0.0043 0.0031 0.0022 0.0019 0.0018 0.0021 0.0024 0.0033 0.0027 0.0022 0.0050 0.0028 0.0030 0.0015 0.0027 0.0024 0.0046 0.0031 0.0017 0.0037 0.0031 0.0043 0.0026 0.0042 0.0028 0.0044 0.0018 0.0029 0.0038 0.0024 0.0036 0.0041 0.0047 0.0042 0.0029 0.0041 0.0027 0.0037 0.0056 0.0026 0.0046 0.0055 0.0037 0.0104 0.0047 0.0037 0.0034 0.0023 0.0023 0.0065 0.0043 0.0033 0.0025 0.0033 0.0047 0.0034 0.0043 0.0021 0.0027 0.0028 0.0043 0.0052 0.0049 0.0029 0.0046 0.0044 0.0054]; false_pairs=[ 0.0050 0.0055 0.0063 0.0040 0.0052 0.0048 0.0047 0.0038 0.0064 0.0033 0.0058 0.0044 0.0043 0.0069 0.0054 0.0071 0.0068 0.0049 0.0054 0.0055 0.0089 0.0055 0.0041 0.0033 0.0039 0.0065 0.0039 0.0062 0.0059 0.0062 0.0053 0.0050 0.0042 0.0051 0.0049 0.0066 0.0087 0.0073 0.0057 0.0082 0.0045 0.0051 0.0057 0.0077 0.0070 0.0066 0.0069 0.0056 0.0056 0.0038 0.0054 0.0072 0.0093 0.0044 0.0056 0.0058 0.0062 0.0091 0.0071 0.0056 0.0055 0.0059 0.0037 0.0038 0.0069 0.0045 0.0054 0.0036 0.0053 0.0065 0.0044 0.0046 0.0060 0.0112 0.0058 0.0058 0.0057 0.0074 0.0034 0.0064 0.0068 0.0096 ]; mean of quad true_pairs=[ 0.0054 0.0052 0.0063 0.0064 0.0081 0.0081 0.0074 0.0080 0.0111 0.0077 0.0051 0.0087 0.0069 0.0089 0.0095 0.0072 0.0101 0.0094 0.0073 0.0065 0.0058 0.0074 0.0077 0.0085 0.0085 0.0070 0.0125 0.0091 0.0095 0.0054 0.0082 0.0070 0.0113 0.0084 0.0061 0.0093 0.0107 0.0133 0.0074 0.0145 0.0070 0.0117 0.0056 0.0070 0.0105 0.0069 0.0086 0.0146 0.0104 0.0098 0.0089 0.0084 0.0082 0.0128 0.0125 0.0084 0.0108 0.0145 0.0106 0.0231 0.0114 0.0088 0.0100 0.0061 0.0061 0.0127 0.0106 0.0100 0.0077 0.0101 0.0114 0.0086 0.0095 0.0065 0.0081 0.0081 0.0107 0.0111 0.0101 0.0092 0.0103 0.0101 0.0171]; false_pairs=[ 0.0104 0.0144 0.0143 0.0101 0.0117 0.0127 0.0100 0.0102 0.0132 0.0080 0.0136 0.0106 0.0102 0.0149 0.0105 0.0151 0.0139 0.0114 0.0127 0.0144 0.0162 0.0123 0.0118 0.0095 0.0097 0.0135 0.0110 0.0142 0.0134 0.0136 0.0143 0.0134 0.0105 0.0116 0.0117 0.0128 0.0166 0.0139 0.0138 0.0164 0.0123 0.0116 0.0127 0.0156 0.0161 0.0155 0.0146 0.0139 0.0121 0.0106 0.0121 0.0145 0.0156 0.0096 0.0139 0.0138 0.0153 0.0173 0.0133 0.0117 0.0121 0.0130 0.0100 0.0074 0.0154 0.0121 0.0107 0.0090 0.0137 0.0129 0.0127 0.0104 0.0150 0.0190 0.0112 0.0130 0.0131 0.0160 0.0084 0.0132 0.0139 0.0173]; median of asbolute differences true_pairs=[ 0.0019 0.0022 0.0019 0.0017 0.0028 0.0023 0.0021 0.0027 0.0024 0.0025 0.0023 0.0022 0.0021 0.0023 0.0023 0.0022 0.0026 0.0025 0.0023 0.0020 0.0022 0.0023 0.0020 0.0023 0.0022 0.0021 0.0023 0.0025 0.0025 0.0021 0.0022 0.0021 0.0023 0.0024 0.0021 0.0023 0.0025 0.0026 0.0025 0.0025 0.0022 0.0023 0.0023 0.0022 0.0016 0.0021 0.0020 0.0026 0.0033 0.0024 0.0024 0.0026 0.0025 0.0024 0.0035 0.0022 0.0024 0.0029 0.0028 0.0040 0.0028 0.0028 0.0023 0.0017 0.0017 0.0029 0.0025 0.0027 0.0024 0.0026 0.0025 0.0025 0.0027 0.0024 0.0022 0.0022 0.0030 0.0031 0.0027 0.0026 0.0025 0.0023 0.0028 ]; checking known correct pairs: false_pairs=[ 0.0032 0.0041 0.0034 0.0034 0.0032 0.0034 0.0036 0.0031 0.0042 0.0032 0.0041 0.0033 0.0035 0.0036 0.0041 0.0035 0.0038 0.0034 0.0037 0.0035 0.0051 0.0041 0.0029 0.0029 0.0030 0.0044 0.0030 0.0042 0.0034 0.0034 0.0036 0.0034 0.0036 0.0030 0.0033 0.0046 0.0042 0.0038 0.0036 0.0047 0.0033 0.0044 0.0038 0.0048 0.0035 0.0044 0.0039 0.0032 0.0031 0.0030 0.0042 0.0040 0.0051 0.0032 0.0037 0.0037 0.0035 0.0049 0.0047 0.0038 0.0034 0.0041 0.0031 0.0038 0.0037 0.0034 0.0041 0.0033 0.0034 0.0042 0.0032 0.0041 0.0032 0.0054 0.0039 0.0028 0.0044 0.0043 0.0036 0.0040 0.0036 0.0057 ]; checking unknown correct pairs: true_pairs=[ 0.0031 0.0030 0.0032 0.0027 0.0034 0.0029 0.0033 0.0028 ]; % target set of ~2,0000 images taken in order, with some overlap between people due to ordering both=[0.0035 0.0025 0.0049 0.0029 0.0037 0.0032 0.0026 0.0036 0.0040 0.0024 0.0035 0.0028 0.0037 0.0035 0.0037 0.0033 0.0040 0.0033 0.0031 0.0041 0.0032 0.0031 0.0028 0.0027 0.0057 0.0049 0.0030 0.0042 0.0029 0.0027 0.0028 0.0030 0.0028 0.0027 0.0028 0.0032 0.0038 0.0028 0.0026 0.0039 0.0030 0.0026 0.0034]; % randomised target set of ~2,0000 images taken in order, with some overlap between people due to ordering results in files % separation changed from 10 to 5 (finer) and set selected to account not just for smiles match_score_concat = 1.0e-03 * Columns 1 through 7 true_pairs=[ 0.1194 0.2026 0.1436 0.1368 0.1047 0.2109 0.1402 0.1933 0.1453 0.1409]; Best concatenated match so far is image #5 match_score_mean = Columns 1 through 7 26.1417 10.9527 10.1586 22.2167 21.8017 87.9939 14.9024 Columns 8 through 10 23.2818 23.3712 18.1523 Columns 1 through 7 false_pairs =[0.2288 0.2256 0.2314 0.2896 0.2245 0.2639 0.1833 0.2400 0.2522 0.2211 0.2142 0.2274]; Best concatenated match so far is image #7 match_score_mean = Columns 1 through 7 26.6547 29.9760 23.7799 42.2037 23.5538 42.2004 40.3173 Columns 8 through 12 21.3234 80.0136 21.3321 95.7807 63.5545 The next experiment explores the impact of granularity level in model-building on the overall performance. In order to make the experiments more defensible, the training set is changed from size that can be described as minimalist (100 images) to 200, which requires further manual work. The model is being built from this set and performance then tested as before. The experiments are still extensible in the sense that they can be rerun with a larger set. the process involves building a model, then doing assessment on the target set with correct matches and another target set with incorrect matches, repeat for each model granularity level. ============================================ >> granularity level 10 (10 pixels apart) ============================================ true ============================================ New model built with probe residual match_score_concat = Columns 1 through 7 true_pairs=[ 0.0029 0.0034 0.0026 0.0022 0.0027 0.0031 0.0021 0.0039 0.0022 0.0028 0.0026 0.0029 0.0030 0.0028 0.0032 0.0026 0.0033 0.0022 0.0031 0.0019]; Best concatenated match so far is image #7 match_score_mean = Columns 1 through 7 24.4106 11.0768 9.5253 20.2803 18.9648 77.0540 14.6632 Columns 8 through 14 22.5876 22.1850 17.2870 22.6915 17.0713 10.1934 12.1835 Columns 15 through 20 18.6013 22.7208 70.9977 3.8462 8.6253 17.5325 Best mean match so far is image #18 % same as abve, with 2-D smoothing, megnitude 6: match_score_concat = Columns 1 through 7 true_pairs=[0.0021 0.0036 0.0017 0.0019 0.0021 0.0046 0.0021 0.0038 0.0032 0.0022 0.0029 0.0026 0.0024 0.0025 0.0029 0.0034 0.0035 0.0018 0.0022 0.0023]; Best concatenated match so far is image #3 match_score_mean = Columns 1 through 7 15.9802 5.5218 4.2924 12.8020 11.2901 51.9451 8.9043 Columns 8 through 14 14.7577 13.1118 11.8087 12.4045 8.5182 5.5876 5.8536 Columns 15 through 20 8.9479 14.0838 44.9127 1.7977 5.7001 10.1735 ============================================ false ============================================ match_score_concat = Columns 1 through 7 false_pairs =[ 0.0040 0.0036 0.0040 0.0036 0.0040 0.0041 0.0037 0.0043 0.0040 0.0040 0.0032 0.0027 0.0043 0.0028 0.0030 0.0035 0.0048 0.0034 0.0032 0.0037 0.0033 0.0042 0.0035 0.0037]; Best concatenated match so far is image #12 match_score_mean = Columns 1 through 7 53.2034 88.2368 45.5857 12.0720 39.8089 62.7185 30.5705 Columns 8 through 14 48.6592 42.9367 89.2026 84.0759 30.0989 35.8482 28.2809 Columns 15 through 21 66.9000 25.1664 46.9933 5.4352 10.4665 20.8447 % same as abve, with 2-D smoothing, megnitude 6: New model built with probe residual match_score_concat = Columns 1 through 7 false_pairs =[ 0.0024 0.0047 0.0041 0.0040 0.0046 0.0040 0.0045 0.0035 0.0033 0.0039 0.0026 0.0030 0.0026 0.0029 0.0033 0.0030 0.0028 0.0028 0.0029 0.0031 0.0034 0.0053 0.0046 0.0037 0.0032 0.0045 0.0032 0.0026 0.0038 0.0049 0.0030 0.0036 0.0036 0.0026 0.0049 0.0043 0.0042] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 1.6819 25.1884 35.8386 12.1532 4.7530 26.8470 42.0459 Columns 8 through 14 36.1538 40.2006 15.3027 11.1528 22.5974 11.3272 6.2531 Columns 15 through 21 29.9630 54.6249 5.9192 12.0094 24.1116 145.5838 12.9215 Columns 22 through 28 18.1428 10.5768 7.9332 33.8025 14.7162 15.6699 17.2884 Columns 29 through 35 23.0758 20.4802 17.6397 9.3488 16.0160 30.6164 70.2247 Columns 36 through 37 25.4339 27.0306 ============================================ >> granularity level 8 (10 pixels apart) ============================================ true ============================================ match_score_concat = Columns 1 through 7 true_pairs=[ 0.0017 0.0014 0.0020 0.0015 0.0014 0.0026 0.0017 0.0017 0.0019 0.0015 0.0019 0.0017 0.0020 0.0023 0.0023 0.0021 0.0021 0.0013 0.0018 0.0014]; Best concatenated match so far is image #18 match_score_mean = Columns 1 through 7 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 Columns 8 through 14 24.2100 23.6129 17.8895 24.2892 19.4168 11.9477 11.1581 Columns 15 through 20 17.9150 23.4196 72.6555 4.7076 9.8430 16.3602 ============================================ false ============================================ match_score_concat = Columns 1 through 7 false_pairs=[ 0.0020 0.0025 0.0019 0.0018 0.0021 0.0025 0.0020 0.0023 0.0023 0.0023 0.0022 0.0026 0.0020 0.0023 0.0019 0.0023 0.0022 0.0021 0.0025 0.0019 0.0018 0.0026 0.0027 0.0026 0.0019 0.0026 0.0024 0.0018 0.0027 0.0023 0.0025 0.0025 0.0026 0.0020 0.0025 0.0024 0.0020]; Best concatenated match so far is image #28 match_score_mean = Columns 1 through 7 false_pairs =[ 90.5976 41.5999 38.0155 39.8922 27.8663 19.0037 30.0603 Columns 8 through 14 34.9602 21.0323 58.4783 51.7050 30.3238 28.0603 60.7249 Columns 15 through 21 23.3427 50.3612 26.3233 40.0124 55.6191 39.2295 16.5392 Columns 22 through 28 146.8811 26.5858 24.3085 9.8339 40.8624 57.7317 25.3215 Columns 29 through 35]; ============================================ >> granularity level 6 (10 pixels apart) ============================================ true ============================================ match_score_concat = 1.0e-03 * Columns 1 through 7 true_pairs=[ 0.4205 0.4856 0.4973 0.4168 0.4242 0.8355 0.5020 0.5762 0.4044 0.3389 0.5119 0.5403 0.5735 0.5943 0.6294 0.5983 0.6074 0.5757 0.5647 0.5898]; Best concatenated match so far is image #10 match_score_mean = Columns 1 through 7 26.1588 11.7354 9.4181 22.8329 21.5600 90.9607 14.7821 Columns 8 through 14 23.6915 22.6162 17.8235 24.0458 18.5569 11.7125 11.1638 Columns 15 through 20 17.2051 23.6663 71.6877 4.4982 10.1947 16.4981 % same as abve, with 2-D smoothing, megnitude 6: New model built with probe residual match_score_concat = Columns 1 through 7 true_pairs=[ 0.0007 0.0008 0.0006 0.0008 0.0007 0.0012 0.0008 0.0009 0.0007 0.0008 0.0008 0.0007 0.0007 0.0007 0.0010 0.0008 0.0008 0.0006 0.0007 0.0007]; Best concatenated match so far is image #3 match_score_mean = Columns 1 through 7 16.1050 4.8835 4.5193 13.1120 11.5816 53.8629 8.6419 Columns 8 through 14 14.1693 13.4328 11.2354 13.0780 8.7783 5.7808 5.8339 Columns 15 through 20 9.1235 13.9527 46.6764 1.8958 5.7829 10.4460 ============================================ false ============================================ Columns 1 through 7 false_pairs =[ 0.6526 0.5793 0.5958 0.6007 0.7924 0.6627 0.5569 0.7413 0.6337 0.4839 0.5990 0.6514 0.5892 0.6479 0.5806 0.7842 0.5388 0.5277 0.6837 0.6544 0.6113 0.5608 0.6065 0.6416 0.6404 0.4421 0.5870 0.7998 0.6711 0.6764 0.5623]; Best concatenated match so far is image #26 match_score_mean = Columns 1 through 7 13.6310 16.8717 19.8221 42.6331 90.5575 41.0474 71.6995 Columns 8 through 14 72.2057 10.7525 11.0665 62.0916 24.5504 27.7458 43.6811 Columns 15 through 21 48.5479 33.7595 28.3650 30.6362 44.6062 24.0505 34.9339 Columns 22 through 28 41.1947 55.5136 34.0240 55.2549 38.1384 47.4358 39.4722 Columns 29 through 31 73.8487 43.7393 23.4990 % same as abve, with 2-D smoothing, megnitude 6: match_score_concat = false_pairs =[ 0.0009 0.0008 0.0009 0.0011 0.0009 0.0010 0.0010 0.0010 0.0010 0.0009 0.0007 0.0007 0.0009 0.0009 0.0010 0.0007 0.0009 0.0009 0.0009 0.0008 0.0008 0.0008 0.0010 0.0009 0.0007 0.0009 0.0008 0.0008 0.0008 0.0007 0.0008 0.0011 0.0009 0.0010 0.0009 0.0009 0.0008 0.0009 0.0009 0.0008 0.0009 0.0007 0.0008 0.0008 0.0009 0.0010 0.0008 0.0009 0.0008 0.0008 0.0009 0.0009 0.0009 0.0010 0.0009 0.0009 0.0010 0.0009 0.0007 0.0009 0.0011 0.0008 0.0009 0.0009 0.0010 0.0010 0.0010 0.0009 0.0007 0.0010 0.0009 0.0009 0.0008 0.0009 0.0008 0.0007 0.0010 0.0009 0.0012 0.0009 0.0008 0.0010 0.0007 0.0009 0.0008 0.0007 0.0009 0.0008 0.0010 0.0009 0.0009 0.0009 0.0007 0.0009 0.0009 0.0007 0.0007 0.0010 0.0008 0.0009 0.0009 0.0009 0.0010 0.0007 0.0010 0.0007 0.0011 0.0008 0.0009 0.0009 0.0009 0.0011 0.0008 0.0008 0.0009 0.0010 0.0008 0.0008 0.0009 0.0010 0.0009 0.0008 0.0008 0.0010 0.0010 0.0007 0.0009 0.0009 0.0010 0.0009 0.0010 0.0010 0.0008 0.0007 0.0008 0.0007 0.0008 0.0010 0.0009]; Best concatenated match so far is image #69 match_score_mean = Columns 1 through 7 15.6226 20.8704 17.4217 44.4096 12.6573 44.5570 34.9438 Columns 8 through 14 24.6150 48.0496 4.8556 9.0358 8.4887 4.1138 10.5953 Columns 15 through 21 20.6511 10.3008 43.3066 28.2395 5.9251 5.6895 26.5840 Columns 22 through 28 16.4410 14.4744 39.2675 7.7297 19.3080 34.0404 49.5416 Columns 29 through 35 10.8350 16.0484 5.7742 27.6038 21.2259 15.9546 7.1995 Columns 36 through 42 49.3492 17.8365 12.3696 13.6261 26.9894 15.0650 37.0134 Columns 43 through 49 26.2976 20.7653 84.7259 96.1844 12.0590 11.0490 20.2178 Columns 50 through 56 25.8457 22.0304 18.1803 18.3491 21.6679 22.9150 71.1965 21.2979 21.6618 11.3543 46.9373 26.2123 8.1003 10.3478 12.1156 21.6832 41.1053 43.4697 26.8989 5.4016 49.9492 92.3573 25.5442 6.5643 14.9283 34.0536 47.0871 28.4432 24.5795 41.1066 40.9287 47.8494 33.6693 15.9996 8.9578 11.1082 19.5665 7.7789 32.3493 27.6279 13.3994 54.6442 25.7303 7.2170 24.9304 10.0391 9.9582 30.5781 20.3147 15.1950 108.8140 2.8533 18.1847 58.1216 17.1240 18.6878 38.0832 17.5419 105.3914 10.3240 46.1613 8.9765 46.0007 25.1192 72.2408 4.2061 31.4035 11.4960 39.4296 18.4093 48.5623 16.3341 24.2311 30.0702 5.8924 41.8103 8.6158 20.7615 29.2615 40.6325 31.2522 14.3023 32.2286 7.7646 14.1360 26.2552 8.6356 22.3366 37.8278 39.4481 % 10 spacing, rotation and translation added (Ajmal) match_score_concat = Columns 1 through 9 AJMAL ICPv1 true_pairs=[ 0.0033 0.0038 0.0019 0.0023 0.0035 0.0059 0.0032 0.0045 0.0022 0.0021 0.0045 0.0050 0.0045 0.0043 0.0042 0.0042 0.0034 0.0036 0.0033 0.0024]; ICPv2 true_pairs=[ 0.0033 0.0032 0.0025 0.0046 0.0034 0.0028 0.0021 0.0043 0.0042 0.0022 0.0038 0.0047 0.0031 0.0028 0.0026]; ans = 86 336 ans = 336 1 match_score_concat_score = Columns 1 through 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 16 through 20 0 0 0 0 0 match_score_concat_latent = Columns 1 through 15 true_pairs=[ 0.0032 0.0025 0.0019 0.0022 0.0036 0.0059 0.0020 0.0029 0.0023 Columns 10 through 12 0.0033 0.0045 0.0042 match_score_concat_score = 0 0 0 0 0 0 0 0 0 0 0 0 match_score_concat_latent = 1.0e-202 * Columns 1 through 9 true_pairs=[ 0.0022 0.0050 0.0013 0.0011 0.0098 0.1110 0.0013 0.0072 0.0027 0.0013 0.0119 0.0332]; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 16 through 20 0 0 0 0 0 Best concatenated match so far is image #3 match_score_mean = Columns 1 through 9 21.0289 34.6934 7.0969 17.8523 7.9538 49.9305 13.0333 17.1382 8.0727 Columns 10 through 18 9.7988 24.9820 21.6011 36.9936 51.4024 15.7841 18.6421 37.4978 36.1418 Columns 19 through 20 4.1674 17.7014 Columns 1 through 9 false_pairs =[ 0.0040 0.0053 0.0044 0.0036 0.0042 0.0044 0.0029 0.0038 0.0040 0.0037 0.0035 0.0042 0.0026]; match_score_concat = 0.0038 0.0040 0.0046 0.0043 0.0046 0.0031 0.0038 0.0040 0.0036 match_score_concat_score = 0 0 0 0 0 0 0 0 0 match_score_concat_latent = 1.0e-203 * false_pairs =[ 0.5796 0.1605 0.0946 0.3168 0.0639 0.1271 0.0801 0.2530 0.1042] Best concatenated match so far is image #6 match_score_mean = 49.1194 49.2159 13.2361 15.2429 10.2602 7.7609 5.3423 25.4370 14.3591 % Resolution 8 - no ICP applied - testing latent % lambda=0.11 %true pairs match_score_concat = Columns 1 through 9 0.0017 0.0014 0.0020 0.0015 0.0014 0.0026 0.0017 0.0017 0.0019 Columns 10 through 18 0.0015 0.0019 0.0017 0.0020 0.0023 0.0023 0.0021 0.0021 0.0013 Columns 19 through 20 0.0018 0.0014 match_score_concat_score = Columns 1 through 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 16 through 20 0 0 0 0 0 match_score_concat_latent = 1.0e-299 * Columns 1 through 9 true_pairs=[ 0.0085 0.0167 0.0027 0.0033 0.0318 0.3250 0.0027 0.0143 0.0087 0.0030 0.0372 0.0053 0.0107 0.0068 0.0337 0.0334 0.0103 0.0065 0.0111 0.0051]; Best concatenated match so far is image #18 match_score_mean = Columns 1 through 9 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 24.2100 23.6129 Columns 10 through 18 17.8895 24.2892 19.4168 11.9477 11.1581 17.9150 23.4196 72.6555 4.7076 Columns 19 through 20 9.8430 16.3602 % false (Random) New model built with probe residual match_score_concat = Columns 1 through 9 =[ 0.0018 0.0026 0.0021 0.0024 0.0025 0.0019 0.0026 0.0026 0.0019 0.0015 0.0026 0.0023 0.0017 0.0021 0.0020 0.0022 0.0028 0.0024 0.0019 0.0019 0.0018 0.0025 0.0019 0.0021 0.0025 0.0022 0.0023 0.0023 0.0019 0.0023 0.0029 0.0018 0.0019 0.0018 0.0018 0.0024 0.0022 0.0020 0.0022 0.0022 0.0013 0.0022 0.0022 0.0025 0.0023 0.0026 0.0021]; match_score_concat_score = Columns 1 through 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 16 through 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 31 through 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 46 through 47 0 0 match_score_concat_latent = 1.0e-299 * Columns 1 through 9 false_pairs =[ 0.0407 0.0337 0.0338 0.1520 0.0783 0.0705 0.0552 0.0482 0.0410 0.0477 0.0390 0.0531 0.0789 0.0661 0.0411 0.0419 0.0296 0.0479 0.0474 0.0305 0.0412 0.0461 0.0566 0.0339 0.1574 0.0393 0.0386 0.0267 0.0280 0.0456 0.0421 0.0213 0.0579 0.0283 0.0297 0.0482 0.0492 0.0390 0.0661 0.0315 0.0054 0.1014 0.0930 0.1292 0.0765 0.0416 0.0554]; Best concatenated match so far is image #41 match_score_mean = Columns 1 through 9 25.8455 17.7198 26.7624 43.3999 56.6285 49.2693 46.5920 55.1966 32.1100 Columns 10 through 18 44.8851 17.2364 32.6720 56.2893 52.9962 67.8455 36.4536 13.0180 28.3029 Columns 19 through 27 37.7822 27.2518 29.0705 43.8784 40.6750 44.6120 51.6792 30.0026 28.7495 Columns 28 through 36 35.4123 18.4045 39.9095 25.3617 19.2222 27.0124 14.7175 22.1422 37.2261 Columns 37 through 45 43.9869 28.9748 46.8364 12.5660 2.5046 62.5197 99.9738 49.9752 47.5053 Columns 46 through 47 20.6251 37.9429 % lambda=0.3 New model built with probe residual match_score_concat = Columns 1 through 9 0.0017 0.0014 0.0020 0.0015 0.0014 0.0026 0.0017 0.0017 0.0019 Columns 10 through 18 0.0015 0.0019 0.0017 0.0020 0.0023 0.0023 0.0021 0.0021 0.0013 Columns 19 through 20 0.0018 0.0014 match_score_concat_score = Columns 1 through 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 16 through 20 0 0 0 0 0 match_score_concat_latent = 1.0e-119 * Columns 1 through 9 true_pairs=[ 0.0077 0.0148 0.0027 0.0032 0.0273 0.2752 0.0026 0.0133 0.0079 0.0031 0.0321 0.0052 0.0095 0.0061 0.0294 0.0290 0.0092 0.0060 0.0101 0.0048]; Best concatenated match so far is image #18 match_score_mean = Columns 1 through 9 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 24.2100 23.6129 Columns 10 through 18 17.8895 24.2892 19.4168 11.9477 11.1581 17.9150 23.4196 72.6555 4.7076 Columns 19 through 20 9.8430 16.3602 match_score_concat = Columns 1 through 9 0.0027 0.0022 0.0020 0.0021 0.0023 0.0021 0.0019 0.0017 0.0020 Columns 10 through 18 0.0026 0.0017 0.0026 0.0023 0.0022 0.0025 0.0021 0.0020 0.0018 Columns 19 through 24 0.0021 0.0025 0.0022 0.0020 0.0021 0.0017 match_score_concat_score = Columns 1 through 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 16 through 24 0 0 0 0 0 0 0 0 0 match_score_concat_latent = 1.0e-119 * Columns 1 through 9 false_pairs =[ 0.1433 0.1346 0.0425 0.0301 0.0463 0.0705 0.0272 0.0276 0.0148 0.0361 0.0296 0.0296 0.0370 0.0274 0.0169 0.0645 0.0343 0.0670 0.0410 0.0280 0.0564 0.0366 0.0285 0.0544]; Best concatenated match so far is image #8 match_score_mean = Columns 1 through 9 74.6205 200.0701 29.7006 35.8080 50.6190 54.3916 18.7936 12.0144 8.2434 Columns 10 through 18 29.3247 24.6262 13.8448 22.2136 12.9047 14.4410 78.8720 41.8343 101.1839 Columns 19 through 24 42.5187 15.9259 49.4639 21.9966 55.4732 54.5238 % lambda=0.8; match_score_concat = Columns 1 through 9 0.0017 0.0014 0.0020 0.0015 0.0014 0.0026 0.0017 0.0017 0.0019 Columns 10 through 18 0.0015 0.0019 0.0017 0.0020 0.0023 0.0023 0.0021 0.0021 0.0013 Columns 19 through 20 0.0018 0.0014 match_score_concat_score = Columns 1 through 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 16 through 20 0 0 0 0 0 match_score_concat_latent = 1.0e+58 * Columns 1 through 9 true_pairs=[ 0.0748 0.1411 0.0320 0.0366 0.2427 2.3736 0.0308 0.1348 0.0790 0.0380 0.2891 0.0595 0.0899 0.0608 0.2680 0.2642 0.0897 0.0607 0.1005 0.0506]; Best concatenated match so far is image #18 match_score_mean = Columns 1 through 9 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 24.2100 23.6129 Columns 10 through 18 17.8895 24.2892 19.4168 11.9477 11.1581 17.9150 23.4196 72.6555 4.7076 Columns 19 through 20 9.8430 16.3602 % false New model built with probe residual match_score_concat = Columns 1 through 9 0.0019 0.0021 0.0027 0.0027 0.0016 0.0021 0.0020 0.0025 0.0024 Columns 10 through 18 0.0025 0.0026 0.0019 0.0024 0.0024 0.0018 0.0016 0.0017 0.0025 Columns 19 through 21 0.0017 0.0017 0.0024 match_score_concat_score = Columns 1 through 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 16 through 21 0 0 0 0 0 0 match_score_concat_latent = 1.0e+58 * Columns 1 through 9 false_pairs =[ 0.3032 0.3615 1.2660 0.1480 0.2737 0.2355 0.4776 0.7755 0.1667 1.3084 0.4372 0.3552 0.7754 0.4239 0.2680 0.0904 0.3278 0.1975 0.3059 0.1886 0.4022]; Best concatenated match so far is image #5 match_score_mean = Columns 1 through 9 53.1260 31.9435 127.3200 9.9189 20.0325 19.9274 41.0432 104.1326 29.7027 Columns 10 through 18 46.8297 58.0350 34.4557 124.6325 28.9655 28.6450 5.2004 36.2410 9.2101 Columns 19 through 21 63.8876 10.1486 30.9080 % n_eigen experimens % 10 eigenvalues, 0.01 delta New model built with probe residual match_score_concat_latent = 1.0e+26 * Columns 1 through 9 true_pairs=[ 1.4837 1.4390 1.3607 1.4578 1.6701 3.1568 1.3883 1.4239 1.4278 1.3611 1.4864 2.0636 1.4650 1.4189 1.4027 1.3951 1.6479 1.3155 1.3180 1.3474]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 24.2100 23.6129 Columns 10 through 18 17.8895 24.2892 19.4168 11.9477 11.1581 17.9150 23.4196 72.6555 4.7076 Columns 19 through 20 9.8430 16.3602 false_pairs =[ 2.4029 1.4584 1.9474 3.9801 1.6489 1.9015 1.4778 1.3172 1.3080 1.9681 1.6126 2.9494 1.8725 2.8172 1.5004 3.1569 1.4263 1.6468 3.1604 1.8315 2.1615 2.4192 1.9628 1.5472 2.0021 2.0645 1.6448 2.2821 2.1118 2.0497 1.9792 1.5504 1.8908 1.8164 1.6760 1.7363 4.3540 1.7030 2.2981 1.4156 2.5018 2.1287 1.7026 1.5014 1.7192 2.0652 1.5183 2.0673 1.7647 1.9857 2.5545 3.1699 2.2207 1.7067 1.9842 3.4281 4.1640 1.6235 1.4775 1.6475 1.7112 2.2598 1.5346 1.6412 1.5887 1.6109 1.4952 1.8180 3.5813 2.7850 1.7261 1.9859 1.8837; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 58.5035 17.1857 44.9589 131.6900 26.2634 42.7654 37.0059 4.8837 8.8718 Columns 10 through 18 34.1570 64.3545 84.5819 52.5189 83.8828 13.4353 103.4349 12.2451 30.5634 Columns 19 through 27 85.9995 36.5586 29.8446 46.4763 36.8696 20.0696 31.2236 70.8226 60.7485 Columns 28 through 36 49.2152 55.4798 78.0106 34.0273 34.6363 31.9702 63.3291 62.5046 32.7547 Columns 37 through 45 118.2424 34.0910 56.9215 10.8395 60.6726 76.1542 30.4227 30.9701 36.6898 Columns 46 through 54 57.1989 17.4563 57.0911 34.7404 34.8068 52.3368 109.7453 53.1478 14.7292 Columns 55 through 63 31.8213 84.4144 154.5018 26.9527 24.1289 23.7021 32.9190 75.9565 33.0513 Columns 64 through 72 32.1790 26.3653 31.6535 19.5904 28.5570 127.6594 51.2365 23.9271 30.1141 Column 73 35.3216 % eigen=20 match_score_concat_latent = 1.0e+45 * Columns 1 through 9 true_pairs=[ 0.3962 0.4558 0.3582 0.3824 0.4811 1.9804 0.3676 0.4449 0.4044 0.3778 0.4957 0.6197 0.4122 0.3854 0.4612 0.4777 0.4706 0.3438 0.4024 0.3850]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 24.2100 23.6129 Columns 10 through 18 17.8895 24.2892 19.4168 11.9477 11.1581 17.9150 23.4196 72.6555 4.7076 Columns 19 through 20 9.8430 16.3602 match_score_concat_latent = 1.0e+45 * Columns 1 through 9 false_pairs =[ 0.7115 0.5472 0.5467 0.7157 0.4350 0.5743 0.6349 0.4646 0.5572 0.5516 0.5696 0.9107 1.1734 0.3748 0.8435 0.6660 0.6824 0.5856 0.4859 0.9985 0.4201 1.6039 0.5335 0.9352 0.6552 1.1442 0.4950 0.7379 0.7415 0.4209 0.6785]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 49.7692 29.1507 18.6309 40.4409 12.8817 44.4213 40.9799 30.1604 36.6839 Columns 10 through 18 38.2475 24.2168 63.3136 89.7421 14.5951 42.5939 28.4348 68.2861 21.3056 Columns 19 through 27 21.2254 66.8114 21.9226 122.8103 17.4835 57.9532 47.8902 41.7733 31.5854 Columns 28 through 31 51.9029 44.5140 13.4613 51.9458 % eigen=40 match_score_concat_latent = 1.0e+71 * Columns 1 through 9 true_pairs=[ 0.0866 0.1497 0.0735 0.0843 0.1596 1.5100 0.0740 0.1630 0.0935 0.1067 0.1985 0.1378 0.0891 0.0843 0.2093 0.1912 0.1110 0.0881 0.1050 0.0859] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 24.2100 23.6129 Columns 10 through 18 17.8895 24.2892 19.4168 11.9477 11.1581 17.9150 23.4196 72.6555 4.7076 Columns 19 through 20 9.8430 16.3602 match_score_concat_latent = 1.0e+71 * Columns 1 through 9 false_pairs =[ 0.2969 0.1033 0.2733 0.1344 0.4738 0.2751 0.6933 0.2216 0.6528 0.1649 0.8165 0.2747 0.9919 0.2102 2.1825 0.2637 0.4018 0.5939 0.4635 0.2252 0.1861]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 32.8978 7.8735 17.2623 17.0893 84.7704 29.1887 35.7051 17.3733 82.5335 Columns 10 through 18 21.6810 110.2405 38.2367 133.5213 19.4278 110.6062 34.3124 47.8902 73.4323 Columns 19 through 21 52.9594 26.6508 14.0012 % eigen=80 match_score_concat_latent = 1.0e+92 * Columns 1 through 9 true_pairs=[ 0.1303 0.2506 0.0441 0.0498 0.4672 4.8352 0.0446 0.2419 0.1331 0.0529 0.5559 0.0832 0.1545 0.0976 0.5188 0.5048 0.1489 0.1023 0.1771 0.0717]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 24.2100 23.6129 Columns 10 through 18 17.8895 24.2892 19.4168 11.9477 11.1581 17.9150 23.4196 72.6555 4.7076 Columns 19 through 20 9.8430 16.3602 match_score_concat_latent = 1.0e+92 * Columns 1 through 9 false_pairs =[ 0.3858 0.6802 0.5246 0.4526 0.5382 1.4255 0.3089 0.5083 1.0671 0.2478 0.6141 0.2913 0.7085 0.9697 0.4392 0.9517 0.6819 0.3670 1.0940 1.2419 2.7918 1.0166 0.7032 0.8389 0.8496 0.4138 0.8123 0.4664 0.3025 1.1298 0.6260 0.5473 2.2939 0.4406 0.4873 2.4713 0.8595]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 46.7719 41.6878 28.1031 24.4617 17.6233 147.3174 20.3440 20.6653 55.0782 Columns 10 through 18 6.7822 32.7462 20.3926 37.8806 32.9813 21.2684 56.9518 32.1469 13.4565 Columns 19 through 27 31.9644 82.1146 49.3637 36.1170 44.0416 50.1369 53.3062 27.0017 51.3821 Columns 28 through 36 17.9640 8.7776 64.5865 58.2384 28.7011 131.7805 27.1375 37.5715 43.4008 Column 37 51.4645 % same as above, but delta=0.1, not 0.01 match_score_concat_latent = 1.0e+93 * Columns 1 through 9 true_pairs=[ 0.0534 0.1023 0.0190 0.0213 0.1879 1.9317 0.0190 0.0994 0.0549 0.0229 0.2240 0.0355 0.0632 0.0403 0.2094 0.2040 0.0612 0.0422 0.0728 0.0302]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 24.2100 23.6129 Columns 10 through 18 17.8895 24.2892 19.4168 11.9477 11.1581 17.9150 23.4196 72.6555 4.7076 Columns 19 through 20 9.8430 16.3602 New model built with probe residual match_score_concat_latent = 1.0e+93 * Columns 1 through 9 false_pairs =[ 0.8958 0.8335 0.2605 0.1294 0.0833 0.2179 0.2111 1.2483 0.5506 0.3935 0.1337 0.1099]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 107.7769 113.8161 33.5098 24.4098 8.0558 43.0366 37.0288 53.8789 37.0621 Columns 10 through 12 67.8538 11.9197 16.1227 % now 0.001 true_pairs=[ 0.1128 0.2170 0.0380 0.0429 0.4051 4.1956 0.0384 0.2092 0.1151 0.0455 0.4819 0.0717 0.1338 0.0844 0.4497 0.4375 0.1289 0.0884 0.1532 0.0618]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 27.0275 12.6990 9.6159 24.2407 24.9617 97.1169 14.9779 24.2100 23.6129 Columns 10 through 18 17.8895 24.2892 19.4168 11.9477 11.1581 17.9150 23.4196 72.6555 4.7076 Columns 19 through 20 9.8430 16.3602 match_score_concat_latent = 1.0e+92 * Columns 1 through 9 false_pairs =[ 0.5731 0.4977 1.0103 0.4466 0.8843 1.0509 0.8124 1.3026 0.3559 0.3925 0.2442 0.8088 0.4872 1.5863 0.6977 0.7588 0.3684 2.2305 0.6812 0.6323 0.8191 0.8765 1.2225 0.4272 0.5923 0.4640 1.0640 0.3263 0.7130 0.3222 0.8902 3.6396 1.1919 0.3184 1.1545]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 9 30.3594 41.8886 78.1399 40.5240 31.8195 56.5951 93.3166 95.9040 14.2582 Columns 10 through 18 26.7595 11.9480 49.6820 27.3437 105.4827 34.0781 44.9622 22.9903 134.1690 Columns 19 through 27 27.3268 37.4590 56.3323 45.3782 33.5091 11.4224 27.9685 27.6672 53.4703 Columns 28 through 35 22.1437 26.0270 16.4446 89.9421 132.3261 59.5507 16.9896 93.9166 # translation and rotation, determinant, 8 points apart, GIP v1 ICP, with apparent bug, model of Fall applied to spring match_score_concat_latent = 1.0e+156 * Columns 1 through 7 true_pairs = 0.1 * [ 2.2416 1.0937 0.1914 2.3554 1.4115 0.8379 3.7418 3.7093 1.7078 1.3945 2.6659 2.1124 1.1908 3.1586 5.7729 1.3039 0.6460 0.1609 1.4695 0.2305 0.5099 0.5997 1.2670 2.3577 5.0103 2.8487 8.9882 3.3560 3.0638 3.5711 0.2957 1.7799 0.8300 2.5991 0.8446 2.6094 0.2022 1.3071 3.1913 1.8489 2.0667 4.9947 1.2572 4.2799 1.1893 1.2664 0.7432 1.0918 0.6922 0.5126 0.6848 3.4609 0.5301 1.1643 1.4820 0.8698]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 79.9203 209.9405 13.8210 40.1915 55.1448 63.5857 72.2942 Columns 8 through 14 216.2911 45.8258 110.2534 35.4438 180.1105 85.4431 140.1741 Columns 15 through 21 100.6247 31.5806 68.0425 6.0258 36.4308 32.2514 97.0436 Columns 22 through 28 50.6504 59.3188 127.3255 95.0576 27.2682 239.1873 51.7610 Columns 29 through 35 64.4368 73.0163 4.6679 39.4717 33.0816 142.3962 43.5773 Columns 36 through 42 34.9446 14.0819 53.7350 94.0958 145.7112 65.7015 169.1981 Columns 43 through 49 120.9968 113.2932 33.8054 22.6578 55.0881 37.6431 20.0878 Columns 50 through 56 31.4428 77.0208 56.1883 24.8618 24.0990 54.8530 23.8715 match_score_concat_latent = 1.0e+157 * Columns 1 through 7 false_pairs =[ 0.1705 0.2257 0.0814 0.7186 0.1604 0.0525 1.7181 0.0741 0.3109 0.5804 0.1983 0.3466 0.1164 0.1867 0.0826 0.5204 0.2889 0.3424 0.2117 0.4900 0.8655 0.2537 0.1459 0.1552 0.1032 0.4005 0.7441 0.1154 0.1382 0.3245 0.5815 0.1068 0.1534 0.2709 0.6942 0.1196 0.0917 0.5085 0.0924 0.2949 0.5308 0.0662 0.1654 0.4525 0.1200 0.3453 0.8255 0.6638 0.1246 0.1528 0.1058 0.1091 0.1150 0.5234 0.1915 0.1897 0.0684 0.4188 0.4085 0.3489 0.1585 0.5714 0.3965 0.1739 0.2420 0.6997 0.5743 0.1307 0.1560 0.2238 0.2090 0.1994 0.5968 0.3414 0.2870 0.3430 0.8316 0.1734 0.1376 0.2375 0.2651 0.0947 0.9816 0.3658 0.1313 0.2023 0.1718 0.6636 0.1467 0.1126 0.1005 0.1769 0.0948 0.1198 0.3612 0.1332 0.5511 0.8447 0.1054 0.7280 0.2260 0.0818 0.4761 0.2561 0.5988 0.1684 0.1166 1.5353 0.1241 0.2647 0.6580 0.1963 0.1506 0.0813 0.0996 1.6106 0.1394 0.0698 0.3148 0.1533 1.3346 0.5580 0.1133 0.1218 0.1115]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 158.9637 148.0571 86.9865 275.5089 263.2884 7.6024 785.1472 Columns 8 through 14 24.5957 84.8228 150.3892 105.0878 64.2261 35.1123 141.0231 Columns 15 through 21 27.0913 161.9148 67.9558 42.5351 87.3732 202.9183 425.7920 Columns 22 through 28 75.3817 130.8178 34.4190 285.1578 63.4668 145.9060 136.7996 Columns 29 through 35 45.9284 76.2578 155.0910 13.2016 108.4291 41.2124 136.8276 Columns 36 through 42 170.1228 82.9186 114.9046 58.2298 94.1504 138.7231 62.1279 Columns 43 through 49 117.9454 85.0107 208.5895 147.0110 162.6072 218.1704 57.1008 Columns 50 through 56 131.3098 60.2833 55.3815 19.1751 89.1338 235.7428 267.7762 Columns 57 through 63 46.3758 94.2651 188.3685 95.7295 53.9673 152.5812 167.5449 Columns 64 through 70 110.8672 54.9310 402.5546 268.9913 35.2755 31.2379 208.1920 Columns 71 through 77 114.5011 132.4036 169.8117 68.9997 494.5541 170.1266 402.3932 Columns 78 through 84 184.3978 74.4119 45.7040 131.7109 61.6284 250.8865 75.8516 Columns 85 through 91 125.8758 229.4942 28.6329 197.5996 125.9773 27.4347 70.9071 Columns 92 through 98 57.0712 10.7583 112.4829 46.1018 25.2807 210.6280 397.6203 Columns 99 through 105 78.2428 124.4489 48.7965 51.8096 111.3603 387.5274 77.9600 Columns 106 through 112 32.0930 17.5548 296.0620 137.4655 89.3816 130.7972 20.2618 Columns 113 through 119 42.1675 117.7937 123.7017 481.4636 151.0053 18.5737 86.3524 Columns 120 through 125 35.5339 320.1844 113.3335 53.7385 183.3373 225.2652 % after improvements 1.0e+156 * Columns 1 through 7 true_pairs =[ 0.4532 0.4386 0.2601 0.9277 0.3272 0.1558 0.3926 0.6319 0.4822 0.2399 3.5809 0.6630 0.3662 0.3895]; false_pairs =[ 0.8215 0.3186 0.4985 0.6372 0.3909 9.5334 0.5126 0.6626 0.5138 1.5178 0.9763 0.9284 0.8714 1.1520 0.9898 2.1434 3.6179 0.7118] % Springer Model (not cmplete, about 250 pairs) with sampling separation of 8 % tested on Spring set of real pairs versus random match_score_concat_latent = 1.0e+125 * Columns 1 through 7 true_pairs =[ 1.1130 1.0412 0.9382 1.1987 0.9507 0.7910 1.0013 1.0662 1.1581 0.9020 6.0698 1.0349 0.9267 0.9897 0.9381 1.2155 1.0423 0.9634 1.0334 0.8085 0.8750 0.8281 0.9615 0.9951 0.8164 0.8167 0.8292 1.0281 0.8005 0.8576 0.8530 0.9232 0.8528 1.1249 0.8804 0.9382 0.7984 1.5043 1.0322 0.9863 0.8426 6.7197 0.8286 0.8177 1.0606 1.1005 0.9665 0.8443 0.9668 1.0773 0.8317 0.8839] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 true_pairs =[ 22.2758 31.1435 5.9929 19.3497 11.1878 2.5681 9.5261 22.9328 19.5616 7.7996 73.9405 13.1098 6.0163 7.0072 11.0037 15.0657 15.8216 13.6433 16.1796 5.4793 5.1130 5.0140 8.1390 9.7264 2.3156 3.6813 6.2732 9.9414 4.5508 11.2261 4.1950 9.2071 3.3817 27.6725 17.4223 21.4861 4.3137 22.8047 8.9179 12.3390 3.6080 109.8160 8.0826 2.5329 10.3862 7.2762 6.0188 6.5765 6.7733 19.7253 9.0184 3.9288] Best mean match so far is image #25 * Preparing for ICP applied to image #106 - Sorting data types - Affine registration of:~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04472d334.abs ??? Error using ==> svd Input to SVD must not contain NaN or Inf. New model built with probe residual match_score_concat_latent = 1.0e+125 * Columns 1 through 7 v 1.2605 2.4573 1.0035 1.1650 1.1095 1.5957 1.1302 1.5327 1.4305 1.3331 1.3262 1.0158 1.0118 1.5114 1.9551 1.2059 1.1178 2.0798 2.4586 1.5072 1.0638 1.4004 2.3328 1.5879 1.2941 1.3263 1.2207 1.9724 1.0172 1.5793 1.0790 2.4553 1.2767 0.9875 2.9968 1.3482 1.1551 7.5101 1.0123 1.4296 1.8246 2.1481 1.5179 2.4418 1.3116 0.9555 1.2747 1.3738 1.1143 1.6543 1.5065 1.6913 2.1957 3.0429 1.0417 1.8119 1.7750 1.8776 3.2383 1.1970 2.4020 1.3918 5.8638 1.4003 0.9516 1.1974 0.9610 1.0932 2.7888 2.1290 1.3246 1.7002 1.2325 1.1944 1.2411 1.0176 1.1036 1.5364 1.7399 1.9240 1.0560 1.1990 1.3773 1.8931 6.3745 1.4034 7.8059 1.1554 1.2476 1.2918 1.7338 1.6154 9.0953 1.2569 1.3478 1.1159 1.5430 2.7590 1.9774 1.3731 1.0927 1.4437 1.8246 1.3138 2.1173 1.8209 1.0865 1.3510 1.5123 1.8231 0.9973 1.3194 1.0146 1.2816 3.4176 1.5054 1.2192 0.9548 2.1082 1.9818 1.0189 2.3494 0.9672 3.5120 2.4797 1.2171 1.3836 2.0057 1.2322 4.6501 3.3191 1.2707 1.3439 1.0295 1.5662 1.9546 1.7068 1.1001 1.1859 0.9575 1.5793 1.4398 1.7948 1.5150 1.1559 3.0838] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 false_pairs =[ 19.7142 115.8083 11.7963 25.7308 18.4047 41.6819 32.3097 39.7367 54.5344 29.7719 63.6660 27.5488 17.9729 35.8142 87.8150 59.6322 23.5127 105.6689 49.0573 21.3079 19.9307 40.7312 94.1172 30.9600 41.3890 40.9507 36.6018 42.7019 37.5308 50.8397 9.7871 51.1216 26.3426 11.2172 187.7649 30.4631 27.9038 244.2453 6.9112 13.0770 28.4097 80.5180 45.5663 46.4455 14.5190 6.2498 16.7777 67.7063 24.8702 49.7534 50.8268 38.5776 68.4110 115.2061 32.0614 53.4559 54.4854 71.3156 30.4022 33.3354 69.8911 24.1791 101.8946 25.8194 9.7541 21.0413 9.2269 20.2034 41.3344 52.2203 12.9045 23.1958 15.6263 33.2904 66.4759 8.3977 