1. Example data of MountCurve - Nonlinear Regression Problem
|
No x y |
No x y |
No x y |
No x y |
No x y |
No x y |
|
1 400 0.00000 2 401 0.00000 3 402 0.00000 4 403 0.00000 5 404 0.00000 6 405 0.00000 7 406 0.00000 8 407 0.00000 9 408 0.00000 10 409 0.00000 11 410 0.00000 12 411 0.00000 13 412 0.00000 14 413 0.00000 15 414 0.00000 16 415 0.00000 17 416 0.00000 18 417 0.00000 19 418 0.00000 20 419 0.00000 21 420 0.00000 22 421 0.00000 23 422 0.00000 24 423 0.00000 25 424 0.00000 26 425 0.00000 27 426 0.00000 28 427 0.00000 29 428 0.00000 30 429 0.00000 31 430 0.00000 32 431 0.00000 33 432 0.00001 34 433 0.00001 35 434 0.00005 |
36 435 0.00014 37 436 0.00033 38 437 0.00084 39 438 0.00214 40 439 0.00401 41 440 0.00846 42 441 0.01573 43 442 0.02942 44 443 0.05303 45 444 0.08089 46 445 0.13059 47 446 0.16849 48 447 0.25326 49 448 0.28538 50 449 0.36135 51 450 0.35706 52 451 0.38473 53 452 0.35831 54 453 0.34594 55 454 0.26469 56 455 0.23151 57 456 0.18391 58 457 0.11498 59 458 0.08957 60 459 0.05728 61 460 0.02904 62 461 0.01612 63 462 0.00936 64 463 0.00456 65 464 0.00205 66 465 0.00081 67 466 0.00033 68 467 0.00013 69 468 0.00004 70 469 0.00002 |
71 470 0.00000 72 471 0.00000 73 472 0.00000 74 473 0.00000 75 474 0.00000 76 475 0.00000 77 476 0.00000 78 477 0.00000 79 478 0.00000 80 479 0.00000 81 480 0.00000 82 481 0.00000 83 482 0.00000 84 483 0.00000 85 484 0.00000 86 485 0.00000 87 486 0.00000 88 487 0.00000 89 488 0.00000 90 489 0.00000 91 490 0.00000 92 491 0.00000 93 492 0.00000 94 493 0.00000 95 494 0.00000 96 495 0.00000 97 496 0.00000 98 497 0.00000 99 498 0.00000 100 499 0.00000 101 500 0.00000 102 501 0.00000 103 400 0.00000 104 401 0.00000 105 402 0.00000 |
106 403 0.00000 107 404 0.00000 108 405 0.00000 109 406 0.00000 110 407 0.00000 111 408 0.00000 112 409 0.00000 113 410 0.00000 114 411 0.00000 115 412 0.00000 116 413 0.00000 117 414 0.00000 118 415 0.00000 119 416 0.00000 120 417 0.00000 121 418 0.00000 122 419 0.00000 123 420 0.00000 124 421 0.00000 125 422 0.00000 126 423 0.00000 127 424 0.00000 128 425 0.00000 129 426 0.00000 130 427 0.00000 131 428 0.00000 132 429 0.00000 133 430 0.00000 134 431 0.00000 135 432 0.00000 136 433 0.00002 137 434 0.00005 138 435 0.00013 139 436 0.00037 140 437 0.00080 |
141 438 0.00210 142 439 0.00438 143 440 0.00933 144 441 0.01623 145 442 0.03124 146 443 0.05060 147 444 0.09114 148 445 0.13773 149 446 0.17647 150 447 0.21935 151 448 0.31303 152 449 0.36742 153 450 0.39079 154 451 0.37866 155 452 0.35692 156 453 0.31019 157 454 0.29462 158 455 0.24586 159 456 0.17016 160 457 0.11542 161 458 0.07642 162 459 0.04941 163 460 0.03222 164 461 0.01849 165 462 0.00881 166 463 0.00403 167 464 0.00195 168 465 0.00087 169 466 0.00033 170 467 0.00012 171 468 0.00004 172 469 0.00001 173 470 0.00000 174 471 0.00000 175 472 0.00000 |