71.9931 49.1927 68.4722 103.3751 45.1795 15.1329 37.6986 109.5900 104.7898 60.0301 183.8432 30.8432 28.1943 25.8548 90.0667 33.5840 121.2930 13.6397 27.3032 26.8292 34.2511 26.5845 62.4035 31.6311 59.1771 83.0484 49.4595 28.4362 67.2323 119.0553 20.2210 28.6140 31.0863 41.9440 11.3150 70.6551 8.0800 38.8062 46.0249 88.8793 27.9409 34.0061 32.6795 57.4678 12.2687 99.0524 16.7672 197.5660 56.7799 36.9115 41.2540 71.2427 20.2392 90.0942 56.5023 25.6073 40.0995 55.0845 40.1146 94.5735 35.4861 19.9001 40.1085 44.4319 40.7451 35.5592 83.0668 49.3572 50.3964 49.6394] Best mean match so far is image #46 ??? Operation terminated by user during ==> mean 5 * Preparing for ICP applied to image #106 - Sorting data types - Affine registration of:~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04472d334.abs ??? Error using ==> svd Input to SVD must not contain NaN or Inf. % no translation, true, GIPv2 ICP 1.0e+126 * Columns 1 through 7 true_pairs =[ 0.1331 0.2764 0.1397 0.1731 0.1831 0.0947 0.1745 0.7268 0.1397 0.1049 3.3097 0.1220 0.1176 0.1021 0.1183 0.1295 0.5802 0.3449 0.1463 0.1475 0.0938 0.1580 0.4358 0.2301 0.1391] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 true_pairs =[ 67.4256 312.7544 17.0936 205.1212 155.9517 16.2763 113.2706 668.3448 74.4684 15.7928 767.3469 41.5230 33.1939 16.1457 48.3868 37.4864 709.3709 336.4411 68.3552 121.7351 13.1356 109.3246 371.3165 248.5114 109.1443] false/random match_score_concat_latent = false_pairs =[ 0.1218 0.1136 1.2401 0.5437 0.5951 1.2421 0.1293 0.3312 0.4499] Best concatenated match so far is image #1 match_score_mean = false_pairs = 1.0e+03 * [0.0326 0.0144 1.6644 0.7252 0.6270 1.7584 0.0435 0.3756 0.6362] GIP v1 ICP true 1.0e+125 * Columns 1 through 7 true_pairs =[ 1.2350 1.3894 0.9255 1.0716 0.9152 0.8753 1.3365 1.4044 1.1802 0.9300 5.8399 1.0012 0.9148 0.9645 0.9706 1.0681 0.9169 0.9010 0.9717 0.9054 0.8195 1.0518 0.9714]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 true_pairs =[ 23.5696 66.0244 8.5180 19.3172 21.2124 3.7910 23.6413 41.8917 10.3846 6.5910 72.6145 20.6304 10.5137 8.9215 9.4321 10.2768 17.7099 11.7455 10.2182 15.8901 4.2883 11.6746 11.2511]; false New model built with probe residual match_score_concat_latent = 1.0e+125 * Columns 1 through 7 false_pairs = [ 1.0865 3.3634 1.0996 8.9816 1.5651 1.8938 2.4338 2.3081 1.6540 1.9172 2.1917 2.5725 2.3713 3.7424 1.1583 1.2767 2.1508 1.9097]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 false_pairs = [ 18.0745 62.4047 35.6414 86.3434 85.1306 271.9675 41.4227 185.1500 61.5879 225.3703 47.9745 134.9276 27.8077 41.7894 19.4481 18.2813 244.1207 74.9877]; true 1.0e+131 * Columns 1 through 7 true_pairs =[ 0.3975 0.3818 0.4143 0.7749 0.4972 0.2920 0.9096 0.3735 0.8208 0.4171 0.9023 2.3840 0.3236 0.3298 0.4224 0.9509 0.3938 0.4089 1.2619 0.4425 0.7527 0.4817 0.3908 0.3230 0.4056 0.4418 0.3687 0.3622 0.9516 0.3936 0.3620 3.1141 0.3548 0.3684 0.3716 0.3613 0.3074 0.3965 0.3562 0.3539 0.3346 0.3049 0.3674 0.4369 0.3428 0.3330 0.3490 0.3073 0.3572 0.3416 0.3253 0.3347 0.3801 0.3320 0.3522 0.5596 0.4002 0.3167 0.5684 0.3749 0.3811 0.3200 2.3565 0.3109 0.3080 0.4184 0.3201 0.3281 0.3299 0.3336 0.4146 0.3549 0.3930 0.4444 0.3856 0.4411 0.3433] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 true_pairs =[ 117.1720 126.2110 64.0405 96.9090 34.0170 0 83.0960 29.2164 72.5026 40.1247 84.9490 281.2955 9.4981 14.8898 37.0502 59.4630 381.7047 41.3170 97.9681 46.8975 88.4275 26.5252 86.5649 10.2317 111.7654 79.7689 10.6468 63.1925 425.8835 51.8376 22.5931 88.4580 42.1569 8.5554 51.3623 14.3487 26.6836 527.9347 391.5125 93.9907 98.9867 16.5949 108.4508 235.0069 172.5127 118.8500 174.2814 24.9312 31.2462 304.0027 109.8703 94.4590 50.5703 16.4623 149.9938 629.8848 222.0868 148.2792 131.4909 7.3134 294.4058 39.1378 175.8963 17.2280 3.0883 13.1293 6.1584 120.9341 78.8116 143.5169 79.4720 211.2655 9.0615 88.6571 9.8284 63.9693 10.7651] false match_score_concat_latent = 1.0e+131 * Columns 1 through 7 false_pairs = [ 1.6648 0.5419 0.3859 1.3984 0.3968 0.8152 0.7687 0.5754 0.6048 0.5891 0.5984 0.4999 0.7700 0.4922 0.6321 0.5877 1.2633 0.7087 0.6434 0.4819 0.6001 0.4736 1.8381 0.8285 0.6243 0.8370 0.6809 0.5067 1.6722 0.4879 0.8779 0.4863 0.6054 1.1085 0.6334 0.5962 0.4587 0.6941 0.8299 0.5992 0.3584 0.5772 0.5758 0.5841 0.5388 0.4253 0.5704 0.4096 0.5588 0.6332 0.4530 0.6170 0.5833 0.5381 0.7949] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 false_pairs = [ 712.8858 28.0483 49.9721 25.9744 83.8231 189.7269 99.7335 117.2318 49.4288 26.9306 29.8121 34.0331 68.0617 159.9729 316.3293 413.9327 105.4327 35.5815 145.5238 222.2460 278.3644 57.8500 81.9764 154.4849 115.0602 75.3337 53.0159 62.3805 35.7656 161.9273 55.9681 19.6323 40.0532 54.5121 63.1157 39.2325 69.3628 48.8983 101.9405 78.9702 26.8156 22.5811 165.0863 329.7790 66.4964 133.5508 36.5559 72.7754 26.5525 63.7686 49.6470 93.1254 90.9187 280.4398 110.4550] %%%% y scale gradient True true_pairs = 1.0e-91 * [0.0241 0.0333 0.0164 0.0234 0.0295 0.2515 0.0551 0.0400 0.0291 0.0127 0.1466 0.0373 0.0220 0.0151 0.0805 0.0992 0.0205 0.0303 0.0784 0.0582 0.0474 0.1655 0.0429 0.0237 0.1141 0.0797 0.3176 0.0682 0.1467 0.1394 0.1558 0.6893 0.0558 0.0317 0.0706 0.1406 0.2752 0.0453 0.0124 0.0375 0.0283 0.1058 0.0182 0.0393 0.0129 0.0216 0.0269 0.0410 0.0838 0.0171 0.0173 0.0302 0.0374 0.0194 0.0341 0.0233 0.0464 0.0127 0.1230 0.1170 0.0127 0.0135 0.2110 0.0118 0.0155 0.3038 0.0320 0.0157 0.0195 0.0098 0.0861 0.0285 0.0912 0.0437 0.0385 0.0616 0.0532] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 11 0.0639 0.0514 0.0931 0.0813 0.0589 0.0531 0.1185 0.0389 0.0675 0.0770 0.0818 Columns 12 through 22 0.3968 0.0234 0.0259 0.0642 0.0977 0.0525 0.0233 0.0903 0.0325 0.0856 0.1406 Columns 23 through 33 0.0595 0.0113 0.1006 0.0500 0.1430 0.0320 0.4282 0.0550 0.1084 0.2066 0.0223 Columns 34 through 44 0.0611 0.0970 0.0677 0.0709 0.0266 0.0042 0.0118 0.0153 0.0562 0.0072 0.2082 Columns 45 through 55 0.0078 0.0091 0.0079 0.0170 0.0947 0.0060 0.0067 0.0102 0.0209 0.0121 0.0232 Columns 56 through 66 0.0139 0.0313 0.0056 0.0680 0.0973 0.0080 0.0049 0.0651 0.0095 0.0114 0.0888 Columns 67 through 77 0.0110 0.0077 0.0091 0.0032 0.0438 0.0122 0.0321 0.0755 0.1187 0.0402 0.0229 false_pairs = 1.0e-91 * [ 0.0416 0.0583 0.0990 0.0761 0.1931 0.0621 0.0609 0.0527 0.0864 0.0676 0.0620 0.0377 0.0641 0.1413 0.0922 0.2989 0.0436 0.0679 0.0500 0.0911 0.3520 0.1374 0.0452 0.0726 0.0674 0.0379 0.0478 0.0615 0.0836 0.1078 0.0773 0.1060 0.0910 0.0487 0.2110 0.2087 0.0919 0.0786 0.1029 0.0587 0.0650 0.0241 0.0839 0.0180 0.0723 0.3130 0.0606 0.4605 0.1029 0.0874 0.0351 0.0810 0.0525 ]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 11 0.0234 0.0389 0.0543 0.0533 0.1260 0.0367 0.0352 0.0481 0.1019 0.1387 0.0432 Columns 12 through 22 0.0167 0.0441 0.0890 0.0441 0.1425 0.0276 0.0648 0.0228 0.1000 0.1811 0.0845 Columns 23 through 33 0.0522 0.0585 0.1091 0.0260 0.0336 0.0973 0.0521 0.0535 0.0274 0.0295 0.1445 Columns 34 through 44 0.0392 0.0854 0.1949 0.0280 0.0654 0.1752 0.0261 0.0530 0.0119 0.0607 0.0141 Columns 45 through 50 0.0650 0.1306 0.1837 0.1282 0.0602 0.0462 %% both x and y deriva combined (with BUGS) New model built with probe residual match_score_concat_latent = true_pairs = 1.0e-84 * [ 0.0178 0.0248 0.0114 0.0197 0.0259 0.1761 0.0441 0.0288 0.0228 0.0107 0.0639 0.0514 0.0931 0.0813 0.0589 0.0531 0.1185 0.0389 0.0675 0.0770] false_pairs = 1.0e-84 * [ 0.0636 0.1911 0.1909 0.2462 0.0657 0.0223 0.0233 0.0407 0.0315 0.0771 0.0694 0.0289 0.0594 0.0372 0.0362 0.0274 0.0648 0.0471 0.8993 0.0367 0.0734 0.0344 0.6787 0.0466 0.1262 0.0235 0.0230 0.0520 0.0956 0.0570 0.0606 0.1271 0.0719 0.0454 0.0988 0.0230 0.0487 0.0410 0.0524 0.0687 0.0389 0.0386 0.1209 0.0467 0.0275 0.0941 0.0421 0.0765 0.0436] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 11 0.0639 0.1261 0.1006 0.1034 0.0571 0.0164 0.0255 0.0449 0.0292 0.1233 0.0533 Columns 12 through 22 0.0282 0.0879 0.0430 0.0213 0.0319 0.0938 0.0904 0.1426 0.0454 0.1673 0.0308 Columns 23 through 33 0.1317 0.0463 0.0819 0.1251 0.0293 0.0320 0.0724 0.0420 0.2192 0.0815 0.0616 Columns 34 through 44 0.0477 0.0483 0.0263 0.1304 0.0542 0.0958 0.0615 0.0358 0.0237 0.1067 0.0914 Columns 45 through 49 0.0181 0.1329 0.0439 0.1197 0.0368 %% both x and y derivatives combined (withOUT BUGS) match_score_concat_latent = true_pairs = [0.0757 0.1027 0.0373 0.1510 0.1560 0.3991 0.1565 0.1154 0.1054 0.042] Best concatenated match so far is image #1 match_score_mean = 0.0639 0.0514 0.0931 0.0813 0.0589 0.0531 0.1185 New model built with probe residual match_score_concat_latent = false_pairs = [ 0.1193 0.2219 0.2581 0.3607 0.6544 0.2431 0.1271 0.2183 0.4354 0.2834] Best concatenated match so far is image #1 match_score_mean = 0.0132 0.1289 0.0548 0.0390 0.1397 0.0559 0.0227 0.0283 0.0547 0.0581 # Intial go at MSD, same as training set fiest, then random (bad test) true/identical to training: match_score_concat_latent = 1.0e-206 * 0.7478 0.7477 0.7478 0.7478 0.7477 0.7477 0.7477 0.7477 0.7477 0.7477 Best concatenated match so far is image #1 match_score_mean = 0.0885 0.3390 0.1915 0.1721 0.1048 0.0389 0.0885 0.1730 0.3585 0.0759 random match_score_concat_latent = 1.0e-202 * 0.1541 0.2139 0.2425 0.2064 0.1541 0.1796 Best concatenated match so far is image #1 match_score_mean = 0.1142 0.1003 0.0231 0.0935 0.0314 0.1741 Best mean match so far is image #3 unssen match_score_concat_latent = 1.0e-202 * Columns 1 through 10 0.1224 0.1842 0.1315 0.1252 0.1887 0.2159 0.1294 0.1442 0.1738 0.1486 Columns 11 through 20 0.2488 0.3199 0.2775 0.2275 0.1960 0.1690 0.2500 0.2813 0.2393 0.1364 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0.0207 0.1379 0.0822 0.2953 0.0686 0.4630 0.4017 0.0107 0.0345 0.0316 Columns 11 through 20 0.2371 0.0518 0.0164 0.1038 0.1211 0.0180 0.1155 0.0646 0.0718 0.3882 % fall semester, prooper, as above true_pairs= 1.0e-45 * [0.0835 0.0515 0.0989 0.1371 0.0996 0.0424 0.0865 0.0454 0.0497 0.0500]; Best concatenated match so far is image #1 match_score_mean = 0.0885 0.3390 0.1915 0.1721 0.1048 0.0389 0.0885 0.1730 0.3585 0.0759 match_score_concat_latent = false_pairs= 1.0e-45 * [ 0.0543 0.0484 0.0280 0.0774 0.0569 0.0580 0.0733 0.0685 0.0559 0.0664 0.0584 0.0655 0.0620 0.0383 0.0742 0.1426 0.0518 0.0333 0.0794 0.0777 0.0861 0.0315 0.0805 0.1011 0.0532 0.0726 0.0859 0.1275 0.0822 0.0876 0.0753 0.0836 0.0409 0.0682 0.0888 0.0536 0.0987 0.0592 0.0848 0.0698 0.0529 0.0585 0.0650 0.0408 0.0610 0.0826 0.0384 0.0989 0.0745 0.0634 0.0645 0.1037 0.0654 0.0401 0.0502 0.0760 0.0959 0.0705 0.0441 0.0359 0.0235 0.0871 0.0581 0.0830 0.0521 0.0639 0.0775 0.0689 0.0552 0.0862 0.1367 0.0758 0.0786 0.0529 0.0831 0.0665 0.0908 0.0781 0.0611 0.0906 0.0993 0.0460 0.0746 0.0490 0.0526 0.0539 0.0870 0.0821 0.0671 0.1035 0.0971 0.0890 0.0889 0.0633 0.0640 0.0630 0.0806 0.0685 0.0697 0.0774]; Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0.0807 0.1505 0.1054 0.0681 0.0290 0.1446 0.2542 0.1132 0.0498 0.0427 Columns 11 through 20 0.0048 0.3118 0.1152 0.1689 0.0171 0.0665 0.0444 0.0196 0.0372 0.0184 Columns 21 through 30 0.0166 0.1647 0.1266 0.3364 0.0701 0.2017 0.0486 0.0020 0.1736 0.0683 Columns 31 through 40 0.1547 0.1631 0.0594 0.2719 0.2382 0.2025 0.1140 0.1038 0.1764 0.1902 Columns 41 through 50 0.0232 0.2254 0.1568 0.1564 0.0482 0.2281 0.0330 0.1506 0.0471 0.0645 Columns 51 through 60 0.0587 0.1043 0.0449 0.0504 0.1743 0.0372 0.0184 0.0970 0.1175 0.1416 Columns 61 through 70 0.4427 0.2079 0.0330 0.1096 0.1916 0.1297 0.2780 0.0520 0.0435 0.1573 Columns 71 through 80 0.0531 0.0021 0.0217 0.1622 0.0665 0.1517 0.0959 0.1621 0.0098 0.0545 Columns 81 through 90 0.3035 0.2890 0.0774 0.3568 0.5186 0.6508 0.3132 0.4069 0.3254 0.1200 Columns 91 through 100 0.0708 0.3038 0.1138 0.0934 0.2159 0.0433 0.2790 0.4019 0.0468 0.1141 -=================-=================-=================-=================-=================-================= GMDS tests random sum_ux = 32.6678 31.1429 34.3079 33.1793 34.0419 28.0082 36.4221 sum_uy = 31.9023 33.8917 37.4061 33.6113 33.8452 36.3723 29.3923 f_acc = 2.7636 2.9069 3.2184 7.0357 78.8340 0.9537 31.7030 rmsdist_acc = 1.6624 1.7050 1.7940 2.6525 8.8789 0.9766 5.6305 maxdist_acc = 9.8143 9.8091 11.1569 15.4335 61.5681 4.9297 29.5838 false_pairs = 1.0e+05 * [ 0.0308 0.0439 0.0270 0.1883 1.0496 0.8053 0.9199 0.3491 0.9303] sum_ux = 34.5782 34.9779 39.4516 32.4753 35.7454 31.2681 32.8290 35.3017 30.7568 sum_uy = 33.5568 30.9491 34.2457 34.1796 25.8024 31.3421 29.7248 39.5632 33.4116 f_acc = 1.2317 1.7543 1.0810 7.5333 41.9834 32.2104 36.7975 13.9655 37.2113 rmsdist_acc = 1.1098 1.3245 1.0397 2.7447 6.4795 5.6754 6.0661 3.7370 6.1001 maxdist_acc = 6.1663 5.2601 6.8920 14.0138 36.3429 33.2624 27.3032 23.3474 44.6073 correct sum_ux = 31.9259 33.4665 32.5390 37.4033 sum_uy = 35.6071 34.3490 31.6017 32.9031 f_acc = 0.4835 0.8717 8.6144 7.7355 rmsdist_acc = 0.6954 0.9336 2.9350 2.7813 maxdist_acc = 3.2090 8.9898 8.4369 13.8531 local_stress_sum = 1.0e+04 * 0.1209 0.2179 2.1536 sum_ux = 31.9259 33.4665 32.5390 sum_uy = 35.6071 34.3490 31.6017 f_acc = 0.4835 0.8717 8.6144 rmsdist_acc = 0.6954 0.9336 2.9350 maxdist_acc = 3.2090 8.9898 8.4369 true_pairs = 1.0e+04 *[ 0.1209 0.2813 2.1536 1.9339 0.0849 1.1021 4.5330 1.9175 6.2653] sum_ux = 31.9259 35.3425 32.5390 37.4033 32.6844 31.1426 29.3369 34.4090 31.8121 sum_uy = 35.6071 34.7487 31.6017 32.9031 32.6022 32.3572 27.4989 32.7534 32.9592 f_acc = 0.4835 1.1251 8.6144 7.7355 0.3397 4.4085 18.1320 7.6700 25.0612 rmsdist_acc = 0.6954 1.0607 2.9350 2.7813 0.5828 2.0996 4.2582 2.7695 5.0061 maxdist_acc = 3.2090 9.2102 8.4369 13.8531 5.3469 13.7891 25.3119 14.5614 31.6051 images 3 5-6 (with and without smoothing) =================== after bug fixes random local_stress_sum = false_pairs = 1.0e+04 *[ 0.1915 0.1055 0.2687 0.1462 0.7822 0.1617 1.1590 0.2950 0.4078 0.5726] sum_ux = 32.9321 35.6030 31.5456 34.9178 32.1677 35.9222 31.3427 36.4203 31.2999 31.6891 sum_uy = 34.5341 33.1055 33.5769 32.7724 35.3096 33.6105 34.4030 34.0133 31.1005 33.9838 f_acc = false_pairs = [ 0.7661 0.4221 1.0748 0.5848 3.1288 0.6470 4.6358 1.1799 1.6312 2.2906] rmsdist_acc = 0.8753 0.6497 1.0367 0.7647 1.7688 0.8043 2.1531 1.0862 1.2772 1.5135 maxdist_acc = 4.2927 2.9877 7.1232 3.4101 12.5550 3.9031 10.4620 4.4910 6.8613 8.7373 True 'unseen_targets_expression_pairs' local_stress_sum = true_pairs = 1.0e+03 *[ 1.3272 1.6752 2.8014 0.0000 0.6603 0.9952 1.1964 0.4260] sum_ux = 36.0996 37.3684 34.6988 30.0000 35.4115 32.4293 30.1785 34.5365 sum_uy = 34.1307 32.1298 33.0201 34.0000 33.3482 34.6630 30.4962 34.4880 f_acc = local_stress_sum = [0.5309 0.6701 1.1206 0.0000 0.2641 0.3981 0.4786 0.1704]; rmsdist_acc = 0.7286 0.8186 1.0586 0.0000 0.5139 0.6309 0.6918 0.4128 maxdist_acc = 3.9301 5.4595 4.4121 0.0000 3.8742 3.8929 2.9131 1.9066 'targets_expression_pairs' local_stress_sum = 1.0e+03 * Columns 1 through 11 true_pairs = 1.0e+03 *[1.1696 1.1867 2.1815 0.3721 2.1204 1.2531 1.5526 0.6728 0.9614 1.2883] sum_ux = Columns 1 through 11 33.5993 31.4473 33.6043 24.3529 31.6078 35.5419 35.8267 33.6463 30.8970 34.0304 34.4882 Column 12 31.9626 sum_uy = Columns 1 through 11 30.8571 32.4144 34.4809 28.0770 32.8076 35.1088 32.5218 33.0432 33.9644 33.7212 33.3311 Column 12 33.1582 f_acc = Columns 1 through 11 true_pairs = [0.4679 0.4747 0.8726 0.1488 0.8482 0.5013 0.6211 0.2691 0.3846 0.7256 0.3937 0.5153]; rmsdist_acc = Columns 1 through 11 0.6840 0.6890 0.9341 0.3858 0.9210 0.7080 0.7881 0.5188 0.6201 0.8518 0.6274 Column 12 0.7179 maxdist_acc = Columns 1 through 11 3.2697 4.6918 8.3448 2.7623 3.9847 4.2131 4.4233 3.2735 3.0690 6.1010 3.2179 Column 12 3.3949 OLD: True -'targets_fall_and_more' local_stress_sum = true_pairs = 1.0e+03 * 9.7377 1.2672 0.5723 1.5781 0.5493 sum_ux = 31.6797 29.5393 33.9974 31.8617 33.2540 sum_uy = 32.9328 34.9936 30.8745 33.3199 32.8255 f_acc = 3.8951 0.5069 0.2289 0.6312 0.2197 rmsdist_acc = 1.9736 0.7120 0.4784 0.7945 0.4687 maxdist_acc = 9.8317 4.1958 2.4797 3.7539 1.9504 skip size now 5 % [images_list]=load_images('targets_expression_pairs'); correct matches local_stress_sum = true_pairs = 1.0e+04 * [ 0.1171 0.1713 0.0955 0.1081 0.1152 0.1036 0.0895 0.0424 0.0681 0.0775 0.0869 0.1263 0.0443 0.0722 1.2786 0.0545] sum_ux = Columns 1 through 7 35.3765 29.9490 31.6483 32.7466 33.7373 34.7697 34.6306 Columns 8 through 14 33.7416 26.4333 35.1823 34.1520 35.4912 32.9488 33.3220 Columns 15 through 16 28.1859 31.0124 sum_uy = Columns 1 through 7 32.7986 32.1002 31.9492 32.6528 30.8056 32.2795 34.6805 Columns 8 through 14 36.1655 33.5282 29.3703 32.9476 35.3347 35.3256 33.6135 Columns 15 through 16 32.6933 33.1762 true_pairs = [0.4682 0.6851 0.3818 0.4324 0.4607 0.4142 0.3580 0.1694 0.2724 0.3099 0.3477 0.5053 0.1771 0.2890 0.2180] rmsdist_acc = Columns 1 through 7 0.6843 0.8277 0.6179 0.6576 0.6787 0.6436 0.5984 Columns 8 through 14 0.4116 0.5219 0.5567 0.5897 0.7109 0.4208 0.5376 Columns 15 through 16 2.2615 0.4669 maxdist_acc = Columns 1 through 7 3.3034 3.6779 5.0389 4.2033 3.1898 4.0818 2.7602 Columns 8 through 14 3.0762 2.0329 4.8950 3.0180 4.0253 2.0498 2.7418 Columns 15 through 16 12.9183 3.2332 random local_stress_sum = false_pairs = 1.0e+04 [ 0.1442 0.0961 0.1052 0.1284 0.5327 0.2038 0.5262 0.2080 0.3329 0.4416 4.4629 0.2698 0.2647 0.2154 0.2055] sum_ux = Columns 1 through 7 33.2860 35.3531 34.8495 33.0838 33.8639 36.9599 33.6153 Columns 8 through 14 33.8304 30.4867 33.1771 35.0016 29.6682 27.5545 29.0637 Column 15 36.1239 sum_uy = Columns 1 through 7 35.3899 30.0741 35.0178 33.7892 34.2362 31.6569 36.5104 Columns 8 through 14 32.9957 35.9506 31.1937 38.5092 33.6614 36.6027 36.5843 Column 15 35.0795 f_acc = Columns 1 through 7 false_pairs = [0.5767 0.3843 0.4208 0.5135 2.1306 0.8152 2.1048 0.8319 1.3315 1.7663 17.8516 1.0793 1.0588 0.8615 0.8222] rmsdist_acc = Columns 1 through 7 0.7594 0.6199 0.6487 0.7166 1.4597 0.9029 1.4508 Columns 8 through 14 0.9121 1.1539 1.3290 4.2251 1.0389 1.0290 0.9282 Column 15 0.9067 maxdist_acc = Columns 1 through 7 5.2270 2.6004 2.5104 2.9917 6.6453 5.2704 8.7418 Columns 8 through 14 2.9350 5.8899 6.6941 27.8845 4.7731 6.3095 5.3126 Column 15 3.3164 skip size now 2 % [images_list]=load_images('targets_expression_pairs'); random f_acc = false_pairs = [4.5113 3.4247 0.3961 2.0070 1.2704 2.9823 1.8546 0.7926 1.7693 0.9683 1.7065 0.2427 3.4449 0.8295 3.1985 0.4064 2.3892 0.5588 0.5774 0.7512 1.2906 3.3348 1.1378 0.3485 0.4672 1.5396 1.2911 1.7044 0.6211 1.7864 4.8265 4.4867 1.0134 0.6337 1.1533 2.0367 0.9687 5.8822 5.2696 0.5655 1.5216 5.6433 1.6275 0.7655 0.7062 1.0771 2.3081 2.3754 2.3970 0.8836 1.3215 0.5133 1.6340 1.2911 0.6814 1.3119 1.4894 0.5925 0.7552 2.0609 1.3160 1.0718 32.5832 2.5934 10.9713 0.8368 1.0910 0.4563 0.8468 2.4290 0.2091 0.8136 2.5823 0.7917 0.5367 0.6635 1.1136 2.1412 4.2438 0.5610 1.6113 1.9685 0.7688 7.5033 1.1571 2.2284 1.0963 2.6323 1.4055 0.8065 0.5900 0.6682 1.0076 0.5837 0.3767 0.9634 12.6310 2.8967 0.8007 1.9611 1.3793 0.8118 4.7302 0.3502 0.8014 1.0716 0.6618 1.3534 10.5468 1.2175 7.1123 1.2246 1.7824 1.2694 0.7602 1.0223 0.7072 1.7233 1.0558 1.0224 0.6633 0.7335 1.8858 1.2868 1.0011 1.4613 0.8542 1.6895 0.8437 1.4384 0.2839 1.2405 1.5414 6.2328 0.6943 0.7830 1.3726 0.5479 0.6224 1.1868 0.9739 1.5673 0.6723 0.9499 6.7562 1.1039 0.8255 1.4898 1.1307 0.7666 0.7930 3.4448 0.9115 0.7967 0.9459 5.3395 3.4886 4.9916 3.7578 1.4741 1.5465 1.0707 2.7628 1.2790 1.9314 0.3908 1.7497 0.9440 1.2379 0.8112 0.7795 1.1612 1.9614 2.3611 2.3147 0.9710 0.7915 2.7301 1.2764 1.9365 1.9717 0.9307 0.8254 0.6500 0.5612 0.2904 1.3339 5.2906 7.5403 0.6213 1.5117 1.2789 2.5833 1.3165 1.8248 1.2543 1.1913 1.1753 11.2486 0.6158 1.1118 1.8066 1.4065 1.1154 0.7876 0.7441 2.1189 0.6077 1.2648 1.2213 0.6260 0.8335 1.7753 2.2091 0.9084 2.7760 1.4399 2.0311 0.7836 0.5973 4.4829 2.6669 1.5629 12.0555 2.1947 0.9060 0.7313 0.8987 2.5578 0.7298 0.2834 3.4870 0.3291 0.4934 0.9600 0.4604 2.0283 2.5258 0.6834 1.5262 2.2128 1.1805 1.0117 4.2444 0.4895 0.6480 13.0996 0.6098 10.0214 2.1615 0.7848 2.2148 1.5695 0.7401 0.7095 6.9993 0.2766 0.9250 1.7060 5.8734 0.8525 0.8350 7.3691 1.2239 1.3973 8.4036 1.1120 0.4064 1.8773 0.6799 1.0966] rmsdist_acc = Columns 1 through 7 2.1240 1.8506 0.6294 1.4167 1.1271 1.7269 1.3618 Columns 8 through 14 0.8903 1.3301 0.9840 1.3063 0.4926 1.8561 0.9108 Columns 15 through 21 1.7884 0.6375 1.5457 0.7475 0.7599 0.8667 1.1360 Columns 22 through 28 1.8261 1.0667 0.5903 0.6835 1.2408 1.1363 1.3055 Columns 29 through 35 0.7881 1.3365 2.1969 2.1182 1.0067 0.7961 1.0739 Columns 36 through 42 1.4271 0.9842 2.4253 2.2956 0.7520 1.2335 2.3756 Columns 43 through 49 1.2757 0.8749 0.8403 1.0378 1.5193 1.5412 1.5482 Columns 50 through 56 0.9400 1.1496 0.7164 1.2783 1.1363 0.8255 1.1454 Columns 57 through 63 1.2204 0.7698 0.8690 1.4356 1.1472 1.0353 5.7082 Columns 64 through 70 1.6104 3.3123 0.9147 1.0445 0.6755 0.9202 1.5585 Columns 71 through 77 0.4573 0.9020 1.6069 0.8898 0.7326 1.2089 0.7572 Columns 78 through 84 0.8145 1.0553 1.4633 2.0600 0.7490 1.2694 1.4030 Columns 85 through 91 0.8768 2.7392 1.0757 1.4928 1.0470 1.6224 1.1855 Columns 92 through 98 0.8980 0.7681 0.8174 1.0038 0.7640 0.6138 0.9815 Columns 99 through 105 3.5540 1.7020 0.8948 1.4004 1.1744 0.9010 2.1749 Columns 106 through 112 0.5918 0.8952 1.0352 0.8135 1.1634 3.2476 1.1034 Columns 113 through 119 2.6669 1.1066 1.3351 1.1267 0.8719 1.0111 0.8409 Columns 120 through 126 1.3127 1.0275 1.0111 0.8144 0.8565 1.3733 1.1344 Columns 127 through 133 1.0006 1.2088 0.9242 1.2998 0.9185 1.1994 0.5328 Columns 134 through 140 1.1138 1.2415 2.4966 0.8332 0.8849 1.1716 0.7402 Columns 141 through 147 0.7889 1.0894 0.9869 1.2519 0.8200 0.9746 2.5993 Columns 148 through 154 1.0507 0.9086 1.2206 1.0633 0.8756 0.8905 1.8560 Columns 155 through 161 0.9547 0.8926 0.9726 2.3107 1.8678 2.2342 1.9385 Columns 162 through 168 1.2141 1.2436 1.0347 1.6622 1.1309 1.3897 0.6252 Columns 169 through 175 1.3227 0.9716 1.1126 0.9006 0.8829 1.0776 1.4005 Columns 176 through 182 1.5366 1.5214 0.9854 0.8897 1.6523 1.1298 1.3916 Columns 183 through 189 1.4042 0.9647 0.9085 0.8062 0.7491 0.5389 1.1549 Columns 190 through 196 2.3001 2.7460 0.7882 1.2295 1.1309 1.6073 1.1474 Columns 197 through 203 1.3508 1.1200 1.0915 1.0841 3.3539 0.7848 1.0544 Columns 204 through 210 1.3441 1.1860 1.0561 0.8875 0.8626 1.4556 0.7795 Columns 211 through 217 1.1246 1.1051 0.7912 0.9130 1.3324 1.4863 0.9531 Columns 218 through 224 1.6661 1.2000 1.4252 0.8852 0.7729 2.1173 1.6331 Columns 225 through 231 1.2502 3.4721 1.4815 0.9518 0.8552 0.9480 1.5993 Columns 232 through 238 0.8543 0.5323 1.8674 0.5737 0.7023 0.9798 0.6785 Columns 239 through 245 1.4242 1.5893 0.8267 1.2354 1.4875 1.0865 1.0058 Columns 246 through 252 2.0602 0.6997 0.8050 3.6193 0.7809 3.1657 1.4702 Columns 253 through 259 0.8859 1.4882 1.2528 0.8603 0.8423 2.6456 0.5259 Columns 260 through 266 0.9618 1.3062 2.4235 0.9233 0.9138 2.7146 1.1063 Columns 267 through 273 1.1821 2.8989 1.0545 0.6375 1.3701 0.8245 1.0472 maxdist_acc = Columns 1 through 7 12.3640 9.8211 3.5667 13.7907 6.6934 7.0047 8.0341 Columns 8 through 14 3.2178 4.7957 3.7474 4.8524 3.1773 8.0735 3.4199 Columns 15 through 21 10.6926 3.0493 7.0457 3.5245 2.5166 4.4724 5.0606 Columns 22 through 28 7.0429 4.6827 2.2808 3.6070 5.8492 5.9143 9.2974 Columns 29 through 35 3.0401 5.8164 14.8764 10.9202 4.5321 4.3232 5.0758 Columns 36 through 42 5.5324 3.9527 17.6159 15.9674 3.8332 5.4830 9.8965 Columns 43 through 49 5.2183 4.4037 4.3544 4.1527 5.2934 8.2103 10.7218 Columns 50 through 56 3.9433 5.1560 2.6115 9.4968 3.5889 4.0263 4.1286 Columns 57 through 63 4.6419 5.4588 4.5473 6.2772 4.2040 3.9439 39.7528 Columns 64 through 70 8.5304 13.7886 5.2069 3.4863 3.2342 3.5762 24.7907 Columns 71 through 77 3.6332 3.4486 6.1294 4.7460 4.4776 5.7372 3.3622 Columns 78 through 84 5.1465 3.1524 7.7829 10.0172 3.5157 6.0041 7.0191 Columns 85 through 91 4.2889 18.0687 5.0263 6.5400 5.0868 7.5297 5.2691 Columns 92 through 98 3.6532 3.4759 3.7306 6.3421 5.2397 2.9781 4.4640 Columns 99 through 105 21.2003 7.6291 3.4007 4.1718 4.1346 3.7963 13.6817 Columns 106 through 112 2.9480 4.5405 4.1633 4.2071 5.8682 25.0560 4.3829 Columns 113 through 119 15.1846 4.9030 4.7234 3.9519 3.8145 4.4880 3.4088 Columns 120 through 126 6.3042 4.7665 5.5492 5.1370 4.1755 5.3241 3.4115 Columns 127 through 133 5.2481 4.4385 5.1180 5.0939 3.2053 10.0919 2.8217 Columns 134 through 140 3.6715 10.4239 17.4152 4.6690 4.1834 5.1827 3.5152 Columns 141 through 147 3.5667 4.3751 5.5940 5.2468 3.4571 6.1753 16.4116 Columns 148 through 154 6.4333 3.2373 5.3190 4.6020 3.4157 5.8394 11.6215 Columns 155 through 161 5.5437 4.2182 8.7191 11.8986 6.8394 15.3552 11.4089 Columns 162 through 168 4.2543 4.7530 4.8652 5.5037 5.6847 4.9528 2.3752 Columns 169 through 175 5.8332 5.3668 5.1553 5.6350 5.2653 4.9688 5.5791 Columns 176 through 182 4.9320 7.1466 6.6694 3.4748 6.0586 4.1656 4.8323 Columns 183 through 189 5.4926 5.9997 3.3275 3.6147 3.5023 2.4910 7.4401 Columns 190 through 196 11.2394 11.9831 4.4652 7.1317 4.6420 8.5754 4.0085 Columns 197 through 203 6.3182 6.9497 5.7147 4.4888 16.5478 6.0784 5.7079 Columns 204 through 210 4.8471 8.5510 6.8611 3.9404 5.1579 7.1532 2.9074 Columns 211 through 217 6.1069 5.6282 3.7465 4.5594 5.6731 5.2303 3.9296 Columns 218 through 224 8.1689 6.4602 7.1339 3.4256 4.7498 10.3908 6.2579 Columns 225 through 231 5.5118 16.2479 5.5904 4.3232 4.1113 4.5491 6.4992 Columns 232 through 238 4.0997 2.4449 7.4944 2.8516 3.3968 6.0192 2.2671 Columns 239 through 245 7.7822 6.1082 3.9264 4.2730 6.1046 7.9356 6.1380 Columns 246 through 252 12.9365 3.4992 3.9821 14.6103 3.7040 18.7938 7.1775 Columns 253 through 259 4.3308 4.8221 6.6621 3.5673 3.4902 15.0163 2.6930 Columns 260 through 266 4.6656 8.1499 15.4710 5.0338 3.8275 19.8200 5.5745 Columns 267 through 273 4.3889 14.5505 4.1077 2.3752 6.0351 3.3807 4.4906 * Preparing for ICP applied to image #548 - Sorting data types - Affine registration of:~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04309d247.abs local_stress_sum = 1.0e+03 *[ 1.1962 4.8538 5.1723 1.6487 4.2681 3.8150 2.3772] sum_ux = 33.0498 31.5982 33.0169 35.1694 38.0943 35.8387 34.3361 sum_uy = 34.0281 34.7517 33.9116 31.4801 35.2570 35.3546 33.0439 f_acc = false_pairs1 = [ 0.4785 1.9415 2.0689 0.6595 1.7073 1.5260 0.9509] rmsdist_acc = 0.6917 1.3934 1.4384 0.8121 1.3066 1.2353 0.9751 maxdist_acc = 3.1690 8.9941 8.7456 4.1416 4.9245 3.7343 5.2375 local_stress_sum = 1.0e+04 * Columns 1 through 7 0.1297 0.0704 0.1223 0.1545 0.5862 0.3966 1.8995 Columns 8 through 14 0.4705 0.3134 1.3678 4.9259 0.3983 0.3316 0.3643 Columns 15 through 21 0.1995 0.2957 0.3116 0.3412 0.2511 2.3625 0.4777 Columns 22 through 23 0.3755 0.2096 sum_ux = Columns 1 through 7 32.7952 28.5686 27.0607 31.1549 33.1865 37.1461 30.7319 Columns 8 through 14 31.5575 35.9311 30.8799 33.5344 37.6185 34.2992 29.0022 Columns 15 through 21 35.5428 36.1394 35.1805 34.0992 34.9571 32.6273 34.3157 Columns 22 through 23 31.9913 33.7643 sum_uy = Columns 1 through 7 30.5840 31.9925 33.7789 34.5407 32.8354 36.0099 29.2314 Columns 8 through 14 34.0530 34.2765 30.7516 37.3759 32.9196 30.4383 33.3216 Columns 15 through 21 35.5665 33.2764 30.9088 31.7406 33.4353 30.3126 32.8939 Columns 22 through 23 33.9602 34.2381 f_acc = false_pairs = [0.5187 0.2814 0.4893 0.6178 2.3447 1.5862 7.5981 1.8821 1.2536 5.4711 19.7036 1.5931 1.3263 1.4570 0.7978 1.1827 1.2465 1.3648 1.0044 9.4501 1.9108 1.5021 0.8383] rmsdist_acc = Columns 1 through 7 0.7202 0.5305 0.6995 0.7860 1.5312 1.2595 2.7565 Columns 8 through 14 1.3719 1.1197 2.3390 4.4389 1.2622 1.1516 1.2071 Columns 15 through 21 0.8932 1.0875 1.1165 1.1682 1.0022 3.0741 1.3823 Columns 22 through 23 1.2256 0.9156 maxdist_acc = Columns 1 through 7 3.9809 3.3090 3.1607 4.1348 7.3592 7.6689 15.6115 Columns 8 through 14 5.1308 4.0714 12.6390 16.2097 4.9794 4.9615 6.6505 Columns 15 through 21 3.9017 5.0715 6.0724 5.1202 4.0963 12.3369 6.0940 Columns 22 through 23 6.4403 4.7782 local_stress_sum = 1.0e+04 * false_pairs = 1.0e+04 * [ 0.2762 6.3601 0.2064 0.1292 1.1651 0.4469 1.0894 0.2319 0.9029 0.5498 0.3612 0.3475 0.2986] sum_ux = Columns 1 through 7 33.1949 30.6242 34.2417 30.5292 30.7064 32.7664 36.9077 Column 8 35.3331 sum_uy = Columns 1 through 7 30.7554 36.7144 32.5999 36.3647 34.7240 31.9596 35.1905 Column 8 35.9394 f_acc = Columns 1 through 7 false_pairs = [ 1.1046 25.4404 0.8257 0.5169 4.6605 1.7875 4.3575 0.9274 3.6117 2.1993 1.4450 1.3901 1.3901 1.1945 1.0487] Columns 15 through 16 1.2407 1.5483 ] rmsdist_acc = Columns 1 through 7 1.0510 5.0438 0.9087 0.7189 2.1588 1.3370 2.0875 Column 8 0.9630 maxdist_acc = Columns 1 through 7 4.4775 25.3897 4.4121 3.2859 10.6228 4.7573 9.3754 Column 8 4.1900 local_stress_sum = true_pairs = 1.0e+03 *[0.6200 2.2824 0.6550 2.0059 1.3834 1.7805 0.9589 0.3415 0.6596 1.0651] sum_ux = 35.5523 34.7680 31.6068 36.4887 31.1227 31.1278 sum_uy = 31.8061 31.9979 31.2366 33.8016 31.8635 30.2710 f_acc = true_pairs = [ 0.2480 0.9130 0.2620 0.8024 0.5534 0.7122 0.3836 0.1366 0.2638 0.4260] rmsdist_acc = 0.4980 0.9555 0.5119 0.8957 0.7439 0.8439 maxdist_acc = 3.1881 5.0121 3.7066 2.9945 4.1113 4.1341 1.7729 2.2290 5.3953 % [images_list]=load_images('targets_fall_and_more'); local_stress_sum = 1.0e+03 * Columns 1 through 7 0.4844 1.0815 0.3097 0.7125 0.1497 2.7745 0.2455 Columns 8 through 14 0.9880 1.4196 0.3019 1.5213 4.8912 0.7909 5.1481 Columns 15 through 18 0.6294 1.0376 1.6656 0.4202 sum_ux = Columns 1 through 7 30.9085 34.4409 35.5014 39.2982 31.2655 36.1958 35.8762 Columns 8 through 14 27.6861 36.0951 33.4531 37.2311 33.1110 32.3641 34.6718 Columns 15 through 18 32.3913 36.2454 34.4153 31.9900 sum_uy = Columns 1 through 7 32.3688 34.3475 35.4276 32.3950 34.1380 26.0217 32.4558 Columns 8 through 14 32.4169 34.7242 32.5183 34.9636 30.8090 32.6545 33.2899 Columns 15 through 18 35.1524 31.7457 35.9402 30.4645 f_acc = Columns 1 through 7 true_pairs = [0.1938 0.4326 0.1239 0.2850 0.0599 1.1098 0.0982 0.3952 0.5678 0.1208 0.6085 0.3164 0.2517 0.4150 0.6663 0.1681] rmsdist_acc = Columns 1 through 7 0.4402 0.6577 0.3520 0.5338 0.2447 1.0535 0.3133 Columns 8 through 14 0.6287 0.7536 0.3475 0.7801 1.3987 0.5625 1.4350 Columns 15 through 18 0.5017 0.6442 0.8162 0.4100 maxdist_acc = Columns 1 through 7 2.8838 5.8372 1.4530 3.2164 1.0214 7.1773 2.2204 Columns 8 through 14 4.1395 3.4257 2.0318 5.0634 7.9404 3.0709 7.4104 Columns 15 through 18 2.0724 5.3056 4.1565 1.9276 local_stress_sum = 1.0e+04 * Columns 1 through 7 0.0502 0.1169 0.0308 0.0713 0.0148 0.0787 0.0245 Columns 8 through 14 0.0978 0.1303 0.0305 0.1517 0.3130 0.0787 0.2730 Columns 15 through 21 0.0637 0.1137 0.1663 0.0420 0.1079 0.0764 0.0598 Columns 22 through 28 0.1406 0.2028 0.0619 0.1456 0.0714 0.0195 0.0608 Columns 29 through 35 0.1070 0.0371 0.0334 5.4304 0.0506 0.0592 0.0271 Columns 36 through 42 0.0729 0.0381 0.2472 0.0401 0.3128 0.0410 0.0656 Columns 43 through 49 0.0443 0.0624 0.0346 0.0413 0.0188 0.0231 0.0431 Columns 50 through 56 0.0543 1.1432 0.1989 0.0671 0.0525 0.0926 0.0957 Columns 57 through 63 0.1139 0.0442 0.1146 0.0457 0.0517 0.0329 0.5629 Columns 64 through 70 0.2153 0.0360 0.0748 0.4000 0.0790 0.0505 0.0237 Columns 71 through 77 0.0355 0.0433 3.5539 0.1450 0.0601 0.2197 0.0814 sum_ux = Columns 1 through 7 31.5348 31.5890 34.5885 40.8370 32.5661 35.8035 36.5883 Columns 8 through 14 28.5286 35.2028 33.7589 35.4834 32.8213 32.7861 32.2876 Columns 15 through 21 34.6215 33.4208 31.6686 31.1332 37.4062 35.3421 32.3205 Columns 22 through 28 33.0995 32.2581 32.5202 38.0080 30.1556 34.3697 34.8398 Columns 29 through 35 34.9713 34.6123 37.7957 31.4209 33.8595 35.5571 32.3484 Columns 36 through 42 29.5556 36.1967 30.3410 30.5727 34.8411 31.7479 32.5645 Columns 43 through 49 31.0953 32.4878 27.9316 31.7018 35.0303 39.2180 35.2404 Columns 50 through 56 32.7977 31.6419 31.9928 31.5118 32.5732 32.6948 35.0772 Columns 57 through 63 36.9507 36.8319 30.7207 34.9092 33.0917 33.9143 33.9378 Columns 64 through 70 33.7686 29.3349 32.4590 33.0363 33.0322 31.9241 32.1752 Columns 71 through 77 34.3638 32.5815 33.8215 34.5214 34.0003 35.3933 33.4755 sum_uy = Columns 1 through 7 33.2962 35.7656 31.8528 32.4265 36.0025 27.1295 33.7343 Columns 8 through 14 33.7298 32.2401 33.8791 34.8405 32.0291 32.2587 31.4064 Columns 15 through 21 34.8372 30.8522 31.4344 29.7535 36.1112 34.1365 34.0089 Columns 22 through 28 34.5723 32.7218 31.4301 35.3683 31.2368 33.0858 35.4737 Columns 29 through 35 31.6004 29.7656 33.1760 33.0415 32.6910 33.3201 32.7906 Columns 36 through 42 32.1158 31.0867 32.5559 35.0837 32.9817 33.0991 35.4551 Columns 43 through 49 31.4655 33.6500 36.0766 34.3747 34.3785 31.6295 33.8265 Columns 50 through 56 31.1017 35.6859 34.2994 29.2942 30.9318 30.4854 34.8518 Columns 57 through 63 32.2470 31.7517 30.2635 36.5906 32.9424 34.0731 37.4354 Columns 64 through 70 33.2593 33.2403 33.7873 32.2922 31.2121 33.3359 34.6592 Columns 71 through 77 33.4812 33.0845 34.1131 32.0395 35.6732 33.4928 33.1667 f_acc = Columns 1 through 7 true_pairs = [0.2007 0.4675 0.1234 0.2851 0.0594 0.3148 0.0981 0.3912 0.5212 0.1221 0.6070 1.2519 0.3149 1.0918 0.2547 0.4548 0.6651 0.1680 0.4315 0.3054 0.2394 0.5626 0.8114 0.2477 0.5824 0.2857 0.0779 0.2432 0.4280 0.1485 0.1336 21.7215 0.2025 0.2366 0.1083 0.2915 0.1773 0.2494 0.1383 0.1652 0.0752 0.0925 0.1725 0.2171 4.5727 0.7955 0.2682 0.2098 0.3705 0.3827 0.4556 0.1770 0.4585 0.1827 0.2069 0.1318 2.2515 0.8611 0.1441 0.2993 1.6000 0.3159 0.2020 0.0946 0.1422 0.1732 14.2156 0.5802 0.2403 0.8788 0.3256] rmsdist_acc = Columns 1 through 7 0.4480 0.6837 0.3512 0.5339 0.2437 0.5611 0.3133 Columns 8 through 14 0.6255 0.7219 0.3495 0.7791 1.1189 0.5612 1.0449 Columns 15 through 21 0.5047 0.6744 0.8155 0.4099 0.6569 0.5526 0.4893 Columns 22 through 28 0.7501 0.9008 0.4977 0.7632 0.5345 0.2791 0.4932 Columns 29 through 35 0.6542 0.3854 0.3655 4.6606 0.4500 0.4865 0.3291 Columns 36 through 42 0.5399 0.3906 0.9943 0.4003 1.1185 0.4048 0.5121 Columns 43 through 49 0.4211 0.4994 0.3719 0.4064 0.2741 0.3042 0.4153 Columns 50 through 56 0.4660 2.1384 0.8919 0.5179 0.4581 0.6087 0.6187 Columns 57 through 63 0.6750 0.4207 0.6771 0.4274 0.4549 0.3630 1.5005 Columns 64 through 70 0.9280 0.3796 0.5471 1.2649 0.5621 0.4494 0.3076 Columns 71 through 77 0.3770 0.4161 3.7704 0.7617 0.4902 0.9374 0.5706 maxdist_acc = Columns 1 through 7 2.8974 5.8201 1.4491 3.2223 1.0211 5.0082 2.2175 Columns 8 through 14 4.0529 3.5679 2.0486 5.1553 6.7108 3.0106 7.7185 Columns 15 through 21 2.0957 6.1956 4.1422 1.9186 3.1808 2.4098 3.2349 Columns 22 through 28 3.6040 4.4584 2.5007 4.1227 2.4188 1.3838 2.2856 Columns 29 through 35 4.5363 2.7299 2.0204 21.4977 2.0293 3.1638 1.6328 Columns 36 through 42 4.4060 1.5585 5.8770 1.7376 6.9665 1.7850 4.5259 Columns 43 through 49 1.7995 2.0807 3.3923 2.6554 1.1875 1.6786 2.7551 Columns 50 through 56 2.3533 8.9335 5.2024 3.0617 2.2358 4.3168 2.8520 Columns 57 through 63 3.2565 2.5995 3.2691 1.7002 2.5172 2.1514 7.4276 Columns 64 through 70 7.0373 1.4680 2.3902 6.3308 3.4384 2.4538 1.8735 Columns 71 through 77 1.8464 1.7513 23.1488 5.0554 2.7109 3.6386 3.0188 % 1x1, some changes randomised local_stress_sum = 1.0e+04 * Columns 1 through 7 0.6886 2.0310 2.2132 0.9149 0.7366 1.0087 0.4964 Columns 8 through 13 1.1991 1.6635 1.1890 1.4357 2.9611 2.5323 sum_ux = Columns 1 through 7 66.2739 65.5691 65.2244 64.1253 59.9574 59.9113 69.9501 Columns 8 through 13 72.7554 70.5089 64.1829 68.9799 60.4473 66.4209 sum_uy = Columns 1 through 7 63.1915 67.8068 72.7565 62.1675 71.3551 65.4559 64.0095 Columns 8 through 13 68.9990 64.2051 65.6788 62.0317 67.0297 67.0079 f_acc = false_pairs = [ 0.6886 2.0310 2.2132 0.9149 0.7366 1.0087 0.4964 1.1991 1.6635 1.1890 1.4357 2.9611 2.5323 1.3226 0.9649 1.4354 1.2074 0.2763 5.8065 2.7381 7.0047 5.1668 1.8227 2.1085 12.0302 1.7008 0.8285] rmsdist_acc = Columns 1 through 7 0.8298 1.4251 1.4877 0.9565 0.8583 1.0043 0.7045 Columns 8 through 13 1.0950 1.2898 1.0904 1.1982 1.7208 1.5913 maxdist_acc = Columns 1 through 7 5.3401 7.3176 6.4132 5.5015 4.6019 4.7937 3.2854 Columns 8 through 13 5.3654 5.5498 6.6093 5.6084 7.4898 9.5397 local_stress_sum = 1.0e+05 * Columns 1 through 7 0.1323 0.0965 