176 473 0.00000 177 474 0.00000 178 475 0.00000 179 476 0.00000 180 477 0.00000 181 478 0.00000 182 479 0.00000 183 480 0.00000 184 481 0.00000 185 482 0.00000 186 483 0.00000 187 484 0.00000 188 485 0.00000 189 486 0.00000 190 487 0.00000 191 488 0.00000 192 489 0.00000 193 490 0.00000 194 491 0.00000 195 492 0.00000 196 493 0.00000 197 494 0.00000 198 495 0.00000 199 496 0.00000 200 497 0.00000 201 498 0.00000 202 499 0.00000 203 500 0.00000 204 501 0.00000
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2. Example data of Cos Function - Multi-nonlinear Regression Problem
|
No x1 x2 x3 y |
No x1 x2 x3 y |
No x1 x2 x3 y |
|
1 0.5797 2.5708 5.0022 98.5408 2 0.5797 2.4431 4.8947 95.8701 3 0.5797 2.5916 5.0202 94.2456 4 0.5797 2.5611 4.9939 91.0629 5 0.5797 2.3325 4.8055 93.2027 6 0.5797 2.4668 4.9143 90.8112 7 0.5797 2.4686 4.9158 88.2381 8 0.5797 2.4323 4.8858 87.6292 9 0.5797 1.9414 4.5161 85.5226 10 0.5797 2.5539 4.9877 85.5457 11 0.5797 2.0865 4.6188 86.8441 12 0.5797 1.8235 4.4363 85.0530 13 0.5797 1.8248 4.4372 85.5197 14 0.5797 1.9375 4.5133 87.0217 |
15 0.5797 1.8336 4.443 82.5568 16 0.5797 1.8372 4.4453 80.7657 17 0.5797 2.1773 4.6859 82.9873 18 0.5797 2.4783 4.9239 82.5369 19 0.5797 2.4794 4.9248 80.7739 20 0.5797 2.298 4.7783 80.9289 21 0.5797 2.3524 4.8213 78.2137 22 0.5797 1.9696 4.5356 78.2423 23 0.5797 1.9703 4.5361 79.9485 24 0.5797 2.1049 4.6322 72.5405 25 0.5797 1.9778 4.5413 73.9879 26 0.5797 1.794 4.4168 72.8660 27 0.5797 2.1633 4.6754 70.3091 28 0.5797 1.9828 4.5448 68.9918 |
29 0.5797 2.0354 4.582 66.5300 30 0.5797 2.1154 4.6399 64.1288 31 0.5797 2.5007 4.9427 62.5733 32 0.5797 1.9915 4.551 62.4885 33 0.5797 2.5063 4.9474 61.3855 34 0.5797 2.2745 4.76 60.5194 35 0.5797 2.5091 4.9496 60.5927 36 0.5797 2.5164 4.9558 59.6154 37 0.5797 2.272 4.758 60.8071 38 0.5797 1.8681 4.466 60.1644 39 0.5797 1.9958 4.5539 60.0994 40 0.5797 2.3907 4.852 59.9757 41 0.5797 1.8992 4.4871 57.5347
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3. Example data of SinCos Function - Multi-nonlinear Regression Problem
|
No x1 x2 x3 y |
No x1 x2 x3 y |
No x1 x2 x3 y |
|
1 0.2671 8.1681 0.4279 -0.0800 2 0.0166 1.3885 1.6640 -1.1242 3 0.6063 6.8171 1.7050 0.8925 4 0.8753 8.0373 2.6867 -0.1802 5 0.6257 4.2429 1.6018 0.0572 6 0.6713 6.8907 1.5692 0.1857 7 0.9401 6.2108 1.9125 0.2150 8 0.6107 5.6197 0.6365 -0.0755 9 0.4577 1.2314 0.4368 0.3377 10 0.7234 7.2440 1.7970 -0.8115 11 0.2419 5.1765 1.4256 -0.0123 12 0.2318 7.1781 1.6261 0.2129 13 0.6524 6.3351 2.2626 1.5248 14 0.7723 7.6326 2.1093 -0.0444 15 0.0245 3.0222 1.8720 -0.1738 16 0.6304 3.6835 0.1315 -0.2854 17 0.0520 4.0654 0.4002 -0.1172 18 0.7023 6.3920 1.0638 -0.0426 19 0.8217 7.8193 2.0572 -0.0328 20 0.7904 4.9634 1.5143 0.1358 21 0.9032 8.4108 2.5776 -0.1446 |
22 0.1264 5.2207 1.1570 1.0164 23 0.3472 4.3368 0.9912 -0.0230 24 0.2706 3.7466 1.7182 -0.3333 25 0.7040 4.9362 1.2282 0.5671 26 0.8083 9.0500 0.3757 -0.2993 27 0.5407 0.2659 1.9618 0.3689 28 0.1243 1.1628 2.1960 -0.7721 29 0.2427 0.1572 2.9357 -1.3249 30 0.0464 2.2911 0.4871 0.3013 31 0.4246 4.3301 1.2939 -0.8624 32 0.8681 1.6665 2.2688 -0.0886 33 0.8769 8.0334 2.0431 -0.2522 34 0.2534 0.6777 0.1429 0.0493 35 0.9410 2.3731 0.1855 -0.0084 36 0.3568 7.8533 1.8709 -0.0108 37 0.3474 5.9253 0.1892 1.0326 38 0.2172 0.4230 1.6284 -0.0823 39 0.1934 0.0198 0.7623 0.3311 40 0.6758 8.1376 0.3833 0.9690 41 0.6766 4.8568 0.2908 -0.2979 42 0.7765 8.9104 0.8317 -0.1419 |
43 0.7619 3.0023 2.2406 -0.1220 44 0.4089 2.7600 2.0099 0.0320 45 0.9456 1.4291 0.2337 0.1728 46 0.8961 3.6353 2.7642 0.0854 47 0.0171 8.7467 1.8384 -0.0045 48 0.8918 6.0939 2.8982 -0.1546 49 0.9986 1.2281 0.2899 -0.0096 50 0.3757 8.6184 0.7082 -0.1888 51 0.6018 5.3124 2.8273 0.1165 52 0.3100 5.7199 1.9586 0.2425 53 0.3707 6.0697 0.0259 0.4101 54 0.6771 0.0077 1.8203 0.5241 55 0.5042 6.2797 2.8635 -0.9349 56 0.7515 1.2359 0.0847 -0.0406 57 0.9864 9.9275 0.2176 -0.2601 58 0.6036 5.0897 2.7701 0.1951 59 0.3932 2.6776 2.4910 -0.3780 60 0.5714 8.2727 1.2285 -0.2521 61 0.4258 0.8845 2.0383 -0.6213 |
4. Example data for multi-output regression
|
No x1 x2 x3 y1 y2 |
No x1 x2 x3 y1 y2 |
No x1 x2 x3 y1 y2 |
|
1 1 0.987 0.987 0.916 0.237 2 2 0.618 1.237 -2.901 0.709 3 3 1.098 3.294 1.973 1.710 4 4 1.485 5.942 5.234 2.392 5 5 3.317 16.587 16.311 4.086 6 6 5.961 35.764 24.425 5.381 7 7 6.663 46.644 28.746 5.814 8 8 2.740 21.924 13.316 4.509 9 9 7.354 66.182 28.386 6.470 10 10 6.937 69.372 30.676 6.944 11 11 3.698 40.680 19.531 5.764 12 12 9.445 113.345 37.937 8.149 13 13 2.569 33.391 13.450 6.653 14 14 1.327 18.581 5.675 6.074 15 15 5.758 86.368 25.422 8.187 16 16 15.772 252.359 48.365 10.965 17 17 12.542 213.210 42.797 10.915 18 18 4.601 82.826 24.540 8.638 19 19 2.203 41.854 13.290 8.508 20 20 8.507 170.130 39.382 11.240 21 21 11.120 233.511 41.252 12.135 22 22 14.865 327.038 47.892 11.892 23 23 22.511 517.762 57.542 13.860 24 24 19.974 479.375 59.715 12.039 25 25 10.741 268.518 40.322 12.288 26 26 13.279 345.260 49.653 12.262 27 27 23.226 627.096 62.851 13.860 28 28 26.368 738.292 75.883 14.203 29 29 9.923 287.766 41.788 12.713 30 30 5.170 155.112 28.346 13.232 31 31 0.776 24.056 -0.588 10.426 32 32 27.559 881.882 80.120 16.553 33 33 15.658 516.708 53.915 14.895 34 34 27.052 919.775 69.354 17.725 |