0.1435 0.1207 0.0276 0.5807 0.2738 Columns 8 through 14 0.7005 0.5167 0.1823 0.2108 1.2030 0.1701 0.0829 sum_ux = Columns 1 through 7 63.4154 68.2392 66.5396 61.1333 64.0015 63.2674 66.7641 Columns 8 through 14 67.2893 68.5516 69.8424 64.8532 64.7700 65.7029 68.9854 sum_uy = Columns 1 through 7 70.4839 63.2195 64.7657 67.5394 68.1758 65.7276 62.7947 Columns 8 through 14 62.8754 67.4706 70.1366 66.8078 68.4652 66.2214 64.1174 f_acc = Columns 1 through 7 rmsdist_acc = Columns 1 through 7 1.1501 0.9823 1.1981 1.0988 0.5256 2.4097 1.6547 Columns 8 through 14 2.6466 2.2731 1.3501 1.4521 3.4685 1.3041 0.9102 maxdist_acc = Columns 1 through 7 5.1995 7.2574 5.8772 5.7168 2.7961 16.9038 8.8830 Columns 8 through 14 14.2959 14.9158 8.7803 8.6647 25.8772 8.8266 6.6038 after smoothing fix, no smoothing applied below local_stress_sum = 1.0e+04 * Columns 1 through 7 2.9045 0.7991 1.0899 1.6700 1.3282 5.2447 1.1418 Columns 8 through 12 1.0574 9.5105 8.2300 2.3571 1.4217 sum_ux = Columns 1 through 7 62.9061 70.9862 64.6927 71.0873 66.6356 65.9744 63.0603 Columns 8 through 12 72.6884 62.4200 70.0383 62.8374 65.6645 sum_uy = Columns 1 through 7 67.3772 64.0699 66.8698 61.5588 66.0495 66.5457 70.0154 Columns 8 through 12 62.3120 73.3297 65.5820 66.6676 66.9487 f_acc = Columns 1 through 7 2.9045 0.7991 1.0899 1.6700 1.3282 5.2454 1.1418 Columns 8 through 12 1.0574 9.5105 8.2300 2.3571 1.4217 rmsdist_acc = Columns 1 through 7 1.7043 0.8939 1.0440 1.2923 1.1525 2.2901 1.0686 Columns 8 through 12 1.0283 3.0839 2.8688 1.5353 1.1924 maxdist_acc = Columns 1 through 7 9.1744 5.0088 5.0801 6.2362 5.7677 15.7979 8.2595 Columns 8 through 12 11.4438 17.1465 18.5868 8.7694 8.0804 after bugfix local_stress_sum = false_pairs = 1.0e+04 * [ 4.2129 1.6657 0.7476 0.7583 1.0858 2.0215 3.1581 4.4319 0.6812 3.7909 0.5295 1.2753 1.0064 1.3104 1.4462 1.1936 0.4973 8.3280 1.5021 0.6023 1.0676] sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 64.7505 63.4750 65.9120 68.4528 Columns 21 through 30 72.2824 70.3999 64.8605 69.0066 68.4146 68.4677 65.2356 68.9121 71.7629 62.0456 Columns 31 through 37 64.3192 65.3217 64.7402 72.6063 63.0195 64.7031 68.8163 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 65.1955 62.2876 61.8562 65.5537 Columns 21 through 30 63.6084 67.3177 70.4155 65.5518 63.1188 65.8311 68.7419 64.4664 65.0624 63.4750 Columns 31 through 37 68.5314 65.3349 64.1310 68.8133 63.6623 70.7717 68.4261 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 4.2129 1.6657 0.7476 0.7583 Columns 21 through 30 1.0858 2.0215 3.1581 4.4319 0.6812 3.7909 0.5295 1.2753 1.0064 1.3104 Columns 31 through 37 1.4462 1.1936 0.4973 8.3280 1.5021 0.6023 1.0676 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 2.0525 1.2906 0.8646 0.8708 Columns 21 through 30 1.0420 1.4218 1.7771 2.1052 0.8253 1.9470 0.7277 1.1293 1.0032 1.1447 Columns 31 through 37 1.2026 1.0925 0.7052 2.8858 1.2256 0.7761 1.0332 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 11.2211 4.7721 4.0603 7.1005 Columns 21 through 30 4.5546 5.6964 13.0065 12.3786 3.2638 15.1138 4.5202 10.8107 5.6029 5.7442 Columns 31 through 37 5.4643 6.7333 4.5641 14.6154 4.2204 4.9730 5.8511 local_stress_sum = 1.0e+06 * Columns 1 through 7 0.0029 0.0023 0.0014 0.0028 0.0006 2.3502 0.0014 Column 8 0.0030 sum_ux = Columns 1 through 7 72.9183 59.4021 66.5541 66.4404 67.0801 69.3936 66.9301 Column 8 59.9128 sum_uy = Columns 1 through 7 66.5312 63.7688 62.1429 67.3321 68.9445 70.0598 64.5601 Column 8 64.1229 f_acc = true_pairs = [0.2853 0.2280 0.1387 0.2788 0.0585 235.0151 0.1378 0.2989] true_pairs = [0.2853 0.2280 0.1387 0.2788 0.0585 0.1378 0.2989] true_pairs = [0.2861 0.2181 0.1390 0.2759 0.0585 205.4608 0.1400 0.2961 0.6766 0.0877 0.7172 4.1001 0.2337 0.3686 0.2678 1.0073 0.8706] rmsdist_acc = Columns 1 through 7 0.5341 0.4775 0.3724 0.5280 0.2419 15.3302 0.3712 Column 8 0.5468 maxdist_acc = Columns 1 through 7 3.5912 2.1353 1.7504 3.3381 1.3724 64.0795 1.8918 Column 8 3.2033 after smoothing fix local_stress_sum = true_pairs = 1.0e+05 *[ 0.0455 0.0143 0.0273 0.0399 0.0184 0.0305 0.5634 0.0188 0.0484 0.0144 0.0063 0.0205 0.0626 0.0059 0.0076 1.3960 0.0209 0.0133 0.0102 0.0239 0.0123 0.0297 0.0208 0.0121 0.0077 0.0084 0.0212 0.0404 0.0160] sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 62.6529 62.6676 63.2733 65.2641 Columns 21 through 30 63.4898 66.5218 63.3734 71.1350 61.4549 63.2478 64.1324 65.1894 60.5486 68.7346 Columns 31 through 40 67.5066 64.4646 62.2821 70.4742 68.9340 73.2076 71.5902 64.8885 64.6411 63.5082 Columns 41 through 45 65.8424 71.1797 69.2028 68.6872 69.5844 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 66.3813 71.0922 66.5874 64.5999 Columns 21 through 30 66.9821 65.3167 65.1246 63.9101 63.8430 66.3046 67.1705 69.2867 64.4831 65.2624 Columns 31 through 40 63.0769 61.3746 65.1015 70.7272 66.3715 69.6255 66.6767 65.1855 67.0830 62.6275 Columns 41 through 45 64.8186 61.7531 61.8673 64.1270 68.1844 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0.4552 0.1429 0.2725 0.3988 Columns 21 through 30 0.1841 0.3054 5.6338 0.1875 0.4842 0.1445 0.0632 0.2054 0.6260 0.0586 Columns 31 through 40 0.0760 13.9600 0.2092 0.1326 0.1021 0.2385 0.1228 0.2967 0.2075 0.1206 Columns 41 through 45 0.0772 0.0841 0.2119 0.4042 0.1603 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0.6747 0.3780 0.5220 0.6315 Columns 21 through 30 0.4291 0.5526 2.3736 0.4331 0.6958 0.3801 0.2514 0.4532 0.7912 0.2422 Columns 31 through 40 0.2757 3.7363 0.4574 0.3641 0.3195 0.4884 0.3504 0.5447 0.4555 0.3472 Columns 41 through 45 0.2779 0.2900 0.4604 0.6357 0.4004 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 5.0634 1.8526 2.9494 11.1158 Columns 21 through 30 3.5592 3.5093 14.1658 2.6076 4.7192 1.8331 1.5403 3.3895 5.6773 1.2516 Columns 31 through 40 1.5576 27.2165 1.9721 2.8224 1.7823 4.6564 2.4312 2.4978 2.3710 1.9679 Columns 41 through 45 2.8801 2.7423 2.4713 3.6624 4.0588 local_stress_sum = 1.0e+06 * Columns 1 through 7 0.0029 0.0022 0.0014 0.0028 0.0006 2.0546 0.0014 Columns 8 through 14 0.0030 0.0068 0.0009 0.0072 0.0410 0.0023 0.0037 Columns 15 through 17 0.0027 0.0101 0.0087 sum_ux = Columns 1 through 7 71.3707 68.8254 67.4673 71.1469 58.9588 62.9164 66.6215 Columns 8 through 14 58.5590 71.1346 68.5781 64.1557 72.9819 66.7701 70.3428 Columns 15 through 17 65.8442 66.3289 73.9764 sum_uy = Columns 1 through 7 68.2556 67.7131 65.6859 64.4248 69.7291 62.9906 64.5564 Columns 8 through 14 64.9012 66.6999 65.0535 66.4491 67.9716 65.7106 66.8109 Columns 15 through 17 65.2302 65.6090 65.8107 f_acc = Columns 1 through 7 rmsdist_acc = Columns 1 through 7 0.5349 0.4671 0.3728 0.5252 0.2419 14.3339 0.3741 Columns 8 through 14 0.5442 0.8225 0.2961 0.8469 2.0249 0.4834 0.6071 Columns 15 through 17 0.5175 1.0036 0.9331 maxdist_acc = Columns 1 through 7 3.5081 2.4113 1.7316 3.3865 1.3640 54.5519 1.9404 Columns 8 through 14 3.3708 4.0881 1.4556 4.3618 9.8729 2.6720 3.3338 Columns 15 through 17 2.8435 5.9331 6.0520 1/8/2011 - after bugfixes, bad nose detection still false local_stress_sum = false_pairs = 1.0e+04 * [ 1.6452 0.5028 1.8826 0.6242 2.1809 2.3843 2.0060 0.9877 0.5029 2.5431 1.4060 2.8394 5.5336 7.1895 0.4094 5.6535 4.4514 1.6379 0.7355 0.7600 1.0568 2.0268 3.1322 4.6849 0.6953 3.8663 0.5383] sum_ux = Columns 1 through 10 71.7163 72.1390 67.4371 64.9073 63.6614 69.7821 70.4582 68.9420 61.6989 67.1588 Columns 11 through 20 63.2804 66.8649 68.3061 68.0495 63.6303 67.3383 65.8170 66.0318 67.6690 68.4360 Columns 21 through 27 63.6613 70.9988 68.3496 66.3821 68.7417 66.8765 63.2979 sum_uy = Columns 1 through 10 64.5262 67.4164 68.9748 63.7508 62.3567 62.9407 64.8011 64.6196 68.2755 63.4603 Columns 11 through 20 64.9663 67.2594 68.9777 65.1655 65.0847 67.5740 60.2835 66.9974 64.3824 64.7027 Columns 21 through 27 64.0914 65.9598 70.0407 65.8875 61.2836 66.8464 67.1482 f_acc = Columns 1 through 10 1.6452 0.5028 1.8826 0.6242 2.1809 2.3843 2.0060 0.9877 0.5029 2.5431 Columns 11 through 20 1.4060 2.8394 5.5336 7.1895 0.4094 5.6535 4.4514 1.6379 0.7355 0.7600 Columns 21 through 27 1.0568 2.0268 3.1322 4.6849 0.6953 3.8664 0.5383 rmsdist_acc = Columns 1 through 10 1.2827 0.7091 1.3721 0.7900 1.4768 1.5441 1.4163 0.9938 0.7092 1.5947 Columns 11 through 20 1.1858 1.6851 2.3524 2.6813 0.6399 2.3777 2.1098 1.2798 0.8576 0.8718 Columns 21 through 27 1.0280 1.4237 1.7698 2.1645 0.8339 1.9663 0.7337 maxdist_acc = Columns 1 through 10 8.3494 2.9590 7.8134 3.0519 8.9773 5.1613 7.4191 4.1906 3.6012 10.4435 Columns 11 through 20 5.3020 14.0045 16.6354 28.6600 5.4787 14.4558 12.6140 4.8879 4.0997 6.8735 Columns 21 through 27 4.6989 5.5876 12.8895 11.9622 3.1333 17.5663 3.7337 true local_stress_sum = true_pairs = 1.0e+05 * [ 0.0102 0.0211 0.0102 0.0220 0.0270 0.3056 0.0104 0.0185 0.0313 0.0098 0.0457 0.9016 0.0154 0.0311 0.0194 0.0201 0.0456 0.0358 0.0272 0.0233 0.0185 0.0305 0.5301 0.0188 0.0490 0.0146 0.0065 0.0204 0.0398 0.0059 0.0077 1.3037 0.0209 0.0137 0.0102 0.0238 0.0122 0.0321 0.0206 0.0122 0.0076 0.0085 0.0233 0.0381 0.0161 0.0091 0.0068 0.0193 0.0117 0.0058 0.0109 0.0088 0.0357 0.0143 0.0434 0.0438 0.0528 0.0136 0.0221 0.0219 0.0123 0.0056 0.1436 0.0272 0.0132 0.0100 0.0173 0.0126 0.0226 0.0098 0.0253 0.0095 1.0394 0.0201 0.0172 0.0844 0.0281] sum_ux = Columns 1 through 7 59.0445 73.6844 64.1919 67.9518 66.2392 72.4427 72.6985 Columns 8 through 14 67.1001 70.0846 63.0780 69.0883 63.9810 66.5634 75.0610 Columns 15 through 21 68.4846 62.7212 67.4915 61.3389 60.0746 63.0560 68.1378 Columns 22 through 28 66.9949 69.6846 70.8037 62.9999 65.1617 67.5071 67.3066 Columns 29 through 35 66.2407 69.3623 65.6207 67.0312 61.3519 61.5850 65.7939 Columns 36 through 42 67.2664 72.9389 65.7244 68.3252 64.7419 67.3785 66.4500 Columns 43 through 49 64.9992 65.8451 68.2449 73.6959 68.6379 67.7442 62.0644 Columns 50 through 56 64.9726 69.8177 63.3665 67.3586 65.7253 63.9385 65.4325 Columns 57 through 63 65.7857 64.0285 57.8622 67.7249 67.3444 68.1946 69.9043 Columns 64 through 70 60.8311 65.8885 71.9039 62.8201 69.0476 67.3336 69.2882 Columns 71 through 77 66.4361 61.1539 64.2892 67.7631 68.8906 67.3710 62.8440 sum_uy = Columns 1 through 7 66.2033 63.5345 68.1027 62.8554 61.9516 70.0464 68.6124 Columns 8 through 14 67.1596 64.5756 65.7467 68.1201 65.2254 69.1130 66.3775 Columns 15 through 21 68.0413 65.7973 65.5675 68.4437 66.2266 67.1633 65.3712 Columns 22 through 28 66.3142 61.9011 63.2755 66.2148 65.2269 68.9984 68.3962 Columns 29 through 35 68.4452 66.8937 65.5945 60.3613 62.9823 69.2082 66.3603 Columns 36 through 42 65.8924 65.5838 64.5975 68.0276 60.0870 66.0382 62.9181 Columns 43 through 49 63.2561 63.6942 69.3068 66.2635 65.5203 64.2767 64.9040 Columns 50 through 56 68.7419 67.0557 69.5090 72.0782 62.9762 72.9562 61.2414 Columns 57 through 63 61.4582 69.3306 57.2047 70.9337 71.2758 66.3783 69.0082 Columns 64 through 70 66.5962 69.0505 64.9412 69.4730 65.3176 66.7605 66.3774 Columns 71 through 77 72.2749 66.8114 68.1243 62.6991 68.0847 60.9092 67.9807 f_acc = Columns 1 through 7 0.1019 0.2112 0.1023 0.2195 0.2697 3.0558 0.1044 Columns 8 through 14 0.1853 0.3132 0.0979 0.4570 9.0161 0.1544 0.3107 Columns 15 through 21 0.1944 0.2008 0.4558 0.3580 0.2715 0.2327 0.1849 Columns 22 through 28 0.3049 5.3009 0.1876 0.4901 0.1464 0.0655 0.2045 Columns 29 through 35 0.3984 0.0591 0.0772 13.0368 0.2089 0.1374 0.1017 Columns 36 through 42 0.2383 0.1224 0.3206 0.2061 0.1218 0.0758 0.0849 Columns 43 through 49 0.2334 0.3811 0.1615 0.0914 0.0684 0.1929 0.1169 Columns 50 through 56 0.0581 0.1089 0.0883 0.3566 0.1431 0.4343 0.4375 Columns 57 through 63 0.5283 0.1362 0.2213 0.2188 0.1225 0.0557 1.4364 Columns 64 through 70 0.2721 0.1316 0.1001 0.1731 0.1263 0.2264 0.0977 Columns 71 through 77 0.2532 0.0950 10.3938 0.2012 0.1717 0.8436 0.2812 rmsdist_acc = Columns 1 through 7 0.3192 0.4596 0.3199 0.4685 0.5193 1.7481 0.3231 Columns 8 through 14 0.4304 0.5597 0.3128 0.6760 3.0027 0.3929 0.5574 Columns 15 through 21 0.4410 0.4481 0.6751 0.5983 0.5211 0.4824 0.4300 Columns 22 through 28 0.5521 2.3024 0.4332 0.7001 0.3826 0.2558 0.4522 Columns 29 through 35 0.6312 0.2431 0.2778 3.6107 0.4571 0.3707 0.3189 Columns 36 through 42 0.4882 0.3498 0.5662 0.4540 0.3490 0.2754 0.2914 Columns 43 through 49 0.4831 0.6173 0.4019 0.3024 0.2615 0.4392 0.3420 Columns 50 through 56 0.2410 0.3300 0.2972 0.5971 0.3783 0.6590 0.6615 Columns 57 through 63 0.7269 0.3690 0.4704 0.4678 0.3500 0.2359 1.1985 Columns 64 through 70 0.5216 0.3627 0.3164 0.4160 0.3554 0.4758 0.3126 Columns 71 through 77 0.5032 0.3083 3.2239 0.4485 0.4143 0.9185 0.5303 maxdist_acc = Columns 1 through 7 2.3768 2.3646 1.5811 2.4343 4.1170 8.8891 1.9951 Columns 8 through 14 2.6051 3.3449 1.8554 5.3634 16.1103 3.0677 6.6505 Columns 15 through 21 2.5169 3.2004 5.0522 2.6586 2.9129 2.5014 3.4842 Columns 22 through 28 3.4735 13.8736 2.5602 4.7677 1.8898 1.5568 3.5803 Columns 29 through 35 5.2649 1.2543 1.5624 28.7373 1.9861 2.8125 1.7677 Columns 36 through 42 4.6591 2.4219 2.8626 2.2575 1.9764 2.9128 2.7134 Columns 43 through 49 2.7416 3.6476 3.9125 1.9743 1.9639 2.3345 1.8160 Columns 50 through 56 1.4202 3.3008 2.3741 3.7252 2.2978 4.7991 4.1636 Columns 57 through 63 3.4971 2.1620 2.3629 2.2858 2.1563 2.0890 10.7592 Columns 64 through 70 3.9373 1.6207 2.0084 3.1171 2.2799 2.9199 1.7453 Columns 71 through 77 2.5330 1.9045 28.0708 2.3182 2.6822 3.6526 3.6523 after mask fix local_stress_sum = 1.0e+04 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0.1903 0.1583 0.2517 0.2453 0.1975 0.4715 0.1117 0.3235 4.9223 Columns 21 through 30 0.1792 0.2912 0.4664 0.2020 0.4689 0.1974 0.0805 0.3143 0.3124 0.0593 Columns 31 through 40 0.0661 0.1329 0.1960 0.2555 0.0937 0.2862 0.1286 0.3238 6.4962 0.1575 sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 65.7600 64.7183 66.6948 65.2146 67.2947 67.6099 65.9802 69.8312 66.6955 Columns 21 through 30 67.7214 63.7049 62.3143 61.8503 66.4284 66.6363 69.0659 64.5105 63.5573 66.1219 Columns 31 through 40 68.7233 72.5764 65.7102 66.7059 73.0394 69.4003 72.3788 65.3887 66.3318 71.5021 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 68.4576 67.0114 61.9897 66.3339 65.3090 63.5838 67.6146 63.7897 69.0545 Columns 21 through 30 62.6736 67.8823 63.1802 67.8899 68.8553 65.4226 65.0752 65.8572 68.5170 62.8820 Columns 31 through 40 66.4367 65.9124 65.2119 66.5582 64.3400 66.4991 64.2570 65.0929 72.1040 64.8202 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0.1903 0.1583 0.2517 0.2453 0.1975 0.4715 0.1117 0.3235 4.9223 Columns 21 through 30 0.1792 0.2912 0.4664 0.2020 0.4689 0.1974 0.0805 0.3143 0.3124 0.0593 Columns 31 through 40 0.0661 0.1329 0.1960 0.2555 0.0937 0.2862 0.1286 0.3238 6.4962 0.1575 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0.4363 0.3978 0.5017 0.4953 0.4444 0.6867 0.3343 0.5688 2.2186 Columns 21 through 30 0.4233 0.5397 0.6829 0.4495 0.6848 0.4443 0.2837 0.5607 0.5589 0.2435 Columns 31 through 40 0.2570 0.3646 0.4427 0.5055 0.3061 0.5350 0.3585 0.5691 2.5488 0.3969 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 3.2789 2.3655 2.5359 3.5312 3.2397 4.1951 2.1179 3.5458 13.3529 Columns 21 through 30 3.2585 3.3442 3.8842 2.1215 4.0708 2.5970 2.1661 3.4492 5.1245 1.7150 Columns 31 through 40 1.2437 2.4513 3.0556 3.6204 1.5597 4.7389 1.5284 2.8451 15.4016 3.4442 random smoothing=13 local_stress_sum = 1.0e+04 * Columns 1 through 7 1.4249 0.9991 0.5749 0.4705 0.8107 1.1857 0.4073 Columns 8 through 13 1.9962 0.6029 0.3501 1.4616 0.6166 0.7789 sum_ux = Columns 1 through 7 68.6021 67.8838 66.3489 71.8692 66.5103 70.1077 67.6967 Columns 8 through 13 63.6949 70.8651 68.6380 70.4672 66.6879 71.5919 sum_uy = Columns 1 through 7 68.4152 71.5066 67.3159 69.2586 62.3427 66.9230 63.7558 Columns 8 through 13 68.5926 64.1541 67.1793 65.3397 62.6837 65.0245 f_acc = Columns 1 through 7 1.4249 0.9991 0.5749 0.4705 0.8107 1.1857 0.4073 Columns 8 through 13 1.9962 0.6029 0.3501 1.4616 0.6166 0.7789 rmsdist_acc = Columns 1 through 7 1.1937 0.9995 0.7582 0.6859 0.9004 1.0889 0.6382 Columns 8 through 13 1.4129 0.7764 0.5917 1.2090 0.7853 0.8826 maxdist_acc = Columns 1 through 7 4.0627 5.3395 4.8882 5.4462 2.8835 4.3962 3.5158 Columns 8 through 13 8.2620 3.0389 3.8457 4.9346 4.3167 4.0394 true local_stress_sum = 1.0e+03 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 29 0 0 8.3842 2.4426 2.1876 1.5018 0.7389 2.2188 3.6488 sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 29 0 0 65.8102 68.6545 65.9246 63.4902 64.2737 67.5865 63.5663 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 29 0 0 64.8199 68.0885 66.2032 69.6144 63.4677 64.0949 70.2803 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 29 0 0 0.8384 0.2443 0.2188 0.1502 0.0739 0.2219 0.3649 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 29 0 0 0.9157 0.4942 0.4677 0.3875 0.2718 0.4710 0.6041 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 29 0 0 6.8284 3.5539 3.5569 2.4650 3.9863 3.6708 4.2166 nose bottom - 6 px changed to from 12 px random false_pairs = 1.0e+06 *[ 0.0172 0.0186 0.0066 0.0317 0.0063 0.0195 0.0078 0.0204 0.0128 0.0131 0.0254 0.0069 0.0174 0.0042 0.0194 0.0313 0.0091 0.0050 0.0040 0.0116 0.0033 0.0056 1.4921 0.0038 0.0070 0.0421 0.0090 0.0059 0.0108 0.0031 0.0055 0.0156 0.0215 0.0261 0.0039 0.0150 0.0113 0.0301 0.0070 0.0116 0.0169] local_stress_sum = 1.0e+06 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0.0172 0.0186 0.0066 0.0317 0.0063 0.0195 0.0078 0.0204 Columns 31 through 40 0.0128 0.0131 0.0254 0.0069 0.0174 0.0042 0.0194 0.0313 0.0091 0.0050 Columns 41 through 50 0.0040 0.0116 0.0033 0.0056 1.4921 0.0038 0.0070 0.0421 0.0090 0.0059 Columns 51 through 60 0.0108 0.0031 0.0055 0.0156 0.0215 0.0261 0.0039 0.0150 0.0113 0.0301 Columns 61 through 63 0.0070 0.0116 0.0169 local_stress_sum_sqr = 1.0e+09 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0.0002 0.0004 0.0000 0.0006 0.0000 0.0002 0.0000 0.0002 Columns 31 through 40 0.0001 0.0001 0.0008 0.0000 0.0001 0.0000 0.0006 0.0011 0.0000 0.0000 Columns 41 through 50 0.0000 0.0000 0.0000 0.0000 5.9174 0.0000 0.0000 0.0008 0.0001 0.0000 Columns 51 through 60 0.0001 0.0000 0.0000 0.0001 0.0002 0.0004 0.0000 0.0001 0.0001 0.0006 Columns 61 through 63 0.0000 0.0000 0.0001 sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 63.5588 62.0136 68.9824 72.3937 62.8797 66.0515 65.9764 59.6721 Columns 31 through 40 65.8832 66.7096 68.8440 61.0136 67.3445 61.2911 64.9969 65.8619 72.4995 65.9320 Columns 41 through 50 69.4330 62.8673 59.6757 71.0103 63.7502 63.6964 67.2372 60.0544 59.7395 70.3569 Columns 51 through 60 64.3649 70.0447 60.8466 70.3857 68.2572 69.7338 65.4023 63.5847 70.3568 66.3076 Columns 61 through 63 66.9691 62.1061 68.6028 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 73.2811 64.0445 63.8374 68.7821 64.3742 66.4737 68.1657 63.2747 Columns 31 through 40 66.1245 68.3286 68.8364 68.9574 63.0666 65.1766 68.9813 62.9109 68.0550 63.6560 Columns 41 through 50 72.4359 67.0968 67.6867 70.1111 66.8783 63.5062 64.5512 66.8284 67.2344 67.2111 Columns 51 through 60 66.2365 65.0719 64.5894 67.7222 64.4497 60.8885 64.0583 66.5376 67.8562 67.4657 Columns 61 through 63 64.1136 63.0579 64.8408 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 1.7176 1.8594 0.6603 3.1734 0.6348 1.9505 0.7849 2.0442 Columns 31 through 40 1.2754 1.3105 2.5431 0.6878 1.7372 0.4177 1.9358 3.1294 0.9062 0.4998 Columns 41 through 50 0.4024 1.1609 0.3330 0.5614 149.2133 0.3819 0.7019 4.2129 0.8986 0.5854 Columns 51 through 60 1.0772 0.3107 0.5464 1.5599 2.1472 2.6088 0.3931 1.5035 1.1329 3.0107 Columns 61 through 63 0.6965 1.1576 1.6932 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 1.3106 1.3636 0.8126 1.7814 0.7968 1.3966 0.8859 1.4298 Columns 31 through 40 1.1293 1.1448 1.5947 0.8293 1.3180 0.6463 1.3913 1.7690 0.9520 0.7070 Columns 41 through 50 0.6344 1.0775 0.5770 0.7493 12.2153 0.6180 0.8378 2.0525 0.9479 0.7651 Columns 51 through 60 1.0379 0.5574 0.7392 1.2489 1.4653 1.6152 0.6270 1.2262 1.0644 1.7351 Columns 61 through 63 0.8345 1.0759 1.3012 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 9.1438 10.6944 3.1022 13.4700 4.0739 9.7413 5.3782 9.2718 Columns 31 through 40 5.2676 7.5490 12.1203 4.5405 6.5595 3.2329 10.3237 12.6998 4.8720 3.2450 Columns 41 through 50 8.2296 3.7332 5.3147 3.4955 131.4028 4.6130 3.1945 13.5357 6.7259 4.6222 Columns 51 through 60 6.0659 2.3648 5.2854 5.3865 8.5253 8.2112 3.8607 7.6526 6.4050 13.8469 Columns 61 through 63 5.2074 7.6778 6.2340 true_pairs true_pairs = 1.0e+04 * [0.7957 0.1571 0.2126 0.1612 0.0948 0.3091 3.5969 0.0671 0.0610 0.1325 0.2171 0.1134 0.0768 0.2635 0.1204 0.5480 0.3071 0.1182 0.0932 0.0891 0.1236 2.2602] local_stress_sum = 1.0e+04 * Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0.7957 0.1571 0.2126 0.1612 0.0948 0.3091 Columns 29 through 35 3.5969 0.0671 0.0610 0.1325 0.2171 0.1134 0.0768 Columns 36 through 42 0.2635 0.1204 0.5480 0.3071 0.1182 0.0932 0.0891 Columns 43 through 44 0.1236 2.2602 local_stress_sum_sqr = 1.0e+05 * Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0.3654 0.0125 0.0266 0.0142 0.0053 0.0562 Columns 29 through 35 6.5327 0.0027 0.0018 0.0086 0.0172 0.0094 0.0027 Columns 36 through 42 0.1094 0.0063 0.2450 0.0409 0.0128 0.0080 0.0046 Columns 43 through 44 0.0172 4.8000 sum_ux = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 57.8026 68.6893 64.2245 61.8476 65.5517 67.2350 Columns 29 through 35 64.5834 61.7512 72.3436 78.0250 64.8068 59.2498 65.4877 Columns 36 through 42 68.2751 60.1321 64.6649 66.3205 64.0879 65.9268 74.7384 Columns 43 through 44 65.9340 68.6231 sum_uy = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 67.7513 65.3849 59.3592 71.0428 66.8644 64.8933 Columns 29 through 35 70.3205 61.9330 67.0520 69.2549 65.6648 70.0129 61.6006 Columns 36 through 42 67.0356 67.4872 67.4818 67.3151 67.0428 64.4503 67.3901 Columns 43 through 44 70.8755 69.6417 f_acc = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0.7957 0.1571 0.2126 0.1612 0.0948 0.3091 Columns 29 through 35 3.5969 0.0671 0.0610 0.1325 0.2171 0.1134 0.0768 Columns 36 through 42 0.2635 0.1204 0.5480 0.3071 0.1182 0.0932 0.0891 Columns 43 through 44 0.1236 2.2602 rmsdist_acc = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0.8920 0.3963 0.4610 0.4014 0.3079 0.5560 Columns 29 through 35 1.8966 0.2590 0.2470 0.3639 0.4659 0.3368 0.2771 Columns 36 through 42 0.5133 0.3470 0.7403 0.5541 0.3438 0.3052 0.2984 Columns 43 through 44 0.3516 1.5034 maxdist_acc = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 6.2025 3.3400 2.6532 2.1434 2.2055 3.5498 Columns 29 through 35 10.8759 2.2041 1.9204 1.9605 2.1947 2.8689 2.2150 Columns 36 through 42 5.0130 2.1517 5.1978 3.1204 3.6974 3.0566 2.2295 Columns 43 through 44 3.9808 8.2590 PCA - start, 100, unordered true (identical to training) local_stress_sum = true_pairs = 1.0e+04 * [ 0.2861 0.2548 1.2737 0.0000 3.7721 0.4696 0.1939 0.1223] local_stress_sum_sqr = 1.0e+05 * 0.0928 0.0429 3.1377 0.0000 7.9566 0.2397 0.0213 0.0075 sum_ux = 75.2840 64.7825 66.6210 71.0000 62.7281 67.4545 66.8414 72.7894 sum_uy = 68.7486 62.4527 60.3993 79.0000 64.1970 63.0622 64.6132 68.8895 f_acc = 0.2861 0.2548 1.2737 0.0000 3.7721 0.4696 0.1939 0.1223 rmsdist_acc = 0.5349 0.5048 1.1286 0.0000 1.9422 0.6853 0.4403 0.3497 maxdist_acc = 4.9978 5.1375 12.0233 0.0000 12.8252 5.0179 2.7786 2.0579 New model built with probe residual match_score_concat_latent = true_pairs = 1.0e-189 * [0.0001 0.0001 0.0214 0.0000 0.1315 0.0002 0.0000 0.0000] Best concatenated match so far is image #1 match_score_mean = 0.2861 0.2548 1.2737 0.0000 3.7721 0.4696 0.1939 0.1223 same faces, not identical to training, [images_list]=load_images('same_person_training_fall'); local_stress_sum = 1.0e+03 * 1.3831 2.3324 local_stress_sum_sqr = 1.0e+03 * 4.6711 2.9441 sum_ux = 62.7369 66.2792 sum_uy = 64.2167 65.1887 f_acc = 0.1383 0.2332 rmsdist_acc = 0.3719 0.4829 maxdist_acc = 4.6801 3.9906 New model built with probe residual match_score_concat_latent = true_pairs = 1.0e-191 * [ 0.5592 0.3216 0.0558 0.0022 0.0001 0.6472 0.0041 0.0022 0.1633 0.0008] Best concatenated match so far is image #1 match_score_mean = 0.1383 0.2332 local_stress_sum = 1.0e+04 * Columns 1 through 10 0.1383 0.2332 0.1180 0.2131 0.0563 4.0848 0.2051 0.2029 0.8761 0.1358 Column 11 0.3486 local_stress_sum_sqr = 1.0e+05 * Columns 1 through 10 0.0467 0.0294 0.0054 0.0203 0.0013 6.3480 0.0362 0.0209 1.3866 0.0074 Column 11 0.0988 sum_ux = Columns 1 through 10 62.7369 66.2792 53.2304 57.8184 67.8873 64.6488 70.0928 68.6303 70.7793 65.0101 Column 11 66.8152 sum_uy = Columns 1 through 10 64.2167 65.1887 67.5565 69.5176 66.3590 68.2532 64.4104 66.7693 66.4052 67.3458 Column 11 70.5854 f_acc = Columns 1 through 10 0.1383 0.2332 0.1180 0.2131 0.0563 4.0848 0.2051 0.2029 0.8761 0.1358 Column 11 0.3486 rmsdist_acc = Columns 1 through 10 0.3719 0.4829 0.3435 0.4617 0.2373 2.0211 0.4529 0.4504 0.9360 0.3685 Column 11 0.5904 maxdist_acc = Columns 1 through 10 4.6801 3.9906 2.0157 2.6729 1.1049 9.5718 3.3471 2.2718 9.3902 1.8171 Column 11 4.7551 New model built with probe residual match_score_concat_latent = true_pairs = 1.0e-189 * [ 0.0056 0.0032 0.0006 0.0022 0.0001 0.6472 0.0041 0.0022 0.1633 0.0008] true_pairs = 1.0e-191 * [ 0.5654 0.3265] Column 11 0.0113 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0.1383 0.2332 0.1180 0.2131 0.0563 4.0848 0.2051 0.2029 0.8761 0.1358 Column 11 0.3486 false local_stress_sum = 1.0e+05 * Columns 1 through 10 0.1178 0.0833 0.0853 0.1336 0.0216 0.2880 1.2060 0.6948 0.0895 0.1805 Columns 11 through 20 0.1751 0.0910 0.4134 0.0440 0.0673 0.0581 0.0735 0.2469 0.0950 0.0606 Columns 21 through 30 0.0503 0.0416 0.1661 0.0682 0.0279 0.0378 0.0956 0.1871 0.0419 0.3331 Columns 31 through 40 0.0852 0.0783 0.1124 0.0834 0.0166 0.1454 0.1199 0.3864 0.1431 0.0752 Columns 41 through 47 0.0414 1.2031 0.8385 0.1784 0.0658 0.0594 0.1410 local_stress_sum_sqr = 1.0e+07 * Columns 1 through 10 0.0042 0.0047 0.0033 0.0068 0.0002 0.0619 2.2456 0.2627 0.0255 0.0252 Columns 11 through 20 0.0168 0.0032 0.0766 0.0011 0.0029 0.0015 0.0022 0.0521 0.0034 0.0017 Columns 21 through 30 0.0013 0.0011 0.0292 0.0022 0.0004 0.0024 0.0033 0.0151 0.0008 0.0396 Columns 31 through 40 0.0032 0.0052 0.0060 0.0038 0.0002 0.0241 0.0065 0.0840 0.0123 0.0028 Columns 41 through 47 0.0007 0.4109 0.6533 0.0150 0.0020 0.0014 0.0066 sum_ux = Columns 1 through 10 71.1168 66.0653 70.6667 69.2774 61.0923 60.5079 62.6343 66.0688 68.5304 67.2920 Columns 11 through 20 66.5700 71.0805 65.8708 65.3426 69.3152 66.9279 66.0536 64.8207 67.0333 63.9132 Columns 21 through 30 66.0624 69.0189 67.5021 61.7804 64.8770 67.8532 70.9717 69.7906 60.8907 61.7310 Columns 31 through 40 69.5778 70.8832 64.2569 64.0187 60.2020 66.8464 61.4546 68.3574 69.4768 66.5806 Columns 41 through 47 67.5043 66.1245 66.0653 67.0170 61.3852 67.8008 67.1007 sum_uy = Columns 1 through 10 67.4626 66.3091 61.2065 64.4850 68.4297 65.1071 69.1350 66.0034 64.5494 63.5942 Columns 11 through 20 69.9336 66.2541 70.3622 63.9297 69.7232 70.7225 71.2441 64.6449 66.5185 67.3804 Columns 21 through 30 67.7816 66.1101 64.4284 69.2422 66.9372 69.7996 70.9481 69.2547 67.9537 64.1039 Columns 31 through 40 66.9556 64.8076 72.3583 69.5350 63.6683 62.7751 68.4562 63.4878 67.6315 65.1220 Columns 41 through 47 61.2813 59.5438 64.9299 70.2856 66.4974 67.4166 60.8438 f_acc = Columns 1 through 10 1.1775 0.8331 0.8535 1.3365 0.2158 2.8803 12.0599 6.9482 0.8948 1.8045 Columns 11 through 20 1.7512 0.9099 4.1345 0.4404 0.6734 0.5809 0.7347 2.4686 0.9498 0.6065 Columns 21 through 30 0.5025 0.4160 1.6614 0.6823 0.2788 0.3784 0.9563 1.8709 0.4189 3.3307 Columns 31 through 40 0.8521 0.7833 1.1241 0.8338 0.1661 1.4540 1.1985 3.8636 1.4308 0.7519 Columns 41 through 47 0.4139 12.0306 8.3855 1.7840 0.6575 0.5941 1.4096 rmsdist_acc = Columns 1 through 10 1.0851 0.9127 0.9238 1.1561 0.4646 1.6971 3.4727 2.6359 0.9459 1.3433 Columns 11 through 20 1.3233 0.9539 2.0333 0.6637 0.8206 0.7622 0.8572 1.5712 0.9746 0.7788 Columns 21 through 30 0.7089 0.6450 1.2889 0.8260 0.5280 0.6152 0.9779 1.3678 0.6472 1.8250 Columns 31 through 40 0.9231 0.8850 1.0603 0.9131 0.4075 1.2058 1.0948 1.9656 1.1962 0.8671 Columns 41 through 47 0.6434 3.4685 2.8957 1.3357 0.8109 0.7708 1.1873 maxdist_acc = Columns 1 through 10 4.1272 6.0067 6.0629 5.1846 2.6012 12.4103 35.4528 17.0018 11.7977 7.8266 Columns 11 through 20 7.7971 4.0341 10.9856 4.8251 4.7723 5.9369 3.9796 11.8729 5.0757 4.3204 Columns 21 through 30 5.7563 4.7256 11.0528 4.3287 2.6741 6.3314 4.8274 8.2371 3.2501 7.8685 Columns 31 through 40 4.4429 5.7185 5.8837 5.0761 2.1502 10.9168 7.0297 11.6709 5.8914 4.3671 Columns 41 through 47 2.7787 14.1819 25.6392 7.2750 5.0471 3.5391 4.5536 New model built with probe residual match_score_concat_latent = false_pairs = 1.0e-187 * [ 0.0004 0.0005 0.0004 0.0007 0.0000 0.0070 0.2610 0.0289 0.0031 0.0029 0.0018 0.0003 0.0082 0.0001 0.0003 0.0002 0.0002 0.0060 0.0004 0.0002 0.0001 0.0001 0.0034 0.0002 0.0000 0.0003 0.0003 0.0016 0.0001 0.0041 0.0003 0.0006 0.0006 0.0004 0.0000 0.0028 0.0007 0.0093 0.0013 0.0003 0.0001 0.0408 0.0715 0.0016 0.0002 0.0001 0.0007] false_pairs = 1.0e-188 * [ 0.0110 0.1427 0.0050 0.0047 0.0040 0.0029 0.0024 0.0009 0.0117 0.0049 0.0889 0.0036 0.0051 0.0287 0.0063 0.0146 0.0027] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 1.1775 0.8331 0.8535 1.3365 0.2158 2.8803 12.0599 6.9482 0.8948 1.8045 Columns 11 through 20 1.7512 0.9099 4.1345 0.4404 0.6734 0.5809 0.7347 2.4686 0.9498 0.6065 Columns 21 through 30 0.5025 0.4160 1.6614 0.6823 0.2788 0.3784 0.9563 1.8709 0.4189 3.3307 Columns 31 through 40 0.8521 0.7833 1.1241 0.8338 0.1661 1.4540 1.1985 3.8636 1.4308 0.7519 Columns 41 through 47 0.4139 12.0306 8.3851 1.7840 0.6575 0.5941 1.4096 local_stress_sum = 1.0e+04 * Columns 1 through 10 1.9313 3.6101 0.8721 1.0810 1.0578 0.7528 0.7598 0.3419 1.6370 1.0796 Columns 11 through 20 3.3597 0.6173 0.7973 1.7755 1.2731 1.6431 0.7083 0.6058 0.5656 1.7868 Columns 21 through 22 4.2363 1.1176 local_stress_sum_sqr = 1.0e+06 * Columns 1 through 10 0.1078 1.3123 0.0454 0.0446 0.0395 0.0271 0.0225 0.0079 0.1121 0.0455 Columns 11 through 20 0.7810 0.0312 0.0447 0.2570 0.0618 0.1331 0.0250 0.0135 0.0128 0.6742 Columns 21 through 22 0.9940 0.0653 sum_ux = Columns 1 through 10 67.8231 68.1842 65.0586 59.1097 63.0480 65.2404 63.0189 64.0121 63.2294 65.7892 Columns 11 through 20 62.4627 62.6796 70.8489 63.7075 66.2311 59.1313 65.4885 62.0874 66.7595 63.2287 Columns 21 through 22 69.6599 70.8865 sum_uy = Columns 1 through 10 65.5357 68.6726 66.9661 68.3827 64.8888 64.0241 64.0906 63.7813 69.8267 70.0438 Columns 11 through 20 63.7496 66.4665 63.2289 66.9034 66.4512 68.0120 70.5434 69.1979 58.8115 68.1227 Columns 21 through 22 69.3085 68.6986 f_acc = Columns 1 through 10 1.9313 3.6101 0.8721 1.0810 1.0578 0.7528 0.7598 0.3419 1.6370 1.0796 Columns 11 through 20 3.3597 0.6173 0.7973 1.7755 1.2731 1.6431 0.7083 0.6058 0.5656 1.7868 Columns 21 through 22 4.2363 1.1176 rmsdist_acc = Columns 1 through 10 1.3897 1.9000 0.9339 1.0397 1.0285 0.8676 0.8717 0.5848 1.2795 1.0390 Columns 11 through 20 1.8330 0.7857 0.8929 1.3325 1.1283 1.2818 0.8416 0.7783 0.7521 1.3367 Columns 21 through 22 2.0582 1.0572 maxdist_acc = Columns 1 through 10 5.7451 15.6027 4.5303 4.5151 3.7832 5.7097 3.9238 4.4893 6.2124 5.4971 Columns 11 through 20 13.6722 8.0957 5.3805 10.5376 4.9812 7.3738 5.2265 4.7312 3.0714 14.0895 Columns 21 through 22 14.4104 5.1340 New model built with probe residual match_score_concat_latent = false_pairs = 1.0e-188 * [0.0110 0.1427 0.0050 0.0047 0.0040 0.0029 0.0024 0.0009 0.0117 0.0049 0.0889 0.0036 0.0051 0.0287 0.0063 0.0146 0.0027 0.0014 0.0014 0.0805 0.1107 0.0071] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 1.9313 3.6101 0.8721 1.0810 1.0578 0.7528 0.7598 0.3419 1.6370 1.0796 Columns 11 through 20 3.3597 0.6173 0.7973 1.7755 1.2731 1.6431 0.7083 0.6058 0.5656 1.7868 Columns 21 through 22 4.2363 1.1176 local_stress_sum = 1.0e+04 * Columns 1 through 7 0.1386 0.2316 0.1199 0.2138 0.0561 4.2104 0.2153 Columns 8 through 14 0.2032 0.8349 0.1363 0.3475 0.2131 0.1933 0.3004 Columns 15 through 21 0.3188 0.4410 0.4039 0.0764 0.1814 0.2755 0.4050 Columns 22 through 28 2.2039 0.7809 0.1579 0.2109 0.1600 0.0920 0.3170 Columns 29 through 35 3.7201 0.0668 0.0588 0.1338 0.2169 0.1142 0.0770 Columns 36 through 42 0.2631 0.1206 0.5412 0.2711 0.1202 0.0930 0.0893 Columns 43 through 49 0.1245 2.2592 0.1675 0.0867 0.3285 1.6430 0.1071 Columns 50 through 56 0.1711 0.1790 0.0800 0.3573 0.1394 0.2773 0.2268 Columns 57 through 63 0.5407 0.1321 0.1896 0.1447 0.0775 0.0534 2.0839 Columns 64 through 70 0.1938 0.2169 1.8207 0.0822 0.1189 0.1060 0.0868 Columns 71 through 77 0.1067 0.7257 0.2861 0.3571 0.1269 1.3172 0.2729 local_stress_sum_sqr = 1.0e+05 * Columns 1 through 7 0.0472 0.0290 0.0055 0.0203 0.0013 6.0995 0.0380 Columns 8 through 14 0.0207 1.4580 0.0074 0.0969 0.0335 0.0174 0.0556 Columns 15 through 21 0.0820 0.1331 0.1139 0.0026 0.0182 0.0596 0.4119 Columns 22 through 28 2.3053 0.3490 0.0130 0.0266 0.0142 0.0051 0.0583 Columns 29 through 35 6.2055 0.0027 0.0017 0.0087 0.0170 0.0093 0.0028 Columns 36 through 42 0.1092 0.0064 0.2514 0.0333 0.0110 0.0081 0.0046 Columns 43 through 49 0.0176 4.6716 0.0170 0.0030 0.1193 2.8156 0.0107 Columns 50 through 56 0.0100 0.0239 0.0036 0.0845 0.0119 0.0715 0.0394 Columns 57 through 63 0.0945 0.0071 0.0143 0.0083 0.0043 0.0016 2.5180 Columns 64 through 70 0.0372 0.0320 7.6477 0.0041 0.0059 0.0056 0.0065 Columns 71 through 77 0.0095 3.0933 0.0806 0.1101 0.0127 0.5348 0.0369 sum_ux = Columns 1 through 7 64.1174 68.5790 63.6978 53.4769 64.6335 64.2238 73.6483 Columns 8 through 14 66.3092 65.4769 65.4079 68.0487 59.2889 69.6085 72.0331 Columns 15 through 21 68.8638 68.1371 67.5184 66.3034 60.8175 57.8450 64.5026 Columns 22 through 28 66.9484 64.0503 68.3195 62.2039 66.3597 67.5465 68.4004 Columns 29 through 35 68.6092 66.1714 69.1219 75.5516 69.1712 65.0989 61.8361 Columns 36 through 42 68.0607 59.7586 70.7121 64.7228 59.7885 61.7130 75.7509 Columns 43 through 49 63.7629 67.7636 63.2986 70.9331 72.1489 58.7722 65.1114 Columns 50 through 56 65.1864 70.7597 71.6243 68.9718 70.3304 63.7645 67.5368 Columns 57 through 63 65.8177 69.8722 71.4984 76.7558 63.6526 69.9561 63.9219 Columns 64 through 70 66.4805 74.8179 67.6975 69.8860 66.6435 63.5154 64.3291 Columns 71 through 77 69.7703 75.3343 70.6754 55.0565 65.1577 64.9289 66.8482 sum_uy = Columns 1 through 7 66.4751 69.7700 66.8444 69.9167 69.7628 67.3834 66.6859 Columns 8 through 14 66.9957 64.6416 66.8135 67.1641 65.1254 69.5150 67.9025 Columns 15 through 21 61.4408 68.8561 69.9288 71.7508 67.6299 65.6662 65.1487 Columns 22 through 28 67.2094 66.4445 67.1630 61.4719 69.0375 65.6053 65.0525 Columns 29 through 35 68.3010 61.2593 66.1033 68.6188 67.5214 65.0033 63.9722 Columns 36 through 42 66.4157 66.9407 64.5597 63.4922 66.2880 67.8901 71.8366 Columns 43 through 49 69.3114 69.4029 66.4638 66.4987 68.2134 65.4530 69.4095 Columns 50 through 56 65.9930 66.5524 69.7575 66.8708 61.1019 65.4141 70.3578 Columns 57 through 63 69.4273 69.0331 63.1399 65.9048 69.6465 67.6714 66.1169 Columns 64 through 70 66.7849 67.3439 65.1511 64.1516 71.6700 68.4479 65.9583 Columns 71 through 77 71.8610 67.5610 67.6909 67.7486 63.3557 66.6797 62.7570 f_acc = Columns 1 through 7 0.1386 0.2316 0.1199 0.2138 0.0561 4.2104 0.2153 Columns 8 through 14 0.2032 0.8349 0.1363 0.3475 0.2131 0.1933 0.3004 Columns 15 through 21 0.3188 0.4410 0.4039 0.0764 0.1814 0.2755 0.4050 Columns 22 through 28 2.2039 0.7809 0.1579 0.2109 0.1600 0.0920 0.3170 Columns 29 through 35 3.7201 0.0668 0.0588 0.1338 0.2169 0.1142 0.0770 Columns 36 through 42 0.2631 0.1206 0.5412 0.2711 0.1202 0.0930 0.0893 Columns 43 through 49 0.1245 2.2592 0.1675 0.0867 0.3285 1.6430 0.1071 Columns 50 through 56 0.1711 0.1790 0.0800 0.3573 0.1394 0.2773 0.2268 Columns 57 through 63 0.5407 0.1321 0.1896 0.1447 0.0775 0.0534 2.0839 Columns 64 through 70 0.1938 0.2169 1.8207 0.0822 0.1189 0.1060 0.0868 Columns 71 through 77 0.1067 0.7257 0.2861 0.3571 0.1269 1.3172 0.2729 rmsdist_acc = Columns 1 through 7 0.3723 0.4812 0.3462 0.4624 0.2368 2.0519 0.4640 Columns 8 through 14 0.4507 0.9137 0.3693 0.5895 0.4617 0.4396 0.5481 Columns 15 through 21 0.5646 0.6640 0.6355 0.2764 0.4260 0.5249 0.6364 Columns 22 through 28 1.4846 0.8837 0.3974 0.4593 0.4000 0.3033 0.5630 Columns 29 through 35 1.9288 0.2585 0.2425 0.3657 0.4657 0.3380 0.2774 Columns 36 through 42 0.5130 0.3473 0.7357 0.5207 0.3468 0.3050 0.2989 Columns 43 through 49 0.3529 1.5031 0.4093 0.2944 0.5731 1.2818 0.3272 Columns 50 through 56 0.4137 0.4231 0.2828 0.5978 0.3733 0.5266 0.4763 Columns 57 through 63 0.7353 0.3635 0.4354 0.3804 0.2784 0.2310 1.4436 Columns 64 through 70 0.4403 0.4657 1.3493 0.2868 0.3449 0.3255 0.2947 Columns 71 through 77 0.3267 0.8519 0.5349 0.5976 0.3563 1.1477 0.5224 maxdist_acc = Columns 1 through 7 4.6774 3.7911 2.0088 2.6614 1.1076 8.2026 3.1536 Columns 8 through 14 2.2744 10.1716 1.7320 4.6908 4.3313 2.4557 4.1856 Columns 15 through 21 3.7554 3.8283 4.2002 1.3561 2.6685 4.1847 6.5131 Columns 22 through 28 8.9061 6.2115 3.3120 2.6279 2.1380 2.1305 3.5833 Columns 29 through 35 10.4695 2.1763 1.9565 1.9862 2.0509 2.8695 2.2531 Columns 36 through 42 5.0128 2.1475 5.6827 2.9794 3.3800 3.0629 2.2391 Columns 43 through 49 3.9440 8.2931 3.4410 1.5091 5.1603 10.4064 3.4867 Columns 50 through 56 1.7863 2.4786 1.9308 4.0426 3.3000 3.7078 5.4191 Columns 57 through 63 4.7827 1.8669 2.2918 2.2805 2.0718 1.6385 8.9267 Columns 64 through 70 3.4495 5.1694 13.1226 1.9611 2.3308 1.9486 2.2955 Columns 71 through 77 2.8315 12.3358 5.4646 4.5574 3.3718 3.7813 3.1711 New model built with probe residual match_score_concat_latent = true_pairs = 1.0e-189 * [0.0056 0.0032 0.0006 0.0022 0.0001 0.6113 0.0043 0.0022 0.1726 0.0008 0.0111 0.0038 0.0019 0.0062 0.0091 0.0145 0.0127 0.0003 0.0020 0.0068 0.0493 0.2460 0.0382 0.0014 0.0030 0.0015 0.0006 0.0064 0.6576 0.0003 0.0002 0.0010 0.0018 0.0010 0.0003 0.0126 0.0007 0.0281 0.0035 0.0013 0.0009 0.0005 0.0021 0.5211 0.0019 0.0003 0.0136 0.3307 0.0012 0.0010 0.0027 0.0004 0.0094 0.0013 0.0081 0.0045 0.0096 0.0008 0.0014 0.0008 0.0005 0.0002 0.2749 0.0043 0.0036 0.9145 0.0005 0.0006 0.0006 0.0007 0.0011 0.3739 0.0093 0.0126 0.0014 0.0539 0.0039] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 0.1386 0.2316 0.1199 0.2138 0.0561 4.2104 0.2153 Columns 8 through 14 0.2032 0.8349 0.1363 0.3475 0.2131 0.1933 0.3004 Columns 15 through 21 0.3188 0.4410 0.4039 0.0764 0.1814 0.2755 0.4050 Columns 22 through 28 2.2039 0.7809 0.1579 0.2109 0.1600 0.0920 0.3170 Columns 29 through 35 3.7201 0.0668 0.0588 0.1338 0.2169 0.1142 0.0770 Columns 36 through 42 0.2631 0.1206 0.5412 0.2711 0.1202 0.0930 0.0893 Columns 43 through 49 0.1245 2.2592 0.1675 0.0867 0.3285 1.6430 0.1071 Columns 50 through 56 0.1711 0.1790 0.0800 0.3573 0.1394 0.2773 0.2268 Columns 57 through 63 0.5407 0.1321 0.1896 0.1447 0.0775 0.0534 2.0839 Columns 64 through 70 0.1938 0.2169 1.8207 0.0822 0.1189 0.1060 0.0868 Columns 71 through 77 0.1067 0.7257 0.2861 0.3571 0.1269 1.3172 0.2729 50x50 model on gip2, from [images_list]=load_images('same_person_training_fall'); true_pairs local_stress_sum = 1.0e+04 * 0.0380 0.0565 0.0299 0.0669 0.0138 1.2884 0.0835 0.0559 0.2484 0.0305 local_stress_sum_sqr = 1.0e+05 * 0.0074 0.0049 0.0017 0.0074 0.0004 2.1167 0.0296 0.0060 0.3621 0.0016 sum_ux = 31.0730 35.1173 32.3325 29.3115 35.9449 33.4666 35.8724 34.1821 31.5075 29.7807 sum_uy = 33.5388 34.3275 33.5828 32.4006 34.5310 34.1968 35.2503 31.4444 32.5593 34.6460 f_acc = 0.1521 0.2259 0.1196 0.2676 0.0551 5.1537 0.3341 0.2236 0.9936 0.1219 rmsdist_acc = 0.3900 0.4752 0.3459 0.5173 0.2347 2.2702 0.5779 0.4728 0.9968 0.3491 maxdist_acc = 2.9209 2.2253 1.6227 2.4385 1.0259 8.6484 4.0968 2.0753 8.5878 1.7078 New model built with probe residual match_score_concat_latent = true_pairs= 1.0e+74 * [0.0097 0.0062 0.0022 0.0090 0.0008 4.0391 0.0129 0.0114 0.0578 0.0013] Best concatenated match so far is image #1 match_score_mean = 0.1521 0.2259 0.1196 0.2676 0.0551 5.1537 0.3339 0.2236 0.9936 0.1219 random: local_stress_sum = 1.0e+03 * Columns 1 through 10 3.7721 2.0214 1.4841 1.8353 2.4609 3.2925 1.2015 4.5602 1.6775 0.9245 Columns 11 through 20 4.9758 1.6165 3.1686 1.1275 1.6790 4.2733 1.8719 3.4929 2.7139 4.1072 local_stress_sum_sqr = 1.0e+04 * Columns 1 through 10 1.9142 0.6809 0.6877 0.5966 0.7394 1.4873 0.2650 3.6102 0.3672 0.1659 Columns 11 through 20 3.3480 0.5062 1.6898 0.2198 1.3110 3.3241 0.6436 4.1420 1.2507 2.7088 sum_ux = Columns 1 through 10 32.9773 32.3328 35.1814 30.8939 35.5367 32.4927 38.6942 31.3835 35.4471 34.7762 Columns 11 through 20 35.4152 31.8925 34.7812 36.1432 31.7783 31.5070 33.0979 34.1719 28.4686 32.8761 sum_uy = Columns 1 through 10 34.1960 34.5396 33.2550 31.9604 34.3829 33.2480 31.7271 30.9689 34.4646 32.1699 Columns 11 through 20 33.0893 32.5148 31.5302 31.3506 37.2721 33.7670 33.5386 33.2621 34.7662 31.8557 f_acc = Columns 1 through 10 1.5088 0.8086 0.5936 0.7341 0.9843 1.3170 0.4806 1.8241 0.6711 0.3698 Columns 11 through 20 1.9903 0.6466 1.2674 0.4510 0.6716 1.7093 0.7488 1.3972 1.0856 1.6429 rmsdist_acc = Columns 1 through 10 1.2283 0.8992 0.7705 0.8568 0.9921 1.1476 0.6933 1.3506 0.8192 0.6081 Columns 11 through 20 1.4108 0.8041 1.1258 0.6716 0.8195 1.3074 0.8653 1.1820 1.0419 1.2817 maxdist_acc = Columns 1 through 10 4.2536 4.0726 4.4636 3.7346 4.1764 4.7715 3.1444 6.1181 2.8733 2.8613 Columns 11 through 20 5.2734 3.8504 5.2476 3.0792 7.5558 5.9541 3.7861 7.9075 3.7655 5.6328 New model built with probe residual match_score_concat_latent = 1.0e+73 * Columns 1 through 10 3.9780 1.4221 1.3092 1.2383 1.5270 3.0725 0.5257 7.4676 0.7804 0.3687 Columns 11 through 20 7.3245 1.0590 3.6000 0.4598 2.3463 6.5360 1.3313 7.4195 2.6933 5.7806 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 1.5088 0.8086 0.5936 0.7341 0.9843 1.3170 0.4806 1.8241 0.6710 0.3698 Columns 11 through 20 1.9903 0.6466 1.2674 0.4510 0.6716 1.7093 0.7488 1.3972 1.0856 1.6429 Best mean match so far is image #10 false_pairs= 1.0e+73 * [3.9780 1.4221 1.3092 1.2383 1.5270 3.0725 0.5257 7.4676 0.7804 0.3687 7.3245 1.0590 3.6000 0.4598 2.3463 6.5360 1.3313] after buxfixes-ish true_pairs local_stress_sum = 242.7248 559.6180 259.1174 571.7267 765.7307 145.2472 359.2585 489.5492 888.7119 252.3106 local_stress_sum_sqr = 1.0e+03 * 0.1499 0.9991 0.1012 0.5137 1.8369 0.0322 0.3051 0.4267 1.7279 0.1216 sum_ux = 31.4337 34.7070 30.7261 34.7502 30.1645 31.3225 34.1330 38.5554 35.3868 25.7713 sum_uy = 35.6492 30.9902 32.7822 30.5325 28.2920 36.7658 33.2216 30.9859 33.8022 31.3899 f_acc = 0.0971 0.2238 0.1036 0.2287 0.3063 0.0581 0.1437 0.1958 0.3555 0.1009 rmsdist_acc = 0.3116 0.4731 0.3219 0.4782 0.5534 0.2410 0.3791 0.4425 0.5962 0.3177 maxdist_acc = 2.2197 3.6743 1.2801 2.4559 3.3675 1.0220 2.2122 1.7992 3.5102 1.6777 New model built with probe residual match_score_concat_latent = true_pairs= 1.0e+72 *[ 0.3394 2.3991 0.2122 1.1098 4.1836 0.0839 0.6830 0.8160 3.9307 0.2535] Best concatenated match so far is image #1 match_score_mean = 0.0971 0.2238 0.1036 0.2287 0.3063 0.0581 0.1437 0.1958 0.3555 0.1009 with smoothin 9 model and 13 in targets: true local_stress_sum = 0 0 0 0 134.6802 234.1389 249.4885 546.2134 517.6379 320.9108 local_stress_sum_sqr = 0 0 0 0 32.3475 81.3932 97.7692 493.1359 435.3512 168.2316 sum_ux = 0 0 0 0 28.0139 32.1881 32.3197 29.5860 33.8672 32.8316 sum_uy = 0 0 0 0 34.8677 30.4303 32.8990 35.4734 34.9294 35.4897 f_acc = 0 0 0 0 0.0539 0.0937 0.0998 0.2185 0.2071 0.1284 rmsdist_acc = 0 0 0 0 0.2321 0.3060 0.3159 0.4674 0.4550 0.3583 maxdist_acc = 0 0 0 0 1.2404 1.3538 1.3418 1.9140 2.0598 1.4653 New model built with probe residual match_score_concat_latent = true_pairs= 1.0e+72 * [0.2808 0.8516 0.1043 1.4046 0.0795 0.1764 0.2017 0.9804 0.9044 0.3168] true_pairs= 1.0e+72 * [0.2654 0.8470 0.1010 1.3404 0.0827 0.1759 0.2009 0.9783 0.8867 0.3380] Best concatenated match so far is image #1 match_score_mean = 0 0 0 0 0.0539 0.0937 0.0998 0.2185 0.2071 0.1284 false_pairs= 1.0e+75 * [ 0.0368 0.0443 2.1012 0.0068] false_pairs= 1.0e+73 * [2.8654 2.8099 1.4621 0.2778 0.6559 4.9439 3.9101 4.873 1.3790] 301 onwards: false_pairs= 1.0e+75 * [0.0604 0.0075 0.0476 0.0082 0.0335 0.0088 0.4327 0.1105 0.0060 0.0013 0.0080 1.6870 0.0098 0.3426 0.0101 0.0112] false_pairs= 1.0e+74 * [0.4325 2.6488 1.7354 0.1504] unfamiliar faces (not in training) true_pairs= 1.0e+72 * [0.4085 0.4219 2.1309 0.1135 0.8446 2.3994 0.8284 0.5389 0.6370 1.0481 0.3619 3.8940 0.3250] true_pairs= 1.0e+72 * [0.4085 0.4219 2.1309 0.1135 0.8446 2.3994 0.8284 0.5389 0.6370 1.0481 0.3619 3.8940 0.3250 3.4704 1.0555 8.1634 0.6984 3.8266 1.2130] local_stress_sum = 1.0e+03 * Columns 1 through 10 0.3461 0.3601 0.4630 0.1599 0.4425 0.7467 0.3489 0.3490 0.3990 0.4703 Columns 11 through 20 0.2995 0.7773 0.2763 0.9935 0.4666 1.4430 0.3722 0.9151 0.4867 0.3885 Columns 21 through 25 0.1320 1.8984 0.1893 0.1373 0.3833 local_stress_sum_sqr = 1.0e+03 * Columns 1 through 10 0.1926 0.2152 0.9467 0.0475 0.3831 1.1386 0.3742 0.2430 0.2858 0.4713 Columns 11 through 20 0.1662 1.5948 0.1465 1.5725 0.5194 4.5191 0.3558 1.8016 0.5494 0.2899 Columns 21 through 25 0.0275 7.1057 0.0632 0.0359 0.2419 sum_ux = Columns 1 through 10 31.7537 31.9818 34.0951 28.8186 33.6132 36.3789 33.5621 33.2466 34.2981 35.9111 Columns 11 through 20 30.8569 31.4218 36.7063 32.8877 32.7699 31.1672 28.3059 34.0241 35.3305 33.4968 Columns 21 through 25 32.4900 35.3183 32.0488 36.3835 29.8666 sum_uy = Columns 1 through 10 34.1467 36.9123 32.6608 33.1225 35.1455 32.3712 31.4950 33.2618 33.0806 34.4788 Columns 11 through 20 36.6793 33.2127 33.3245 31.1059 28.8932 34.5463 30.3331 32.8858 32.1167 32.5618 Columns 21 through 25 34.0402 32.1630 30.6871 35.0695 30.9614 f_acc = Columns 1 through 10 0.1385 0.1440 0.1852 0.0639 0.1770 0.2987 0.1395 0.1396 0.1596 0.1881 Columns 11 through 20 0.1198 0.3109 0.1105 0.3974 0.1866 0.5772 0.1489 0.3660 0.1947 0.1554 Columns 21 through 25 0.0528 0.7594 0.0757 0.0549 0.1533 rmsdist_acc = Columns 1 through 10 0.3721 0.3795 0.4303 0.2529 0.4207 0.5465 0.3736 0.3736 0.3995 0.4337 Columns 11 through 20 0.3461 0.5576 0.3325 0.6304 0.4320 0.7597 0.3859 0.6050 0.4412 0.3942 Columns 21 through 25 0.2298 0.8714 0.2752 0.2343 0.3915 maxdist_acc = Columns 1 through 10 1.5701 1.9364 3.7357 1.3440 2.3066 2.2019 3.0681 2.0247 2.0460 2.5015 Columns 11 through 20 1.7628 3.3209 1.8269 2.6326 2.6562 3.4369 2.4889 2.6835 2.3120 1.8766 Columns 21 through 25 0.9136 4.6224 1.5819 1.1456 1.4953 New model built with probe residual match_score_concat_latent = true_pairs= 1.0e+73 * [0.0408 0.0422 0.2131 0.0113 0.0845 0.2399 0.0828 0.0539 0.0637 0.1048 0.0362 0.3894 0.0325 0.3470 0.1056 0.8163 0.0698 0.3827 0.1213 0.0623 0.0079 1.4612 0.0139 0.0088 0.0512] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0.1385 0.1440 0.1852 0.0639 0.1770 0.2987 0.1395 0.1396 0.1596 0.1881 Columns 11 through 20 0.1198 0.3109 0.1105 0.3974 0.1866 0.5772 0.1489 0.3660 0.1947 0.1554 Columns 21 through 25 0.0528 0.7594 0.0757 0.0549 0.1533 86 pairs, 13 smoothing local_stress_sum = 1.0e+03 * Columns 1 through 7 0.4941 0.3101 0.5024 0.3617 0.4654 0.4450 0.2697 Columns 8 through 14 0.4814 0.3019 0.3903 0.3086 0.1821 0.2911 0.2539 Columns 15 through 21 0.2488 0.4327 0.1602 0.2542 0.3087 0.6582 0.2057 Columns 22 through 28 0.3511 0.4570 0.5856 0.5315 0.8360 5.0711 0.0847 Columns 29 through 35 0.0857 0.2139 0.1057 0.1131 0.3883 0.1992 0.2159 Columns 36 through 42 0.2235 0.2397 0.5755 0.1587 0.2181 0.1925 0.2034 Columns 43 through 49 0.6944 6.9000 0.6767 0.6894 0.6712 0.8426 0.3572 Columns 50 through 56 0.1324 0.3212 0.3330 0.3066 0.5386 0.9515 0.2423 Columns 57 through 63 0.7341 0.3497 0.2485 0.1498 0.3109 0.6772 0.1924 Columns 64 through 70 0.4747 0.2475 0.2235 0.2530 0.4564 0.4884 0.1577 Columns 71 through 77 1.1334 0.6680 1.3793 2.0367 0.1858 0.6344 0.2313 Columns 78 through 84 0.2133 0.7509 0.3018 0.4347 0.8258 0.4034 0.3339 Column 85 0.5419 local_stress_sum_sqr = 1.0e+05 * Columns 1 through 7 0.0042 0.0018 0.0072 0.0020 0.0048 0.0042 0.0015 Columns 8 through 14 0.0046 0.0017 0.0036 0.0014 0.0005 0.0012 0.0017 Columns 15 through 21 0.0013 0.0065 0.0004 0.0012 0.0014 0.0082 0.0022 Columns 22 through 28 0.0027 0.0034 0.0050 0.0052 0.0112 0.6873 0.0001 Columns 29 through 35 0.0002 0.0009 0.0002 0.0003 0.0030 0.0006 0.0009 Columns 36 through 42 0.0009 0.0011 0.0080 0.0004 0.0013 0.0012 0.0007 Columns 43 through 49 0.0082 1.6753 0.0076 0.0072 0.0059 0.0132 0.0021 Columns 50 through 56 0.0003 0.0016 0.0031 0.0017 0.0045 0.0241 0.0014 Columns 57 through 63 0.0150 0.0017 0.0011 0.0003 0.0014 0.0104 0.0006 Columns 64 through 70 0.0042 0.0010 0.0006 0.0010 0.0041 0.0054 0.0003 Columns 71 through 77 0.0234 0.0109 0.0451 0.1127 0.0006 0.0063 0.0016 Columns 78 through 84 0.0011 0.0277 0.0012 0.0035 0.0424 0.0035 0.0029 Column 85 0.0105 sum_ux = Columns 1 through 7 34.1341 28.4879 28.0495 34.3888 31.4939 33.1830 34.1487 Columns 8 through 14 35.5443 32.3951 34.2486 31.4592 31.2317 37.0623 30.7544 Columns 15 through 21 29.3965 32.0114 34.5043 34.0311 30.6332 30.6280 32.8089 Columns 22 through 28 30.4548 29.9437 31.2208 32.7810 28.4635 32.8540 27.8139 Columns 29 through 35 33.2656 27.3004 38.1851 35.8383 34.9757 29.7304 31.7938 Columns 36 through 42 33.9006 33.5927 34.6742 31.6280 39.9494 32.8291 34.2041 Columns 43 through 49 34.1448 36.8775 33.0667 35.6878 32.0402 31.7900 34.8609 Columns 50 through 56 34.5083 31.7809 38.2902 36.4034 33.9282 30.8720 34.4325 Columns 57 through 63 34.4891 34.7045 34.4767 35.5180 36.3404 36.1097 34.6617 Columns 64 through 70 34.2863 32.6839 33.9401 35.5275 30.8256 30.8478 33.1203 Columns 71 through 77 37.8095 34.0064 33.1758 33.6212 33.7792 34.6190 34.1533 Columns 78 through 84 33.1825 36.8016 32.1029 36.3451 35.2790 35.5296 37.5286 Column 85 34.4399 sum_uy = Columns 1 through 7 34.7417 36.2170 35.1324 33.0470 31.4656 33.1136 32.8578 Columns 8 through 14 36.3920 33.2438 31.7147 32.7153 36.3754 34.9656 32.7222 Columns 15 through 21 30.9773 31.5610 34.2269 35.3950 36.7689 33.4956 33.1446 Columns 22 through 28 32.7968 30.2644 33.4590 33.3450 33.7578 35.0688 29.0857 Columns 29 through 35 31.6682 29.9544 35.2094 34.3672 33.6843 32.5413 31.4490 Columns 36 through 42 34.8090 36.6804 33.5821 36.8458 31.6246 32.9721 33.7044 Columns 43 through 49 34.0262 32.7338 31.7412 35.9010 32.3326 33.2830 33.1934 Columns 50 through 56 31.4233 36.4540 33.2258 33.4349 33.6394 32.5183 31.2921 Columns 57 through 63 34.3137 32.7856 32.4829 34.5294 34.1480 32.6307 36.7418 Columns 64 through 70 33.1491 34.6682 32.4548 35.1653 33.2835 29.6186 34.1521 Columns 71 through 77 34.4815 33.3045 33.2553 34.4243 35.7014 32.1211 33.6945 Columns 78 through 84 32.5852 33.4971 32.0620 33.7068 33.8304 34.7810 35.7649 Column 85 31.2968 f_acc = Columns 1 through 7 0.1976 0.1240 0.2010 0.1447 0.1862 0.1780 0.1079 Columns 8 through 14 0.1926 0.1208 0.1561 0.1234 0.0728 0.1164 0.1016 Columns 15 through 21 0.0995 0.1731 0.0641 0.1017 0.1235 0.2633 0.0823 Columns 22 through 28 0.1404 0.1828 0.2342 0.2126 0.3344 2.0285 0.0339 Columns 29 through 35 0.0343 0.0856 0.0423 0.0453 0.1553 0.0797 0.0864 Columns 36 through 42 0.0894 0.0959 0.2302 0.0635 0.0872 0.0770 0.0814 Columns 43 through 49 0.2778 2.7600 0.2707 0.2758 0.2685 0.3370 0.1429 Columns 50 through 56 0.0529 0.1285 0.1332 0.1226 0.2155 0.3806 0.0969 Columns 57 through 63 0.2937 0.1399 0.0994 0.0599 0.1244 0.2709 0.0770 Columns 64 through 70 0.1899 0.0990 0.0894 0.1012 0.1825 0.1954 0.0631 Columns 71 through 77 0.4533 0.2672 0.5517 0.8147 0.0743 0.2537 0.0925 Columns 78 through 84 0.0853 0.3004 0.1207 0.1739 0.3303 0.1614 0.1336 Column 85 0.2168 rmsdist_acc = Columns 1 through 7 0.4446 0.3522 0.4483 0.3804 0.4315 0.4219 0.3284 Columns 8 through 14 0.4388 0.3475 0.3951 0.3513 0.2699 0.3412 0.3187 Columns 15 through 21 0.3155 0.4160 0.2532 0.3189 0.3514 0.5131 0.2869 Columns 22 through 28 0.3747 0.4276 0.4840 0.4611 0.5783 1.4242 0.1840 Columns 29 through 35 0.1852 0.2925 0.2056 0.2127 0.3941 0.2823 0.2939 Columns 36 through 42 0.2990 0.3096 0.4798 0.2519 0.2954 0.2775 0.2852 Columns 43 through 49 0.5270 1.6613 0.5203 0.5251 0.5181 0.5806 0.3780 Columns 50 through 56 0.2301 0.3585 0.3650 0.3502 0.4642 0.6169 0.3113 Columns 57 through 63 0.5419 0.3740 0.3153 0.2448 0.3527 0.5205 0.2774 Columns 64 through 70 0.4357 0.3146 0.2990 0.3181 0.4273 0.4420 0.2511 Columns 71 through 77 0.6733 0.5169 0.7428 0.9026 0.2726 0.5037 0.3041 Columns 78 through 84 0.2921 0.5480 0.3475 0.4170 0.5747 0.4017 0.3655 Column 85 0.4656 maxdist_acc = Columns 1 through 7 1.9885 1.7690 2.6126 2.0388 2.7151 2.2253 1.9979 Columns 8 through 14 2.0192 1.5016 2.2214 1.2240 1.4063 1.7437 1.6047 Columns 15 through 21 1.9059 2.7378 1.3268 1.7562 1.5564 2.5106 2.8447 Columns 22 through 28 1.7701 1.5769 2.2821 1.7862 2.4691 8.9514 0.7043 Columns 29 through 35 0.9740 1.2102 0.8146 1.1149 2.4313 1.1376 1.1869 Columns 36 through 42 1.3776 1.3264 2.0524 1.2060 1.8882 1.6802 1.2842 Columns 43 through 49 1.9530 12.3108 2.2231 2.3884 2.0854 2.5103 1.9299 Columns 50 through 56 1.0027 1.6690 2.6930 1.5761 2.0106 3.3157 1.7562 Columns 57 through 63 3.3644 1.5174 1.3391 0.9692 1.2860 2.9018 1.0658 Columns 64 through 70 2.0557 1.8118 1.0467 1.2463 1.9316 2.3720 0.8899 Columns 71 through 77 3.1276 3.2750 3.2794 5.1734 1.5110 2.1551 2.5951 Columns 78 through 84 1.8980 4.1798 1.2675 1.7686 4.6298 2.0800 2.0576 Column 85 3.9567 ans = 1.0e+03 * 1.9784 0.7542 0.1641 0.0744 0.0271 0.0238 0.0199 0.0186 0.0153 0.0149 percent_explained = 64.0131 24.4029 5.3091 2.4058 0.8763 0.7706 0.6454 0.6020 0.4935 0.4813 true local_stress_sum = 210.7423 524.5314 202.3073 532.5545 134.6675 235.9266 local_stress_sum_sqr = 111.3566 416.0038 49.9767 590.3091 32.1173 79.6270 sum_ux = 29.6081 33.5982 28.4900 29.5032 30.0438 31.2680 sum_uy = 33.2820 37.0102 34.1387 35.4154 35.7750 30.4822 f_acc = 0.0843 0.2098 0.0809 0.2130 0.0539 0.0944 rmsdist_acc = 0.2903 0.4581 0.2845 0.4615 0.2321 0.3072 maxdist_acc = 1.9063 1.9546 0.9525 2.0912 1.2318 1.3138 New model built with probe residual match_score_concat_latent = 1.0e+28 * 0.4343 1.9034 0.2037 1.7375 0.2026 0.3579 Best concatenated match so far is image #1 match_score_mean = 0.0843 0.2098 0.0809 0.2130 0.0539 0.0944 local_stress_sum = Columns 1 through 7 0 0 0 0 0 0 243.3452 Columns 8 through 10 545.2013 513.2490 318.7617 local_stress_sum_sqr = Columns 1 through 7 0 0 0 0 0 0 92.5539 Columns 8 through 10 497.3260 428.3467 165.0327 sum_ux = Columns 1 through 7 0 0 0 0 0 0 30.2871 Columns 8 through 10 31.0157 33.9827 32.8599 sum_uy = Columns 1 through 7 0 0 0 0 0 0 34.4447 Columns 8 through 10 31.7384 34.4283 35.1808 f_acc = Columns 1 through 7 0 0 0 0 0 0 0.0973 Columns 8 through 10 0.2181 0.2053 0.1275 rmsdist_acc = Columns 1 through 7 0 0 0 0 0 0 0.3120 Columns 8 through 10 0.4670 0.4531 0.3571 maxdist_acc = Columns 1 through 7 0 0 0 0 0 0 1.3492 Columns 8 through 10 1.8953 2.0577 1.4538 New model built with probe residual match_score_concat_latent = 1.0e+28 * Columns 1 through 7 0 0 0 0 0 0 0.2910 Columns 8 through 10 2.9600 1.7071 0.8497 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 0 0 0 0 0 0 0.0973 Columns 8 through 10 0.2181 0.2053 0.1275 true_pairs = 1.0e+28 * [0.4343 1.9034 0.2037 1.7375 0.2026 0.3579 0.2910 2.9600 1.7071 0.8497] false local_stress_sum = 1.0e+03 * Columns 1 through 7 2.8528 2.8222 2.8910 2.2682 1.6802 2.2674 1.7480 Columns 8 through 10 1.8924 1.3991 2.5292 local_stress_sum_sqr = 1.0e+04 * Columns 1 through 7 2.1241 1.1523 1.4829 0.7543 0.4285 0.6857 0.7044 Columns 8 through 10 0.4611 0.3047 1.3028 sum_ux = Columns 1 through 7 31.1898 35.8333 30.1369 36.9639 31.7536 35.6553 29.5436 Columns 8 through 10 35.4040 35.0791 31.9269 sum_uy = Columns 1 through 7 33.0101 33.2981 34.1183 32.3418 35.9913 35.4297 33.4765 Columns 8 through 10 32.9570 32.4932 34.3749 f_acc = Columns 1 through 7 1.1411 1.1289 1.1564 0.9073 0.6721 0.9070 0.6992 Columns 8 through 10 0.7570 0.5596 1.0117 rmsdist_acc = Columns 1 through 7 1.0682 1.0625 1.0754 0.9525 0.8198 0.9523 0.8362 Columns 8 through 10 0.8700 0.7481 1.0058 maxdist_acc = Columns 1 through 7 6.3248 4.0883 5.4354 3.3026 3.2596 3.8395 4.4242 Columns 8 through 10 2.9288 2.8701 4.9504 New model built with probe residual match_score_concat_latent = Columns 1 through 7 false_pairs=1.0e+30 * [1.4587 0.7947 1.0777 0.4841 0.3108 0.4634 0.5163 0.3209 0.1996 0.9191 0.7528 0.3726 0.8976 2.8188 0.1048 0.4620 0.4186 0.0868] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 1.1411 1.1289 1.1564 0.9073 0.6721 0.9070 0.6992 Columns 8 through 10 0.7570 0.5596 1.0117 false_pairs=1.0e+30 * [ 0.7528 0.3726 0.8976 2.8188 0.1048 0.4620 0.4186 0.0868 1.2215 1.4385 0.1447 0.6823 4.5549 2.4208 0.9814 5.0784 0.8731 5.1190 0.6655] MANY MORE RANDOMS local_stress_sum = 1.0e+03 * Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 3.3969 1.9504 2.5844 4.5964 1.0311 1.7395 Columns 57 through 63 2.4063 0.7953 2.0265 3.6009 1.1958 2.5011 3.4861 Columns 64 through 70 4.3762 3.0710 5.7514 2.3124 4.5553 2.7019 6.3725 local_stress_sum_sqr = 1.0e+04 * Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 1.3144 0.6161 1.1829 3.8473 0.1602 0.6298 Columns 57 through 63 0.6578 0.1303 2.0513 2.4618 0.2291 0.9354 6.6129 Columns 64 through 70 3.5384 1.4367 6.7318 1.1842 6.7521 0.9751 9.7494 sum_ux = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 31.2895 33.2111 33.8396 35.9064 30.5706 32.8830 Columns 57 through 63 31.8778 31.8168 31.5256 29.4166 35.0734 35.5362 32.7767 Columns 64 through 70 31.4723 29.5642 28.2382 32.8922 32.6239 37.5288 33.2511 sum_uy = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 33.8349 33.5454 33.3301 30.9435 34.2467 34.8030 Columns 57 through 63 33.5368 34.3654 37.0526 32.0750 34.1163 33.4005 31.9452 Columns 64 through 70 35.7871 33.1327 33.2267 34.2763 35.7814 32.9335 32.7822 f_acc = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 false_pairs=[ 1.3588 0.7802 1.0337 1.8386 0.4124 0.6958 0.9625 0.3181 0.8106 1.4404 0.4783 1.0004 1.3944 1.7505 1.2284 2.3006 0.9250 1.8221 1.0807 2.5490] rmsdist_acc = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 1.1657 0.8833 1.0167 1.3559 0.6422 0.8342 Columns 57 through 63 0.9811 0.5640 0.9003 1.2002 0.6916 1.0002 1.1809 Columns 64 through 70 1.3231 1.1083 1.5168 0.9618 1.3499 1.0396 1.5966 maxdist_acc = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 4.7480 4.5737 4.0541 6.6318 3.1066 4.1313 Columns 57 through 63 3.1534 2.7081 6.5200 5.8169 2.8303 4.1646 8.7860 Columns 64 through 70 5.7162 4.8662 9.4946 5.0909 7.9400 4.0982 9.1362 New model built with probe residual match_score_concat_latent = 1.0e+30 * Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 0.7528 0.3726 0.8976 2.8188 0.1048 0.4620 Columns 57 through 63 0.4186 0.0868 1.2215 1.4385 0.1447 0.6823 4.5549 Columns 64 through 70 2.4208 0.9814 5.0784 0.8731 5.1190 0.6655 6.8156 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 1.3588 0.7802 1.0337 1.8386 0.4124 0.6958 Columns 57 through 63 0.9625 0.3181 0.8106 1.4404 0.4783 1.0004 1.3944 Columns 64 through 70 1.7505 1.2284 2.3006 0.9250 1.8221 1.0807 2.5490 true - unseen people local_stress_sum = 1.0e+03 * Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 1.2387 0.5070 0.8182 0.3910 1.2476 0.4111 Columns 57 through 63 0.8695 0.1966 0.4944 0.2249 0.3163 0.1356 3.6651 Columns 64 through 69 0.7511 0.8836 0.1936 0.4471 0.3441 0.2145 local_stress_sum_sqr = 1.0e+04 * Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 0.2023 0.0527 0.1229 0.0213 0.5852 0.0437 Columns 57 through 63 0.1094 0.0053 0.0824 0.0067 0.0360 0.0026 4.2037 Columns 64 through 69 0.1580 0.1520 0.0056 0.0519 0.0178 0.0090 sum_ux = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 32.8430 34.3814 34.8235 34.5432 35.7654 38.2519 Columns 57 through 63 31.5815 28.2465 34.9547 34.4119 34.0822 32.9134 33.8721 Columns 64 through 69 33.6310 33.5676 35.0354 33.4505 34.4000 34.3153 sum_uy = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 31.4237 33.6464 31.6406 35.1190 33.9935 31.9800 Columns 57 through 63 32.0784 32.7533 33.0398 31.8667 34.0724 32.3042 34.8458 Columns 64 through 69 38.1820 35.9267 32.1207 32.8768 32.5107 33.1561 f_acc = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 true_pairs= [0.4955 0.2028 0.3273 0.1564 0.4991 0.1644 0.3478 0.0787 0.1978 0.0899 0.1265 0.0542 1.4660 0.3005 0.3534 0.0775 0.1788 0.1376 0.0858] rmsdist_acc = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 0.7039 0.4503 0.5721 0.3955 0.7064 0.4055 Columns 57 through 63 0.5897 0.2805 0.4447 0.2999 0.3557 0.2329 1.2108 Columns 64 through 69 0.5481 0.5945 0.2783 0.4229 0.3710 0.2929 maxdist_acc = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 2.3944 2.2830 2.6292 1.6808 4.4407 2.6885 Columns 57 through 63 2.6794 1.2053 2.9871 1.4633 2.3514 0.8761 7.8621 Columns 64 through 69 3.1992 2.7287 1.2293 2.0809 1.5754 1.4116 New model built with probe residual match_score_concat_latent = true_pairs= 1.0e+30 * [0.1387 0.0356 0.0788 0.0140 0.3835 0.0340 0.0701 0.0035 0.0704 0.0043 0.0240 0.0019 3.0631 0.1237 0.1163 0.0040 0.0400 0.0122 0.0060] Best concatenated match so far is image #1 match_score_mean = Columns 1 through 7 0 0 0 0 0 0 0 Columns 8 through 14 0 0 0 0 0 0 0 Columns 15 through 21 0 0 0 0 0 0 0 Columns 22 through 28 0 0 0 0 0 0 0 Columns 29 through 35 0 0 0 0 0 0 0 Columns 36 through 42 0 0 0 0 0 0 0 Columns 43 through 49 0 0 0 0 0 0 0 Columns 50 through 56 0 0.4955 0.2028 0.3273 0.1564 0.4991 0.1644 Columns 57 through 63 0.3478 0.0787 0.1978 0.0899 0.1265 0.0542 1.4660 Columns 64 through 69 0.3005 0.3534 0.0775 0.1788 0.1376 0.0858 209 pairs, 13 smoothing true local_stress_sum = 1.0e+03 * Columns 1 through 10 0.2110 0.5283 0.1937 0.5253 0.1366 0.2341 0.2496 0.5566 0.5124 0.3105 Column 11 1.3515 local_stress_sum_sqr = 1.0e+03 * Columns 1 through 10 0.1080 0.4272 0.0483 0.5829 0.0351 0.0806 0.0983 0.5091 0.4268 0.1592 Column 11 3.9807 sum_ux = Columns 1 through 10 31.5473 34.6028 33.1687 31.4417 30.8853 31.0018 34.1655 33.3665 34.9968 32.1218 Column 11 41.1455 sum_uy = Columns 1 through 10 32.3670 35.8529 37.2049 34.5762 34.7165 31.0992 34.0202 32.9868 34.9570 33.7955 Column 11 30.5635 f_acc = Columns 1 through 10 0.0844 0.2113 0.0775 0.2101 0.0547 0.0937 0.0998 0.2226 0.2050 0.1242 Column 11 0.5406 rmsdist_acc = Columns 1 through 10 0.2905 0.4597 0.2783 0.4584 0.2338 0.3060 0.3160 0.4718 0.4527 0.3524 Column 11 0.7352 maxdist_acc = Columns 1 through 10 1.8408 1.9568 1.1016 2.1485 1.3507 1.3439 1.3403 1.9262 2.0628 1.4212 Column 11 3.1866 New model built with probe residual match_score_concat_latent = 1.0e+73 * Columns 1 through 10 0.1621 0.2035 0.1545 0.2076 0.1536 0.1563 0.1592 0.2160 0.1848 0.1565 Column 11 1.5890 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0.0844 0.2113 0.0775 0.2101 0.0547 0.0937 0.0998 0.2226 0.2050 0.1242 Column 11 0.5406 local_stress_sum = 1.0e+03 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 1.3444 0.5037 0.6143 0.2723 0.4561 0.5826 0.8755 0.2185 0.6228 0.5379 Columns 21 through 30 0.4187 0.6471 1.1504 0.6165 0.7926 0.3247 0.2003 0.4620 5.5720 0.1295 Columns 31 through 38 0.1924 0.3046 0.3837 0.6053 0.2133 0.6145 0.2303 0.7836 local_stress_sum_sqr = 1.0e+04 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0.3994 0.0539 0.0991 0.0124 0.0362 0.0737 0.1924 0.0062 0.0809 0.0566 Columns 21 through 30 0.1286 0.1092 0.2419 0.0779 0.1964 0.0280 0.0078 0.0673 6.4546 0.0028 Columns 31 through 38 0.0047 0.0178 0.0224 0.0730 0.0083 0.2385 0.0079 0.1052 sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 40.1322 25.8275 31.6651 37.8877 33.3659 32.2712 30.1471 27.0544 31.5572 36.9685 Columns 21 through 30 36.3359 31.8301 36.4752 32.7463 32.6987 30.9743 37.4323 39.4446 30.9674 32.0967 Columns 31 through 38 28.9181 35.5971 32.2713 32.3045 34.5952 30.2180 34.2532 33.3696 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 34.6696 33.8376 32.6202 32.5349 30.9938 32.6369 33.9690 33.6764 30.5212 35.8210 Columns 21 through 30 33.2115 35.3325 34.8190 30.6125 29.2234 32.0600 32.3640 33.2962 27.3579 33.6613 Columns 31 through 38 33.5153 33.4086 33.6527 34.8888 32.2217 35.7837 28.6831 33.2462 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0.5377 0.2015 0.2457 0.1089 0.1824 0.2330 0.3502 0.0874 0.2491 0.2152 Columns 21 through 30 0.1675 0.2588 0.4602 0.2466 0.3170 0.1299 0.0801 0.1848 2.2288 0.0518 Columns 31 through 38 0.0769 0.1218 0.1535 0.2421 0.0853 0.2458 0.0921 0.3134 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0.7333 0.4489 0.4957 0.3300 0.4271 0.4827 0.5918 0.2956 0.4991 0.4639 Columns 21 through 30 0.4093 0.5087 0.6783 0.4966 0.5631 0.3604 0.2830 0.4299 1.4929 0.2276 Columns 31 through 38 0.2774 0.3491 0.3918 0.4921 0.2921 0.4958 0.3035 0.5599 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 3.2181 2.1704 2.4795 1.6006 1.8796 2.9311 3.8576 1.1893 2.3483 2.5380 Columns 21 through 30 4.5804 3.1774 3.6798 2.4904 3.8453 2.2793 1.4397 2.9844 8.6792 1.2374 Columns 31 through 38 0.9752 1.6120 1.4654 2.2348 1.2455 4.5391 1.2283 2.3930 New model built with probe residual match_score_concat_latent = 1.0e+74 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0.1616 0.0272 0.0446 0.0156 0.0176 0.0304 0.0473 0.0154 0.0291 0.0245 Columns 21 through 30 0.0289 0.0310 0.0669 0.0230 0.0833 0.0170 0.0155 0.0307 3.0556 0.0154 Columns 31 through 38 0.0154 0.0176 0.0168 0.0202 0.0164 0.1109 0.0154 0.0239 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0.5377 0.2015 0.2457 0.1089 0.1824 0.2330 0.3502 0.0874 0.2491 0.2152 Columns 21 through 30 0.1675 0.2588 0.4602 0.2466 0.3170 0.1299 0.0801 0.1848 2.2288 0.0518 Columns 31 through 38 0.0769 0.1218 0.1535 0.2421 0.0853 0.2458 0.0921 0.3134 local_stress_sum = true_pairs= 1.0e+04 * [0.0772 0.0403 0.0316 0.0387 0.0273 0.0274 0.0488 0.0149 0.0165 0.0224 0.0155 0.0346 0.0161 0.1337 0.0685 0.0819 0.0371 0.1252 0.0406 0.0871 0.0196 0.0497 0.0226 0.0316 0.0135 0.8673 0.0751 0.0881 0.0196 0.0452 0.0341 0.0219 0.0163 0.0344 0.0233 0.0696 0.0324 0.0282 0.0623] local_stress_sum_sqr = true_pairs= 1.0e+05 *[ 0.0107 0.0025 0.0016 0.0044 0.0017 0.0013 0.0047 0.0004 0.0004 0.0009 0.0009 0.0018 0.0006 0.0238 0.0097 0.0123 0.0020 0.0590 0.0032 0.0109 0.0005 0.0082 0.0007 0.0035 0.0003 1.4582 0.0158 0.0151 0.0006 0.0053 0.0018 0.0009 0.0005 0.0021 0.0014 0.0468 0.0025 0.0022 0.0081] sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 34.6028 33.2385 33.8770 Columns 41 through 50 33.8483 35.5206 37.0650 36.3338 34.0888 35.8839 36.9517 29.5598 31.1237 33.0850 Columns 51 through 60 32.3109 33.3958 36.6488 32.1704 36.3958 33.6737 31.6422 30.8773 32.5978 33.1326 Columns 61 through 70 33.9860 31.5476 27.8751 32.1807 33.3965 32.9669 31.0903 34.4200 36.3509 33.0561 Columns 71 through 77 33.7981 31.4170 33.2568 30.3933 35.2911 36.2585 29.9691 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 34.4754 34.6858 35.3723 Columns 41 through 50 36.8876 32.7689 32.4743 33.6422 32.7016 30.1708 31.1670 36.1938 31.8968 29.7645 Columns 51 through 60 34.7904 31.0590 30.7934 32.3644 34.9021 34.1504 31.4034 33.9342 33.3821 33.4909 Columns 61 through 70 35.2893 33.0079 32.8489 38.0605 37.4766 32.8244 34.8408 33.8755 33.1675 32.0491 Columns 71 through 77 33.7288 34.7707 29.4076 33.3832 33.0129 35.0061 34.3662 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0.3089 0.1613 0.1262 Columns 41 through 50 0.1549 0.1094 0.1095 0.1950 0.0597 0.0658 0.0895 0.0621 0.1386 0.0644 Columns 51 through 60 0.5348 0.2740 0.3277 0.1484 0.5008 0.1622 0.3483 0.0785 0.1988 0.0903 Columns 61 through 70 0.1264 0.0542 3.4691 0.3005 0.3524 0.0786 0.1809 0.1366 0.0877 0.0652 Columns 71 through 77 0.1376 0.0931 0.2785 0.1295 0.1126 4.9471 0.2492 true_pairs= [0.3089 0.1613 0.1262 0.1549 0.1094 0.1095 0.1950 0.0597 0.0658 0.0895 0.0621 0.1386 0.0644 0.5348 0.2740 0.3277 0.1484 0.5008 0.1622 0.3483 0.0785 0.1988 0.0903 0.1264 0.0542 3.4691 0.3005 0.3524 0.0786 0.1809 0.1366 0.0877 0.0652 0.1376 0.0931 0.2785 0.1295 0.1126 0.2492] rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0.5557 0.4016 0.3553 Columns 41 through 50 0.3936 0.3307 0.3309 0.4416 0.2443 0.2566 0.2991 0.2492 0.3723 0.2538 Columns 51 through 60 0.7313 0.5235 0.5725 0.3852 0.7077 0.4028 0.5902 0.2802 0.4459 0.3005 Columns 61 through 70 0.3555 0.2327 1.8626 0.5482 0.5936 0.2803 0.4253 0.3696 0.2962 0.2553 Columns 71 through 77 0.3710 0.3051 0.5277 0.3599 0.3356 2.2242 0.4992 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 2.4994 1.6365 1.4543 Columns 41 through 50 2.2130 2.3617 1.4669 1.9519 1.1189 1.2113 1.3713 2.3361 1.7632 1.6806 Columns 51 through 60 2.3055 2.5990 2.6269 1.6457 4.4303 2.0151 2.6328 1.2373 3.0013 1.4693 Columns 61 through 70 2.3296 0.8553 8.0541 3.1900 2.7030 1.2539 2.1103 1.6339 1.4991 1.5129 Columns 71 through 77 1.5422 2.1277 6.3156 1.9572 1.9674 9.8035 2.2085 New model built with probe residual match_score_concat_latent = 1.0e+75 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0.0025 0.0017 0.0016 Columns 41 through 50 0.0020 0.0016 0.0016 0.0020 0.0015 0.0015 0.0016 0.0018 0.0016 0.0015 Columns 51 through 60 0.0095 0.0044 0.0049 0.0016 0.0229 0.0017 0.0047 0.0015 0.0043 0.0015 Columns 61 through 70 0.0019 0.0015 0.6395 0.0077 0.0071 0.0015 0.0027 0.0015 0.0016 0.0015 Columns 71 through 77 0.0016 0.0016 0.0271 0.0016 0.0016 1.5755 0.0037 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0.3089 0.1613 0.1262 Columns 41 through 50 0.1549 0.1094 0.1095 0.1950 0.0597 0.0658 0.0895 0.0621 0.1386 0.0644 Columns 51 through 60 0.5348 0.2740 0.3277 0.1484 0.5008 0.1622 0.3483 0.0785 0.1988 0.0903 Columns 61 through 70 0.1264 0.0542 3.4691 0.3005 0.3524 0.0786 0.1809 0.1366 0.0877 0.0652 Columns 71 through 77 0.1376 0.0931 0.2785 0.1295 0.1126 4.9471 0.2492 Best mean match so far is image #37 Best man match overall is image #% Run this function in matching mode true_pairs= 1.0e+73 * [100 * [ 0.0025 0.0017 0.0016 0.0020 0.0016 0.0016 0.0020 0.0015 0.0015 0.0016 0.0018 0.0016 0.0015 0.0095 0.0044 0.0049 0.0016 0.0229 0.0017 0.0047 0.0015 0.0043 0.0015 0.0019 0.0015 0.6395 0.0077 0.0071 0.0015 0.0027 0.0015 0.0016 0.0015 0.0016 0.0016 0.0271 0.0016 0.0016 0.0037],10*[0.1616 0.0272 0.0446 0.0156 0.0176 0.0304 0.0473 0.0154 0.0291 0.0245 0.0289 0.0310 0.0669 0.0230 0.0833 0.0170 0.0155 0.0307 3.0556 0.0154 0.0154 0.0176 0.0168 0.0202 0.0164 0.1109 0.0154 0.0239],[ 0.1621 0.2035 0.1545 0.2076 0.1536 0.1563 0.1592 0.2160 0.1848 0.1565]] sort(true_pairs) ans = 1.0e+75 * Columns 1 through 10 0.0015 0.0015 0.0015 0.0015 0.0015 0.0015 0.0015 0.0015 0.0015 0.0015 Columns 11 through 20 0.0015 0.0015 0.0015 0.0015 0.0015 0.0016 0.0016 0.0016 0.0016 0.0016 Columns 21 through 30 0.0016 0.0016 0.0016 0.0016 0.0016 0.0016 0.0016 0.0016 0.0016 0.0016 Columns 31 through 40 0.0016 0.0016 0.0016 0.0017 0.0017 0.0017 0.0017 0.0018 0.0018 0.0018 Columns 41 through 50 0.0018 0.0019 0.0020 0.0020 0.0020 0.0020 0.0021 0.0022 0.0023 0.0024 Columns 51 through 60 0.0025 0.0025 0.0027 0.0027 0.0029 0.0029 0.0030 0.0031 0.0031 0.0037 Columns 61 through 70 0.0043 0.0044 0.0045 0.0047 0.0047 0.0049 0.0067 0.0071 0.0077 0.0083 Columns 71 through 78 0.0095 0.0111 0.0162 0.0229 0.0271 0.3056 0.6395 1.5755 figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04233d403.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04233d391.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04313d63.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04313d57.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04339d301.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04339d291.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04407d322.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04407d316.ppm')) error figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04475d131.