35 35 13.678 478.745 55.640 15.390 36 36 4.987 179.515 30.265 15.296 37 37 4.209 155.731 27.332 14.766 38 38 11.764 447.040 44.773 15.041 39 39 31.855 1242.341 90.286 19.462 40 40 35.712 1428.484 80.451 17.299 41 41 29.560 1211.977 84.244 20.135 42 42 3.949 165.873 26.787 14.990 43 43 14.069 604.958 59.887 18.335 44 44 27.231 1198.172 82.514 18.289 45 45 17.511 788.002 59.296 19.108 46 46 21.082 969.770 72.884 18.612 47 47 20.723 974.003 64.811 18.571 48 48 25.131 1206.307 80.290 20.449 49 49 47.161 2310.899 98.398 22.867 50 50 21.262 1063.100 66.327 21.285 51 51 1.520 77.516 8.373 17.994 52 52 26.186 1361.686 74.942 22.315 53 53 19.286 1022.178 68.779 20.085 54 54 29.990 1619.479 88.679 20.424 55 55 20.719 1139.560 74.545 22.539 56 56 6.975 390.588 37.865 18.212 57 57 52.847 3012.288 106.993 23.876 58 58 46.140 2676.110 102.662 23.602 59 59 21.006 1239.379 66.145 23.083 60 60 12.617 757.016 52.665 23.843 61 61 47.628 2905.299 105.337 26.003 62 62 36.337 2252.883 98.262 25.058 63 63 57.201 3603.648 125.039 24.860 64 64 57.715 3693.737 120.399 25.841 65 65 37.752 2453.903 104.168 27.177 66 66 46.968 3099.920 100.816 26.997 67 67 15.310 1025.785 67.903 24.517 68 68 20.088 1366.001 78.163 26.777 |
69 69 9.572 660.495 46.869 22.454 70 70 44.030 3082.113 101.113 28.072 71 71 24.675 1751.903 76.671 26.200 72 72 1.398 100.660 7.936 21.903 73 73 54.174 3954.734 120.694 30.253 74 74 19.028 1408.086 75.614 28.853 75 75 1.195 89.611 5.085 21.028 76 76 27.241 2070.331 95.381 30.613 77 77 16.501 1270.604 62.261 29.627 78 78 36.152 2819.885 99.146 31.726 79 79 5.892 465.497 37.484 26.684 80 80 62.813 5025.060 131.298 30.525 81 81 0.835 67.626 0.481 25.615 82 82 59.934 4914.564 137.646 32.669 83 83 60.298 5004.759 143.983 30.500 84 84 0.725 60.879 -1.384 26.458 85 85 76.135 6471.487 153.899 35.532 86 86 63.602 5469.734 146.077 33.984 87 87 33.414 2907.059 95.391 29.914 88 88 79.062 6957.485 143.915 35.678 89 89 0.483 42.987 -5.707 23.604 90 90 76.355 6871.995 147.017 31.764 91 91 1.245 113.317 6.853 24.226 92 92 82.284 7570.150 144.438 36.643 93 93 30.622 2847.818 99.408 34.316 94 94 66.766 6276.047 154.828 38.483 95 95 14.145 1343.765 60.311 33.949 96 96 73.040 7011.871 143.466 34.306 97 97 89.344 8666.335 161.146 37.300 98 98 49.522 4853.178 134.013 32.844 99 99 98.477 9749.244 172.948 38.723 100 100 79.506 7950.601 159.859 35.861 |