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04473d242.ppm')) false local_stress_sum = 1.0e+04 * 0.2324 0.2481 0.1850 0.1047 0.2779 0.3859 2.3749 local_stress_sum_sqr = 1.0e+06 * 0.0127 0.0119 0.0086 0.0015 0.0165 0.0227 1.4177 sum_ux = 35.8376 30.9816 34.7146 33.6380 33.5344 31.8372 30.1878 sum_uy = 32.2537 33.4850 33.1256 32.7759 33.0005 33.9863 34.4158 f_acc = 0.9296 0.9923 0.7400 0.4187 1.1116 1.5438 9.4996 rmsdist_acc = 0.9642 0.9961 0.8602 0.6471 1.0543 1.2425 3.0821 maxdist_acc = 6.3164 5.1072 5.0523 2.4961 4.9024 5.3278 19.2679 New model built with probe residual match_score_concat_latent = 1.0e+75 * 0.0530 0.0551 0.0388 0.0063 0.0795 0.0962 7.0127 0.0679 1.9624 Best concatenated match so far is image #1 match_score_mean = 0.9296 0.9923 0.7400 0.4187 1.1116 1.5438 9.4996 GMDSPCA false_pairs= 1.0e+75 * [0.0530 0.0551 0.0388 0.0063 0.0795 0.0962 7.0127 0.0679 1.9624 0.044491 0.032300] false_pairs= 1.0e+74 * [0.8654 0.0784 1.6135 0.3495 0.2776 1.0718 0.0613 0.1642 0.7754 0.5125 0.9447 0.1772 1.0807 0.4827 0.0253 0.0892 0.3796 0.4593 0.2329 0.0592 0.0421 0.7926 5.2217 0.4662 0.1924 0.5476 1.5221 0.0698 0.2990 0.2547 0.0541 0.9356 0.8226 0.0913 0.4082 3.1301 1.3852 0.5804 3.1179 0.5376 2.9634 0.3767 3.8619 0.6957 0.1025 0.0780 0.1172 0.3326 0.4745 0.5944 0.7956 1.8661 0.4031 0.8190 0.1415 0.1378 0.3521 0.2095 0.4336] CLOSE ===== figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04336d394.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04575d415.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04394d408.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04535d262.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04603d148.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04379d295.ppm')) ===== DISTANT (false positive): ==== figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04428d246.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04370d228.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04542d113.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04313d63.ppm')) FAC false_pairs= [1.2893 0.5080 2.1630 0.7841 0.7717 1.6890 0.4248 0.7133 1.4837 1.0675 1.5257 0.7614 1.7552 0.8956 0.2590 0.5078 0.8902 0.8919 0.8119 0.3744 0.3194 1.0852] SQR-distance false_pairs= 1.0e+05 * [0.2049 0.0193 0.4666 0.0775 0.0697 0.2684 0.0153 0.0424 0.1919 0.1296 0.2240 0.0478 0.2606 0.1141 0.0059 0.0223 0.0942 0.1186 0.0545 0.0133 0.0094 0.1838 1.1455] distance false_pairs= 1.0e+03 * [3.2232 1.2699 5.4075 1.9602 1.9293 4.2224 1.0619 1.7833 3.7093 2.6686 3.8143 1.9035 4.3881 2.2389 0.6474 1.2694 2.2256 2.2298 2.0298 0.9359 0.7984 2.7130 7.6020 ] local_stress_sum = 1.0e+03 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 3.2232 1.2699 5.4075 1.9602 1.9293 Columns 81 through 90 4.2224 1.0619 1.7833 3.7093 2.6686 3.8143 1.9035 4.3881 2.2389 0.6474 Columns 91 through 98 1.2694 2.2256 2.2298 2.0298 0.9359 0.7984 2.7130 7.6020 local_stress_sum_sqr = 1.0e+05 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0.2049 0.0193 0.4666 0.0775 0.0697 Columns 81 through 90 0.2684 0.0153 0.0424 0.1919 0.1296 0.2240 0.0478 0.2606 0.1141 0.0059 Columns 91 through 98 0.0223 0.0942 0.1186 0.0545 0.0133 0.0094 0.1838 1.1455 sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 34.0642 34.8520 33.7837 33.7922 30.2334 Columns 81 through 90 30.8329 39.0343 33.3885 36.6222 36.3165 32.9924 29.0883 32.8178 37.4832 36.9522 Columns 91 through 98 28.0454 34.3602 32.0123 32.7559 31.8824 35.8783 31.5088 33.5433 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 33.9622 34.9492 33.3421 32.1919 32.1701 Columns 81 through 90 39.8245 34.1062 32.1761 33.8764 33.0785 33.0082 31.0840 30.4702 35.3991 35.1835 Columns 91 through 98 29.5723 33.2558 34.3910 32.1083 32.0100 33.3033 31.8177 34.9867 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 1.2893 0.5080 2.1630 0.7841 0.7717 Columns 81 through 90 1.6890 0.4248 0.7133 1.4837 1.0675 1.5257 0.7614 1.7552 0.8956 0.2590 Columns 91 through 98 0.5078 0.8902 0.8919 0.8119 0.3744 0.3194 1.0852 3.0408 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 1.1355 0.7127 1.4707 0.8855 0.8785 Columns 81 through 90 1.2996 0.6517 0.8446 1.2181 1.0332 1.2352 0.8726 1.3248 0.9463 0.5089 Columns 91 through 98 0.7126 0.9435 0.9444 0.9011 0.6119 0.5651 1.0417 1.7438 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 5.5575 2.3119 6.7122 5.3521 3.7484 Columns 81 through 90 5.5916 2.5874 2.9894 4.5802 4.6702 4.2533 3.2784 5.6904 5.4950 2.0075 Columns 91 through 98 2.6474 3.8530 4.9162 3.4133 2.3697 2.2030 5.8956 8.5461 New model built with probe residual match_score_concat_latent = 1.0e+74 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0.8654 0.0784 1.6135 0.3495 0.2776 Columns 81 through 90 1.0718 0.0613 0.1642 0.7754 0.5125 0.9447 0.1772 1.0807 0.4827 0.0253 Columns 91 through 98 0.0892 0.3796 0.4593 0.2329 0.0592 0.0421 0.7926 5.2217 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 1.2893 0.5080 2.1630 0.7841 0.7717 Columns 81 through 90 1.6890 0.4248 0.7133 1.4837 1.0675 1.5257 0.7614 1.7552 0.8956 0.2590 Columns 91 through 98 0.5078 0.8902 0.8919 0.8119 0.3744 0.3194 1.0852 3.0408 local_stress_sum = 1.0e+03 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 3.3934 1.8895 2.6235 4.3445 1.0431 Columns 81 through 90 1.7608 2.4353 0.7859 2.0461 3.4555 1.1955 2.4814 3.3456 4.1261 3.0862 Columns 91 through 100 5.6078 2.4048 4.3832 2.6292 6.5072 3.0465 1.0389 1.2647 0.8424 1.9373 Columns 101 through 110 3.1717 3.2047 2.4984 5.6551 2.4335 3.6056 1.4746 1.6129 2.1522 2.0284 Column 111 1.6702 false_pairs= 1.0e+03 * [3.3934 1.8895 2.6235 4.3445 1.0431 1.7608 2.4353 0.7859 2.0461 3.4555 1.1955 2.4814 3.3456 4.1261 3.0862 5.6078 2.4048 4.3832 2.6292 6.5072 3.0465 1.0389 1.2647 0.8424 1.9373 3.1717 3.2047 2.4984 5.6551 2.4335 3.6056 1.4746 1.6129 2.1522 2.0284 1.6702] local_stress_sum_sqr = 1.0e+04 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 1.3107 0.5867 1.2421 3.3111 0.1651 Columns 81 through 90 0.6366 0.6763 0.1273 2.2281 2.3752 0.2278 0.9342 6.8475 3.2232 1.4547 Columns 91 through 100 6.6402 1.1986 6.2276 0.9247 9.7603 1.6333 0.2455 0.1955 0.2932 1.0108 Columns 101 through 110 1.1357 1.4024 1.8512 4.4869 0.9153 2.0124 0.3393 0.3461 0.8136 0.5025 Column 111 0.9186 sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 30.6816 33.8534 34.2992 32.1820 31.5037 Columns 81 through 90 31.8907 31.4812 37.1899 32.2989 33.4232 35.1133 36.8944 33.8212 34.0742 34.0918 Columns 91 through 100 32.3788 29.6423 30.8975 36.8038 33.0731 34.2409 31.7342 32.6903 36.5184 34.4939 Columns 101 through 110 31.0224 33.0894 32.3795 36.4906 30.5774 32.4494 31.2927 35.6432 34.9192 36.6871 Column 111 31.8886 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 33.0196 33.1528 30.6021 34.0267 33.2624 Columns 81 through 90 34.9249 33.6306 34.6158 34.7577 31.3792 32.9062 31.9600 32.1939 33.5941 32.3649 Columns 91 through 100 34.4334 31.8523 33.8450 31.1622 31.3216 32.0644 33.6613 32.2167 32.3641 34.8542 Columns 101 through 110 31.2468 32.8546 32.2104 31.1727 34.2659 32.3905 28.9887 36.0373 33.6936 32.5104 Column 111 31.5100 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 1.3573 0.7558 1.0494 1.7378 0.4172 Columns 81 through 90 0.7043 0.9741 0.3144 0.8184 1.3822 0.4782 0.9926 1.3382 1.6504 1.2345 Columns 91 through 100 2.2431 0.9619 1.7533 1.0517 2.6029 1.2186 0.4156 0.5059 0.3370 0.7749 Columns 101 through 110 1.2687 1.2819 0.9994 2.2620 0.9734 1.4422 0.5898 0.6452 0.8609 0.8114 Column 111 0.6681 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 1.1651 0.8694 1.0244 1.3183 0.6459 Columns 81 through 90 0.8392 0.9870 0.5607 0.9047 1.1757 0.6915 0.9963 1.1568 1.2847 1.1111 Columns 91 through 100 1.4977 0.9808 1.3241 1.0255 1.6133 1.1039 0.6446 0.7112 0.5805 0.8803 Columns 101 through 110 1.1264 1.1322 0.9997 1.5040 0.9866 1.2009 0.7680 0.8032 0.9278 0.9008 Column 111 0.8174 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 4.7354 4.6074 4.3288 6.5629 3.2342 Columns 81 through 90 4.0590 3.0928 2.6878 6.5312 6.8314 2.7884 3.7366 8.8787 5.5054 4.9102 Columns 91 through 100 9.4075 4.9343 7.8172 4.0446 9.1758 4.3941 3.0485 2.4013 4.8444 6.8570 Columns 101 through 110 3.6818 3.7701 5.1258 5.0944 5.0429 5.0727 3.2163 2.6682 4.2597 3.3183 Column 111 6.3266 New model built with probe residual match_score_concat_latent = 1.0e+74 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0.4662 0.1924 0.5476 1.5221 0.0698 Columns 81 through 90 0.2990 0.2547 0.0541 0.9356 0.8226 0.0913 0.4082 3.1301 1.3852 0.5804 Columns 91 through 100 3.1179 0.5376 2.9634 0.3767 3.8619 0.6957 0.1025 0.0780 0.1172 0.3326 Columns 101 through 110 0.4745 0.5944 0.7956 1.8661 0.4031 0.8190 0.1415 0.1378 0.3521 0.2095 Column 111 0.4336 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 1.3573 0.7558 1.0494 1.7378 0.4172 Columns 81 through 90 0.7043 0.9741 0.3144 0.8184 1.3822 0.4782 0.9926 1.3382 1.6504 1.2345 Columns 91 through 100 2.2431 0.9619 1.7533 1.0517 2.6029 1.2186 0.4156 0.5059 0.3370 0.7749 Columns 101 through 110 1.2687 1.2819 0.9994 2.2620 0.9734 1.4422 0.5898 0.6452 0.8609 0.8114 Column 111 0.6681 more randoms with big model 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 0.0610 0.0179 0.0525 0.0259 0.0048 Columns 231 through 240 0.0009 0.0005 0.0246 0.3107 0.0099 0.0308 0.0659 0.1208 0.0049 0.0164 Columns 241 through 250 0.0721 0.0016 0.0081 0.0408 0.0863 0.0063 0.0120 0.1535 0.0457 3.1752 Columns 251 through 260 0.2257 0.0037 0.0029 0.0173 0.0100 0.0290 0.0012 0.0004 0.0138 0.0021 Columns 261 through 270 0.0107 0.0097 0.1339 0.0076 0.0016 0.0133 0.0263 0.0149 0.0017 0.0008 Columns 271 through 280 0.0050 0.0247 0.0165 0.0569 0.0427 0.0102 0.0074 0.0438 0.0017 0.0113 Columns 281 through 290 0.0020 0.0019 0.0108 0.0002 0.0052 0.0817 0.0827 0.0037 0.0007 0.0198 Columns 291 through 300 0.0217 0.0465 0.0102 0.0745 0.0048 0.0022 0.0036 0.0744 0.0017 0.0040 Column 301 0.0109 sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 32.0391 34.2796 36.4056 33.9505 31.7592 Columns 231 through 240 28.9608 31.1157 34.2811 32.5405 34.3475 34.2753 32.2553 34.0298 33.7820 32.5059 Columns 241 through 250 32.4702 37.2733 33.9964 30.5259 37.3676 35.0451 35.2104 33.9617 29.9389 34.9669 Columns 251 through 260 31.0904 33.3839 36.4606 33.0919 31.9264 33.3861 35.1776 36.1793 36.5284 34.5444 Columns 261 through 270 33.8776 35.8513 33.0954 33.0231 35.3796 32.0537 30.3624 31.5584 35.1926 35.1178 Columns 271 through 280 31.7097 33.6364 33.6512 32.9389 30.4167 33.1990 33.4044 32.0119 35.0410 31.8813 Columns 281 through 290 35.3381 33.0598 28.1763 34.2803 34.8826 34.5092 33.2214 31.0175 32.2486 33.8733 Columns 291 through 300 33.8763 33.0922 33.0772 30.1863 31.6100 32.7257 34.9097 32.2845 30.6988 38.6858 Column 301 39.0354 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 33.4170 35.5762 31.2403 33.5458 30.7893 Columns 231 through 240 31.9422 36.9168 32.9449 32.7348 34.5691 38.6864 33.7351 33.7303 32.9743 31.0126 Columns 241 through 250 32.8889 34.0119 35.5556 36.1661 33.3533 34.2665 32.7102 32.1141 32.6121 33.1935 Columns 251 through 260 33.3465 32.9631 33.8896 33.7241 36.3083 30.8908 36.5907 35.8358 34.5932 31.3438 Columns 261 through 270 33.7229 34.0001 31.9581 36.7162 31.8092 33.1098 32.9096 33.7195 34.1116 35.3619 Columns 271 through 280 32.3808 33.5147 32.6439 33.7073 33.2095 33.2493 32.4340 32.3004 33.6137 32.6396 Columns 281 through 290 32.4767 32.4986 34.0694 31.5409 33.3620 33.7843 34.3251 31.6418 30.6782 36.2214 Columns 291 through 300 32.7615 34.1347 38.0498 36.8202 34.5548 33.8569 30.6963 32.2772 34.2086 30.5294 Column 301 33.0706 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 1.7687 1.4278 2.3265 1.6144 0.5362 Columns 231 through 240 0.3201 0.2203 1.5438 4.0397 0.9880 1.8196 2.0274 2.9789 0.7563 1.0890 Columns 241 through 250 2.4852 0.4008 1.0042 2.1878 2.5718 0.7197 1.1659 2.5367 1.4533 8.9796 Columns 251 through 260 4.2691 0.6541 0.5837 1.3213 1.0568 1.8286 0.3707 0.2192 1.0279 0.5033 Columns 261 through 270 1.1023 0.9419 2.7363 0.8658 0.3680 1.0380 1.4040 1.2323 0.4466 0.2529 Columns 271 through 280 0.7159 1.5020 1.1180 1.9086 1.7489 0.9265 0.8320 1.9356 0.4223 1.0058 Columns 281 through 290 0.4283 0.4107 1.1484 0.1730 0.6439 2.7301 2.7049 0.5819 0.2702 1.1260 Columns 291 through 300 1.3276 1.5939 0.9164 2.1257 0.6486 0.4605 0.6102 2.3644 0.3810 0.6729 Column 301 1.1349 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 1.3299 1.1949 1.5253 1.2706 0.7323 Columns 231 through 240 0.5658 0.4694 1.2425 2.0099 0.9940 1.3489 1.4239 1.7259 0.8697 1.0436 Columns 241 through 250 1.5764 0.6331 1.0021 1.4791 1.6037 0.8484 1.0798 1.5927 1.2055 2.9966 Columns 251 through 260 2.0662 0.8087 0.7640 1.1495 1.0280 1.3523 0.6088 0.4682 1.0139 0.7094 Columns 261 through 270 1.0499 0.9705 1.6542 0.9305 0.6066 1.0188 1.1849 1.1101 0.6683 0.5029 Columns 271 through 280 0.8461 1.2255 1.0573 1.3815 1.3225 0.9626 0.9122 1.3912 0.6498 1.0029 Columns 281 through 290 0.6544 0.6408 1.0716 0.4159 0.8024 1.6523 1.6447 0.7628 0.5198 1.0611 Columns 291 through 300 1.1522 1.2625 0.9573 1.4580 0.8053 0.6786 0.7811 1.5376 0.6173 0.8203 Column 301 1.0653 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 9.2481 4.7272 6.4554 4.6253 3.7891 Columns 231 through 240 2.3199 2.0014 5.6600 13.9293 4.5321 5.4032 7.3898 11.1580 3.3721 6.0099 Columns 241 through 250 6.6509 2.7210 4.0443 7.1859 7.2231 3.7706 4.2201 11.0319 8.2336 25.4843 Columns 251 through 260 10.5611 2.8897 2.8725 4.4672 4.2351 5.6559 2.2855 1.8221 5.2455 2.3201 Columns 261 through 270 3.8861 4.6782 9.9123 4.5461 2.7860 6.8036 6.6216 3.8754 2.4533 2.4743 Columns 271 through 280 3.4314 5.2601 4.7659 7.2820 7.9340 5.4467 4.0764 7.2101 2.8135 5.7074 Columns 281 through 290 3.1917 3.4691 4.5711 1.5142 4.1402 8.7808 8.4658 3.8800 1.9865 5.9222 Columns 291 through 300 5.2392 9.3273 4.8089 8.6143 3.3194 2.8614 2.9915 8.9551 2.6794 3.4516 Column 301 3.8791 New model built with probe residual match_score_concat_latent = 1.0e+76 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 0.0266 0.0069 0.0218 0.0103 0.0016 Columns 231 through 240 0.0004 0.0002 0.0102 0.1604 0.0044 0.0134 0.0268 0.0600 0.0020 0.0079 Columns 241 through 250 0.0311 0.0007 0.0033 0.0160 0.0404 0.0024 0.0045 0.0770 0.0230 1.6817 Columns 251 through 260 0.1068 0.0015 0.0011 0.0072 0.0038 0.0123 0.0005 0.0002 0.0060 0.0009 Columns 261 through 270 0.0042 0.0034 0.0682 0.0032 0.0007 0.0055 0.0126 0.0062 0.0007 0.0003 Columns 271 through 280 0.0022 0.0095 0.0068 0.0257 0.0192 0.0041 0.0030 0.0202 0.0008 0.0050 Columns 281 through 290 0.0009 0.0008 0.0044 0.0002 0.0023 0.0367 0.0386 0.0017 0.0003 0.0085 Columns 291 through 300 0.0083 0.0217 0.0045 0.0295 0.0021 0.0010 0.0015 0.0339 0.0007 0.0017 Column 301 0.0044 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 0.0059 0.0032 0.0101 0.0019 0.0033 Columns 231 through 240 0.0068 0.0076 0.0061 0.0004 0.0034 2.0796 0.0013 0.0087 0.0027 0.0068 Columns 241 through 250 0.0008 0.0749 0.1198 0.0048 0.0405 0.0021 0.2780 0.0542 0.0154 0.0062 Columns 251 through 254 0.0103 0.0271 0.0060 0.0144 sum_ux = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 38.1808 33.1716 31.2357 31.0667 33.0732 Columns 231 through 240 31.0363 37.1403 30.4747 29.9152 34.3007 34.9390 35.5771 33.3414 35.5594 34.5810 Columns 241 through 250 29.7797 31.0712 31.5954 35.8846 37.2791 37.5847 34.4637 33.0176 34.7479 35.8283 Columns 251 through 254 29.8484 29.8814 32.2791 29.9506 sum_uy = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 30.0567 33.4703 35.6119 36.7552 34.3484 Columns 231 through 240 33.0476 35.9437 32.3678 34.5085 33.2937 29.1152 35.6354 34.4838 34.2163 36.0961 Columns 241 through 250 34.4173 33.2618 34.9702 34.8446 31.3777 35.3490 32.6865 30.3358 32.8404 32.2092 Columns 251 through 254 32.6819 34.1342 31.9065 35.1854 f_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 0.7866 0.5238 1.0536 0.4625 0.6607 Columns 231 through 240 0.8969 0.8415 0.5053 0.2321 0.5446 9.1475 0.3616 0.8990 0.5286 0.7200 Columns 241 through 250 0.2838 2.7821 1.9392 0.6018 1.6296 0.4557 3.9199 2.3252 1.2837 0.8054 Columns 251 through 254 1.0693 1.7655 0.7096 1.1912 rmsdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 0.8869 0.7237 1.0265 0.6801 0.8128 Columns 231 through 240 0.9471 0.9174 0.7108 0.4817 0.7380 3.0245 0.6013 0.9482 0.7270 0.8486 Columns 241 through 250 0.5327 1.6680 1.3926 0.7757 1.2766 0.6750 1.9799 1.5249 1.1330 0.8974 Columns 251 through 254 1.0341 1.3287 0.8424 1.0914 maxdist_acc = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 3.5681 4.4927 4.5920 3.0172 2.6093 Columns 231 through 240 3.7432 3.7596 4.4214 1.6795 3.4726 24.3040 2.7119 3.8302 2.9484 4.0215 Columns 241 through 250 2.3812 6.0104 11.0439 4.3062 8.0248 2.6432 14.6875 5.4836 4.9631 3.4867 Columns 251 through 254 4.0670 5.6743 3.4479 5.0892 New model built with probe residual match_score_concat_latent = 1.0e+75 * Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 0.0217 0.0141 0.0422 0.0077 0.0135 Columns 231 through 240 0.0233 0.0322 0.0243 0.0017 0.0142 9.7365 0.0056 0.0398 0.0106 0.0324 Columns 241 through 250 0.0031 0.3191 0.5682 0.0094 0.1831 0.0084 1.3822 0.2114 0.0648 0.0269 Columns 251 through 254 0.0435 0.1150 0.0241 0.0644 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 10 0 0 0 0 0 0 0 0 0 0 Columns 11 through 20 0 0 0 0 0 0 0 0 0 0 Columns 21 through 30 0 0 0 0 0 0 0 0 0 0 Columns 31 through 40 0 0 0 0 0 0 0 0 0 0 Columns 41 through 50 0 0 0 0 0 0 0 0 0 0 Columns 51 through 60 0 0 0 0 0 0 0 0 0 0 Columns 61 through 70 0 0 0 0 0 0 0 0 0 0 Columns 71 through 80 0 0 0 0 0 0 0 0 0 0 Columns 81 through 90 0 0 0 0 0 0 0 0 0 0 Columns 91 through 100 0 0 0 0 0 0 0 0 0 0 Columns 101 through 110 0 0 0 0 0 0 0 0 0 0 Columns 111 through 120 0 0 0 0 0 0 0 0 0 0 Columns 121 through 130 0 0 0 0 0 0 0 0 0 0 Columns 131 through 140 0 0 0 0 0 0 0 0 0 0 Columns 141 through 150 0 0 0 0 0 0 0 0 0 0 Columns 151 through 160 0 0 0 0 0 0 0 0 0 0 Columns 161 through 170 0 0 0 0 0 0 0 0 0 0 Columns 171 through 180 0 0 0 0 0 0 0 0 0 0 Columns 181 through 190 0 0 0 0 0 0 0 0 0 0 Columns 191 through 200 0 0 0 0 0 0 0 0 0 0 Columns 201 through 210 0 0 0 0 0 0 0 0 0 0 Columns 211 through 220 0 0 0 0 0 0 0 0 0 0 Columns 221 through 230 0 0 0 0 0 0.7866 0.5238 1.0536 0.4625 0.6607 Columns 231 through 240 0.8969 0.8415 0.5053 0.2321 0.5446 9.1475 0.3616 0.8990 0.5286 0.7200 Columns 241 through 250 0.2838 2.7821 1.9392 0.6018 1.6296 0.4557 3.9199 2.3252 1.2837 0.8054 Columns 251 through 254 1.0693 1.7655 0.7096 1.1912 6/8/2011 Full face, masked PCA model real pairs local_stress_sum = 1.0e+04 * Columns 1 through 11 0.0513 0.2479 0.0307 0.0910 0.2719 0.0458 0.0596 0.1216 0.4229 0.0434 0.2824 Columns 12 through 22 0.0609 0.0497 0.1579 0.1464 0.4633 0.1348 0.0625 0.0825 0.0945 0.2922 0.6853 Columns 23 through 33 0.1483 1.5487 0.1948 0.1004 0.0394 1.1000 0.3197 0.0753 0.0177 1.1284 1.7862 local_stress_sum_sqr = 1.0e+05 * Columns 1 through 11 0.0052 0.2718 0.0017 0.0127 0.1036 0.0029 0.0053 0.0191 0.3415 0.0033 0.1645 Columns 12 through 22 0.0050 0.0050 0.0503 0.0317 0.4335 0.0246 0.0055 0.0099 0.0157 0.1625 0.6757 Columns 23 through 33 0.0339 3.2981 0.0597 0.0151 0.0041 1.3625 0.3673 0.0114 0.0005 1.4755 6.5378 sum_ux = Columns 1 through 11 37.6175 34.5633 29.9838 30.6086 32.8779 33.3203 36.8971 35.6107 31.2089 35.1103 33.7444 Columns 12 through 22 35.5201 36.1248 29.8134 32.6943 29.4392 35.6076 36.5493 33.1032 35.4805 32.6377 36.5214 Columns 23 through 33 34.0564 34.9725 34.4348 35.5951 37.8958 34.7672 34.2975 35.6502 34.2177 38.3936 31.9094 sum_uy = Columns 1 through 11 31.9716 33.0909 33.0095 31.8288 34.4138 31.3086 33.3857 35.1958 31.6955 32.2755 32.0849 Columns 12 through 22 34.1455 32.7593 31.3180 30.6348 32.2022 30.8388 32.4082 34.4373 32.2702 32.6787 34.3576 Columns 23 through 33 35.4302 32.7379 33.0583 31.4691 28.8446 31.7583 33.7106 28.2382 31.7238 32.2504 33.6133 f_acc = Columns 1 through 11 0.2053 0.9915 0.1226 0.3640 1.0878 0.1831 0.2385 0.4864 1.6915 0.1735 1.1298 Columns 12 through 22 0.2435 0.1987 0.6318 0.5857 1.8532 0.5392 0.2499 0.3299 0.3782 1.1689 2.7414 Columns 23 through 33 0.5931 6.1948 0.7794 0.4017 0.1577 4.4000 1.2789 0.3012 0.0709 4.5136 7.1447 rmsdist_acc = Columns 1 through 11 0.4531 0.9958 0.3502 0.6034 1.0430 0.4279 0.4884 0.6974 1.3006 0.4165 1.0629 Columns 12 through 22 0.4934 0.4457 0.7948 0.7653 1.3613 0.7343 0.4999 0.5744 0.6150 1.0812 1.6557 Columns 23 through 33 0.7701 2.4889 0.8828 0.6338 0.3971 2.0976 1.1309 0.5488 0.2663 2.1245 2.6730 maxdist_acc = Columns 1 through 11 2.2207 7.6203 1.5575 2.9158 4.7321 1.5887 1.9195 2.9331 5.4986 2.1454 4.8646 Columns 12 through 22 1.6601 2.5122 3.4541 2.7961 7.1671 2.4746 2.0469 2.1585 2.3900 4.9557 6.0706 Columns 23 through 33 3.4073 8.0613 3.3714 2.9464 2.1972 6.7619 9.0987 2.9965 1.2643 6.6172 13.1915 New model built with probe residual match_score_concat_latent = 1.0e+31 * Columns 1 through 11 0.0039 0.2179 0.0012 0.0088 0.0711 0.0019 0.0038 0.0134 0.2235 0.0024 0.1262 Columns 12 through 22 0.0033 0.0037 0.0364 0.0217 0.3229 0.0163 0.0038 0.0070 0.0113 0.1134 0.4877 Columns 23 through 33 0.0243 2.4012 0.0417 0.0100 0.0032 0.9218 0.2891 0.0089 0.0004 1.0068 5.0875 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 11 0.2053 0.9915 0.1226 0.3640 1.0878 0.1831 0.2385 0.4864 1.6915 0.1735 1.1298 Columns 12 through 22 0.2435 0.1987 0.6318 0.5857 1.8532 0.5392 0.2499 0.3299 0.3782 1.1689 2.7414 Columns 23 through 33 0.5931 6.1948 0.7794 0.4017 0.1577 4.4000 1.2789 0.3012 0.0709 4.5136 7.1447 local_stress_sum = 1.0e+04 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0.0361 0.0658 0.1507 0.5103 0.2206 Columns 56 through 61 0.0914 0.4687 1.2791 1.6310 0.0353 0.0554 local_stress_sum_sqr = 1.0e+05 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0.0018 0.0068 0.0360 0.3647 0.0653 Columns 56 through 61 0.0118 0.3790 2.6705 4.4953 0.0013 0.0066 sum_ux = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 35.6079 34.4355 30.1564 30.2320 34.7270 Columns 56 through 61 39.0775 32.6640 34.3901 33.0819 32.4990 36.2266 sum_uy = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 32.5017 32.5043 32.5415 29.9988 31.6015 Columns 56 through 61 33.4614 31.9471 32.2244 28.2552 31.7778 34.9286 f_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0.1446 0.2632 0.6027 2.0411 0.8823 Columns 56 through 61 0.3655 1.8748 5.1165 6.5241 0.1411 0.2214 true_pairs= [0.1446 0.2632 0.6027 2.0411 0.8823 0.3655 1.8748 5.1165 6.5241 0.1411 0.2214 0.2053 0.9915 0.1226 0.3640 1.0878 0.1831 0.2385 0.4864 1.6915 0.1735 1.1298 0.2435 0.1987 0.6318 0.5857 1.8532 0.5392 0.2499 0.3299 0.3782 1.1689 2.7414 0.5931 6.1948 0.7794 0.4017 0.1577 4.4000 1.2789 0.3012 0.0709 4.5136 7.1447] rmsdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0.3802 0.5130 0.7764 1.4287 0.9393 Columns 56 through 61 0.6045 1.3692 2.2620 2.5542 0.3756 0.4706 maxdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 1.4823 2.5364 3.3213 6.1844 3.5745 Columns 56 through 61 2.0889 6.2878 8.2235 10.5423 1.1029 2.7149 New model built with probe residual match_score_concat_latent = 1.0e+31 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0.0013 0.0046 0.0238 0.2535 0.0461 Columns 56 through 61 0.0076 0.2902 1.9683 3.3345 0.0008 0.0045 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0.1446 0.2632 0.6027 2.0411 0.8823 Columns 56 through 61 0.3655 1.8748 5.1165 6.5241 0.1411 0.2214 random local_stress_sum = 1.0e+04 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0.4495 0.3412 1.3010 0.4139 1.0616 Columns 56 through 66 0.1657 0.8451 1.1412 0.9640 1.1069 1.1953 1.6517 0.4799 2.3420 3.1875 0.3085 Columns 67 through 77 1.6428 0.4286 1.6592 2.2700 0.3579 1.7304 0.2898 0.4535 0.7642 0.8940 0.8461 Columns 78 through 88 1.2504 6.3114 0.6135 0.9816 0.1546 1.0635 0.3171 0.3043 0.6838 1.2026 1.4125 Columns 89 through 92 0.4722 2.3279 0.2155 0.3169 local_stress_sum_sqr = 1.0e+07 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0.0091 0.0017 0.0218 0.0036 0.0148 Columns 56 through 66 0.0004 0.0109 0.0223 0.0176 0.0181 0.0250 0.0512 0.0041 0.1033 0.1333 0.0013 Columns 67 through 77 0.0524 0.0026 0.0393 0.0776 0.0024 0.0524 0.0012 0.0035 0.0082 0.0142 0.0106 Columns 78 through 88 0.0264 5.0417 0.0054 0.0129 0.0004 0.0137 0.0016 0.0065 0.0093 0.0250 0.0272 Columns 89 through 92 0.0032 0.0903 0.0010 0.0012 sum_ux = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 34.4783 35.4952 33.9553 34.3223 35.9401 Columns 56 through 66 34.9574 33.7825 32.4916 31.7344 31.8171 29.5075 31.5234 33.9507 33.7543 33.8849 33.7645 Columns 67 through 77 31.1159 32.9777 36.3495 35.4729 31.6673 34.0791 33.8038 28.4768 30.9720 32.2998 36.8929 Columns 78 through 88 32.9522 32.0057 33.5921 32.3546 31.9497 30.6902 35.0906 34.9727 34.3486 33.7856 31.7825 Columns 89 through 92 34.7428 36.0552 35.7905 34.0284 sum_uy = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 31.9771 32.4979 32.0112 31.9004 33.5267 Columns 56 through 66 35.7900 32.7497 31.8025 36.6653 30.9678 34.8452 33.9477 32.1704 33.4988 31.3692 32.4300 Columns 67 through 77 34.1918 34.0857 35.1012 32.2603 36.0719 31.7750 31.4973 30.0032 31.6433 38.8072 30.4336 Columns 78 through 88 35.1460 30.9179 33.6066 33.7548 32.9258 35.3759 35.7717 32.4803 33.3360 34.3741 34.7658 Columns 89 through 92 32.2260 26.2471 32.0115 37.8979 f_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 1.7980 1.3650 5.2039 1.6558 4.2465 Columns 56 through 66 0.6626 3.3803 4.5649 3.8559 4.4275 4.7813 6.6070 1.9195 9.3680 12.7499 1.2341 Columns 67 through 77 6.5710 1.7143 6.6367 9.0799 1.4316 6.9215 1.1592 1.8139 3.0569 3.5761 3.3845 Columns 78 through 88 5.0017 25.2455 2.4542 3.9262 0.6183 4.2540 1.2685 1.2174 2.7352 4.8105 5.6501 Columns 89 through 92 1.8887 9.3116 0.8620 1.2675 Columns 1 through 11 false_pairs= [ 1.7980 1.3650 5.2039 1.6558 4.2465 0.6626 3.3803 4.5649 3.8559 4.4275 4.7813 6.6070 1.9195 9.3680 12.7499 1.2341 6.5710 1.7143 6.6367 9.0799 1.4316 6.9215 1.1592 1.8139 3.0569 3.5761 3.3845 5.0017 25.2455 2.4542 3.9262 0.6183 4.2540 1.2685 1.2174 2.7352 4.8105 5.6501 1.8887 9.3116 0.8620 1.2675] rmsdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 1.3409 1.1683 2.2812 1.2868 2.0607 Columns 56 through 66 0.8140 1.8386 2.1366 1.9636 2.1042 2.1866 2.5704 1.3855 3.0607 3.5707 1.1109 Columns 67 through 77 2.5634 1.3093 2.5762 3.0133 1.1965 2.6309 1.0767 1.3468 1.7484 1.8911 1.8397 Columns 78 through 88 2.2365 5.0245 1.5666 1.9815 0.7863 2.0625 1.1263 1.1033 1.6538 2.1933 2.3770 Columns 89 through 92 1.3743 3.0515 0.9285 1.1258 maxdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 7.4471 4.6816 8.2227 6.4385 6.7474 Columns 56 through 66 3.6077 6.9349 7.5267 9.6077 8.1327 9.0630 12.1645 5.9618 15.2849 12.0591 4.1453 Columns 67 through 77 11.2910 4.6569 10.0861 11.5895 5.4632 12.0337 4.0869 5.1266 9.1863 8.5409 7.8102 Columns 78 through 88 8.2398 46.1572 5.9405 6.8002 3.7983 9.4450 4.7322 11.3084 7.7041 9.4710 7.9087 Columns 89 through 92 5.6846 12.4901 4.0422 3.6699 New model built with probe residual match_score_concat_latent = 1.0e+33 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0.0074 0.0011 0.0157 0.0023 0.0101 Columns 56 through 66 0.0003 0.0076 0.0175 0.0126 0.0129 0.0196 0.0379 0.0031 0.0752 0.0908 0.0008 Columns 67 through 77 0.0411 0.0019 0.0277 0.0561 0.0016 0.0391 0.0008 0.0025 0.0060 0.0109 0.0073 Columns 78 through 88 0.0205 4.1612 0.0038 0.0091 0.0003 0.0096 0.0011 0.0060 0.0069 0.0193 0.0199 Columns 89 through 92 0.0024 0.0672 0.0007 0.0008 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 1.7980 1.3650 5.2039 1.6558 4.2465 Columns 56 through 66 0.6626 3.3803 4.5649 3.8559 4.4275 4.7813 6.6070 1.9195 9.3680 12.7499 1.2341 Columns 67 through 77 6.5710 1.7143 6.6367 9.0799 1.4316 6.9215 1.1592 1.8139 3.0569 3.5761 3.3845 Columns 78 through 88 5.0017 25.2455 2.4542 3.9262 0.6183 4.2540 1.2685 1.2174 2.7352 4.8105 5.6501 Columns 89 through 92 1.8887 9.3116 0.8620 1.2675 narrow band: true local_stress_sum = 1.0e+03 * 1.8933 2.9346 0.9573 1.6031 0.7182 0.3157 0.6791 4.5035 1.8375 2.3368 2.3216 local_stress_sum_sqr = 1.0e+04 * 0.5736 1.8064 0.3148 0.5817 0.1374 0.0199 0.0742 2.3738 0.6031 0.8569 0.9566 sum_ux = 31.8015 34.3870 35.0963 30.6103 30.9975 34.7761 27.6318 34.4949 31.7834 30.7816 31.8451 sum_uy = 30.8818 35.1364 33.3439 35.4394 31.6864 34.4199 34.3608 34.9387 34.2824 33.6688 35.6334 f_acc = 0.7573 1.1738 0.3829 0.6413 0.2873 0.1263 0.2716 1.8014 0.7350 0.9347 0.9287 rmsdist_acc = 0.8702 1.0834 0.6188 0.8008 0.5360 0.3554 0.5212 1.3422 0.8573 0.9668 0.9637 maxdist_acc = 4.7974 5.5541 4.1699 4.6679 3.5714 1.8781 2.2708 4.6312 4.0072 4.2642 4.8366 New model built with probe residual match_score_concat_latent = 1.0e+30 * 0.4157 1.4158 0.2555 0.4596 0.1064 0.0146 0.0476 1.6742 0.4340 0.6366 0.7352 Best concatenated match so far is image #1 match_score_mean = 0.7573 1.1738 0.3829 0.6413 0.2873 0.1263 0.2716 1.8014 0.7350 0.9347 0.9287 nose range =50 all cutoff at 0 random: Columns 1 through 11 false_pairs=[ 0.2333 0.1205 0.5625 0.0677 0.0689 0.3704 0.1873 0.1247 0.0719 0.1224 0.1016 0.1754 0.1870 0.1410 0.1602 0.1461 0.1399 0.2331 0.4234 0.1519 0.6850 0.5485 0.0935 1.1576 0.1478 0.1600 0.0725 0.1596 0.1060 0.8578 0.0662 0.0399 0.0883 0.3527 0.0304 0.0497 0.4942 0.1975 0.0853 0.5107 0.0717 0.0885 0.0416 0.1205 0.1246 0.3579 0.2386 0.1528 0.2488 0.2245 0.0578 0.0732 0.5106 0.0947 0.4949 0.0621 0.1378 0.3536 0.9726 0.3704 0.0521 0.5920 0.0462 0.0937 0.4507 0.3532 0.2915 0.8224 0.7247 0.0649 0.1486 0.0767 0.0936 0.2786 0.0479 0.0918 0.0724 0.2298 0.5136 0.2279 0.5278 0.1247 0.8696 0.0954 0.1256 0.0896 0.1605 0.2095 0.0496 0.0908 0.1041 0.0367 0.1346 0.0673 0.4062 0.1632 0.1290 0.1051 0.0878 0.0461 0.1776 0.0154 0.1629 0.3562 0.0848 0.3476 0.3091 0.0424 0.3524 0.2740 0.1859 0.4017 0.3771 0.0636 0.1261 0.3140 0.1799 0.1281 0.0458 0.1934 0.3686 0.3896 0.1657 0.1240 0.1375 0.1331 0.8283 0.0895 0.1148 0.2280 0.0620 0.0739 0.2056 0.1065 0.0525 0.1889 0.2363 0.0984 0.1969 0.1276 0.0929 0.2180 0.0814 0.3464 0.1632 0.0904 0.2386 0.2094 0.2073 0.1331 0.8434 0.2549 0.1827 0.4410 0.2273 0.0803 0.0544 0.2491 0.0819 0.0351 0.0981 0.0813 1.1028 0.1977 0.0497 0.1116 0.2794 0.1259 0.1904 0.2111 0.0425 0.0978 0.4523 0.5070 0.1837 0.0869 0.3962 0.1922 0.0990 0.0876 0.0273 0.9022 0.0809 0.1857 0.6153 0.3784 0.2055 0.1797 0.0646 0.1293 0.1321 0.8473 0.2266 0.0981 0.1316 0.1981 0.1194 0.0678 0.0255 0.0504 0.1772 0.3431 0.3832 0.1182 0.1749 0.2890 0.0472 0.0593 0.4017 0.0665 0.0809 0.1530 0.0774 0.3490 0.3715 0.1198 0.9443 0.4637 0.1814 0.1424 0.8256 0.2121 0.1685 0.4231 0.0604 0.0656 0.2480 0.1754 0.2236 0.1585 0.1638 0.2855 0.0964 0.0807 0.1218 0.6635 0.2243 0.0267 0.3265 0.1100 0.1372 0.1659 0.3749 0.2083 0.0647 0.2061 0.1089 0.4125 0.0792 0.2684 0.0413 0.2704 0.1459 0.1002 0.2827 0.0659 0.4406 0.4682 0.0983 0.1916 0.0464 0.0703] local_stress_sum_sqr = 1.0e+05 * Columns 1 through 11 0.1178 0.0417 1.0915 0.0101 0.0082 0.6633 0.0773 0.0384 0.0234 0.0713 0.0357 Columns 12 through 22 0.1996 0.1451 0.1627 0.1510 0.0294 0.0554 0.1313 0.6221 0.0729 1.5943 0.9879 Columns 23 through 33 0.0312 4.7450 0.1778 0.1367 0.0085 0.0822 0.0381 2.7500 0.0252 0.0030 0.0364 Columns 34 through 44 0.7479 0.0030 0.0056 0.7044 0.1574 0.0164 2.1093 0.0200 0.0295 0.0071 0.0931 Columns 45 through 55 0.0804 1.1903 0.7195 0.0545 0.3080 0.1539 0.0086 0.0130 1.0151 0.0287 0.4949 Columns 56 through 66 0.0097 0.0723 0.6920 4.3074 0.5882 0.0062 0.6964 0.0071 0.0618 0.5974 0.3830 Columns 67 through 77 0.2788 2.4783 2.6247 0.0121 0.0298 0.0176 0.0172 0.2892 0.0071 0.0131 0.0277 Columns 78 through 88 0.0747 0.7847 0.1236 0.4934 0.1106 2.0440 0.0276 0.0566 0.0151 0.0979 0.1085 Columns 89 through 99 0.0068 0.0591 0.0553 0.0065 0.0490 0.0445 0.7635 0.3671 0.0294 0.0344 0.0451 Columns 100 through 110 0.0084 0.0432 0.0010 0.0533 0.7632 0.0161 1.1551 0.2095 0.0045 0.2130 0.6480 Columns 111 through 121 0.3089 0.8233 0.6186 0.0134 0.1181 0.3420 0.1529 0.0671 0.0059 0.0762 0.4137 Columns 122 through 132 1.0636 0.1089 0.0428 0.0382 0.0241 1.1376 0.0364 0.0425 0.2773 0.0094 0.0168 Columns 133 through 143 0.3755 0.0266 0.0073 0.1370 0.3862 0.0685 0.2045 0.3426 0.0199 0.6940 0.0172 Columns 144 through 154 0.3476 0.3547 0.0420 0.0986 0.2241 0.0979 0.0359 2.2400 0.1886 0.1833 1.1528 Columns 155 through 165 0.1154 0.0233 0.0052 0.4305 0.0088 0.0033 0.0187 0.0221 4.5706 0.0680 0.0057 Columns 166 through 176 0.0567 0.5443 0.0404 0.1743 0.1838 0.0050 0.0496 0.8375 1.9987 0.0877 0.0242 Columns 177 through 187 0.3694 0.1911 0.0438 0.0186 0.0011 2.3270 0.0129 0.2853 2.8740 0.3497 0.0796 Columns 188 through 198 0.1711 0.0096 0.0601 0.0955 2.1552 0.1542 0.0219 0.2489 0.0577 0.0375 0.0136 Columns 199 through 209 0.0048 0.0073 0.1364 0.4333 0.7747 0.0393 0.1454 0.2711 0.0040 0.0096 0.5318 Columns 210 through 220 0.0142 0.0598 0.0345 0.0206 0.7903 0.6979 0.1156 2.7781 0.7128 0.0999 0.0386 Columns 221 through 231 2.9675 0.1411 0.0640 0.5277 0.0121 0.0153 0.3627 0.0740 0.1527 0.2134 0.0490 Columns 232 through 242 0.2366 0.0718 0.0127 0.0242 0.7214 0.1076 0.0020 0.3923 0.0730 0.0466 0.0981 Columns 243 through 253 0.1721 0.1882 0.0414 0.1055 0.0197 0.4614 0.0161 0.2332 0.0041 0.1406 0.0570 Columns 254 through 262 0.0308 0.4018 0.0158 1.1893 1.0192 0.0311 0.3113 0.0081 0.0173 sum_ux = Columns 1 through 11 32.2406 36.7020 31.2348 33.2003 31.8365 32.9006 32.6547 32.9014 33.6507 30.5713 29.3775 Columns 12 through 22 32.2914 32.5080 34.1493 34.6390 35.2279 31.9205 32.6211 35.7972 37.3886 36.0260 32.8715 Columns 23 through 33 39.5794 33.2887 38.3029 34.5317 32.7745 29.1385 34.4656 32.2145 36.6058 36.3558 33.8874 Columns 34 through 44 33.2276 30.0949 30.0972 33.4792 30.5696 28.4826 36.7996 32.0062 34.9949 34.6207 32.8118 Columns 45 through 55 36.9696 35.0412 34.0504 35.5678 35.4866 32.1705 35.1137 30.6738 31.4422 31.7402 35.9785 Columns 56 through 66 34.5817 34.4447 34.2487 29.9703 32.6698 33.8065 32.2269 35.2108 35.4167 35.2237 35.3997 Columns 67 through 77 33.8061 35.0189 32.6278 30.8439 32.2043 34.4179 34.2152 33.2323 30.9834 33.9983 34.1667 Columns 78 through 88 34.9456 34.0650 36.9113 34.2688 33.4215 36.9298 34.0345 30.9049 36.6088 27.0940 36.3571 Columns 89 through 99 33.2332 32.3726 34.1250 35.7959 37.0890 36.9657 32.1148 33.3335 35.7532 35.5800 37.6569 Columns 100 through 110 29.2694 33.7668 32.3417 34.0344 34.7254 31.5012 32.9997 34.3961 33.3219 31.8695 30.6366 Columns 111 through 121 31.5879 35.2130 34.6418 28.4776 30.0896 33.5079 33.0514 33.1729 34.2697 33.0699 34.4068 Columns 122 through 132 34.2265 30.9037 34.8806 38.3651 28.2022 34.7970 26.8166 31.4718 37.9588 31.4253 36.3405 Columns 133 through 143 34.2205 36.1441 31.3449 29.3644 32.2106 34.6002 36.6969 33.0874 31.9796 34.9026 37.3673 Columns 144 through 154 34.0626 36.5597 35.3435 32.7366 33.0155 31.0022 33.5451 34.0779 33.6338 33.6195 35.8218 Columns 155 through 165 33.2123 35.3291 29.1200 31.6001 34.9916 35.3304 32.9116 31.7455 33.5641 29.7101 34.3601 Columns 166 through 176 30.0350 30.6694 32.7996 33.1637 34.8994 32.8367 31.5156 36.6657 32.4381 33.5629 28.9571 Columns 177 through 187 33.6478 35.0784 36.1049 33.2431 33.6772 32.0371 30.1670 29.0929 33.1716 33.3767 30.4376 Columns 188 through 198 34.5235 35.5285 33.0066 32.4890 35.5309 35.4203 32.7926 31.0189 34.2165 30.3471 27.9118 Columns 199 through 209 33.4692 34.8322 33.3533 32.2749 30.8824 33.7605 31.9728 35.8351 32.3997 34.2637 33.1530 Columns 210 through 220 32.7044 34.4954 34.4268 37.9662 35.5526 33.5505 33.8082 32.2059 35.8532 36.7417 29.0043 Columns 221 through 231 32.4249 31.1447 29.6271 31.9853 36.5324 33.4278 32.7732 33.5947 32.6444 34.3397 32.3509 Columns 232 through 242 35.0122 36.2986 34.3279 27.4142 31.3511 36.4638 29.9452 35.1696 32.0639 34.4958 33.3042 Columns 243 through 253 33.7429 31.2409 31.2058 32.6066 32.9109 35.0707 34.0619 29.0970 33.7443 30.3849 32.3089 Columns 254 through 262 35.1269 32.6603 31.0709 34.0831 31.6066 34.7050 37.3936 36.5722 36.0383 sum_uy = Columns 1 through 11 29.8288 33.7963 36.2695 32.1617 34.8278 32.8707 31.5926 29.8076 33.6411 29.2027 32.6471 Columns 12 through 22 35.4985 34.3491 31.3329 32.4070 37.1161 32.0975 30.3085 29.9670 34.4173 32.1994 34.4072 Columns 23 through 33 35.9915 32.7904 33.4246 33.7293 33.6568 32.2944 33.9647 34.6631 32.1011 34.1604 33.7320 Columns 34 through 44 39.1334 33.1968 31.3901 32.8539 34.0056 33.3182 34.7926 35.7744 32.6010 33.3451 31.5329 Columns 45 through 55 31.4553 32.0107 31.3890 32.4856 31.3809 33.1824 34.6499 35.3741 31.8271 32.0980 30.1856 Columns 56 through 66 34.6979 34.0338 35.8276 34.9049 30.8532 32.1549 32.2771 33.2411 31.3199 32.7794 34.5757 Columns 67 through 77 35.3643 31.9300 33.5113 30.6617 31.8841 33.2991 35.6039 36.7951 36.0203 34.1618 33.4334 Columns 78 through 88 32.3807 30.6795 32.1389 35.7361 31.6787 33.0213 31.3658 30.8256 34.9513 33.0619 34.8506 Columns 89 through 99 35.8664 33.7449 31.8577 33.4505 29.8486 33.8549 33.7134 34.8744 32.3622 30.2335 33.9810 Columns 100 through 110 33.6394 33.4479 32.8432 35.2797 32.2906 33.1272 31.5998 32.0707 33.9499 35.2588 33.2877 Columns 111 through 121 33.2547 31.4368 29.8987 29.4515 38.4820 32.9647 35.0936 30.2480 36.5006 33.2227 30.8698 Columns 122 through 132 35.3479 31.4686 34.2057 31.2616 34.1358 31.3432 32.3368 35.0425 34.5902 29.2377 34.5702 Columns 133 through 143 34.6993 33.2846 36.5420 33.1694 33.8031 32.8832 31.0607 33.7704 32.3256 30.3545 31.3899 Columns 144 through 154 31.1028 32.3635 32.5688 32.6088 31.9010 32.1638 35.2698 37.9755 31.9508 32.3710 34.8314 Columns 155 through 165 29.5251 35.8420 32.2709 35.4148 30.8701 34.1453 36.5755 32.5695 35.9955 33.0537 35.0914 Columns 166 through 176 32.5425 30.6819 31.6564 31.8023 35.1959 33.4359 32.4726 34.8429 33.2769 26.7801 33.2703 Columns 177 through 187 40.0549 33.3869 35.5158 35.9270 32.2522 32.0963 33.3724 34.2430 32.1504 39.1770 34.0276 Columns 188 through 198 31.4696 35.4973 31.3205 30.4597 34.8885 32.0089 36.1364 32.8182 36.8890 33.7828 33.6153 Columns 199 through 209 31.6094 32.5026 31.1650 31.2685 36.9797 36.2205 33.5105 33.3003 33.3476 31.2959 32.3707 Columns 210 through 220 32.8128 30.4841 34.4123 33.9232 31.6783 33.7843 29.8599 32.8822 34.6670 35.9915 31.5330 Columns 221 through 231 30.9151 33.1760 33.5116 33.5314 36.4863 34.2627 35.6194 32.9544 32.3442 32.6189 32.4252 Columns 232 through 242 32.9800 32.8627 34.3615 35.5555 36.5641 32.2225 32.8657 32.9747 32.7338 34.3676 31.8432 Columns 243 through 253 32.4897 31.1859 31.5993 31.1499 34.8422 36.8364 30.4711 37.1986 33.0070 32.0339 31.4966 Columns 254 through 262 33.5975 33.5818 33.0410 34.0656 32.7715 33.1698 33.2766 35.3162 33.7495 f_acc = Columns 1 through 11 false_pairs=[0.9332 0.4820 2.2501 0.2708 0.2758 1.4817 0.7493 0.4987 0.2877 0.4896 0.4064 0.7016 0.7481 0.5639 0.6407 0.5843 0.5598 0.9326 1.6937 0.6075 2.7401 2.1941 0.3742 4.6304 0.5911 0.6399 0.2901 0.6382 0.4241 3.4313 0.2647 0.1598 0.3533 1.4109 0.1216 0.1986 1.9767 0.7901 0.3412 2.0427 0.2868 0.3541 0.1664 0.4819 0.4984 1.4314 0.9542 0.6112 0.9953 0.8979 0.2311 0.2927 2.0422 0.3787 1.9795 0.2483 0.5513 1.4144 3.8902 1.4815 0.2083 2.3681 0.1847 0.3748 1.8027 1.4128 1.1660 3.2894 2.8987 0.2594 0.5942 0.3070 0.3745 1.1145 0.1916 0.3671 0.2895 0.9192 2.0545 0.9116 2.1111 0.4987 3.4785 0.3816 0.5025 0.3584 0.6420 0.8380 0.1985 0.3632 0.4164 0.1468 0.5384 0.2692 1.6250 0.6528 0.5159 0.4203 0.3510 0.1843 0.7103 0.0615 0.6517 1.4247 0.3392 1.3902 1.2363 0.1695 1.4094 1.0959 0.7435 1.6068 1.5083 0.2544 0.5045 1.2559 0.7195 0.5123 0.1833 0.7735 1.4745 1.5584 0.6627 0.4959 0.5498 0.5322 3.3133 0.3582 0.4594 0.9120 0.2480 0.2955 0.8224 0.4259 0.2099 0.7554 0.9452 0.3935 0.7878 0.5106 0.3716 0.8719 0.3255 1.3858 0.6530 0.3617 0.9544 0.8377 0.8293 0.5325 3.3735 1.0197 0.7308 1.7639 0.9094 0.3212 0.2176 0.9964 0.3278 0.1403 0.3924 0.3253 4.4111 0.7909 0.1989 0.4465 1.1174 0.5037 0.7617 0.8442 0.1699 0.3911 1.8090 2.0281 0.7348 0.3474 1.5850 0.7686 0.3959 0.3505 0.1093 3.6090 0.3236 0.7426 2.4611 1.5136 0.8219 0.7187 0.2583 0.5174 0.5285 3.3893 0.9064 0.3925 0.5266 0.7925 0.4775 0.2712 0.1018 0.2018 0.7089 1.3725 1.5330 0.4727 0.6994 1.1561 0.1889 0.2371 0.2662 0.3237 0.6120 0.3096 1.3962 1.4860 0.4791 3.7774 1.8547 0.7255 0.5694 3.3024 0.8483 0.6739 1.6923 0.2415 0.2624 0.9919 0.7014 0.8946 0.6339 0.6552 1.1421 0.3855 0.3228 0.4871 2.6540 0.8973 0.1069 1.3059 0.4402 0.5490 0.6637 1.4997 0.8330 0.2587 0.8245 0.4354 1.6500 0.3168 1.0735 0.1652 1.0817 0.5837 0.4010 1.1308 0.2634 1.7626 1.8728 0.3933 0.7664 0.1854 0.2811] rmsdist_acc = Columns 1 through 11 0.9660 0.6943 1.5000 0.5204 0.5252 1.2172 0.8656 0.7062 0.5363 0.6997 0.6375 Columns 12 through 22 0.8376 0.8649 0.7509 0.8005 0.7644 0.7482 0.9657 1.3014 0.7794 1.6553 1.4813 Columns 23 through 33 0.6117 2.1518 0.7688 0.7999 0.5386 0.7989 0.6512 1.8524 0.5145 0.3997 0.5944 Columns 34 through 44 1.1878 0.3488 0.4457 1.4060 0.8889 0.5841 1.4292 0.5356 0.5951 0.4079 0.6942 Columns 45 through 55 0.7060 1.1964 0.9768 0.7818 0.9977 0.9476 0.4807 0.5411 1.4291 0.6154 1.4070 Columns 56 through 66 0.4983 0.7425 1.1893 1.9724 1.2172 0.4564 1.5389 0.4298 0.6122 1.3426 1.1886 Columns 67 through 77 1.0798 1.8137 1.7026 0.5093 0.7709 0.5541 0.6120 1.0557 0.4377 0.6059 0.5381 Columns 78 through 88 0.9587 1.4334 0.9548 1.4530 0.7062 1.8651 0.6177 0.7089 0.5987 0.8013 0.9154 Columns 89 through 99 0.4455 0.6026 0.6453 0.3831 0.7337 0.5188 1.2747 0.8080 0.7182 0.6483 0.5925 Columns 100 through 110 0.4293 0.8428 0.2481 0.8073 1.1936 0.5824 1.1791 1.1119 0.4117 1.1872 1.0468 Columns 111 through 121 0.8623 1.2676 1.2281 0.5043 0.7103 1.1207 0.8482 0.7158 0.4281 0.8795 1.2143 Columns 122 through 132 1.2484 0.8140 0.7042 0.7415 0.7295 1.8203 0.5985 0.6778 0.9550 0.4980 0.5436 Columns 133 through 143 0.9069 0.6526 0.4582 0.8692 0.9722 0.6273 0.8876 0.7146 0.6096 0.9338 0.5705 Columns 144 through 154 1.1772 0.8081 0.6014 0.9769 0.9152 0.9107 0.7297 1.8367 1.0098 0.8549 1.3281 Columns 155 through 165 0.9536 0.5667 0.4665 0.9982 0.5725 0.3746 0.6265 0.5704 2.1003 0.8893 0.4460 Columns 166 through 176 0.6682 1.0571 0.7098 0.8728 0.9188 0.4121 0.6254 1.3450 1.4241 0.8572 0.5894 Columns 177 through 187 1.2590 0.8767 0.6292 0.5920 0.3306 1.8997 0.5688 0.8618 1.5688 1.2303 0.9066 Columns 188 through 198 0.8477 0.5083 0.7193 0.7270 1.8410 0.9520 0.6265 0.7256 0.8902 0.6910 0.5207 Columns 199 through 209 0.3191 0.4492 0.8419 1.1716 1.2381 0.6875 0.8363 1.0752 0.4346 0.4869 1.2676 Columns 210 through 220 0.5159 0.5690 0.7823 0.5564 1.1816 1.2190 0.6922 1.9436 1.3619 0.8518 0.7546 Columns 221 through 231 1.8173 0.9210 0.8209 1.3009 0.4915 0.5122 0.9959 0.8375 0.9458 0.7962 0.8094 Columns 232 through 242 1.0687 0.6209 0.5682 0.6979 1.6291 0.9473 0.3270 1.1428 0.6635 0.7409 0.8147 Columns 243 through 253 1.2246 0.9127 0.5086 0.9080 0.6599 1.2845 0.5628 1.0361 0.4064 1.0400 0.7640 Columns 254 through 262 0.6332 1.0634 0.5132 1.3276 1.3685 0.6272 0.8754 0.4306 0.5302 maxdist_acc = Columns 1 through 11 5.5423 3.8257 8.4159 2.6093 2.1919 8.3758 4.4484 4.7595 3.6352 5.9605 4.2532 Columns 12 through 22 6.8967 5.9492 7.3071 5.9324 2.8014 4.6449 4.7530 8.1949 5.6073 9.9702 9.3402 Columns 23 through 33 4.2783 12.0780 8.7420 5.3200 2.3068 7.3073 3.8513 12.4985 4.0906 1.7860 4.3634 Columns 34 through 44 9.5944 2.1838 2.5695 8.1477 6.2685 3.3033 12.2633 4.1981 3.6131 2.9279 5.6355 Columns 45 through 55 5.8620 10.6795 10.5664 3.9958 7.3440 5.5975 2.4480 3.4005 8.9667 4.1646 7.3720 Columns 56 through 66 3.6056 4.2720 9.2711 13.0123 7.5463 2.4342 8.0092 3.6519 6.4269 8.4715 6.5221 Columns 67 through 77 5.0864 10.8694 13.0856 3.2544 3.6700 2.7558 2.9904 6.1878 3.1527 2.6926 4.6343 Columns 78 through 88 4.2766 7.7938 6.0678 5.3131 7.0243 10.0234 3.9577 3.5075 3.2032 5.2144 4.7886 Columns 89 through 99 2.8491 4.7145 5.9111 3.6551 3.8326 5.8967 9.8563 9.5255 3.3846 3.9959 4.8444 Columns 100 through 110 3.6325 3.3890 1.4800 3.8293 8.2711 2.9053 13.2279 6.4286 2.1291 5.2223 9.4595 Columns 111 through 121 8.3367 11.8684 8.6127 2.7863 7.2688 6.0891 7.5434 4.9381 2.4710 4.1856 8.8984 Columns 122 through 132 10.2404 4.8255 4.2123 3.7259 2.6374 8.7668 4.0547 4.3003 7.8715 3.0237 3.5634 Columns 133 through 143 8.1100 3.5171 2.5725 5.0612 6.8833 6.4958 7.0196 9.5285 3.3135 10.0421 3.5060 Columns 144 through 154 7.3970 9.2437 4.0852 3.9323 7.2892 4.1197 3.7563 11.6144 6.9239 7.0081 11.1662 Columns 155 through 165 4.1797 3.5857 2.6061 7.8051 2.0292 2.3565 2.7175 3.3086 13.5401 3.5580 2.6936 Columns 166 through 176 4.4995 7.5824 4.1524 6.5335 7.1577 2.2427 5.7378 11.3746 12.5639 5.3792 3.1840 Columns 177 through 187 7.3476 7.8647 5.8693 2.8761 1.5896 13.5332 2.7603 8.5782 14.3571 6.3287 3.8830 Columns 188 through 198 6.6447 2.7430 3.3770 5.8440 11.2035 5.7404 3.5431 9.1807 4.1674 3.6020 3.5329 Columns 199 through 209 3.3966 2.3812 5.5906 9.7496 8.9754 4.2909 6.6669 5.5146 2.1328 2.7320 7.2046 Columns 210 through 220 4.0640 5.2660 4.5015 3.0032 10.5090 9.6507 8.0359 11.9924 7.3962 6.5790 3.0140 Columns 221 through 231 12.0950 5.3252 4.8590 8.3778 2.9630 3.1508 8.6171 3.6278 5.9035 7.7767 4.4375 Columns 232 through 242 6.5542 5.8663 2.7216 3.1702 6.9658 5.2567 2.2969 7.6736 4.9757 3.4583 6.7771 Columns 243 through 253 4.4453 5.0766 6.0057 5.7321 2.6108 7.3913 3.4494 6.5348 2.4554 5.6382 3.8713 Columns 254 through 262 4.0893 7.4185 3.5077 11.8613 8.9068 4.0603 9.3585 2.8166 3.1454 New model built with probe residual match_score_concat_latent = 1.0e+31 * Columns 1 through 11 0.0915 0.0335 0.8667 0.0076 0.0062 0.5665 0.0572 0.0302 0.0191 0.0600 0.0275 Columns 12 through 22 0.1633 0.1133 0.1375 0.1301 0.0203 0.0464 0.1040 0.5339 0.0558 1.2829 0.8024 Columns 23 through 33 0.0218 3.8419 0.1583 0.1114 0.0058 0.0716 0.0316 2.2999 0.0201 0.0022 0.0286 Columns 34 through 44 0.6821 0.0024 0.0041 0.5625 0.1245 0.0133 1.8578 0.0160 0.0226 0.0058 0.0779 Columns 45 through 55 0.0677 0.9292 0.6164 0.0395 0.2556 0.1188 0.0070 0.0108 0.8751 0.0232 0.3598 Columns 56 through 66 0.0080 0.0526 0.5848 3.6453 0.4866 0.0043 0.5413 0.0055 0.0580 0.4686 0.3247 Columns 67 through 77 0.2335 1.9468 2.0982 0.0096 0.0209 0.0144 0.0130 0.2385 0.0053 0.0098 0.0228 Columns 78 through 88 0.0524 0.6247 0.1000 0.3512 0.0929 1.6908 0.0225 0.0441 0.0115 0.0814 0.0838 Columns 89 through 99 0.0052 0.0510 0.0459 0.0056 0.0395 0.0417 0.6524 0.2447 0.0227 0.0278 0.0389 Columns 100 through 110 0.0070 0.0287 0.0008 0.0410 0.7032 0.0124 1.0237 0.1691 0.0035 0.1483 0.5717 Columns 111 through 121 0.2680 0.7454 0.4578 0.0105 0.0844 0.2692 0.1284 0.0567 0.0047 0.0570 0.2746 Columns 122 through 132 0.9709 0.0855 0.0341 0.0268 0.0163 0.8474 0.0305 0.0335 0.2419 0.0076 0.0137 Columns 133 through 143 0.3114 0.0213 0.0061 0.1155 0.3464 0.0608 0.1699 0.3272 0.0165 0.5994 0.0134 Columns 144 through 154 0.2874 0.2435 0.0323 0.0726 0.1951 0.0771 0.0285 1.8243 0.1521 0.1599 0.9338 Columns 155 through 165 0.0880 0.0189 0.0039 0.3181 0.0056 0.0025 0.0144 0.0181 3.3853 0.0501 0.0044 Columns 166 through 176 0.0436 0.4472 0.0318 0.1385 0.1449 0.0040 0.0432 0.6781 1.6362 0.0710 0.0180 Columns 177 through 187 0.2688 0.1588 0.0349 0.0139 0.0008 1.8244 0.0097 0.2256 2.5395 0.2840 0.0547 Columns 188 through 198 0.1512 0.0078 0.0486 0.0729 1.7123 0.1270 0.0174 0.2247 0.0404 0.0292 0.0105 Columns 199 through 209 0.0043 0.0059 0.1179 0.3727 0.5495 0.0306 0.1267 0.2115 0.0027 0.0074 0.3961 Columns 210 through 220 0.0121 0.0514 0.0235 0.0163 0.6396 0.4935 0.0981 2.2211 0.6012 0.0816 0.0260 Columns 221 through 231 2.5565 0.1130 0.0485 0.4289 0.0089 0.0119 0.3147 0.0573 0.1231 0.1425 0.0371 Columns 232 through 242 0.1870 0.0654 0.0098 0.0172 0.4949 0.0819 0.0013 0.3433 0.0582 0.0360 0.0828 Columns 243 through 253 0.1094 0.1540 0.0343 0.0804 0.0145 0.3850 0.0120 0.1921 0.0033 0.1089 0.0461 Columns 254 through 262 0.0243 0.3413 0.0134 0.9816 0.8440 0.0255 0.2869 0.0065 0.0148 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 11 0.9332 0.4820 2.2501 0.2708 0.2758 1.4817 0.7493 0.4987 0.2877 0.4896 0.4064 Columns 12 through 22 0.7016 0.7481 0.5639 0.6407 0.5843 0.5598 0.9326 1.6937 0.6075 2.7401 2.1941 Columns 23 through 33 0.3742 4.6304 0.5911 0.6399 0.2901 0.6382 0.4241 3.4313 0.2647 0.1598 0.3533 Columns 34 through 44 1.4109 0.1216 0.1986 1.9767 0.7901 0.3412 2.0427 0.2868 0.3541 0.1664 0.4819 Columns 45 through 55 0.4984 1.4314 0.9542 0.6112 0.9953 0.8979 0.2311 0.2927 2.0422 0.3787 1.9795 Columns 56 through 66 0.2483 0.5513 1.4144 3.8902 1.4815 0.2083 2.3681 0.1847 0.3748 1.8027 1.4128 Columns 67 through 77 1.1660 3.2894 2.8987 0.2594 0.5942 0.3070 0.3745 1.1145 0.1916 0.3671 0.2895 Columns 78 through 88 0.9192 2.0545 0.9116 2.1111 0.4987 3.4785 0.3816 0.5025 0.3584 0.6420 0.8380 Columns 89 through 99 0.1985 0.3632 0.4164 0.1468 0.5384 0.2692 1.6250 0.6528 0.5159 0.4203 0.3510 Columns 100 through 110 0.1843 0.7103 0.0615 0.6517 1.4247 0.3392 1.3902 1.2363 0.1695 1.4094 1.0959 Columns 111 through 121 0.7435 1.6068 1.5083 0.2544 0.5045 1.2559 0.7195 0.5123 0.1833 0.7735 1.4745 Columns 122 through 132 1.5584 0.6627 0.4959 0.5498 0.5322 3.3133 0.3582 0.4594 0.9120 0.2480 0.2955 Columns 133 through 143 0.8224 0.4259 0.2099 0.7554 0.9452 0.3935 0.7878 0.5106 0.3716 0.8719 0.3255 Columns 144 through 154 1.3858 0.6530 0.3617 0.9544 0.8377 0.8293 0.5325 3.3735 1.0197 0.7308 1.7639 Columns 155 through 165 0.9094 0.3212 0.2176 0.9964 0.3278 0.1403 0.3924 0.3253 4.4111 0.7909 0.1989 Columns 166 through 176 0.4465 1.1174 0.5037 0.7617 0.8442 0.1699 0.3911 1.8090 2.0281 0.7348 0.3474 Columns 177 through 187 1.5850 0.7686 0.3959 0.3505 0.1093 3.6090 0.3236 0.7426 2.4611 1.5136 0.8219 Columns 188 through 198 0.7187 0.2583 0.5174 0.5285 3.3893 0.9064 0.3925 0.5266 0.7925 0.4775 0.2712 Columns 199 through 209 0.1018 0.2018 0.7089 1.3725 1.5330 0.4727 0.6994 1.1561 0.1889 0.2371 1.6067 Columns 210 through 220 0.2662 0.3237 0.6120 0.3096 1.3962 1.4860 0.4791 3.7774 1.8547 0.7255 0.5694 Columns 221 through 231 3.3024 0.8483 0.6739 1.6923 0.2415 0.2624 0.9919 0.7014 0.8946 0.6339 0.6552 Columns 232 through 242 1.1421 0.3855 0.3228 0.4871 2.6540 0.8973 0.1069 1.3059 0.4402 0.5490 0.6637 Columns 243 through 253 1.4997 0.8330 0.2587 0.8245 0.4354 1.6500 0.3168 1.0735 0.1652 1.0817 0.5837 Columns 254 through 262 0.4010 1.1308 0.2634 1.7626 1.8728 0.3933 0.7664 0.1854 0.2811 true local_stress_sum = 1.0e+04 * Columns 1 through 11 true_pairs=[ 0.0165 0.0389 0.0160 0.1325 0.0129 0.0139 0.0122 0.0507 0.0524 0.0128 0.0594 1.2020 0.0383 2.2865 0.0607 0.0643 0.0595 0.0220 0.0373 0.0355 0.1791 0.3014 0.4855 0.0660 0.0157 0.0341 0.0441 0.0187 0.2106 0.0328 0.0138 0.0235 0.0345 0.0140 0.0163 0.0079 0.0215 0.1053 0.0192 0.0310 0.0152 0.0258 0.0186 0.5900 0.0314 0.0219 0.0112 0.0114 0.0251 0.0196 0.0822 0.0103 1.0240 0.0401 0.0833 0.0388 0.1296 0.1854 0.0410 0.0115 0.0146 0.0083 0.2212 0.1918 0.0175 0.4691 0.0211 0.0369 0.1318 0.0135 0.0351 0.0064 0.0190 0.0134 0.1144 0.0473] local_stress_sum_sqr = 1.0e+06 * Columns 1 through 11 0.0001 0.0003 0.0001 0.0151 0.0001 0.0002 0.0001 0.0007 0.0017 0.0002 0.0016 Columns 12 through 22 0.2959 0.0004 1.1827 0.0011 0.0013 0.0013 0.0002 0.0005 0.0003 0.0704 0.0520 Columns 23 through 33 0.1015 0.0023 0.0001 0.0005 0.0034 0.0001 0.0649 0.0009 0.0001 0.0001 0.0003 Columns 34 through 44 0.0002 0.0001 0.0000 0.0001 0.0078 0.0001 0.0002 0.0004 0.0004 0.0002 0.1022 Columns 45 through 55 0.0004 0.0001 0.0001 0.0002 0.0003 0.0003 0.0023 0.0000 0.3422 0.0011 0.0016 Columns 56 through 66 0.0004 0.0037 0.0734 0.0025 0.0000 0.0004 0.0000 0.0146 0.0202 0.0002 0.1719 Columns 67 through 76 0.0001 0.0022 0.0287 0.0001 0.0010 0.0000 0.0001 0.0001 0.0055 0.0005 sum_ux = Columns 1 through 11 29.2876 31.1110 26.0782 31.9294 34.8556 34.0126 28.7679 30.6554 30.6684 36.7379 34.9897 Columns 12 through 22 32.3951 31.6818 33.4727 33.4233 33.0280 30.1006 32.1708 30.8214 33.2446 36.0703 34.6150 Columns 23 through 33 31.6202 37.3170 37.3646 32.1219 31.0817 33.4157 33.5847 32.5082 35.9754 37.5126 37.1501 Columns 34 through 44 29.1629 30.3441 32.8560 30.1147 33.1379 31.1885 28.1956 36.7062 29.3427 31.4416 30.8592 Columns 45 through 55 34.3879 34.6450 32.7166 32.7247 34.7019 34.0198 33.7074 31.7069 35.2112 34.9128 33.5049 Columns 56 through 66 31.2947 36.1793 36.0093 36.0550 34.1950 36.2683 35.9640 36.0572 31.1172 31.1768 34.5480 Columns 67 through 76 32.4343 32.9904 35.0413 31.8937 36.7897 32.9134 32.9998 35.1546 32.5992 36.9541 sum_uy = Columns 1 through 11 34.8991 33.6792 31.3875 32.3916 34.9590 30.3327 34.8196 33.0966 34.6691 32.4768 32.6096 Columns 12 through 22 33.3762 33.9974 39.5385 30.5504 32.6574 31.2845 31.6504 36.0772 30.2547 29.5771 30.6638 Columns 23 through 33 33.9224 30.3511 36.2312 31.8799 33.4163 32.0114 32.2580 35.8438 32.5695 35.3412 34.3524 Columns 34 through 44 35.4550 35.7634 31.8863 31.5446 33.3097 31.6688 33.9957 31.9183 34.3156 33.2693 31.6600 Columns 45 through 55 33.1633 33.4634 36.1060 31.0748 32.2848 36.4925 32.9670 33.2878 31.4563 33.5884 29.5310 Columns 56 through 66 34.9325 31.6608 32.1896 33.0823 31.8892 31.4986 33.9307 34.0666 31.9168 34.1978 36.8655 Columns 67 through 76 31.8125 31.0971 37.1134 34.9865 32.8458 34.5344 33.6787 33.9048 32.6310 34.8839 f_acc = Columns 1 through 11 0.0662 0.1557 0.0641 0.5301 0.0517 0.0555 0.0488 0.2027 0.2097 0.0511 0.2377 Columns 12 through 22 4.8079 0.1533 9.1460 0.2429 0.2571 0.2382 0.0880 0.1492 0.1420 0.7163 1.2055 Columns 23 through 33 1.9422 0.2640 0.0627 0.1364 0.1763 0.0746 0.8425 0.1312 0.0553 0.0938 0.1379 Columns 34 through 44 0.0560 0.0651 0.0318 0.0859 0.4210 0.0767 0.1241 0.0608 0.1032 0.0745 2.3602 Columns 45 through 55 0.1255 0.0878 0.0446 0.0456 0.1005 0.0782 0.3290 0.0413 4.0960 0.1603 0.3332 Columns 56 through 66 0.1554 0.5184 0.7418 0.1641 0.0459 0.0584 0.0330 0.8849 0.7673 0.0701 1.8765 Columns 67 through 76 0.0844 0.1477 0.5273 0.0538 0.1405 0.0254 0.0761 0.0535 0.4575 0.1892 true_pairs= [0.0662 0.1557 0.0641 0.5301 0.0517 0.0555 0.0488 0.2027 0.2097 0.0511 0.2377 4.8079 0.1533 9.1460 0.2429 0.2571 0.2382 0.0880 0.1492 0.1420 0.7163 1.2055 1.9422 0.2640 0.0627 0.1364 0.1763 0.0746 0.8425 0.1312 0.0553 0.0938 0.1379 0.0560 0.0651 0.0318 0.0859 0.4210 0.0767 0.1241 0.0608 0.1032 0.0745 2.3602 0.1255 0.0878 0.0446 0.0456 0.1005 0.0782 0.3290 0.0413 4.0960 0.1603 0.3332 0.1554 0.5184 0.7418 0.1641 0.0459 0.0584 0.0330 0.8849 0.7673 0.0701 1.8765 0.0844 0.1477 0.5273 0.0538 0.1405 0.0254 0.0761 0.0535 0.4575 0.1892] rmsdist_acc = Columns 1 through 11 0.2572 0.3946 0.2531 0.7281 0.2273 0.2355 0.2209 0.4502 0.4579 0.2261 0.4875 Columns 12 through 22 2.1927 0.3915 3.0242 0.4928 0.5070 0.4880 0.2967 0.3862 0.3769 0.8463 1.0979 Columns 23 through 33 1.3936 0.5138 0.2505 0.3694 0.4199 0.2732 0.9179 0.3622 0.2351 0.3063 0.3714 Columns 34 through 44 0.2365 0.2552 0.1783 0.2931 0.6489 0.2770 0.3523 0.2465 0.3213 0.2729 1.5363 Columns 45 through 55 0.3542 0.2962 0.2112 0.2136 0.3171 0.2797 0.5736 0.2033 2.0239 0.4004 0.5773 Columns 56 through 66 0.3942 0.7200 0.8613 0.4051 0.2143 0.2416 0.1817 0.9407 0.8760 0.2648 1.3698 Columns 67 through 76 0.2904 0.3843 0.7262 0.2320 0.3749 0.1594 0.2758 0.2312 0.6764 0.4349 maxdist_acc = Columns 1 through 11 2.0129 2.2042 2.0994 7.6240 1.4051 2.4050 1.6946 2.7185 3.8251 2.8057 3.4338 Columns 12 through 22 10.3783 2.4985 13.9953 2.7669 3.1034 2.9967 1.7949 2.8173 2.2076 10.8517 9.3028 Columns 23 through 33 10.9801 3.5457 2.1517 2.3394 4.7274 1.6712 10.7795 3.5218 1.5316 1.6602 1.9123 Columns 34 through 44 3.0436 1.3884 0.9135 1.9039 5.6356 1.7237 2.1043 3.5922 2.8067 2.7092 9.7566 Columns 45 through 55 2.7576 1.6487 2.0743 2.4132 2.0660 2.3764 3.5121 1.4569 12.2615 3.6253 2.6596 Columns 56 through 66 2.3809 3.7084 9.4168 5.3759 0.9758 2.6387 1.5096 6.3968 5.7005 2.4122 12.9344 Columns 67 through 76 1.7725 5.3504 9.0377 1.6823 3.5976 0.7352 1.6190 1.7854 4.4981 2.9258 New model built with probe residual match_score_concat_latent = 1.0e+31 * Columns 1 through 11 0.0010 0.0024 0.0009 0.1138 0.0007 0.0018 0.0005 0.0053 0.0133 0.0017 0.0133 Columns 12 through 22 2.2425 0.0030 9.0472 0.0082 0.0104 0.0112 0.0013 0.0044 0.0027 0.5769 0.4551 Columns 23 through 33 0.8584 0.0196 0.0010 0.0038 0.0300 0.0009 0.6098 0.0082 0.0006 0.0012 0.0021 Columns 34 through 44 0.0014 0.0006 0.0002 0.0010 0.0721 0.0011 0.0015 0.0037 0.0039 0.0017 0.7751 Columns 45 through 55 0.0037 0.0009 0.0006 0.0014 0.0028 0.0028 0.0185 0.0004 2.6796 0.0077 0.0119 Columns 56 through 66 0.0028 0.0294 0.6115 0.0193 0.0002 0.0039 0.0003 0.1136 0.1686 0.0019 1.5292 Columns 67 through 76 0.0006 0.0203 0.2491 0.0007 0.0078 0.0002 0.0008 0.0006 0.0463 0.0042 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 11 0.0662 0.1557 0.0641 0.5301 0.0517 0.0555 0.0488 0.2027 0.2097 0.0511 0.2377 Columns 12 through 22 4.8079 0.1533 9.1460 0.2429 0.2571 0.2382 0.0880 0.1492 0.1420 0.7163 1.2055 Columns 23 through 33 1.9422 0.2640 0.0627 0.1364 0.1763 0.0746 0.8425 0.1312 0.0553 0.0938 0.1379 Columns 34 through 44 0.0560 0.0651 0.0318 0.0859 0.4210 0.0767 0.1241 0.0608 0.1032 0.0745 2.3602 Columns 45 through 55 0.1255 0.0878 0.0446 0.0456 0.1005 0.0782 0.3290 0.0413 4.0960 0.1603 0.3332 Columns 56 through 66 0.1554 0.5184 0.7418 0.1641 0.0459 0.0584 0.0330 0.8849 0.7673 0.0701 1.8765 Columns 67 through 76 0.0844 0.1477 0.5273 0.0538 0.1405 0.0254 0.0761 0.0535 0.4575 0.1892 nose only true 1.0e+04 * Columns 1 through 11 0.0199 0.0116 0.0074 0.0259 0.0144 0.0068 0.0224 0.0574 0.0237 0.0166 0.0805 Columns 12 through 22 0.0234 0.0097 0.0109 0.0308 0.0668 0.0579 0.0091 0.0160 0.0251 0.0223 0.2629 Columns 23 through 33 0.7298 0.0193 0.7083 0.0386 0.0176 0.0145 0.0371 0.0075 0.0080 0.0161 0.0565 Columns 34 through 44 0.0088 0.0051 0.0049 0.0164 0.0840 0.0245 0.0547 0.0103 0.0094 0.0222 1.0763 Columns 45 through 55 0.0055 0.0144 0.0302 0.0063 0.0177 0.0085 0.0441 0.0080 0.9327 0.0108 0.0119 Columns 56 through 66 0.0276 0.0419 0.0659 0.0339 0.0092 0.0141 0.0396 0.0560 0.0607 0.0087 0.1554 Columns 67 through 76 0.0082 0.0136 0.0248 0.0108 0.2229 0.0027 0.0110 0.0094 0.0287 0.0276 local_stress_sum_sqr = 1.0e+05 * Columns 1 through 11 0.0054 0.0007 0.0006 0.0225 0.0050 0.0005 0.0091 0.0171 0.0126 0.0025 0.0403 Columns 12 through 22 0.0038 0.0006 0.0004 0.0051 0.0201 0.0222 0.0026 0.0014 0.0040 0.0152 1.1697 Columns 23 through 33 1.3981 0.0018 1.6413 0.0249 0.0162 0.0014 0.0365 0.0012 0.0027 0.0013 0.0179 Columns 34 through 44 0.0006 0.0001 0.0001 0.0029 0.0645 0.0054 0.0150 0.0005 0.0009 0.0015 2.9968 Columns 45 through 55 0.0002 0.0032 0.0048 0.0004 0.0077 0.0006 0.0208 0.0004 3.2365 0.0004 0.0005 Columns 56 through 66 0.0054 0.0097 0.1258 0.0066 0.0009 0.0024 0.0279 0.0186 0.0736 0.0011 0.6950 Columns 67 through 76 0.0005 0.0021 0.0203 0.0013 0.3360 0.0000 0.0009 0.0006 0.0090 0.0028 sum_ux = Columns 1 through 11 34.5505 40.1417 35.0443 37.2118 35.9819 33.4222 33.2059 29.9993 36.4013 30.9900 33.6568 Columns 12 through 22 33.7380 26.5017 32.2476 31.9185 33.1786 34.3197 31.6314 32.8063 32.0589 29.4833 28.3786 Columns 23 through 33 33.9565 34.6437 32.0995 34.3439 33.9046 39.0634 33.1017 34.6343 34.6662 30.6682 34.8120 Columns 34 through 44 33.4176 35.4585 33.1658 31.6698 31.9620 34.3504 30.9164 32.3434 28.9349 34.1238 34.1668 Columns 45 through 55 30.6227 34.3789 36.5476 32.1652 31.3049 31.0535 32.0481 32.7134 36.1621 33.0283 33.5155 Columns 56 through 66 33.5688 32.4291 31.4999 33.2899 36.8002 31.2896 34.2033 34.4191 32.7873 32.6257 32.9174 Columns 67 through 76 36.4483 31.0971 33.2683 32.7098 33.3756 33.5371 30.0078 35.4223 32.4953 37.5961 sum_uy = Columns 1 through 11 33.9662 33.6754 32.6652 31.9239 32.3261 33.7252 30.0894 31.5384 29.0907 30.7796 34.3855 Columns 12 through 22 33.6927 36.2179 36.4696 31.9119 32.4145 36.1983 36.8928 33.6515 29.8847 30.6102 31.0660 Columns 23 through 33 32.4333 36.7288 34.2775 37.5605 33.0619 32.8399 31.4192 33.0924 32.8777 32.9514 30.7191 Columns 34 through 44 34.0540 32.2200 29.9675 34.8275 35.9670 31.3062 29.6036 27.7873 31.8917 32.7256 28.6081 Columns 45 through 55 35.0038 35.1323 32.0527 33.9871 34.4607 31.0134 32.7000 34.5119 34.2222 32.2719 31.9220 Columns 56 through 66 30.5937 32.1247 34.4895 34.7364 32.2899 32.1851 33.8997 35.1929 30.6937 33.8893 31.4864 Columns 67 through 76 32.4293 31.0727 32.1939 33.9738 34.6270 32.9234 33.4129 37.4199 32.8054 28.7579 f_acc = Columns 1 through 11 true_pairs= [0.0797 0.0465 0.0297 0.1034 0.0574 0.0270 0.0897 0.2295 0.0947 0.0664 0.3222 0.0935 0.0388 0.0435 0.1232 0.2673 0.2315 0.0362 0.0640 0.1003 0.0894 1.0515 2.9192 0.0773 2.8331 0.1545 0.0705 0.0580 0.1483 0.0301 0.0319 0.0645 0.2262 0.0352 0.0202 0.0197 0.0655 0.3359 0.0982 0.2188 0.0412 0.0375 0.0887 4.3053 0.0220 0.0575 0.1210 0.0252 0.0707 0.0340 0.1766 0.0321 3.7307 0.0431 0.0475 0.1104 0.1676 0.2635 0.1354 0.0369 0.0565 0.1583 0.2242 0.2430 0.0349 0.6215 0.0327 0.0545 0.0992 0.0434 0.8914 0.0108 0.0441 0.0376 0.1148 0.1103] rmsdist_acc = Columns 1 through 11 0.2823 0.2156 0.1724 0.3216 0.2396 0.1644 0.2994 0.4790 0.3078 0.2576 0.5676 Columns 12 through 22 0.3059 0.1969 0.2085 0.3510 0.5170 0.4812 0.1903 0.2530 0.3166 0.2990 1.0254 Columns 23 through 33 1.7086 0.2780 1.6832 0.3931 0.2655 0.2407 0.3851 0.1736 0.1787 0.2541 0.4756 Columns 34 through 44 0.1877 0.1422 0.1405 0.2560 0.5795 0.3133 0.4678 0.2029 0.1936 0.2979 2.0749 Columns 45 through 55 0.1485 0.2397 0.3478 0.1587 0.2659 0.1843 0.4202 0.1792 1.9315 0.2075 0.2179 Columns 56 through 66 0.3322 0.4094 0.5134 0.3680 0.1920 0.2377 0.3979 0.4735 0.4929 0.1869 0.7884 Columns 67 through 76 0.1807 0.2333 0.3149 0.2082 0.9441 0.1037 0.2099 0.1939 0.3389 0.3322 maxdist_acc = Columns 1 through 11 2.6530 1.8564 1.5893 4.7489 3.7738 1.4555 3.5540 3.3193 4.5533 2.2723 4.1969 Columns 12 through 22 2.6689 1.5813 1.2634 2.5765 3.5438 3.8940 3.1333 2.1211 2.4905 4.1973 12.1736 Columns 23 through 33 9.9797 1.8352 10.3108 5.1414 4.1457 2.3615 5.1757 2.6220 3.3418 1.9377 3.9534 Columns 34 through 44 1.7280 1.0969 1.0373 2.3691 4.9464 2.8362 3.0363 1.4042 2.1726 1.8291 11.2802 Columns 45 through 55 1.3132 3.1266 2.3860 1.6043 3.7907 1.8307 3.6508 1.6104 12.4861 1.1531 1.4062 Columns 56 through 66 2.7831 2.8712 7.4273 3.1522 2.1203 2.7739 4.5057 3.4197 6.3574 2.3930 11.3171 Columns 67 through 76 1.7524 2.4778 4.6988 2.2595 8.0015 0.5363 1.8062 1.7191 3.9738 1.8312 New model built with probe residual match_score_concat_latent = 1.0e+31 * Columns 1 through 11 0.0048 0.0006 0.0005 0.0204 0.0044 0.0004 0.0082 0.0132 0.0119 0.0021 0.0352 Columns 12 through 22 0.0034 0.0005 0.0003 0.0044 0.0165 0.0187 0.0024 0.0012 0.0033 0.0137 1.0712 Columns 23 through 33 1.1657 0.0015 1.3462 0.0232 0.0154 0.0013 0.0337 0.0012 0.0009 0.0012 0.0154 Columns 34 through 44 0.0006 0.0002 0.0002 0.0027 0.0554 0.0046 0.0122 0.0005 0.0009 0.0011 2.3485 Columns 45 through 55 0.0002 0.0029 0.0037 0.0004 0.0056 0.0006 0.0182 0.0004 2.5469 0.0003 0.0004 Columns 56 through 66 0.0045 0.0084 0.1164 0.0058 0.0008 0.0022 0.0220 0.0163 0.0577 0.0010 0.6591 Columns 67 through 76 0.0004 0.0019 0.0171 0.0013 0.3006 0.0002 0.0007 0.0006 0.0083 0.0023 Best concatenated match so far is image #1 match_score_mean = Columns 1 through 11 0.0797 0.0465 0.0297 0.1034 0.0574 0.0270 0.0897 0.2295 0.0947 0.0664 0.3222 Columns 12 through 22 0.0935 0.0388 0.0435 0.1232 0.2673 0.2315 0.0362 0.0640 0.1003 0.0894 1.0515 Columns 23 through 33 2.9192 0.0773 2.8331 0.1545 0.0705 0.0580 0.1483 0.0301 0.0319 0.0645 0.2262 Columns 34 through 44 0.0352 0.0202 0.0197 0.0655 0.3359 0.0982 0.2188 0.0412 0.0375 0.0887 4.3053 Columns 45 through 55 0.0220 0.0575 0.1210 0.0252 0.0707 0.0340 0.1766 0.0321 3.7307 0.0431 0.0475 Columns 56 through 66 0.1104 0.1676 0.2635 0.1354 0.0369 0.0565 0.1583 0.2242 0.2430 0.0349 0.6215 Columns 67 through 76 0.0327 0.0545 0.0992 0.0434 0.8914 0.0108 0.0441 0.0376 0.1148 0.1103 random local_stress_sum = 1.0e+03 * 0.3877 0.5488 2.0641 0.2594 2.1012 1.4803 2.3789 0.6296 0.3746 0.5311 2.3456 local_stress_sum_sqr = 1.0e+04 * 0.1692 0.1232 1.5225 0.0467 2.1251 0.9878 3.1074 0.3828 0.2695 0.1147 2.9929 sum_ux = 31.9291 33.5716 31.8957 33.7534 34.4050 30.4841 34.1168 34.9217 32.3477 33.7029 33.5786 sum_uy = 30.4543 31.0143 29.2367 35.0013 32.5161 30.1788 32.4065 30.4498 30.1996 31.1043 29.0071 f_acc = 0.1551 0.2195 0.8256 0.1037 0.8405 0.5921 0.9516 0.2518 0.1498 0.2124 0.9383 false_pairs=[0.1551 0.2195 0.8256 0.1037 0.8405 0.5921 0.9516 0.2518 0.1498 0.2124 0.9383] rmsdist_acc = 0.3938 0.4685 0.9086 0.3221 0.9168 0.7695 0.9755 0.5018 0.3871 0.4609 0.9686 maxdist_acc = 4.0425 3.1907 5.5364 2.5710 6.7741 5.7405 6.3072 4.7486 5.1274 3.7014 7.0970 New model built with probe residual match_score_concat_latent = 1.0e+30 * 0.1531 0.1058 1.1924 0.0424 1.8029 0.8750 2.5161 0.3451 0.2346 0.0982 2.4655 Best concatenated match so far is image #1 match_score_mean = 0.1551 0.2195 0.8256 0.1037 0.8405 0.5921 0.9516 0.2518 0.1498 0.2124 0.9383 full face with rotation random local_stress_sum = 1.0e+04 * 2.8643 1.5297 0.3389 0.1176 1.0219 local_stress_sum_sqr = 1.0e+06 * 1.2525 0.8439 0.1355 0.0018 0.6005 sum_ux = 36.8092 35.6082 34.4450 27.0234 31.6679 sum_uy = 34.4438 34.0893 33.6099 34.8754 34.8697 f_acc = 11.4572 6.1187 1.3558 0.4704 4.0876 rmsdist_acc = 3.3848 2.4736 1.1644 0.6859 2.0218 maxdist_acc = 13.2829 13.3893 13.6231 2.6442 17.8864 New model built with probe residual match_score_concat_latent = 1.0e+31 * 8.8955 6.5855 1.2759 0.0131 5.5552 Best concatenated match so far is image #1 match_score_mean = 11.4572 6.1187 1.3558 0.4704 4.0876 true local_stress_sum = 1.0e+04 * 2.1112 2.7392 0.1988 local_stress_sum_sqr = 1.0e+06 * 0.9155 5.2034 0.1315 sum_ux = 36.7104 32.5181 31.9169 sum_uy = 33.6900 32.7224 31.7709 f_acc = 8.4447 10.9569 0.7953 rmsdist_acc = 2.9060 3.3101 0.8918 maxdist_acc = 15.0946 26.3538 11.9677 New model built with probe residual match_score_concat_latent = 1.0e+32 * 0.6471 4.7122 0.1271 Best concatenated match so far is image #1 match_score_mean = 8.4447 10.9569 0.7953 no rotation, 250 geodesic points random local_stress_sum = 1.0e+06 * Columns 1 through 11 0.0493 0.0919 0.0505 1.0043 0.1551 1.0481 0.0910 0.0989 2.3444 0.0526 0.6346 Column 12 0.0619 local_stress_sum_sqr = 1.0e+09 * Columns 1 through 11 0.0001 0.0005 0.0003 0.0823 0.0017 0.1423 0.0010 0.0022 2.2496 0.0002 0.0495 Column 12 0.0002 sum_ux = Columns 1 through 11 165.8047 160.9209 170.2602 161.9314 158.4141 159.2310 163.2366 162.8947 172.8943 158.2993 164.3550 Column 12 154.4986 sum_uy = Columns 1 through 11 170.4663 169.7349 171.6283 153.7771 163.8525 165.5860 163.1064 173.4929 185.6838 163.9225 173.0595 Column 12 168.7999 f_acc = Columns 1 through 11 0.7886 1.4699 0.8072 16.0698 2.4819 16.7691 1.4567 1.5830 37.5103 0.8420 10.1530 Column 12 0.9910 rmsdist_acc = Columns 1 through 11 0.8880 1.2124 0.8985 4.0087 1.5754 4.0950 1.2069 1.2582 6.1246 0.9176 3.1864 Column 12 0.9955 maxdist_acc = Columns 1 through 11 5.0594 5.0588 8.4713 29.4748 10.2330 40.7339 10.2300 10.9656 62.6296 6.0317 30.8017 Column 12 5.0710 after buxfixes local_stress_sum = 1.0e+05 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 18 0 0 0.1338 0.2257 0.4682 5.0107 5.3696 local_stress_sum_sqr = 1.0e+07 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 18 0 0 0.0036 0.0035 0.2661 1.9852 2.0986 sum_ux = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 18 0 0 172.2924 157.4986 161.6320 167.4188 167.9713 sum_uy = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 18 0 0 170.9030 163.8857 171.9004 166.6544 164.4949 f_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 18 0 0 0.2140 0.3611 0.7491 8.0172 8.5914 rmsdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 18 0 0 0.4627 0.6009 0.8655 2.8315 2.9311 maxdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 18 0 0 6.4612 3.7819 19.1961 18.5105 17.6414 local_stress_sum = 1.0e+06 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0.1051 0.2534 0.0472 0.0942 1.2690 0.1465 0.2186 0.2066 0.0839 Columns 23 through 30 0.0371 0.0970 0.0772 0.1233 0.9352 0.0833 0.0374 0.1138 local_stress_sum_sqr = 1.0e+08 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0.0058 0.0624 0.0025 0.0074 1.4700 0.0203 0.0854 0.0330 0.0045 Columns 23 through 30 0.0031 0.0072 0.0059 0.0139 0.5418 0.0052 0.0011 0.0074 sum_ux = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 167.6997 175.8508 167.4769 164.8475 171.8947 160.1515 170.3296 168.5997 168.2019 Columns 23 through 30 157.0728 167.6176 165.9714 167.5537 167.7707 167.5177 165.2985 167.4205 sum_uy = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 168.0985 162.4388 166.9346 161.2658 161.3819 170.8496 171.2551 163.5937 165.2338 Columns 23 through 30 166.9947 156.4009 162.7712 170.2896 165.8473 169.7265 169.5386 164.6342 f_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 1.6816 4.0545 0.7557 1.5068 20.3047 2.3437 3.4971 3.3062 1.3421 Columns 23 through 30 0.5943 1.5513 1.2353 1.9733 14.9635 1.3330 0.5989 1.8201 rmsdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 1.2967 2.0136 0.8693 1.2275 4.5061 1.5309 1.8701 1.8183 1.1585 Columns 23 through 30 0.7709 1.2455 1.1115 1.4047 3.8683 1.1545 0.7739 1.3491 maxdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 5.4554 14.0038 8.3194 12.4700 37.7042 11.4971 15.7484 8.8040 8.9099 Columns 23 through 30 11.3519 7.4757 11.1016 12.4795 24.7982 6.5694 5.8514 6.6203 true local_stress_sum = 1.0e+06 * Columns 1 through 11 0.0080 0.0232 0.0059 0.0205 0.0069 0.0071 0.0085 0.0431 0.0299 0.0075 0.5163 Columns 12 through 19 0.0204 0.0145 1.8784 0.0261 0.2807 0.3604 0.3522 0.0322 local_stress_sum_sqr = 1.0e+08 * Columns 1 through 11 0.0001 0.0008 0.0000 0.0003 0.0003 0.0001 0.0001 0.0019 0.0010 0.0001 0.4089 Columns 12 through 19 0.0003 0.0002 3.9790 0.0006 0.0710 0.1149 0.3617 0.0050 sum_ux = Columns 1 through 11 172.6452 163.2094 175.8682 169.8455 170.4805 166.1359 172.4725 174.1175 167.7914 157.4812 174.8148 Columns 12 through 19 163.5777 155.4055 158.4981 164.6447 163.7799 171.7026 161.2225 177.9567 sum_uy = Columns 1 through 11 168.4860 161.7562 171.1783 156.3863 167.4959 169.0115 169.1020 166.9699 157.8420 169.8258 167.0706 Columns 12 through 19 166.8558 167.1905 159.1507 156.6031 164.3069 159.7969 163.7961 164.2154 f_acc = Columns 1 through 11 0.1273 0.3715 0.0949 0.3276 0.1104 0.1137 0.1358 0.6898 0.4788 0.1193 8.2613 Columns 12 through 19 0.3266 0.2325 30.0543 0.4172 4.4907 5.7662 5.6351 0.5148 rmsdist_acc = Columns 1 through 11 0.3568 0.6095 0.3080 0.5724 0.3322 0.3372 0.3685 0.8305 0.6920 0.3454 2.8743 Columns 12 through 19 0.5715 0.4822 5.4822 0.6459 2.1191 2.4013 2.3738 0.7175 maxdist_acc = Columns 1 through 11 3.8657 5.4208 4.2717 3.1706 7.3246 3.6995 5.5969 5.5243 8.6842 3.7771 27.8316 Columns 12 through 19 4.8900 4.1826 47.5492 5.3056 13.4074 16.3093 40.4724 13.5285 after bugfix local_stress_sum = 1.0e+06 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0.0338 Columns 12 through 14 0.0204 0.0146 1.6162 local_stress_sum_sqr = 1.0e+08 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0.0017 Columns 12 through 14 0.0003 0.0002 3.8935 sum_ux = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 177.0734 Columns 12 through 14 166.3577 147.8604 161.7286 sum_uy = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 158.5362 Columns 12 through 14 167.0388 170.4878 160.6131 f_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0.5410 Columns 12 through 14 0.3262 0.2333 25.8593 rmsdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0.7356 Columns 12 through 14 0.5711 0.4830 5.0852 maxdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 10.2837 Columns 12 through 14 4.9115 4.1966 57.6654 figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04279d290.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04279d284.ppm')) long run after buxfixes true true_pairs= 1.0e+05 * [0.1997 0.1377 0.0682 0.1799 0.1960 0.0350 0.1331 0.2661 0.1999 0.1006 0.3450 0.1893 0.0861 0.0932 0.1843 0.1590 0.2449 0.0810 0.1897 0.1386 0.1619 0.2821 0.4226 0.1442 0.3184 0.1016 0.0560 0.1125 0.4026 0.0836 0.0440 0.1289 0.1394 0.0787 0.0835 0.0903 0.0566 0.4761 0.0692 8.7786 0.0516 0.0565 0.1245 0.1898 0.2279 0.0527 0.0798 0.0628 0.0825 0.1051 0.0678 0.0637 0.3311 0.0805 0.5302 0.2965 0.7466 0.0479 0.1434 0.0607 0.0376 0.0501 3.8890 0.0748 0.1646 0.0575 0.0717 0.0710 0.0588 0.0625 0.1247 0.0860 0.0605 0.0817 0.1229 0.2042] local_stress_sum = 1.0e+05 * Columns 1 through 11 0.1997 0.1377 0.0682 0.1799 0.1960 0.0350 0.1331 0.2661 0.1999 0.1006 0.3450 Columns 12 through 22 0.1893 0.0861 0.0932 0.1843 0.1590 0.2449 0.0810 0.1897 0.1386 0.1619 0.2821 Columns 23 through 33 0.4226 0.1442 0.3184 0.1016 0.0560 0.1125 0.4026 0.0836 0.0440 0.1289 0.1394 Columns 34 through 44 0.0787 0.0835 0.0903 0.0566 0.4761 0.0692 8.7786 0.0516 0.0565 0.1245 0.1898 Columns 45 through 55 0.2279 0.0527 0.0798 0.0628 0.0825 0.1051 0.0678 0.0637 0.3311 0.0805 0.5302 Columns 56 through 66 0.2965 0.7466 0.0479 0.1434 0.0607 0.0376 0.0501 3.8890 0.0748 0.1646 0.0575 Columns 67 through 76 0.0717 0.0710 0.0588 0.0625 0.1247 0.0860 0.0605 0.0817 0.1229 0.2042 f_acc = Columns 1 through 11 0.3195 0.2203 0.1091 0.2878 0.3136 0.0560 0.2129 0.4257 0.3199 0.1609 0.5520 Columns 12 through 22 0.3029 0.1377 0.1491 0.2949 0.2544 0.3918 0.1297 0.3036 0.2217 0.2591 0.4514 Columns 23 through 33 0.6761 0.2307 0.5094 0.1625 0.0896 0.1800 0.6441 0.1338 0.0705 0.2063 0.2230 Columns 34 through 44 0.1259 0.1336 0.1445 0.0906 0.7617 0.1107 14.0457 0.0826 0.0904 0.1992 0.3037 Columns 45 through 55 0.3647 0.0843 0.1277 0.1004 0.1320 0.1682 0.1086 0.1018 0.5297 0.1288 0.8483 Columns 56 through 66 0.4744 1.1946 0.0766 0.2294 0.0970 0.0601 0.0801 6.2224 0.1198 0.2634 0.0919 Columns 67 through 76 0.1148 0.1136 0.0940 0.1000 0.1995 0.1377 0.0968 0.1307 0.1967 0.3267 f_acc = true_pairs= [0.3195 0.2203 0.1091 0.2878 0.3136 0.0560 0.2129 0.4257 0.3199 0.1609 0.5520 0.3029 0.1377 0.1491 0.2949 0.2544 0.3918 0.1297 0.3036 0.2217 0.2591 0.4514 0.6761 0.2307 0.5094 0.1625 0.0896 0.1800 0.6441 0.1338 0.0705 0.2063 0.2230 0.1259 0.1336 0.1445 0.0906 0.7617 0.1107 0.0826 0.0904 0.1992 0.3037 0.3647 0.0843 0.1277 0.1004 0.1320 0.1682 0.1086 0.1018 0.5297 0.1288 0.8483 0.4744 0.0766 0.2294 0.0970 0.0601 0.0801 0.1198 0.2634 0.0919 0.1148 0.1136 0.0940 0.1000 0.1995 0.1377 0.0968 0.1307 0.1967 0.3267] rmsdist_acc = Columns 1 through 11 0.5652 0.4694 0.3304 0.5364 0.5600 0.2366 0.4614 0.6525 0.5656 0.4011 0.7429 Columns 12 through 22 0.5504 0.3711 0.3862 0.5430 0.5043 0.6260 0.3601 0.5510 0.4708 0.5090 0.6719 Columns 23 through 33 0.8223 0.4803 0.7137 0.4031 0.2994 0.4243 0.8026 0.3658 0.2655 0.4542 0.4722 Columns 34 through 44 0.3549 0.3656 0.3802 0.3010 0.8728 0.3328 3.7478 0.2873 0.3007 0.4463 0.5511 Columns 45 through 55 0.6039 0.2903 0.3574 0.3169 0.3633 0.4101 0.3295 0.3191 0.7278 0.3589 0.9210 Columns 56 through 66 0.6887 1.0930 0.2768 0.4789 0.3115 0.2453 0.2831 2.4945 0.3461 0.5132 0.3032 Columns 67 through 76 0.3388 0.3371 0.3066 0.3162 0.4467 0.3710 0.3111 0.3615 0.4435 0.5716 maxdist_acc = Columns 1 through 11 6.3949 3.6273 6.8260 8.7127 7.2205 3.0610 11.4953 4.2538 9.6451 10.9383 13.5270 Columns 12 through 22 5.4666 2.4430 9.5218 2.9109 7.0798 7.9003 8.0953 9.6218 6.0774 11.5076 3.8625 Columns 23 through 33 7.1502 4.9547 6.8734 5.9679 2.4455 4.3473 7.3147 8.4730 3.9693 6.6368 10.2943 Columns 34 through 44 4.8737 5.3705 4.4854 4.2400 6.4749 4.5825 35.7933 2.3736 3.0154 3.7027 6.1715 Columns 45 through 55 5.5625 5.2632 4.6780 2.6529 3.3676 8.0731 3.8556 3.0781 7.1184 5.8831 6.6109 Columns 56 through 66 4.1627 11.8271 6.1766 4.7848 3.5433 2.3417 2.6764 28.1762 3.3158 7.6655 2.7953 Columns 67 through 76 4.0983 5.7138 2.8954 2.9334 6.7744 6.0522 2.6306 3.1351 4.5118 3.3065 random false_pairs= 1.0e+05 * [ 0.4222 0.9061 0.3996 0.9021 1.3434 0.1348 0.4285 0.5005 0.6841 0.5190 6.5885 0.6303 0.5790 0.8560 0.9624 0.7068 0.3466 0.9016 0.3933 1.2190 0.6846 2.8691 0.4257 0.3624 0.5939 0.6997 0.6512 2.0997 0.5110 1.0299 0.4264 1.4071 0.0969 0.4810 0.7599 0.8937 0.2997 0.9318 1.7952 2.6906 0.3044 1.3893 0.3371 0.9263 0.6306 0.6432 0.8639 0.9413 0.3418 1.0959 0.5295 0.5012 0.2305 0.5076 0.6118 6.0797 4.2221 1.5917 2.3437 1.9827 0.1372 0.6855 0.7395 0.4894 0.8676 0.7027 1.0128 0.1671 0.9062 0.3304 1.1733 0.7451 1.1382 0.3424 0.7904 1.6297 0.6016 0.4494 0.3613 1.9850 0.7008 1.2095 0.7484 0.5896 0.4572 0.4571 0.6770 0.1292 1.0324 1.0515 3.3446 0.2243 1.5426 1.1413 0.3028 1.1489 0.9225 0.3327 0.7511 0.7092 0.7484 0.3072 0.8485 1.0464 0.8770 0.8260 0.5037 1.5590 0.6596 0.3553 1.2888 0.5404 7.5999 0.6840 0.5650 1.6995 0.6784 6.7118 3.2306 1.5048 1.5149 0.5387 0.5261 1.9369 1.4686 0.3221 1.3860 0.2565 0.2359 0.5255 1.3759 0.4741 0.3273 2.8445 0.4323 0.5757 0.2886 0.1728 1.3529 2.0128 0.6840 0.8252 1.1738 2.0059 0.4687 1.0743 0.2630 0.2782 0.8363 0.2982 0.9182 2.3028 0.2016 0.8735 1.0891 0.3279 0.4232 6.7992 0.2008 0.4470 1.6260 0.2884 0.7605 0.3560 1.7406 7.2250 7.7841 0.8526 0.7019 0.2918 1.1301 0.2735 0.6737 2.9723 1.0404] local_stress_sum = 1.0e+05 * Columns 1 through 11 0.4222 0.9061 0.3996 0.9021 1.3434 0.1348 0.4285 0.5005 0.6841 0.5190 6.5885 Columns 12 through 22 0.6303 0.5790 0.8560 0.9624 0.7068 0.3466 0.9016 0.3933 1.2190 0.6846 2.8691 Columns 23 through 33 0.4257 0.3624 0.5939 0.6997 0.6512 2.0997 0.5110 1.0299 0.4264 1.4071 0.0969 Columns 34 through 44 0.4810 0.7599 0.8937 0.2997 0.9318 1.7952 2.6906 0.3044 1.3893 0.3371 0.9263 Columns 45 through 55 0.6306 0.6432 0.8639 0.9413 0.3418 1.0959 0.5295 0.5012 0.2305 0.5076 0.6118 Columns 56 through 66 6.0797 4.2221 1.5917 2.3437 1.9827 0.1372 0.6855 0.7395 0.4894 0.8676 0.7027 Columns 67 through 77 1.0128 0.1671 0.9062 0.3304 1.1733 0.7451 1.1382 0.3424 0.7904 1.6297 0.6016 Columns 78 through 88 0.4494 0.3613 1.9850 0.7008 1.2095 0.7484 0.5896 0.4572 0.4571 0.6770 0.1292 Columns 89 through 99 1.0324 1.0515 3.3446 0.2243 1.5426 1.1413 0.3028 1.1489 0.9225 0.3327 0.7511 Columns 100 through 110 0.7092 0.7484 0.3072 0.8485 1.0464 0.8770 0.8260 0.5037 1.5590 0.6596 0.3553 Columns 111 through 121 1.2888 0.5404 7.5999 0.6840 0.5650 1.6995 0.6784 6.7118 3.2306 1.5048 1.5149 Columns 122 through 132 0.5387 0.5261 1.9369 1.4686 0.3221 1.3860 0.2565 0.2359 0.5255 1.3759 0.4741 Columns 133 through 143 0.3273 2.8445 0.4323 0.5757 0.2886 0.1728 1.3529 2.0128 0.6840 0.8252 1.1738 Columns 144 through 154 2.0059 0.4687 1.0743 0.2630 0.2782 0.8363 0.2982 0.9182 2.3028 0.2016 0.8735 Columns 155 through 165 1.0891 0.3279 0.4232 6.7992 0.2008 0.4470 1.6260 0.2884 0.7605 0.3560 1.7406 Columns 166 through 175 7.2250 7.7841 0.8526 0.7019 0.2918 1.1301 0.2735 0.6737 2.9723 1.0404 local_stress_sum_sqr = 1.0e+08 * Columns 1 through 11 0.0009 0.0048 0.0018 0.0370 0.0126 0.0001 0.0013 0.0017 0.0045 0.0020 0.9820 Columns 12 through 22 0.0027 0.0022 0.0074 0.0053 0.0030 0.0011 0.0082 0.0008 0.0128 0.0047 0.0975 Columns 23 through 33 0.0034 0.0011 0.0030 0.0028 0.0027 0.0557 0.0019 0.0077 0.0009 0.0121 0.0001 Columns 34 through 44 0.0013 0.0048 0.0057 0.0008 0.0092 0.0240 0.0883 0.0006 0.0123 0.0023 0.0062 Columns 45 through 55 0.0033 0.0025 0.0041 0.0077 0.0013 0.0090 0.0017 0.0015 0.0006 0.0016 0.0039 Columns 56 through 66 0.9044 0.4292 0.0292 0.0468 0.0197 0.0001 0.0022 0.0041 0.0019 0.0046 0.0037 Columns 67 through 77 0.0059 0.0007 0.0050 0.0010 0.0118 0.0066 0.0096 0.0010 0.0055 0.0201 0.0049 Columns 78 through 88 0.0033 0.0007 0.0233 0.0027 0.0064 0.0048 0.0055 0.0022 0.0018 0.0066 0.0001 Columns 89 through 99 0.0198 0.0079 0.1095 0.0005 0.0232 0.0084 0.0006 0.0094 0.0060 0.0013 0.0051 Columns 100 through 110 0.0040 0.0046 0.0007 0.0047 0.0058 0.0061 0.0060 0.0016 0.0194 0.0025 0.0010 Columns 111 through 121 0.0174 0.0018 1.1410 0.0045 0.0033 0.0222 0.0039 0.2468 0.0953 0.0169 0.0337 Columns 122 through 132 0.0018 0.0030 0.0259 0.0199 0.0007 0.0104 0.0003 0.0005 0.0015 0.0136 0.0017 Columns 133 through 143 0.0011 0.0649 0.0038 0.0040 0.0012 0.0004 0.0111 0.0320 0.0038 0.0042 0.0061 Columns 144 through 154 0.0401 0.0021 0.0104 0.0019 0.0009 0.0055 0.0008 0.0047 0.0576 0.0005 0.0038 Columns 155 through 165 0.0100 0.0008 0.0015 0.4984 0.0003 0.0017 0.0277 0.0006 0.0062 0.0010 0.0335 Columns 166 through 175 1.2441 0.3621 0.0060 0.0062 0.0040 0.0089 0.0004 0.0034 0.0522 0.0187 sum_ux = Columns 1 through 11 160.0015 167.3817 165.5469 157.8512 165.5186 171.7605 166.8763 155.1417 158.9305 162.5984 164.0930 Columns 12 through 22 164.3407 161.6178 164.0617 168.8154 165.6622 174.8466 174.4379 157.7665 157.2219 170.6902 160.9452 Columns 23 through 33 166.0100 181.9368 177.9319 173.3359 165.9838 171.2111 169.3491 172.7318 174.0623 171.5720 166.0612 Columns 34 through 44 169.5956 156.4034 158.5995 160.3605 168.9988 180.0030 167.6489 176.3450 170.5898 168.1624 171.1071 Columns 45 through 55 171.1596 162.8811 160.2569 173.7696 166.2689 166.1760 154.9688 156.2882 166.9561 168.4031 174.9534 Columns 56 through 66 169.7797 165.0332 161.1983 173.0913 163.0211 166.8857 166.1963 159.1409 153.5213 166.8556 165.7781 Columns 67 through 77 159.0241 160.8524 172.5165 164.2883 164.6572 161.0981 162.4912 163.3655 168.8079 178.1372 151.1702 Columns 78 through 88 165.8600 173.7194 168.0785 167.2269 164.1353 174.1671 168.6290 159.9693 176.8074 164.7317 172.5576 Columns 89 through 99 164.1482 162.6675 166.2910 164.8342 160.4073 172.5563 158.6230 166.8487 168.5936 162.8421 165.7558 Columns 100 through 110 165.1487 165.7582 174.7975 166.2744 160.6616 162.2906 169.7586 168.3309 166.0383 174.7547 168.2904 Columns 111 through 121 171.2373 173.6104 172.0114 171.4121 171.5573 161.4048 155.6471 172.8953 160.3821 173.8979 168.6232 Columns 122 through 132 159.1503 161.9259 162.0142 167.4605 173.9177 163.6913 160.9647 168.6417 176.6927 166.0862 157.7514 Columns 133 through 143 163.5973 171.8165 159.0748 165.1845 168.3103 170.0931 163.3925 170.1599 161.6016 168.1100 164.8748 Columns 144 through 154 165.0708 164.6557 175.6359 160.4365 175.3062 163.6668 165.3221 161.9733 157.7336 172.8807 166.4287 Columns 155 through 165 175.1348 161.5170 166.5447 172.7521 174.6925 168.6978 162.9735 159.1619 173.3827 161.5969 169.8689 Columns 166 through 175 160.6797 158.1017 170.5483 169.8288 165.2100 162.5657 165.6538 163.0452 171.5293 163.1538 sum_uy = Columns 1 through 11 167.9891 163.0217 158.7243 165.0509 172.6666 168.7021 167.1122 162.8177 162.0079 162.3206 160.5712 Columns 12 through 22 168.4898 158.0355 161.4392 172.9269 165.1716 165.9478 167.4753 169.6975 169.0066 170.9401 171.6750 Columns 23 through 33 169.2514 169.4955 168.4249 163.9589 166.3663 159.4628 161.3510 174.0855 166.7582 161.8394 159.1879 Columns 34 through 44 165.4267 171.3282 164.6050 160.0805 161.4928 164.0129 172.7826 170.5542 164.9356 165.5550 157.3066 Columns 45 through 55 169.8233 161.4543 162.8718 169.2346 171.7810 178.8157 162.3445 167.9053 164.0893 171.2294 163.5566 Columns 56 through 66 166.7169 169.5344 170.7821 164.3709 162.4210 163.6325 165.5543 165.0712 169.1229 172.2603 162.7691 Columns 67 through 77 160.8647 159.5957 160.8279 161.5236 171.3671 162.9418 163.7473 175.6308 160.0278 172.4907 166.6286 Columns 78 through 88 166.4801 167.9333 167.5686 163.1445 166.0213 165.6334 165.9428 175.3352 161.9094 157.3814 170.8243 Columns 89 through 99 165.8304 165.9150 172.9335 171.4087 171.7403 169.1069 171.6148 166.4255 171.4732 163.9178 168.5636 Columns 100 through 110 169.1285 177.1086 164.0224 161.1656 165.0417 169.2807 168.6298 161.7301 172.1669 175.8840 165.2984 Columns 111 through 121 161.1566 172.1401 156.0250 165.1044 167.1368 167.7913 171.2611 168.9598 156.9387 171.7080 170.5071 Columns 122 through 132 163.8630 163.1991 155.8031 165.6790 165.3932 165.0315 172.4695 167.6608 167.9560 161.5244 163.4536 Columns 133 through 143 157.0276 177.7303 165.2452 165.6323 172.7076 164.3290 170.8044 179.5065 164.0685 168.4942 164.1930 Columns 144 through 154 166.8403 177.1633 168.5322 164.4906 168.8470 159.9606 174.1481 160.1857 175.4700 162.5160 164.3934 Columns 155 through 165 166.6098 155.7959 166.3283 170.2484 166.4788 162.6203 172.8132 171.8828 158.8429 172.2791 168.0274 Columns 166 through 175 170.7472 177.8919 157.3206 164.2121 165.2263 172.6175 156.8342 159.7420 161.1210 167.7401 f_acc = Columns 1 through 11 0.6755 1.4497 0.6394 1.4433 2.1494 0.2156 0.6857 0.8008 1.0945 0.8305 10.5417 Columns 12 through 22 1.0085 0.9264 1.3696 1.5398 1.1309 0.5546 1.4426 0.6293 1.9504 1.0954 4.5906 Columns 23 through 33 0.6811 0.5798 0.9503 1.1195 1.0419 3.3595 0.8176 1.6478 0.6822 2.2513 0.1550 Columns 34 through 44 0.7696 1.2158 1.4300 0.4796 1.4908 2.8723 4.3049 0.4871 2.2229 0.5394 1.4820 Columns 45 through 55 1.0090 1.0291 1.3823 1.5061 0.5469 1.7534 0.8472 0.8020 0.3688 0.8122 0.9789 Columns 56 through 66 9.7275 6.7554 2.5468 3.7499 3.1724 0.2195 1.0968 1.1832 0.7830 1.3882 1.1244 Columns 67 through 77 1.6205 0.2673 1.4499 0.5287 1.8773 1.1921 1.8212 0.5479 1.2647 2.6075 0.9625 Columns 78 through 88 0.7191 0.5780 3.1760 1.1213 1.9353 1.1974 0.9434 0.7316 0.7313 1.0833 0.2067 Columns 89 through 99 1.6518 1.6825 5.3513 0.3589 2.4681 1.8261 0.4844 1.8383 1.4759 0.5324 1.2017 Columns 100 through 110 1.1348 1.1975 0.4915 1.3576 1.6743 1.4033 1.3217 0.8060 2.4944 1.0554 0.5684 Columns 111 through 121 2.0621 0.8646 12.1598 1.0944 0.9040 2.7193 1.0854 10.7389 5.1689 2.4077 2.4239 Columns 122 through 132 0.8620 0.8418 3.0990 2.3498 0.5153 2.2177 0.4104 0.3775 0.8408 2.2014 0.7585 Columns 133 through 143 0.5237 4.5512 0.6917 0.9212 0.4617 0.2764 2.1646 3.2205 1.0945 1.3203 1.8781 Columns 144 through 154 3.2095 0.7500 1.7190 0.4209 0.4451 1.3381 0.4772 1.4691 3.6845 0.3226 1.3977 Columns 155 through 165 1.7426 0.5246 0.6771 10.8787 0.3213 0.7152 2.6016 0.4614 1.2168 0.5696 2.7850 Columns 166 through 175 11.5600 12.4546 1.3642 1.1230 0.4668 1.8082 0.4376 1.0779 4.7557 1.6647 false_pairs=[ 0.6755 1.4497 0.6394 1.4433 2.1494 0.2156 0.6857 0.8008 1.0945 0.8305 10.5417 1.0085 0.9264 1.3696 1.5398 1.1309 0.5546 1.4426 0.6293 1.9504 1.0954 4.5906 0.6811 0.5798 0.9503 1.1195 1.0419 3.3595 0.8176 1.6478 0.6822 2.2513 0.1550 0.7696 1.2158 1.4300 0.4796 1.4908 2.8723 4.3049 0.4871 2.2229 0.5394 1.4820 1.0090 1.0291 1.3823 1.5061 0.5469 1.7534 0.8472 0.8020 0.3688 0.8122 0.9789 9.7275 6.7554 2.5468 3.7499 3.1724 0.2195 1.0968 1.1832 0.7830 1.3882 1.1244 1.6205 0.2673 1.4499 0.5287 1.8773 1.1921 1.8212 0.5479 1.2647 2.6075 0.9625 0.7191 0.5780 3.1760 1.1213 1.9353 1.1974 0.9434 0.7316 0.7313 1.0833 0.2067 1.6518 1.6825 5.3513 0.3589 2.4681 1.8261 0.4844 1.8383 1.4759 0.5324 1.2017 1.1348 1.1975 0.4915 1.3576 1.6743 1.4033 1.3217 0.8060 2.4944 1.0554 0.5684 2.0621 0.8646 12.1598 1.0944 0.9040 2.7193 1.0854 10.7389 5.1689 2.4077 2.4239 0.8620 0.8418 3.0990 2.3498 0.5153 2.2177 0.4104 0.3775 0.8408 2.2014 0.7585 0.5237 4.5512 0.6917 0.9212 0.4617 0.2764 2.1646 3.2205 1.0945 1.3203 1.8781 3.2095 0.7500 1.7190 0.4209 0.4451 1.3381 0.4772 1.4691 3.6845 0.3226 1.3977 1.7426 0.5246 0.6771 10.8787 0.3213 0.7152 2.6016 0.4614 1.2168 0.5696 2.7850 11.5600 12.4546 1.3642 1.1230 0.4668 1.8082 0.4376 1.0779 4.7557 1.6647] rmsdist_acc = Columns 1 through 11 0.8219 1.2041 0.7996 1.2014 1.4661 0.4644 0.8281 0.8949 1.0462 0.9113 3.2468 Columns 12 through 22 1.0042 0.9625 1.1703 1.2409 1.0634 0.7447 1.2011 0.7933 1.3966 1.0466 2.1426 Columns 23 through 33 0.8253 0.7614 0.9748 1.0580 1.0207 1.8329 0.9042 1.2837 0.8260 1.5004 0.3937 Columns 34 through 44 0.8773 1.1026 1.1958 0.6925 1.2210 1.6948 2.0748 0.6979 1.4909 0.7345 1.2174 Columns 45 through 55 1.0045 1.0144 1.1757 1.2272 0.7395 1.3242 0.9204 0.8955 0.6073 0.9012 0.9894 Columns 56 through 66 3.1189 2.5991 1.5959 1.9365 1.7811 0.4685 1.0473 1.0877 0.8849 1.1782 1.0604 Columns 67 through 77 1.2730 0.5170 1.2041 0.7271 1.3702 1.0918 1.3495 0.7402 1.1246 1.6148 0.9811 Columns 78 through 88 0.8480 0.7603 1.7821 1.0589 1.3911 1.0942 0.9713 0.8553 0.8552 1.0408 0.4547 Columns 89 through 99 1.2852 1.2971 2.3133 0.5990 1.5710 1.3513 0.6960 1.3558 1.2149 0.7296 1.0962 Columns 100 through 110 1.0653 1.0943 0.7011 1.1652 1.2939 1.1846 1.1496 0.8978 1.5794 1.0273 0.7540 Columns 111 through 121 1.4360 0.9299 3.4871 1.0461 0.9508 1.6490 1.0418 3.2770 2.2735 1.5517 1.5569 Columns 122 through 132 0.9284 0.9175 1.7604 1.5329 0.7178 1.4892 0.6407 0.6144 0.9170 1.4837 0.8709 Columns 133 through 143 0.7237 2.1334 0.8317 0.9598 0.6795 0.5258 1.4713 1.7946 1.0462 1.1491 1.3705 Columns 144 through 154 1.7915 0.8660 1.3111 0.6487 0.6671 1.1568 0.6908 1.2121 1.9195 0.5680 1.1822 Columns 155 through 165 1.3201 0.7243 0.8229 3.2983 0.5668 0.8457 1.6130 0.6792 1.1031 0.7547 1.6688 Columns 166 through 175 3.4000 3.5291 1.1680 1.0597 0.6833 1.3447 0.6615 1.0382 2.1807 1.2902 maxdist_acc = Columns 1 through 11 4.8236 6.7637 10.0309 13.5991 7.7919 4.0126 5.1093 8.6246 6.7910 4.7519 36.8529 Columns 12 through 22 8.6814 9.8407 10.2969 7.3307 4.8395 8.0748 7.5475 4.8914 8.5573 11.1133 16.9595 Columns 23 through 33 13.7018 8.5141 10.5273 7.8678 7.9296 18.6114 5.7572 11.4999 4.8219 11.3746 2.4961 Columns 34 through 44 4.6453 10.4789 7.8149 5.7093 14.6542 10.8445 18.4358 3.7218 6.6118 9.4409 7.3620 Columns 45 through 55 9.4259 4.7199 10.2352 6.4446 9.3120 7.6239 6.8355 4.2612 7.3470 5.5378 7.7224 Columns 56 through 66 36.9301 35.9025 10.3458 10.7113 7.7113 4.1294 3.8212 7.6384 7.3815 7.6498 5.2400 Columns 67 through 77 4.5291 9.2013 6.5800 4.6988 8.3480 8.2084 7.2398 7.9191 9.5902 10.8225 13.1209 Columns 78 through 88 7.3708 4.0452 9.4517 5.9671 7.4954 9.5136 10.2289 12.0594 6.8256 10.8043 3.3659 Columns 89 through 99 10.2054 6.8052 18.0438 6.6334 8.0739 7.0747 6.2180 8.1094 7.2444 5.9212 5.7711 Columns 100 through 110 5.2952 6.1378 4.7538 5.3834 5.5455 6.0328 10.2637 4.4921 11.5338 7.8443 4.9117 Columns 111 through 121 13.0306 5.4594 38.0765 6.6451 8.6198 10.6957 7.6694 16.7302 16.4970 9.9084 16.0579 Columns 122 through 132 5.6640 8.3117 9.5790 9.2453 5.4775 8.2954 3.3713 5.3758 6.7377 8.3120 6.3697 Columns 133 through 143 5.5531 11.6295 11.3681 10.3161 7.4612 7.7492 12.7070 10.6828 7.9640 7.6992 7.1124 Columns 144 through 154 10.4332 7.4600 6.6876 10.5539 6.9696 5.7135 5.3094 5.6568 11.3531 5.2501 4.9685 Columns 155 through 165 6.9080 6.3807 6.9815 24.0242 2.7210 4.7179 9.8968 4.3279 12.0681 6.9476 12.1664 Columns 166 through 175 36.0648 16.4425 8.5213 14.5511 13.1654 6.3331 3.7919 6.9728 11.5654 9.7376 figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04436d369.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04436d361.ppm')) after nose and mask fixed true local_stress_sum = 1.0e+04 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0 0 0 0 0 Columns 56 through 66 0 0 0 0 0 0 0 0.4071 2.7934 0.4797 0.4791 Columns 67 through 76 0.6091 0.5176 0.4886 0.3654 0.9033 0.6884 0.4500 0.5937 1.2409 1.8304 local_stress_sum_sqr = 1.0e+04 * Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0 0 0 0 0 Columns 56 through 66 0 0 0 0 0 0 0 0.3206 5.0671 0.1407 0.3227 Columns 67 through 76 0.3656 0.2551 0.2781 0.1393 0.9769 0.4679 0.1965 0.2720 1.5327 2.3961 sum_ux = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0 0 0 0 0 Columns 56 through 66 0 0 0 0 0 0 0 160.1537 161.6684 167.8792 172.1651 Columns 67 through 76 177.2315 158.8245 160.0375 163.5267 169.4941 171.8056 155.4469 165.8578 164.3563 168.7854 sum_uy = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0 0 0 0 0 Columns 56 through 66 0 0 0 0 0 0 0 165.1649 166.8827 165.9573 166.6623 Columns 67 through 76 168.1862 162.3067 159.0973 167.7906 166.9330 166.6105 158.7476 169.2845 162.0853 159.4413 f_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0 0 0 0 0 Columns 56 through 66 0 0 0 0 0 0 0 0.0651 0.4469 0.0768 0.0767 Columns 67 through 76 0.0975 0.0828 0.0782 0.0585 0.1445 0.1101 0.0720 0.0950 0.1985 0.2929 true_pairs= [0.0651 0.4469 0.0768 0.0767 0.0975 0.0828 0.0782 0.0585 0.1445 0.1101 0.0720 0.0950 0.1985 0.2929 0.0709 0.1592 0.0865 0.1868 0.0622 0.0765 0.1092 0.3361 0.1421 0.0792 0.2873 0.1655 0.0860 0.1106 0.2083 0.2683 0.3934 0.0934 0.2266 0.1296 0.2013 0.3840 0.9105 0.1377 0.4671 0.1289 0.0721 0.1004 0.5162 0.0446 0.0587 0.1344 0.1112 0.1407 0.0688 0.0775 0.0762 0.3353 0.0751 0.1292] training set: true_pairs= [ 0.2194 0.1890 0.1708 0.1448 0.1985 0.1386 0.1729 0.1761 0.2531 0.2082 0.3287 0.0447 0.0438 0.0846 0.0629 0.0757 0.0535 0.0569 0.0636 0.1291 0.0711 0.1131 0.1103 0.1772 0.1105 0.1698 0.1135 0.0952 0.0767 0.0516 0.0371 0.0518 0.0414 0.0936 0.1057 0.1505 0.1059 0.1488 0.0609 0.0624 0.0430 0.0539 0.1655 0.2018 0.1158 0.1883 0.1780 0.1692 0.1124 0.0418 0.1467 0.1280 0.1283 0.1633 0.1467 0.0629 0.2367 0.0771 0.0796 0.0762 0.1149 0.2077 0.2148 0.2717 0.1331 0.0890 0.0951 0.2123 0.1557 0.0537 0.1800 0.2657 0.5231 0.8014 0.0582 1.6659 0.0707 0.0927 0.2368 0.1014 0.1244 0.2455 0.0872 0.1037 0.1619 0.2374 0.1116 0.1531 0.0465 0.0883 0.0859 0.9662 0.4331 0.2026 0.1349 0.2303 0.1207 0.1352 0.0816 0.0662 0.0515 0.2230 0.0731 0.0634 0.0683 0.0724 0.3235 0.2649 0.1579 0.1345 0.0855 6.5809 6.8026 0.1387 0.1607 0.0873 0.1896 0.1462 0.1604 0.0347 0.0810 0.0680 0.1471 0.1172 0.1729 8.5365 8.6031 0.2067 0.3484 8.3711 8.8407 0.1185 0.1484 0.0350 0.0348 0.0610 0.0911 0.0533 0.0432 0.0407 0.0426 0.0871 0.0394 0.0728 0.0708 0.1280 4.0435 0.0709 0.0809 0.0743 0.1749 0.0696 0.0526 0.1473 4.1581 0.5235 0.4180 0.2542 1.1008 0.3257 0.1099 0.0727 0.1310 0.6129 0.1605 5.0433 0.1123 0.0620 0.0621 0.2277 0.1811 0.0743 0.0815 0.2310 0.1471 0.0821 0.0673 0.3349 0.0485 0.0674 0.0584 0.0635 0.0453 0.0532 0.0666 0.1359 0.5338 0.0883 0.2185 0.0530 0.0750 0.1795 0.0789 0.0698 0.0649 0.0496 0.0836 0.1080 0.1281 0.0869 0.0492 0.0644 0.1135 0.0530 0.1115 0.1039 0.2201 0.0601 0.0572 0.0501 0.1078 0.0984 0.0515 0.0602 0.1764 0.0546 0.0449 0.1217 0.0644] local_stress_sum = 1.0e+05 * Columns 1 through 11 0.1371 0.1181 0.1068 0.0905 0.1241 0.0866 0.1081 0.1100 0.1582 0.1301 0.2054 Columns 12 through 22 0.0279 0.0274 0.0528 0.0393 0.0473 0.0334 0.0355 0.0398 0.0807 0.0444 0.0707 Columns 23 through 33 0.0689 0.1108 0.0691 0.1061 0.0710 0.0595 0.0480 0.0322 0.0232 0.0324 0.0259 Columns 34 through 44 0.0585 0.0660 0.0941 0.0662 0.0930 0.0381 0.0390 0.0269 0.0337 0.1034 0.1261 Columns 45 through 55 0.0724 0.1177 0.1113 0.1058 0.0703 0.0261 0.0917 0.0800 0.0802 0.1020 0.0917 Columns 56 through 66 0.0393 0.1480 0.0482 0.0497 0.0476 0.0718 0.1298 0.1343 0.1698 0.0832 0.0556 Columns 67 through 77 0.0594 0.1327 0.0973 0.0336 0.1125 0.1661 0.3269 0.5009 0.0364 1.0412 0.0442 Columns 78 through 88 0.0579 0.1480 0.0634 0.0777 0.1535 0.0545 0.0648 0.1012 0.1484 0.0698 0.0957 Columns 89 through 99 0.0291 0.0552 0.0537 0.6039 0.2707 0.1266 0.0843 0.1439 0.0755 0.0845 0.0510 Columns 100 through 110 0.0414 0.0322 0.1394 0.0457 0.0396 0.0427 0.0453 0.2022 0.1655 0.0987 0.0840 Columns 111 through 121 0.0534 4.1131 4.2516 0.0867 0.1004 0.0545 0.1185 0.0914 0.1002 0.0217 0.0507 Columns 122 through 132 0.0425 0.0920 0.0732 0.1080 5.3353 5.3769 0.1292 0.2177 5.2320 5.5254 0.0741 Columns 133 through 143 0.0927 0.0218 0.0218 0.0381 0.0570 0.0333 0.0270 0.0255 0.0266 0.0544 0.0246 Columns 144 through 154 0.0455 0.0443 0.0800 2.5272 0.0443 0.0506 0.0464 0.1093 0.0435 0.0329 0.0920 Columns 155 through 165 2.5988 0.3272 0.2613 0.1589 0.6880 0.2036 0.0687 0.0455 0.0819 0.3831 0.1003 Columns 166 through 176 3.1520 0.0702 0.0388 0.0388 0.1423 0.1132 0.0464 0.0510 0.1444 0.0919 0.0513 Columns 177 through 187 0.0421 0.2093 0.0303 0.0421 0.0365 0.0397 0.0283 0.0332 0.0416 0.0850 0.3336 Columns 188 through 198 0.0552 0.1366 0.0331 0.0469 0.1122 0.0493 0.0436 0.0405 0.0310 0.0522 0.0675 Columns 199 through 209 0.0800 0.0543 0.0307 0.0403 0.0709 0.0331 0.0697 0.0649 0.1376 0.0375 0.0357 Columns 210 through 219 0.0313 0.0674 0.0615 0.0322 0.0376 0.1102 0.0341 0.0280 0.0761 0.0402 local_stress_sum_sqr = 1.0e+07 * Columns 1 through 11 0.0023 0.0015 0.0013 0.0012 0.0024 0.0010 0.0010 0.0015 0.0027 0.0019 0.0040 Columns 12 through 22 0.0001 0.0001 0.0004 0.0002 0.0002 0.0001 0.0001 0.0001 0.0009 0.0004 0.0009 Columns 23 through 33 0.0005 0.0008 0.0008 0.0007 0.0005 0.0004 0.0002 0.0001 0.0000 0.0001 0.0001 Columns 34 through 44 0.0004 0.0005 0.0010 0.0004 0.0010 0.0001 0.0002 0.0001 0.0001 0.0011 0.0030 Columns 45 through 55 0.0005 0.0012 0.0011 0.0012 0.0004 0.0001 0.0014 0.0009 0.0007 0.0011 0.0011 Columns 56 through 66 0.0001 0.0031 0.0002 0.0002 0.0003 0.0005 0.0026 0.0024 0.0094 0.0012 0.0002 Columns 67 through 77 0.0004 0.0016 0.0009 0.0001 0.0018 0.0028 0.0174 0.0339 0.0002 0.1102 0.0002 Columns 78 through 88 0.0005 0.0057 0.0006 0.0009 0.0073 0.0002 0.0004 0.0016 0.0046 0.0019 0.0035 Columns 89 through 99 0.0001 0.0003 0.0003 0.0371 0.0125 0.0024 0.0007 0.0032 0.0005 0.0005 0.0003 Columns 100 through 110 0.0002 0.0001 0.0030 0.0002 0.0002 0.0002 0.0002 0.0067 0.0058 0.0018 0.0011 Columns 111 through 121 0.0003 1.4344 1.5456 0.0006 0.0008 0.0002 0.0012 0.0008 0.0009 0.0001 0.0003 Columns 122 through 132 0.0001 0.0011 0.0010 0.0016 4.1502 2.0420 0.0015 0.0036 2.0069 3.1931 0.0004 Columns 133 through 143 0.0007 0.0000 0.0001 0.0002 0.0003 0.0002 0.0001 0.0001 0.0001 0.0002 0.0001 Columns 144 through 154 0.0002 0.0002 0.0008 0.4937 0.0002 0.0002 0.0005 0.0016 0.0008 0.0001 0.0008 Columns 155 through 165 0.6726 0.0093 0.0099 0.0033 0.0452 0.0037 0.0009 0.0003 0.0006 0.0185 0.0010 Columns 166 through 176 0.9611 0.0005 0.0001 0.0001 0.0037 0.0016 0.0002 0.0007 0.0022 0.0018 0.0002 Columns 177 through 187 0.0002 0.0060 0.0001 0.0002 0.0001 0.0002 0.0001 0.0001 0.0002 0.0009 0.0128 Columns 188 through 198 0.0004 0.0055 0.0001 0.0003 0.0015 0.0002 0.0002 0.0001 0.0001 0.0003 0.0004 Columns 199 through 209 0.0006 0.0003 0.0001 0.0001 0.0008 0.0001 0.0004 0.0004 0.0019 0.0002 0.0001 Columns 210 through 219 0.0001 0.0004 0.0006 0.0001 0.0001 0.0012 0.0001 0.0001 0.0005 0.0001 sum_ux = Columns 1 through 11 174.7249 186.3432 184.2543 173.3002 173.7582 170.1577 171.7832 153.0555 165.0123 164.1402 170.8208 Columns 12 through 22 176.6340 160.6020 170.8971 172.4364 174.4169 162.7548 176.7389 165.8126 167.3159 160.1736 166.1290 Columns 23 through 33 168.1637 173.0181 173.2980 165.4225 163.9269 176.2317 179.3074 180.6826 175.2562 174.0779 172.2146 Columns 34 through 44 163.5269 172.3969 169.1741 176.1937 171.0260 169.3625 168.0737 168.3579 170.3823 174.6313 170.9012 Columns 45 through 55 163.2416 168.4745 172.8563 176.6115 177.5429 173.4826 165.8780 163.2183 165.8594 176.1291 168.5931 Columns 56 through 66 164.4817 157.9956 175.2646 157.5402 158.7802 166.0708 170.1271 178.6672 169.8893 164.4406 161.7673 Columns 67 through 77 162.4551 167.4963 169.7856 164.2083 167.5986 163.2610 161.6941 164.3609 173.9987 159.9583 166.5297 Columns 78 through 88 164.3899 164.6192 169.8750 167.3587 162.6817 169.6508 169.8408 170.0632 177.0447 176.3430 155.0664 Columns 89 through 99 175.7191 157.9733 164.3457 159.7859 157.5728 174.7707 172.5320 159.2773 157.2627 163.1228 176.5834 Columns 100 through 110 169.3343 166.4498 167.7784 177.9474 159.1657 167.7983 173.9979 166.5879 172.1352 174.8550 163.1360 Columns 111 through 121 166.9912 167.8024 173.7858 167.4026 164.0491 158.6959 163.7475 170.6675 168.3128 170.5391 153.9704 Columns 122 through 132 174.8189 146.2609 162.7468 167.9360 165.3251 162.4961 159.0583 163.2786 168.3965 161.0849 166.6603 Columns 133 through 143 164.7272 164.6416 170.6200 174.7080 170.6855 167.8953 164.7145 176.3389 170.4939 176.1975 177.9522 Columns 144 through 154 174.3940 171.1431 181.4835 173.6293 170.8888 172.3070 173.0757 176.2050 174.4568 169.3786 170.8541 Columns 155 through 165 161.1816 172.5943 175.3440 172.5961 170.0794 159.1834 167.8675 175.5060 166.0870 167.0070 172.5661 Columns 166 through 176 161.4967 175.7591 164.4292 158.3497 156.3296 163.5508 161.2594 171.1419 160.2668 170.2240 181.8690 Columns 177 through 187 181.1042 171.9510 177.5686 174.5919 165.1212 164.5967 159.8200 162.5519 157.3316 166.4876 173.2068 Columns 188 through 198 164.4662 167.8638 182.9054 171.8462 166.3115 161.7832 154.5187 165.8130 153.4337 162.2011 167.9126 Columns 199 through 209 167.2107 157.8894 164.1550 161.3913 161.3903 164.4537 179.3821 180.2401 169.0573 161.3159 157.2487 Columns 210 through 219 170.6748 156.9867 159.6709 157.5869 158.3394 159.8163 147.0575 156.2174 160.9459 163.4824 sum_uy = Columns 1 through 11 165.2563 165.5795 166.1995 170.9152 163.6844 166.4196 167.6353 164.3084 164.3194 165.2355 167.5823 Columns 12 through 22 168.2803 169.2835 162.9216 167.0631 160.2461 167.8332 169.5117 174.8860 171.1718 164.1210 166.2988 Columns 23 through 33 167.0086 164.9682 166.5209 162.2844 166.6866 167.9411 165.1581 166.7926 160.2144 165.3253 167.8167 Columns 34 through 44 166.6829 169.0154 167.4451 165.2335 166.7266 163.3553 160.9022 168.5189 170.4437 166.3809 165.6549 Columns 45 through 55 172.8555 162.9250 166.5391 168.8128 165.6671 164.9514 168.2179 171.1405 168.0393 165.4957 172.6453 Columns 56 through 66 166.1628 169.4516 164.9904 167.9538 163.7687 164.0050 170.4196 165.5426 163.1279 175.4676 161.4074 Columns 67 through 77 163.0493 163.7688 167.3249 163.5510 161.5910 165.2694 167.1262 168.0834 158.4944 171.0608 166.7465 Columns 78 through 88 165.5484 162.0160 168.3853 165.3217 158.6229 165.4265 167.7379 163.2385 170.1646 167.9603 163.2186 Columns 89 through 99 168.6188 173.4607 165.6139 162.4525 178.8317 168.1632 159.4673 162.7666 164.0704 173.8091 160.7991 Columns 100 through 110 163.7047 165.6171 168.0516 173.7905 162.5437 165.3834 171.4767 164.8168 171.3565 161.0105 160.9255 Columns 111 through 121 163.1510 168.4366 166.4470 168.8021 167.6726 167.0674 165.0700 167.0253 169.8833 160.8127 168.4143 Columns 122 through 132 160.3481 157.2100 170.4292 167.9136 166.7632 163.7882 168.6650 163.5936 162.1007 180.7796 163.5770 Columns 133 through 143 163.6172 164.9055 171.0022 161.7652 163.5815 167.0784 169.0006 168.2613 164.0774 164.8563 166.6608 Columns 144 through 154 169.8005 169.1029 169.7164 157.3982 169.8301 168.6651 170.1656 162.8833 160.5731 159.6538 159.3710 Columns 155 through 165 173.3246 167.9013 161.6300 168.6266 170.7362 166.7189 169.7284 165.8737 170.5912 173.7643 167.8026 Columns 166 through 176 164.1637 166.2759 165.7092 167.8649 167.9781 165.1915 159.7562 167.0045 164.4183 163.9133 161.3147 Columns 177 through 187 170.5880 163.8068 163.7659 173.5798 166.1039 175.8241 174.0372 165.6473 168.5011 166.9730 166.5521 Columns 188 through 198 165.3801 167.5947 160.4842 160.8629 163.7599 170.0116 171.8991 166.2369 168.2465 163.4443 168.6906 Columns 199 through 209 165.0517 166.4950 167.2727 174.1411 165.7266 165.2047 170.4458 165.1037 161.5600 163.6611 161.6673 Columns 210 through 219 172.4295 167.7160 163.5795 165.3646 163.4575 165.5055 163.7666 167.1725 164.3511 170.2986 f_acc = Columns 1 through 11 0.2194 0.1890 0.1708 0.1448 0.1985 0.1386 0.1729 0.1761 0.2531 0.2082 0.3287 Columns 12 through 22 0.0447 0.0438 0.0846 0.0629 0.0757 0.0535 0.0569 0.0636 0.1291 0.0711 0.1131 Columns 23 through 33 0.1103 0.1772 0.1105 0.1698 0.1135 0.0952 0.0767 0.0516 0.0371 0.0518 0.0414 Columns 34 through 44 0.0936 0.1057 0.1505 0.1059 0.1488 0.0609 0.0624 0.0430 0.0539 0.1655 0.2018 Columns 45 through 55 0.1158 0.1883 0.1780 0.1692 0.1124 0.0418 0.1467 0.1280 0.1283 0.1633 0.1467 Columns 56 through 66 0.0629 0.2367 0.0771 0.0796 0.0762 0.1149 0.2077 0.2148 0.2717 0.1331 0.0890 Columns 67 through 77 0.0951 0.2123 0.1557 0.0537 0.1800 0.2657 0.5231 0.8014 0.0582 1.6659 0.0707 Columns 78 through 88 0.0927 0.2368 0.1014 0.1244 0.2455 0.0872 0.1037 0.1619 0.2374 0.1116 0.1531 Columns 89 through 99 0.0465 0.0883 0.0859 0.9662 0.4331 0.2026 0.1349 0.2303 0.1207 0.1352 0.0816 Columns 100 through 110 0.0662 0.0515 0.2230 0.0731 0.0634 0.0683 0.0724 0.3235 0.2649 0.1579 0.1345 Columns 111 through 121 0.0855 6.5809 6.8026 0.1387 0.1607 0.0873 0.1896 0.1462 0.1604 0.0347 0.0810 Columns 122 through 132 0.0680 0.1471 0.1172 0.1729 8.5365 8.6031 0.2067 0.3484 8.3711 8.8407 0.1185 Columns 133 through 143 0.1484 0.0350 0.0348 0.0610 0.0911 0.0533 0.0432 0.0407 0.0426 0.0871 0.0394 Columns 144 through 154 0.0728 0.0708 0.1280 4.0435 0.0709 0.0809 0.0743 0.1749 0.0696 0.0526 0.1473 Columns 155 through 165 4.1581 0.5235 0.4180 0.2542 1.1008 0.3257 0.1099 0.0727 0.1310 0.6129 0.1605 Columns 166 through 176 5.0433 0.1123 0.0620 0.0621 0.2277 0.1811 0.0743 0.0815 0.2310 0.1471 0.0821 Columns 177 through 187 0.0673 0.3349 0.0485 0.0674 0.0584 0.0635 0.0453 0.0532 0.0666 0.1359 0.5338 Columns 188 through 198 0.0883 0.2185 0.0530 0.0750 0.1795 0.0789 0.0698 0.0649 0.0496 0.0836 0.1080 Columns 199 through 209 0.1281 0.0869 0.0492 0.0644 0.1135 0.0530 0.1115 0.1039 0.2201 0.0601 0.0572 Columns 210 through 219 0.0501 0.1078 0.0984 0.0515 0.0602 0.1764 0.0546 0.0449 0.1217 0.0644 rmsdist_acc = Columns 1 through 11 0.4684 0.4348 0.4133 0.3805 0.4455 0.3723 0.4158 0.4196 0.5031 0.4563 0.5733 Columns 12 through 22 0.2114 0.2093 0.2908 0.2507 0.2751 0.2312 0.2385 0.2522 0.3593 0.2666 0.3363 Columns 23 through 33 0.3321 0.4210 0.3324 0.4120 0.3369 0.3085 0.2770 0.2271 0.1926 0.2277 0.2035 Columns 34 through 44 0.3060 0.3251 0.3879 0.3253 0.3858 0.2468 0.2498 0.2073 0.2322 0.4068 0.4492 Columns 45 through 55 0.3403 0.4339 0.4219 0.4114 0.3353 0.2045 0.3830 0.3578 0.3581 0.4041 0.3830 Columns 56 through 66 0.2509 0.4866 0.2776 0.2821 0.2760 0.3389 0.4558 0.4635 0.5213 0.3648 0.2983 Columns 67 through 77 0.3084 0.4608 0.3945 0.2318 0.4243 0.5155 0.7232 0.8952 0.2412 1.2907 0.2659 Columns 78 through 88 0.3044 0.4866 0.3185 0.3527 0.4955 0.2953 0.3220 0.4024 0.4872 0.3341 0.3913 Columns 89 through 99 0.2156 0.2972 0.2931 0.9829 0.6581 0.4501 0.3673 0.4799 0.3475 0.3678 0.2857 Columns 100 through 110 0.2574 0.2270 0.4722 0.2704 0.2519 0.2613 0.2692 0.5688 0.5146 0.3973 0.3667 Columns 111 through 121 0.2924 2.5653 2.6082 0.3724 0.4008 0.2954 0.4355 0.3823 0.4005 0.1862 0.2847 Columns 122 through 132 0.2608 0.3836 0.3423 0.4158 2.9217 2.9331 0.4547 0.5902 2.8933 2.9733 0.3443 Columns 133 through 143 0.3852 0.1870 0.1866 0.2469 0.3019 0.2309 0.2078 0.2018 0.2063 0.2951 0.1985 Columns 144 through 154 0.2697 0.2661 0.3577 2.0108 0.2663 0.2845 0.2725 0.4183 0.2639 0.2294 0.3837 Columns 155 through 165 2.0391 0.7236 0.6466 0.5042 1.0492 0.5707 0.3314 0.2697 0.3619 0.7829 0.4007 Columns 166 through 176 2.2457 0.3351 0.2490 0.2493 0.4772 0.4256 0.2726 0.2856 0.4806 0.3835 0.2866 Columns 177 through 187 0.2595 0.5787 0.2203 0.2596 0.2416 0.2519 0.2127 0.2306 0.2580 0.3687 0.7306 Columns 188 through 198 0.2971 0.4675 0.2303 0.2738 0.4237 0.2808 0.2642 0.2547 0.2227 0.2891 0.3286 Columns 199 through 209 0.3579 0.2949 0.2218 0.2538 0.3369 0.2302 0.3339 0.3223 0.4692 0.2451 0.2391 Columns 210 through 219 0.2239 0.3283 0.3137 0.2268 0.2453 0.4200 0.2337 0.2118 0.3489 0.2537 maxdist_acc = Columns 1 through 11 4.2824 3.5810 3.5985 4.7207 5.4385 3.8055 3.4166 5.0229 6.1936 5.4135 5.3886 Columns 12 through 22 2.2041 1.9717 4.1808 2.6505 2.2916 1.7002 2.6147 2.5464 2.8886 4.6055 3.2016 Columns 23 through 33 2.1750 2.4603 3.0057 3.1410 2.2458 2.1721 2.2230 2.3678 1.9161 2.3062 2.5264 Columns 34 through 44 2.5882 3.0419 2.8719 1.9953 2.6968 1.9666 2.1755 1.6240 2.2504 2.9689 5.1169 Columns 45 through 55 2.9593 3.3672 2.8788 4.1027 2.0939 2.3508 4.7751 4.4828 3.8989 2.7934 5.2354 Columns 56 through 66 2.4327 3.9944 2.5875 2.2861 2.8645 3.5321 4.8799 4.8361 5.1480 3.3774 2.0491 Columns 67 through 77 2.4102 4.2903 3.7133 2.1441 3.6577 3.0893 6.1409 7.7650 4.1265 11.2351 2.9035 Columns 78 through 88 3.2292 5.9374 3.0983 3.4925 6.1007 2.5768 2.3666 3.5302 5.3503 4.8442 4.8874 Columns 89 through 99 1.9657 2.9611 2.7384 6.2864 5.9199 3.8926 4.9104 4.2265 2.7384 3.5012 2.9752 Columns 100 through 110 3.2452 1.9153 3.4790 2.1638 2.4261 2.5726 2.3473 6.0463 6.7477 4.9114 5.2435 Columns 111 through 121 2.8192 16.0082 15.7090 2.5184 2.1752 2.2809 2.7698 3.3888 2.1985 2.6379 2.3358 Columns 122 through 132 2.4083 3.2461 3.3039 3.6205 29.6511 17.8552 3.7627 4.4316 17.2400 24.3194 2.8640 Columns 133 through 143 3.4355 1.6840 1.7278 2.2279 2.0839 2.1777 1.9359 1.9661 1.7655 2.3350 2.4179 Columns 144 through 154 2.8049 2.7591 4.1014 10.8642 2.3735 2.7543 4.0670 3.5696 3.7440 2.2773 2.3915 Columns 155 through 165 15.5217 4.8997 7.6373 7.4614 6.9739 3.6321 4.9769 3.3364 3.3854 7.5646 4.1101 Columns 166 through 176 17.6000 3.2000 2.4661 1.9291 7.1419 4.5419 2.6657 5.0632 3.4315 6.5933 3.2159 Columns 177 through 187 2.3253 4.0223 2.5593 2.6460 1.9666 2.3585 2.1375 2.3822 2.9329 3.2147 4.3114 Columns 188 through 198 3.9447 4.8201 2.9512 3.9999 5.8801 3.1809 1.5786 1.5715 2.7029 3.7611 2.8770 Columns 199 through 209 3.2452 2.5052 3.5452 1.7960 4.3954 2.3949 2.4417 2.5068 3.7361 3.2187 2.1367 Columns 210 through 219 2.1960 3.7337 3.9879 2.4741 2.8811 4.6966 2.1780 3.0018 3.0133 1.6906 rmsdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0 0 0 0 0 Columns 56 through 66 0 0 0 0 0 0 0 0.2552 0.6685 0.2770 0.2769 Columns 67 through 76 0.3122 0.2878 0.2796 0.2418 0.3802 0.3319 0.2683 0.3082 0.4456 0.5412 maxdist_acc = Columns 1 through 11 0 0 0 0 0 0 0 0 0 0 0 Columns 12 through 22 0 0 0 0 0 0 0 0 0 0 0 Columns 23 through 33 0 0 0 0 0 0 0 0 0 0 0 Columns 34 through 44 0 0 0 0 0 0 0 0 0 0 0 Columns 45 through 55 0 0 0 0 0 0 0 0 0 0 0 Columns 56 through 66 0 0 0 0 0 0 0 3.9055 7.5732 2.2790 4.1721 Columns 67 through 76 3.4308 2.8533 3.3929 3.0462 4.1016 2.6683 2.8947 2.2348 4.2098 2.7803 local_stress_sum = 1.0e+04 * Columns 1 through 11 0.4428 0.9949 0.5408 1.1674 0.3885 0.4779 0.6824 2.1009 0.8884 0.4952 1.7959 Columns 12 through 22 1.0343 0.5376 0.6914 1.3018 1.6766 2.4587 0.5837 1.4161 0.8103 1.2582 2.3999 Columns 23 through 33 5.6907 0.8605 2.9196 0.8057 0.4506 0.6273 3.2260 0.2790 0.3671 0.8398 0.6951 Columns 34 through 40 0.8795 0.4299 0.4842 0.4763 2.0957 0.4697 0.8075 local_stress_sum_sqr = 1.0e+05 * Columns 1 through 11 0.0253 0.0916 0.0168 0.1234 0.0152 0.0150 0.0342 0.4962 0.0704 0.0214 0.7008 Columns 12 through 22 0.2196 0.0186 0.1701 0.1366 0.2158 0.6249 0.0237 0.1606 0.0502 0.2189 0.6605 Columns 23 through 33 4.0072 0.0667 1.0155 0.0599 0.0199 0.0816 1.3989 0.0078 0.0106 0.0695 0.0329 Columns 34 through 40 0.0861 0.0179 0.0656 0.0248 0.6226 0.0203 0.0891 sum_ux = Columns 1 through 11 175.2304 174.2729 153.2671 170.5681 148.6992 161.9130 169.9940 162.1843 166.0439 175.9249 162.1607 Columns 12 through 22 177.5096 171.3927 175.4816 176.8488 174.3266 168.1535 170.5348 175.5698 164.4018 164.3001 165.8239 Columns 23 through 33 154.8133 163.1510 161.9875 170.8326 164.9578 148.3440 169.2252 172.3258 171.3121 165.4372 160.7925 Columns 34 through 40 179.8289 171.9090 170.7527 160.6592 165.1850 162.5057 162.1620 sum_uy = Columns 1 through 11 161.4754 160.3056 167.7140 166.2327 159.7178 169.2869 166.3212 165.5131 164.0776 169.1147 167.8949 Columns 12 through 22 166.3024 168.2673 163.0911 174.4194 170.4603 165.2392 172.6239 158.5059 165.7975 175.4805 162.6599 Columns 23 through 33 165.1168 164.0944 168.2662 158.3691 167.2360 171.9976 171.3418 165.2773 176.8102 168.7467 171.0647 Columns 34 through 40 167.7245 165.9866 161.2725 167.3874 163.4614 164.0165 169.5096 f_acc = Columns 1 through 11 0.0709 0.1592 0.0865 0.1868 0.0622 0.0765 0.1092 0.3361 0.1421 0.0792 0.2873 Columns 12 through 22 0.1655 0.0860 0.1106 0.2083 0.2683 0.3934 0.0934 0.2266 0.1296 0.2013 0.3840 Columns 23 through 33 0.9105 0.1377 0.4671 0.1289 0.0721 0.1004 0.5162 0.0446 0.0587 0.1344 0.1112 Columns 34 through 40 0.1407 0.0688 0.0775 0.0762 0.3353 0.0751 0.1292 rmsdist_acc = Columns 1 through 11 0.2662 0.3990 0.2942 0.4322 0.2493 0.2765 0.3304 0.5798 0.3770 0.2815 0.5360 Columns 12 through 22 0.4068 0.2933 0.3326 0.4564 0.5179 0.6272 0.3056 0.4760 0.3601 0.4487 0.6197 Columns 23 through 33 0.9542 0.3711 0.6835 0.3590 0.2685 0.3168 0.7184 0.2113 0.2423 0.3666 0.3335 Columns 34 through 40 0.3751 0.2623 0.2784 0.2761 0.5791 0.2741 0.3594 maxdist_acc = Columns 1 through 11 2.9715 3.5660 1.9177 4.6116 3.1878 2.3931 1.8540 5.1794 2.8796 2.9234 8.1747 Columns 12 through 22 7.2033 2.1872 7.4969 2.9818 4.1008 4.4428 2.7797 3.3141 2.1601 3.6795 3.7791 Columns 23 through 33 7.2023 4.0348 5.4358 3.7517 2.0154 4.9098 5.0764 2.7629 2.0386 4.0320 2.0753 Columns 34 through 40 2.3791 3.4337 5.7040 3.9041 10.2972 2.6419 4.9731 false/random local_stress_sum = 1.0e+05 * Columns 1 through 11 0.7298 0.2448 0.4063 0.8542 5.8667 2.3159 0.2326 0.9147 0.5019 1.7087 0.5262 Columns 12 through 22 0.4816 0.2723 1.9044 1.5014 0.3005 0.8944 1.1746 0.7809 1.7398 0.5748 0.8473 Columns 23 through 31 0.3051 4.4121 0.7138 0.3410 0.4173 1.4370 1.6189 0.2146 0.0819 local_stress_sum_sqr = 1.0e+07 * Columns 1 through 11 0.0294 0.0041 0.0191 0.0888 8.3177 1.0478 0.0044 0.0441 0.0306 0.3096 0.0299 Columns 12 through 22 0.0255 0.0051 0.3835 0.1558 0.0073 0.0614 0.1601 0.0364 0.3804 0.0231 0.0371 Columns 23 through 31 0.0054 4.6452 0.0446 0.0097 0.0144 0.2009 0.2234 0.0039 0.0004 sum_ux = Columns 1 through 11 160.3646 164.2959 163.2688 154.0029 157.6849 166.0314 171.3225 164.7658 173.4130 168.2517 171.4407 Columns 12 through 22 164.6722 160.8291 163.0611 168.0747 152.6677 153.7209 161.2447 166.7906 160.7317 163.7054 164.3584 Columns 23 through 31 164.1638 170.3140 171.5236 174.7552 174.0803 165.5548 173.1007 165.7933 165.8623 sum_uy = Columns 1 through 11 167.9891 166.8348 166.4502 162.2983 162.9082 164.7454 171.6041 163.5720 163.0068 172.9106 165.4543 Columns 12 through 22 159.2349 168.9965 170.1325 170.2536 167.2882 173.4907 166.4940 166.0971 166.8163 161.8250 168.2185 Columns 23 through 31 165.9977 162.7406 160.9665 176.0030 168.9540 173.1083 158.2542 165.8423 161.8059 f_acc = Columns 1 through 11 1.1676 0.3917 0.6500 1.3667 9.3868 3.7054 0.3722 1.4636 0.8031 2.7339 0.8420 Columns 12 through 22 0.7706 0.4357 3.0470 2.4022 0.4807 1.4310 1.8793 1.2495 2.7838 0.9197 1.3557 Columns 23 through 31 0.4881 7.0593 1.1422 0.5455 0.6677 2.2993 2.5902 0.3433 0.1310 rmsdist_acc = Columns 1 through 11 1.0806 0.6259 0.8062 1.1691 3.0638 1.9250 0.6101 1.2098 0.8961 1.6534 0.9176 Columns 12 through 22 0.8778 0.6601 1.7456 1.5499 0.6934 1.1962 1.3709 1.1178 1.6685 0.9590 1.1644 Columns 23 through 31 0.6987 2.6569 1.0687 0.7386 0.8171 1.5163 1.6094 0.5859 0.3619 maxdist_acc = Columns 1 through 11 4.7495 3.0443 5.2550 6.7481 30.6769 20.4265 3.9301 6.2123 6.4409 10.2047 6.2867 Columns 12 through 22 6.0610 3.1056 11.9948 6.8104 4.7879 6.8871 14.3374 7.3330 13.9166 5.0094 5.3038 Columns 23 through 31 3.6914 25.3239 6.9152 4.4233 4.7317 9.8789 7.8750 6.2027 2.3035 false_pairs=[1.1676 0.3917 0.6500 1.3667 9.3868 3.7054 0.3722 1.4636 0.8031 2.7339 0.8420 0.7706 0.4357 3.0470 2.4022 0.4807 1.4310 1.8793 1.2495 2.7838 0.9197 1.3557 0.4881 7.0593 1.1422 0.5455 0.6677 2.2993 2.5902 0.3433 ] figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04297d312.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04297d306.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04313d63.ppm')) figure imshow(imread('~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04313d57.ppm')) no cheeks (default) random local_stress_sum = 1.0e+05 * Columns 1 through 11 0.3406 0.7455 0.1959 0.2187 0.8476 0.1052 0.1777 0.3877 0.3607 0.3318 1.3348 Columns 12 through 22 0.6253 0.3911 0.4665 0.7797 0.3840 0.2696 0.4136 0.2894 0.7413 0.4448 1.5528 Columns 23 through 33 1.3585 0.2857 0.4175 0.6082 0.3382 0.6080 0.2279 0.8114 0.4006 1.0201 0.1133 Columns 34 through 44 0.3852 0.2092 0.7069 0.2134 0.4875 1.0282 0.3301 0.1774 0.9348 0.1445 0.6444 Columns 45 through 55 0.4074 3.2377 0.5323 0.4456 0.2573 0.6984 0.3833 0.3715 0.1766 0.3069 0.6389 Columns 56 through 66 0.3404 1.2012 0.4040 1.4060 1.2952 0.0561 0.6436 0.5945 0.3293 0.7514 0.4526 Columns 67 through 77 0.7874 0.1196 0.9623 0.2505 0.6396 0.6218 0.6123 0.2624 0.7368 0.7509 0.4180 Columns 78 through 84 0.2355 0.2660 1.7210 0.3923 1.0745 0.3314 0.5533 local_stress_sum_sqr = 1.0e+07 * Columns 1 through 11 0.0055 0.0243 0.0054 0.0035 0.0624 0.0008 0.0027 0.0074 0.0201 0.0154 0.1656 Columns 12 through 22 0.0243 0.0101 0.0248 0.0522 0.0105 0.0045 0.0320 0.0039 0.0735 0.0306 0.2691 Columns 23 through 33 0.2061 0.0038 0.0106 0.0175 0.0129 0.0324 0.0081 0.0506 0.0095 0.0711 0.0009 Columns 34 through 44 0.0118 0.0042 0.0435 0.0050 0.0111 0.1377 0.0075 0.0019 0.1752 0.0032 0.0313 Columns 45 through 55 0.0133 4.0077 0.0168 0.0318 0.0038 0.0468 0.0075 0.0091 0.0019 0.0123 0.0401 Columns 56 through 66 0.0126 0.1257 0.0448 0.2115 0.1160 0.0003 0.0202 0.0212 0.0125 0.0303 0.0279 Columns 67 through 77 0.0405 0.0011 0.0572 0.0077 0.0414 0.0695 0.0547 0.0059 0.0427 0.0391 0.0166 Columns 78 through 84 0.0089 0.0061 0.2382 0.0117 0.0523 0.0107 0.0379 sum_ux = Columns 1 through 11 167.7490 166.4185 174.1079 163.0770 157.1062 160.1533 172.0540 161.5163 177.3828 161.7940 169.3274 Columns 12 through 22 166.3578 174.5059 160.0387 174.1012 169.9584 166.0891 173.3008 164.4628 160.1795 161.3629 170.5309 Columns 23 through 33 163.3236 169.1200 159.8306 168.5435 172.8037 167.5944 177.4939 165.4588 170.6373 172.2772 157.3146 Columns 34 through 44 166.5723 167.6909 171.4686 162.0218 162.3330 160.1294 169.5170 165.2524 158.1741 179.3502 169.6942 Columns 45 through 55 161.2769 169.4874 169.7924 169.7829 165.8832 177.4199 160.8783 174.2387 173.3601 162.8204 171.5532 Columns 56 through 66 154.2771 165.6086 164.3349 171.4032 161.2795 169.3652 168.8469 169.8329 169.6262 179.6263 161.8672 Columns 67 through 77 170.2393 163.1118 159.8799 165.2650 169.0759 170.3699 178.3659 176.8257 163.6069 169.6539 168.2808 Columns 78 through 84 160.8577 168.5274 176.4419 161.3497 164.7214 163.8355 158.9202 sum_uy = Columns 1 through 11 170.1531 178.5689 170.3694 165.0228 164.3890 171.7742 166.0872 161.9724 168.3012 167.2081 158.7428 Columns 12 through 22 164.0513 165.4998 163.0541 168.0024 170.7707 170.0895 166.4628 165.6531 170.5058 170.8833 167.4818 Columns 23 through 33 157.0502 163.9279 162.8758 175.3569 163.1840 171.9563 169.5754 168.7069 170.4514 154.1839 163.6020 Columns 34 through 44 163.4353 168.6965 166.2198 163.8141 163.2145 156.9360 170.1748 163.1772 162.2703 172.1834 164.7909 Columns 45 through 55 163.7198 161.6559 166.4409 167.0256 167.3820 163.0450 164.9316 176.4340 169.6028 169.0095 176.2738 Columns 56 through 66 168.8858 163.1474 168.4675 172.0710 170.0419 156.0644 167.6362 179.8850 169.2899 168.1996 171.6930 Columns 67 through 77 156.8415 174.3661 169.4625 170.9881 158.8854 164.7806 168.7501 166.0571 162.7616 173.4475 169.0990 Columns 78 through 84 164.9451 164.9031 160.8477 166.7367 166.3147 163.9889 163.0473 f_acc = Columns 1 through 11 0.5449 1.1928 0.3134 0.3499 1.3562 0.1684 0.2844 0.6203 0.5771 0.5309 2.1356 Columns 12 through 22 1.0005 0.6258 0.7464 1.2475 0.6144 0.4313 0.6618 0.4630 1.1860 0.7117 2.4845 Columns 23 through 33 2.1737 0.4572 0.6680 0.9731 0.5411 0.9727 0.3647 1.2983 0.6410 1.6322 0.1813 Columns 34 through 44 0.6162 0.3346 1.1311 0.3416 0.7801 1.6451 0.5281 0.2838 1.4957 0.2312 1.0310 Columns 45 through 55 0.6518 5.1803 0.8516 0.7130 0.4117 1.1174 0.6132 0.5944 0.2826 0.4911 1.0223 Columns 56 through 66 0.5446 1.9219 0.6464 2.2496 2.0723 0.0897 1.0297 0.9512 0.5269 1.2023 0.7242 Columns 67 through 77 1.2598 0.1914 1.5397 0.4008 1.0234 0.9949 0.9797 0.4198 1.1788 1.2015 0.6688 Columns 78 through 84 0.3768 0.4256 2.7535 0.6277 1.7192 0.5303 0.8853 false_pairs=[0.5449 1.1928 0.3134 0.3499 1.3562 0.1684 0.2844 0.6203 0.5771 0.5309 2.1356 1.0005 0.6258 0.7464 1.2475 0.6144 0.4313 0.6618 0.4630 1.1860 0.7117 2.4845 2.1737 0.4572 0.6680 0.9731 0.5411 0.9727 0.3647 1.2983 0.6410 1.6322 0.1813 0.6162 0.3346 1.1311 0.3416 0.7801 1.6451 0.5281 0.2838 1.4957 0.2312 1.0310 0.6518 5.1803 0.8516 0.7130 0.4117 1.1174 0.6132 0.5944 0.2826 0.4911 1.0223 0.5446 1.9219 0.6464 2.2496 2.0723 0.0897 1.0297 0.9512 0.5269 1.2023 0.7242 1.2598 0.1914 1.5397 0.4008 1.0234 0.9949 0.9797 0.4198 1.1788 1.2015 0.6688 0.3768 0.4256 2.7535 0.6277 1.7192 0.5303 0.8853] rmsdist_acc = Columns 1 through 11 0.7382 1.0922 0.5598 0.5916 1.1646 0.4103 0.5333 0.7876 0.7597 0.7286 1.4614 Columns 12 through 22 1.0002 0.7911 0.8640 1.1169 0.7839 0.6568 0.8135 0.6804 1.0890 0.8436 1.5762 Columns 23 through 33 1.4743 0.6762 0.8173 0.9865 0.7356 0.9863 0.6039 1.1394 0.8006 1.2776 0.4258 Columns 34 through 44 0.7850 0.5785 1.0635 0.5844 0.8832 1.2826 0.7267 0.5327 1.2230 0.4808 1.0154 Columns 45 through 55 0.8073 2.2760 0.9228 0.8444 0.6416 1.0571 0.7831 0.7709 0.5316 0.7008 1.0111 Columns 56 through 66 0.7380 1.3863 0.8040 1.4999 1.4395 0.2996 1.0148 0.9753 0.7259 1.0965 0.8510 Columns 67 through 77 1.1224 0.4375 1.2408 0.6331 1.0116 0.9975 0.9898 0.6479 1.0857 1.0961 0.8178 Columns 78 through 84 0.6138 0.6524 1.6594 0.7923 1.3112 0.7282 0.9409 maxdist_acc = Columns 1 through 11 3.2337 4.1417 7.4058 3.1785 9.1112 3.3918 3.2780 4.5143 6.1313 4.9578 11.3404 Columns 12 through 22 5.1306 4.9333 6.7005 6.6935 5.1822 4.7573 6.9623 5.3519 8.6195 5.9111 9.7852 Columns 23 through 33 10.4197 4.4702 4.7503 4.0999 5.2590 9.2696 4.8981 7.4820 3.8528 6.9208 2.5384 Columns 34 through 44 4.3810 3.9588 6.4710 5.0663 4.0568 11.5401 4.5728 2.9910 10.8882 6.9053 5.9217 Columns 45 through 55 4.8293 21.2600 6.8995 6.6518 3.6234 7.0337 4.7606 4.1995 3.2533 8.6838 6.3129 Columns 56 through 66 7.1965 10.4938 8.5580 7.9358 7.3494 2.4659 4.2064 5.2120 5.7889 5.1108 6.8508 Columns 67 through 77 5.2883 2.6826 6.4656 6.8261 7.2054 8.0214 7.4091 4.8583 6.4705 7.2063 5.6949 Columns 78 through 84 4.8944 4.4154 9.1710 4.6207 5.6745 5.1754 7.0696 * Preparing for ICP applied to image #170 - Sorting data types - Affine registration of:~/NIST/FRGC-2.0-dist/nd1/Spring2004range/04429d459.abs and~/NIST/FRGC-2.0-dist/nd1/Fall2003range/04460d260.abs true local_stress_sum = 1.0e+05 * Columns 1 through 11 0.0791 0.1070 0.0311 0.0853 0.0268 0.0264 0.0512 0.2178 0.0819 0.0376 0.1582 Columns 12 through 22 0.0856 0.0510 0.1208 0.0931 0.0803 0.2436 0.0449 0.1240 0.0661 0.0810 0.1399 Columns 23 through 33 0.2757 0.1057 0.1520 0.0539 0.0318 0.0596 3.0557 0.0266 0.0304 0.0854 0.0706 Columns 34 through 44 0.0746 0.0344 0.1700 0.0438 3.3830 0.0776 0.2235 0.0411 0.0354 0.0724 0.1106 Columns 45 through 55 0.0234 0.0377 0.0554 0.0627 0.0429 0.0686 0.0491 0.0439 0.1237 0.0567 0.2613 Columns 56 through 66 0.0787 0.1885 2.1561 0.1104 0.0401 0.0493 0.0307 0.0343 0.0783 0.0449 0.0514 Columns 67 through 76 0.0536 0.0503 0.0406 0.0388 0.0556 0.0526 0.0407 0.0563 0.0777 0.0889 local_stress_sum_sqr = 1.0e+07 * Columns 1 through 11 0.0009 0.0013 0.0001 0.0010 0.0001 0.0001 0.0002 0.0052 0.0007 0.0002 0.0081 Columns 12 through 22 0.0007 0.0002 0.0014 0.0007 0.0007 0.0095 0.0002 0.0018 0.0004 0.0016 0.0026 Columns 23 through 33 0.0150 0.0013 0.0054 0.0003 0.0001 0.0007 0.6121 0.0001 0.0001 0.0011 0.0004 Columns 34 through 44 0.0007 0.0001 0.0053 0.0002 1.3478 0.0005 0.0092 0.0002 0.0001 0.0007 0.0009 Columns 45 through 55 0.0001 0.0001 0.0003 0.0004 0.0001 0.0003 0.0004 0.0002 0.0014 0.0003 0.0121 Columns 56 through 66 0.0004 0.0045 0.3942 0.0015 0.0001 0.0002 0.0001 0.0002 0.0008 0.0001 0.0003 Columns 67 through 76 0.0003 0.0003 0.0002 0.0002 0.0003 0.0004 0.0001 0.0002 0.0010 0.0009 sum_ux = Columns 1 through 11 177.4085 154.6355 167.6254 168.0514 167.0223 166.6869 177.8097 161.9838 160.4501 171.9858 175.4178 Columns 12 through 22 167.5502 170.4109 184.6651 161.3629 172.8882 168.7968 172.2311 166.7217 160.9809 173.6539 161.7544 Columns 23 through 33 160.8782 171.4345 167.5500 163.2929 166.7604 169.1973 174.5247 159.6755 174.9736 161.7146 161.2920 Columns 34 through 44 166.2807 171.5829 172.5248 166.9115 167.4283 164.6298 165.2796 181.8177 167.9602 172.4038 180.6550 Columns 45 through 55 161.0283 154.1815 168.4215 165.1627 158.2504 172.1743 173.2047 162.9402 170.0067 172.7308 169.8658 Columns 56 through 66 169.4010 169.2140 166.8801 166.5943 163.6289 167.5742 155.2934 161.0482 183.5968 165.7792 167.6062 Columns 67 through 76 160.7795 151.7702 160.3824 156.7325 170.1129 155.5270 151.1741 153.5287 161.3740 170.4637 sum_uy = Columns 1 through 11 159.7076 162.9440 166.6446 163.1441 164.1665 169.1023 161.9080 166.0953 174.0146 166.3768 167.7359 Columns 12 through 22 168.8770 172.8588 161.1972 175.4594 171.1529 159.9216 171.2877 166.6574 165.3308 168.0440 168.7302 Columns 23 through 33 164.6516 166.2424 172.7267 167.1608 170.4772 165.3662 161.8263 171.3356 165.1916 166.1315 167.1149 Columns 34 through 44 164.8919 171.0755 168.6802 167.2120 167.0936 172.2702 168.5590 170.6972 163.9472 169.9426 169.3932 Columns 45 through 55 170.4341 171.5867 175.4089 166.2492 170.7800 170.8502 167.7084 174.5255 164.7580 172.2980 168.2945 Columns 56 through 66 174.1955 165.9002 171.8191 166.5524 160.2038 163.0979 167.9601 174.2424 162.9476 170.8542 165.5452 Columns 67 through 76 171.6148 165.8784 162.7743 168.3629 166.8704 165.8855 172.5247 173.7761 165.0220 178.2914 f_acc = Columns 1 through 11 0.1265 0.1712 0.0498 0.1365 0.0428 0.0423 0.0819 0.3485 0.1311 0.0601 0.2531 Columns 12 through 22 0.1370 0.0816 0.1932 0.1489 0.1284 0.3898 0.0719 0.1984 0.1057 0.1296 0.2238 Columns 23 through 33 0.4410 0.1690 0.2431 0.0862 0.0509 0.0954 4.8891 0.0426 0.0486 0.1366 0.1130 Columns 34 through 44 0.1193 0.0551 0.2720 0.0701 5.4128 0.1242 0.3577 0.0657 0.0566 0.1158 0.1769 Columns 45 through 55 0.0374 0.0604 0.0887 0.1003 0.0686 0.1098 0.0786 0.0703 0.1979 0.0907 0.4181 Columns 56 through 66 0.1259 0.3016 3.4498 0.1766 0.0641 0.0789 0.0492 0.0548 0.1253 0.0719 0.0822 Columns 67 through 76 0.0858 0.0805 0.0649 0.0621 0.0890 0.0841 0.0651 0.0901 0.1243 0.1422 true_pairs=[0.1265 0.1712 0.0498 0.1365 0.0428 0.0423 0.0819 0.3485 0.1311 0.0601 0.2531 0.1370 0.0816 0.1932 0.1489 0.1284 0.3898 0.0719 0.1984 0.1057 0.1296 0.2238 0.4410 0.1690 0.2431 0.0862 0.0509 0.0954 4.8891 0.0426 0.0486 0.1366 0.1130 0.1193 0.0551 0.2720 0.0701 5.4128 0.1242 0.3577 0.0657 0.0566 0.1158 0.1769 0.0374 0.0604 0.0887 0.1003 0.0686 0.1098 0.0786 0.0703 0.1979 0.0907 0.4181 0.1259 0.3016 3.4498 0.1766 0.0641 0.0789 0.0492 0.0548 0.1253 0.0719 0.0822 0.0858 0.0805 0.0649 0.0621 0.0890 0.0841 0.0651 0.0901 0.1243 0.1422] rmsdist_acc = Columns 1 through 11 0.3557 0.4138 0.2232 0.3694 0.2070 0.2057 0.2862 0.5903 0.3620 0.2451 0.5031 Columns 12 through 22 0.3701 0.2856 0.4396 0.3859 0.3584 0.6244 0.2681 0.4454 0.3251 0.3600 0.4731 Columns 23 through 33 0.6641 0.4111 0.4931 0.2936 0.2257 0.3089 2.2111 0.2063 0.2204 0.3695 0.3362 Columns 34 through 44 0.3454 0.2347 0.5215 0.2648 2.3265 0.3524 0.5981 0.2564 0.2380 0.3403 0.4206 Columns 45 through 55 0.1935 0.2457 0.2978 0.3167 0.2619 0.3314 0.2804 0.2652 0.4449 0.3012 0.6466 Columns 56 through 66 0.3548 0.5492 1.8574 0.4202 0.2532 0.2810 0.2217 0.2341 0.3540 0.2682 0.2867 Columns 67 through 76 0.2929 0.2838 0.2547 0.2491 0.2983 0.2900 0.2551 0.3002 0.3526 0.3771 maxdist_acc = Columns 1 through 11 3.7351 5.4343 2.1406 3.3162 1.9657 1.6398 1.9576 3.8438 2.9263 2.6969 8.9297 Columns 12 through 22 3.3496 2.2402 3.7976 3.8260 3.1582 5.1696 2.2233 3.9023 3.2393 4.9222 4.3774 Columns 23 through 33 7.0752 4.6653 3.9446 2.0666 3.3925 4.3018 13.5426 1.8610 1.7231 3.2358 2.7596 Columns 34 through 44 2.7251 1.9560 4.2945 2.9587 19.0480 2.6677 7.8154 2.6038 2.2403 3.9966 2.7239 Columns 45 through 55 2.7783 2.0222 3.1078 3.3468 1.8400 2.6346 3.6884 2.5771 3.2724 2.6793 6.5308 Columns 56 through 66 4.0521 5.9957 12.0249 3.0434 1.8183 2.1761 1.5738 2.7293 3.9422 1.6141 3.4425 Columns 67 through 76 2.5686 4.0279 2.3430 2.2169 2.5285 3.8557 2.4523 1.9628 4.1958 2.4563 TEXAS true pais 95-97 inclusive local_stress_sum = 1.0e+05 * Columns 1 through 11 0.0311 0.0222 0.0113 0.0297 0.0139 0.7620 0.2484 0.0286 0.0205 0.0136 0.0657 Columns 12 through 22 0.0237 0.1075 0.0214 0.0555 0.0185 0.5782 0.0294 0.0390 0.5391 0.0272 0.0339 Columns 23 through 33 0.2885 0.0411 0.0276 0.0191 0.0268 0.0261 0.0195 0.0165 0.0109 0.0076 0.0606 Columns 34 through 44 0.0399 0.0303 0.0169 0.0184 0.0251 0.1269 0.0339 0.3032 0.0922 0.0073 0.0641 Columns 45 through 55 0.0232 0.0733 0.0534 0.1223 0.0264 0.0249 0.1186 0.0285 0.0956 0.0538 0.0548 Columns 56 through 66 0.0853 0.1147 0.0719 0.0291 1.4969 0.0423 0.0677 0.0651 0.0174 0.0844 0.0263 Columns 67 through 77 0.0204 0.1259 0.0359 0.0403 0.0727 0.0260 0.0258 0.0413 0.0274 0.1425 0.0473 Columns 78 through 86 0.1438 0.0221 0.0584 0.0320 0.0406 0.0527 0.0218 0.0118 0.0165 f_acc = Columns 1 through 11 true_pairs=[1.2443 0.8876 0.4539 1.1898 0.5558 30.4790 9.9378 1.1436 0.8220 0.5460 2.6291 0.9476 4.3008 0.8570 2.2215 0.7414 23.1286 1.1746 1.5601 21.5656 1.0885 1.3576 11.5401 1.6445 1.1034 0.7654 1.0701 1.0454 0.7800 0.6582 0.4344 0.3027 2.4235 1.5962 1.2116 0.6766 0.7352 1.0026 5.0768 1.3553 12.1271 3.6885 0.2904 2.5637 0.9297 2.9304 2.1352 4.8931 1.0563 0.9953 4.7439 1.1403 3.8258 2.1501 2.1930 3.4109 4.5877 2.8757 1.1622 59.8751 1.6921 2.7095 2.6025 0.6967 3.3749 1.0517 0.8170 5.0370 1.4350 1.6120 2.9096 1.0397 1.0321 1.6503 1.0951 5.7009 1.8901 5.7500 0.8850 2.3360 1.2815 1.6233 2.1083 0.8735 0.4740 0.6586] ans = 1.0e+05 * 4.8849 2.4852 0.5385 0.3636 0.2206 0.1473 0.0845 0.0358 0.0300 0.0264 percent_explained = 55.4048 28.1875 6.1078 4.1235 2.5025 1.6705 0.9584 0.4056 0.3404 0.2991 false local_stress_sum = 1.0e+05 * Columns 1 through 11 0.3943 0.1479 0.2162 0.4513 0.1746 0.2453 0.0752 1.7305 0.2532 0.1107 0.1636 Columns 12 through 22 0.2114 0.3935 0.1110 0.2183 0.5910 0.0731 0.9675 0.1236 0.3918 1.0442 0.4558 Columns 23 through 33 0.1774 0.2709 0.2269 0.1824 0.1897 0.3387 1.2031 0.3195 0.1286 0.2721 0.3814 Columns 34 through 44 0.3228 1.3469 0.1650 0.3411 0.1349 0.2194 0.1020 0.3995 0.1344 0.1648 0.4136 Columns 45 through 48 0.2562 0.2389 0.2002 0.3661 f_acc = Columns 1 through 11 15.7720 5.9177 8.6495 18.0502 6.9845 9.8127 3.0067 69.2203 10.1264 4.4294 6.5428 Columns 12 through 22 8.4573 15.7396 4.4402 8.7322 23.6416 2.9234 38.6992 4.9458 15.6721 41.7668 18.2334 Columns 23 through 33 7.0980 10.8341 9.0774 7.2969 7.5867 13.5474 48.1258 12.7800 5.1451 10.8843 15.2543 Columns 34 through 44 12.9134 53.8757 6.5989 13.6425 5.3957 8.7758 4.0787 15.9816 5.3778 6.5909 16.5429 Columns 45 through 48 10.2465 9.5577 8.0070 14.6433 Columns 1 through 11 false_pairs=[ 15.7720 5.9177 8.6495 18.0502 6.9845 9.8127 3.0067 69.2203 10.1264 4.4294 6.5428 8.4573 15.7396 4.4402 8.7322 23.6416 2.9234 38.6992 4.9458 15.6721 41.7668 18.2334 7.0980 10.8341 9.0774 7.2969 7.5867 13.5474 48.1258 12.7800 5.1451 10.8843 15.2543 12.9134 53.8757 6.5989 13.6425 5.3957 8.7758 4.0787 15.9816 5.3778 6.5909 16.5429 10.2465 9.5577 8.0070 14.6433] resolution at 5 false local_stress_sum = 1.0e+05 * Columns 1 through 11 3.5793 1.6855 1.3280 4.4554 1.6239 1.9236 0.2991 4.1663 2.1691 1.2339 2.0526 Columns 12 through 22 2.2693 4.5263 4.0404 2.8596 5.3316 0.5559 4.3865 1.2125 2.6199 3.8466 6.5695 Columns 23 through 26 0.5774 2.7534 2.2508 2.0536 f_acc = Columns 1 through 11 false_pairs=[ 15.9079 7.4910 5.9021 19.8019 7.2173 8.5491 1.3293 18.5170 9.6404 5.4841 9.1229 10.0859 20.1171 17.9572 12.7093 23.6958 2.4706 19.4954 5.3890 11.6438 17.0960 29.1976 2.5664 12.2372 10.0036 9.1269] \ true local_stress_sum = 1.0e+05 * Columns 1 through 11 0.3873 0.2620 0.2224 0.2793 0.2693 0.4523 4.2628 0.3439 0.2446 0.2514 0.3739 Columns 12 through 18 0.7674 0.2978 0.3281 0.2719 0.2477 0.4125 0.2677 f_acc = Columns 1 through 11 true_pairs=[ 1.7213 1.1645 0.9887 1.2411 1.1967 2.0104 18.9458 1.5286 1.0872 1.1174 1.6618 3.4107 1.3234 1.4583 1.2086 1.1009 1.8335 1.1897] resolution at 2 ERROR back to 5 figure imshow(imread('/home/schestow/Texas3DFRDatabase/PreprocessedImages/Clean_0183_095_20050912210315_Range.png')) figure imshow(imread('/home/schestow/Texas3DFRDatabase/PreprocessedImages/Clean_0184_095_20050912210957_Range.png')) figure imshow(imread('/home/schestow/Texas3DFRDatabase/PreprocessedImages/Clean_0193_095_20050912224435_Range.png')) figure imshow(imread('/home/schestow/Texas3DFRDatabase/PreprocessedImages/Clean_0194_095_20050912224939_Range.png')) resolution at 2 bebugging nose 0 0 0 0 0 1.9036 1.2678 1.1448 27.7003 1.3948 1.2038 Column 12 1.8696 Columns 1 through 11 1.7971 1.2785 1.0246 2.0885 2.0485 1.7608 1.1536 1.6864 2.0304 0.7347 1.1131 Column 12 2.0084 geodesic 600 vertexes true_pairs=[2.9626 3.8862 3.5716 5.0308 1.7060 4.0890 3.1376 30.9470 1.7519 31.0055 1.6786 1.6857 1.5199 4.0288 7.6341 4.1814 2.5752 5.9269 2.2590 1.6158 5.9950 3.0868 3.0964 7.4941 3.6365 2.7182 5.2884 4.1762 43.4410 1.2647 6.2175 1.7899 0.6350 5.5265 12.7956 1.1867 4.9084 6.5400 12.5895 3.5922 31.7844 29.8773 7.5619 9.8583 6.1141 4.7144 2.6923 2.6960 2.4366 6.5062 3.7440 2.3134 5.8683 7.0087 4.4309 2.3581 9.6349 5.8314 2.5843 1.3607 8.5900 7.4623 4.3363 2.1302 1.8978 7.6408 5.1478 55.6915 2.5734 1.4455 3.4387 2.9330 1.1586 1.6024 15.1222 2.6120] false_pairs=[ 6.6410 7.5985 12.8111 37.1689 9.2791 23.4728 3.8303 29.7797 4.2623 52.3256 2.3049 43.9953 20.9670 14.5642 20.7548 35.6529 14.9009 7.9629 61.2702 89.7027 7.3219 10.8261 16.4616 45.8824 22.9342 7.6887 6.6018 13.5055 10.8339 58.9307 24.5859 19.5364 10.8034 10.4272 8.5679 14.2642] 2400 vertexs true_pairs=[ 1.0701 2.1978 0.6938 0.9326 1.9112 2.7786 2.7708 3.2533 2.9104 1.0459 2.1897 1.1971 1.3107 1.2026 0.8667 2.0882 0.5942 1.1848 1.7379 23.7850 2.2485 8.2421 1.1306 0.8613 1.1788 1.9869 1.9317 1.0862 2.5898 1.4262 0.3894 0.3453 3.0845 4.9467 2.7650 8.2410 4.7616 2.8622 6.6828 0.3424 3.0268 1.3132 2.7713 2.1112 3.3133 0.7338 62.9694 2.6526 0.6597 3.9528 0.9295 24.9043 5.5483 1.9327 2.4503 26.0206 1.7387 5.8230 0.7734 0.8162 6.8091 1.1233 4.3834 6.0689 3.7640 2.0515 1.2197 2.0029 1.1956 0.4711 1.8602 1.7638 4.3353 2.5179 2.9967 22.3523 3.8310 2.4778 1.9891 1.0403 0.4101 0.6936 1.9002 9.0989 0.2875 6.2822] false_pairs=[ 18.1068 4.3427 9.0928 19.1670 33.9847 5.6026 2.9001 13.5408 10.5125 5.1769 7.1843 9.6112 10.5024 3.5095 8.6023 16.8799 3.1804 10.5160 5.4729 17.3035 10.6941 12.2624 3.8046 52.3685 12.6866 13.1589 5.5193 21.3811 51.9557 5.8199 8.7844 23.2455 7.2894 11.3231 15.9507 7.7516 14.0860 4.2613 9.5090 4.2735 9.5307 4.0834 9.5176 14.6374 8.7638 9.9210 5.6607 6.7040] OR f_acc = Columns 1 through 11 17.7863 4.2626 9.5061 19.6362 36.5871 5.4985 3.0142 10.6037 9.8229 5.2201 7.2366 Columns 12 through 22 9.5985 10.1459 3.4412 9.0374 15.8027 3.2701 10.3241 5.8138 19.3461 10.6551 11.7347 Columns 23 through 33 4.0258 52.1395 12.7096 11.6287 5.8048 21.2646 52.7142 5.8711 8.8462 20.8663 7.3457 Columns 34 through 44 11.6345 16.1141 7.6927 14.2241 4.4450 8.8025 4.6498 9.4043 4.1095 9.7249 17.8303 Columns 45 through 48 8.8135 10.1161 5.6949 6.5454 plot_roc(true_pairs, false_pairs) false_pairs=[ 12.8584 5.1717 7.0418 19.0782 34.9676 5.9821 2.1925 8.6357 9.4578 5.7708 7.5887 11.0132 13.2087 4.4003 5.4691 85.5509 2.6669 73.1121 5.8756 17.6565 30.2745 12.6061 1.2584 11.7180 12.3352 11.4404 5.4835 17.0256 16.2154 5.8234 6.9849 22.1817 6.6127 12.5922 12.6870 6.0027 20.9576 4.5397 13.1723 4.658 9.3436 4.5124 5.4500 82.7390 8.2555 9.1475 30.5489 5.9517] true_pairs=[ 1.0338 0.5322 0.3721 1.8876 1.9794 1.3142 0.9995 2.5118 3.9344 0.8381 1.1508 1.1554 1.5223 1.4568 0.8634 1.8954 2.7138 0.4440 1.9638 21.4024 2.1810 0.8074 3.3317 1.2568 1.1135 1.5583 1.9569 1.0921 2.6059 0.4545 2.4574 1.0844 2.1952 6.2227 3.4436 1.7821 3.1074 3.0102 1.6781 1.4636 1.9433] difference_type='mds'; % use 'mds' (also for GMDS), 'gradient' or others % MDS/GMDS options apply_gmds=1; % difference_type should also be set to 'mds' visualise_gmds=0; % whether or not to show GMDS results (assuming it's run) gmds_N = 250; % number of points in GMDS mds_skip = 5; % spacing between points to sample for GMDS scale_index_gmds=1; % used for changing ratio along dimensions show_mds_images=0; % also applies to visualisation of GMDS (before points are placed) show_mds_iterations=0; % whether to animate MDS process after its calculations triangulation_method='delaunay'; % how to turn the set of points into triangles show_local_stress=0; % for a stress map to be dipslayed as a image mds_min=-0.25; mds_max=0.2; mds_leap=0.05; size_factor_mds=0.01; apply_geodesic_mask=1; % bollean - whether to cull out items outside a paticular region geodesic_mask_distance_nose=100; geodesic_mask_distance_eyes=100; support_buffer=2; GMDS_remesh=1; % whether or not to emesh to simplify GMDS remesh_limit=3000; % how many vertices to keep for GMDS true_pairs=[ 0.8902 0.4825 0.3507 1.6955 1.5363 1.2659 0.9596 2.1592 3.1918 0.6602 1.0864 0.9692 1.3731 1.4322 0.8379 1.6617 2.2719 0.4532 1.7858 19.1693 1.5069 0.7501 3.0653 1.2864 1.1135 1.3958 1.7275 1.0043 2.2331 0.4310 2.0960 0.9877 1.9638 4.9598 2.8072 1.6405 2.1427 2.3213 2.0869 1.4430 1.6989 0.9964 2.2365 3.3270 3.5085 0.7452 5.7749 2.7704 1.5718 3.4367 0.7470 65.2320 3.8963 1.5855 4.4400 1.3006 1.7953 8.2081 2.2943 1.5651 3.4561 0.7833 1.8975 2.1207 3.3147 1.5577 2.1043 1.3751 2.7803 0.3652 2.3449 1.2693 1.2215 1.2112 2.1556 1.8828 3.6005 2.8739 2.0679 0.9908 0.4425 0.4260 1.6030 1.0420 1.8640 9.6807] false false_pairs=[ 12.4931 4.8446 8.1928 18.5098 36.5020 6.0183 2.4373 8.0411 9.1968 4.8651 7.3145 9.9989 11.3802 4.3949 5.0650 68.2133 2.7053 41.1277 5.2488 16.7322 26.4348 11.3690 1.2053 9.9105 12.8226 11.0007 5.3994 18.4268 18.1303 6.0267 6.8794 38.0898 5.9014 11.6095 14.2046 6.2516 14.3958 4.6213 12.1867 4.5162 10.1706 4.4384 5.2171 77.9576 8.4037 9.4873 29.3488 5.8193] % ============================================================== % MDS and GMDS % ============================================================== difference_type='mds'; % use 'mds' (also for GMDS), 'gradient' or others % MDS/GMDS options apply_gmds=1; % difference_type should also be set to 'mds' visualise_gmds=0; % whether or not to show GMDS results (assuming it's run) gmds_N = 250; % number of points in GMDS mds_skip = 5; % spacing between points to sample for GMDS scale_index_gmds=1; % used for changing ratio along dimensions show_mds_images=0; % also applies to visualisation of GMDS (before points are placed) show_mds_iterations=0; % whether to animate MDS process after its calculations triangulation_method='delaunay'; % how to turn the set of points into triangles show_local_stress=0; % for a stress map to be dipslayed as a image mds_min=-0.25; % MDS-only option mds_max=0.2; mds_leap=0.05; size_factor_mds=0.01; apply_geodesic_mask=1; % bollean - whether to cull out items outside a paticular region geodesic_mask_distance_nose=110; geodesic_mask_distance_eyes=110; support_buffer=2; GMDS_remesh=1; % whether or not to emesh to simplify GMDS remesh_limit=3000; % how many vertices to keep for GMDS false_pairs=[ 20.8469 4.9360 11.6308 17.4869 6.6809 7.3512 0.9394 11.9586 12.2555 6.5554 8.5589 8.8297 14.3189 2.3485 33.9744 11.8443 2.9064 21.3563 4.9943 11.7603 33.0314 10.7420 2.1592 9.6775 18.6026 10.5477 7.1570 19.1056 16.1032] true_pairs=[ 5.0036 0.5421 2.7477 1.2400 1.4419 1.5882 0.9309 0.7776 0.6770 1.6044 1.9486 1.0672 1.0657 1.7676 1.0863 0.8865 0.7514 2.1480 25.9873 30.7481 0.6112 0.8326 1.4008 1.4591 25.3697 0.7779 1.4830 1.3179 0.5664 1.0717 1.0965 0.4037 1.7745] % ============================================================== % MDS and GMDS % ============================================================== difference_type='mds'; % use 'mds' (also for GMDS), 'gradient' or others % MDS/GMDS options apply_gmds=1; % difference_type should also be set to 'mds' visualise_gmds=0; % whether or not to show GMDS results (assuming it's run) gmds_N = 250; % number of points in GMDS mds_skip = 5; % spacing between points to sample for GMDS scale_index_gmds=1; % used for changing ratio along dimensions show_mds_images=0; % also applies to visualisation of GMDS (before points are placed) show_mds_iterations=0; % whether to animate MDS process after its calculations triangulation_method='delaunay'; % how to turn the set of points into triangles show_local_stress=0; % for a stress map to be dipslayed as a image mds_min=-0.25; % MDS-only option mds_max=0.2; mds_leap=0.05; size_factor_mds=0.01; apply_geodesic_mask=1; % bollean - whether to cull out items outside a paticular region geodesic_mask_distance_nose=95; geodesic_mask_distance_eyes=95; support_buffer=2; GMDS_remesh=1; % whether or not to emesh to simplify GMDS remesh_limit=3000; % how many vertices to keep for GMDS false_pairs=[ 12.5936 3.9913 10.1577 18.9216 27.8996 5.3639 13.0104 8.3893 10.5867 35.7343 7.1267 7.0271 21.7295 3.5577 7.7708 45.1452 2.2798 19.3831 4.7733 10.1894 6.4247 9.8633 1.3270 7.6231 11.1020 7.4798 8.0946 11.1568 72.2482 5.3361 5.5109 11.8068 5.2926 21.7158 11.3844 7.8325 12.6285 3.9645 7.4394 5.0670 8.3857 47.8107 22.7758 22.7270 24.2143 10.1489 6.2019 4.9667] true_pairs=[ 1.5578 0.5472 0.5490 1.2652 1.1053 2.0089 0.7844 1.3964 5.1408 1.1799 1.3453 0.8871 1.7112 1.1697 0.8136 1.2297 0.6406 0.9298 0.9100 11.3341 1.5595 1.6386 2.2684 0.9116 1.6122 0.9397 2.1916 1.3304 2.2485 0.7561 0.6866 0.3025 1.9381 2.5915 1.7314 8.3741 2.8184 2.5902 2.5859 1.7600 1.1202 0.5439 3.6549 1.6376 3.1987 1.5699 33.4187 2.1743 1.4358 2.2551 0.5922 5.5191 42.1218 3.0354 0.7318 1.3861 2.6048 3.0457 3.1326 2.5266 2.5528 2.0532 1.0521 6.7563 8.6767 1.5542 1.3290 1.6005 1.9316 1.0680 2.9921 3.4651 1.2085 1.3718 3.0820 3.2732 3.6141 3.0763 1.0664 1.1578] smoothing over 5 in 2-D true_pairs=[ 1.5033 37.2468 0.7382 1.2305 1.5399 0.9496 2.6083 0.8696 1.4778 1.9265 0.8029 0.9728 1.3115 1.1147 0.8694 2.4615 1.0521 0.9235 1.5244 13.8396 1.0324 0.8244 1.0587 1.8175 1.3916 70.8308 1.4113] false_pairs=[ 12.3391 4.3644 38.3570 10.9097 3.1704 5.3411 1.7603 6.9124 9.8288 4.1866 6.1026 7.2668 9.2044 1.9039 12.3776 10.4248 2.3245 29.9458 44.3838 8.7139 20.1923 11.9111 1.3714 7.4108 12.0924 5.3675 4.7571] geodesic_range_distances = 1.0e+08 * Columns 1 through 7 0.1181 2.1957 0.1721 0.2281 0.0680 0.1643 0.0768 Columns 8 through 14 0.2281 0.0480 0.0689 0.0446 0.3970 0.1495 0.0838 Columns 15 through 21 0.0514 0.0275 0.1849 0.0533 0.0450 0.0327 0.0986 Columns 22 through 28 0.0954 0.0889 0.0901 0.0641 0.0547 0.0599 0.0342 Columns 29 through 35 0.1243 0.1288 0.2182 0.1389 0.0778 0.0486 0.1479 Columns 36 through 42 0.0938 0.0529 0.0576 0.0806 0.0962 0.0516 0.0390 Columns 43 through 44 0.0259 0.0274 false_pairs= 1.0e+08 *[ 0.1181 2.1957 0.1721 0.2281 0.0680 0.1643 0.0768 0.2281 0.0480 0.0689 0.0446 0.3970 0.1495 0.0838 0.0514 0.0275 0.1849 0.0533 0.0450 0.0327 0.0986 0.0954 0.0889 0.0901 0.0641 0.0547 0.0599 0.0342 0.1243 0.1288 0.2182 0.1389 0.0778 0.0486 0.1479 0.0938 0.0529 0.0576 0.0806 0.0962 0.0516 0.0390 0.0259 0.0274] geodesic_range_distances = 1.0e+07 * Columns 1 through 7 0.3059 0.1195 0.1346 0.3120 0.3837 0.2180 0.2072 Columns 8 through 14 0.1958 0.2429 0.2438 0.1283 0.1383 0.2129 0.2269 Columns 15 through 21 0.1719 0.2329 0.1735 0.1255 0.1059 0.1245 0.3345 Columns 22 through 28 0.1985 0.1476 0.1715 0.1829 0.1637 0.1589 0.1394 Columns 29 through 35 2.0254 0.5073 0.4790 0.3290 0.2785 0.2511 0.3041 Columns 36 through 42 0.5185 0.2474 0.1275 0.1706 0.1841 0.2337 0.2408 Columns 43 through 49 0.5018 0.7106 0.2598 0.3072 0.5487 0.7604 0.2386 Columns 50 through 51 0.2744 true_pairs= 1.0e+07 *[ 0.3059 0.1195 0.1346 0.3120 0.3837 0.2180 0.2072 0.1958 0.2429 0.2438 0.1283 0.1383 0.2129 0.2269 0.1719 0.2329 0.1735 0.1255 0.1059 0.1245 0.3345 0.1985 0.1476 0.1715 0.1829 0.1637 0.1589 0.1394] diffusion distance false_pairs=[ 452.0001 337.0001 27.0000 5.0003 408.9995 441.9999 57.9997 37.0003 415.9998 428.9995 27.9028 418.0969 120.0000 426.0000 100.0001 458.9996 13.9716 426.0283 353.0000 80.0341 93.0346 380.9456 65.0001 380.9658 56.9653 2.9395 383.0606 430.9590 401.0411 34.9996 29.0054 80.9822 378.0227 376.9685 45.8064 24.9997 474.0001 45.9998 422.9998 60.0001 395.0046 67.0002 352.0051 26.0002 452.9997 62.9999] true_pairs=[ 26.0004 473.9713 28.9996 416.0000 15.9999 37.0001 370.9997 393.9999 5.0001 25.0000 401.0000 0.0004 451.9996 14.9037 404.0962 33.0002 397.9999 55.9998 7.0004 367.9998 57.9995 401.0000 372.0000 0.0003 34.9997 9.0001 432.0000 71.0000 351.9941 5.9937 43.0279 354.0651 37.8216 50.0003 408.5102 9.0002 326.0002 18.9999 338.9998 13.9334 311.0665 45.0001 389.9998 400.9997 22.9998 344.0391 349.9998 18.6513 27.0001 4.0001 10.0003 333.0001 44.0002 320.9998 55.0002 339.0002 26.8330 312.3183 34.9999 35.0002 6.8094 28.3057 348.9998 76.0002 73.0005 0.0002 51.9998 59.9998 6.0002 413.0000 38.0003 35.0001 474.9421 429.1933 407.9737 24.9696 25.9998 26.9595 373.0403 456.9699 91.0176 432.4588 451.9999 402.0001 28.9996 397.0005 9.0001 23.9994 368.0000 0.0001 0.0003 0.0002 450.9998 391.0003 415.0000 399.0001 56.0466 371.0466 35.0139 18.0145 403.9940 385.8932 20.0229 342.9111 59.9448 388.0330 356.9686 354.0313 91.0001 320.0001 29.9999 350.0018 31.0000 29.9717 9.9837 361.0123 354.9995 42.4576 344.0274 44.9308 23.0000 306.4794 118.0005 0.0002 364.9997 22.0221 11.0067 397.9940 357.0346 9.0005 5.0001 5.0002 275.9997 343.9803 368.0001 60.6847 328.0004 345.9997 22.0003 15.0362 5.0000 0.0002 368.9999 361.9936 15.0060 0.0003 372.0002 26.0001 385.9999 37.9998 16.0001 365.9995 28.0000 362.0001 8.0002 17.0004 0.0001 26.9999] geodesic_range_distances = Columns 1 through 7 92.9999 482.9714 89.9998 376.9997 7.0003 112.0001 407.1994 Columns 8 through 14 424.9998 100.9999 136.2757 465.0000 14.9999 600.9998 22.9039 Columns 15 through 21 429.0964 39.0002 433.9994 82.9998 71.0000 352.0002 52.9997 Columns 22 through 28 408.9998 442.9998 4.9998 73.9999 22.0000 460.9998 30.0003 Columns 29 through 35 388.9934 11.1769 34.0000 367.7668 30.2990 98.0001 415.9995 Columns 36 through 42 31.9999 322.9993 12.0002 396.9995 21.0000 332.3552 53.0005 Columns 43 through 49 454.0002 428.0001 98.0000 382.0390 326.0002 16.0694 30.0692 Columns 50 through 56 26.0000 199.0003 335.9999 23.0151 355.0001 66.9999 349.9997 Columns 57 through 63 28.0469 339.5772 52.0002 42.0003 43.7095 37.9717 363.9998 Columns 64 through 70 96.0001 202.0004 9.9997 24.9998 250.0001 11.0003 437.9993 Column 71 57.0006