A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 1 Input 0 called attribute0 range: 394 to 399 Input 1 called attribute1 range: 43 to 44.2 Input 2 called attribute2 range: 352 to 357 Input 3 called attribute3 range: 0 to 1.2 Input 4 called attribute4 range: 48.8 to 50 Output 0 called output0 range: 57.4 to 58.6 Output 1 called output1 range: 5640 to 5700 Output 2 called output2 range: 0.2 to 1.4 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 **************************************************************** * * 1 points. Chose rsnum -1 (thus using 1 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 282.318 19.8323 16.183 0.632247 20.6292 * experiment 309.185 4.66456 357.179 0.970989 10.1891 * est rs center 293.286 25.7942 310.561 0.538521 24.9108 * est rs spread 59.4339 14.3333 172.27 0.268388 12.8639 * * ------------------------------ * att0: (399.601567-201.378202)/200.000000 = 0.991117 * att1: (49.886588-1.031188)/49.000000 = 0.997049 * att2: (592.743988-5.394077)/600.000000 = 0.978917 * att3: (0.993648-0.014206)/0.995000 = 0.984364 * att4: (48.877389-2.658505)/50.000000 = 0.924378 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 2 Input 0 called attribute0 range: 280 to 400 Input 1 called attribute1 range: 15 to 45 Input 2 called attribute2 range: 0 to 400 Input 3 called attribute3 range: 0.1 to 0.7 Input 4 called attribute4 range: 20 to 50 Output 0 called output0 range: 56 to 72 Output 1 called output1 range: 5600 to 6100 Output 2 called output2 range: 0.3 to 0.5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 **************************************************************** * * 2 points. Chose rsnum -1 (thus using 2 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 50 374.526 0.005 50 * experiment 360.434 4.90081 588.837 0.187922 1.11735 * est rs center 299.345 27.9771 314.184 0.456378 26.9143 * est rs spread 55.7466 13.4876 172.669 0.29834 14.1833 * * ------------------------------ * att0: (399.963444-202.830326)/200.000000 = 0.985666 * att1: (49.304429-1.486695)/49.000000 = 0.975872 * att2: (589.796503-3.831089)/600.000000 = 0.976609 * att3: (0.996903-0.007725)/0.995000 = 0.994148 * att4: (49.927591-0.723326)/50.000000 = 0.984085 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 3 Input 0 called attribute0 range: 280 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0.1 to 0.7 Input 4 called attribute4 range: 0 to 50 Output 0 called output0 range: 20 to 80 Output 1 called output1 range: 1000 to 7000 Output 2 called output2 range: 0.2 to 1.2 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 **************************************************************** * * 3 points. Chose rsnum -1 (thus using 3 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 1 600 0.005 0 * experiment 268.778 41.2652 516.473 0.904859 19.4481 * est rs center 299.355 26.3751 323.88 0.49281 25.9777 * est rs spread 63.3176 14.0152 176.816 0.296831 15.2656 * * ------------------------------ * att0: (399.513952-204.716600)/200.000000 = 0.973987 * att1: (49.711402-1.103341)/49.000000 = 0.992001 * att2: (596.748699-1.329154)/600.000000 = 0.992366 * att3: (0.997930-0.006067)/0.995000 = 0.996847 * att4: (49.872585-0.152265)/50.000000 = 0.994406 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 4 Input 0 called attribute0 range: 280 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 0.8 Input 4 called attribute4 range: 0 to 50 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 **************************************************************** * * 4 points. Chose rsnum -1 (thus using 4 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 1 524.901 0.005 0 * experiment 272.607 42.4309 597.802 0.918811 6.65728 * est rs center 303.072 26.2448 291.998 0.490689 24.3922 * est rs spread 59.2442 13.8847 179.994 0.28564 14.2043 * * ------------------------------ * att0: (399.607343-201.057217)/200.000000 = 0.992751 * att1: (49.056604-1.699960)/49.000000 = 0.966462 * att2: (599.293770-0.939730)/600.000000 = 0.997257 * att3: (0.986793-0.015182)/0.995000 = 0.976494 * att4: (49.366636-0.667572)/50.000000 = 0.973981 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 5 Input 0 called attribute0 range: 260 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 50 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 **************************************************************** * * 5 points. Chose rsnum -1 (thus using 5 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 50 600 1 3.46229 * experiment 218.07 33.2215 268.822 0.0892636 5.70945 * est rs center 298.325 25.3133 319.646 0.499777 24.7622 * est rs spread 54.8102 14.2601 164.91 0.299365 15.0298 * * ------------------------------ * att0: (399.047558-204.393958)/200.000000 = 0.973268 * att1: (49.335253-1.276435)/49.000000 = 0.980792 * att2: (594.516824-10.334904)/600.000000 = 0.973637 * att3: (0.994662-0.008511)/0.995000 = 0.991107 * att4: (49.773201-0.434831)/50.000000 = 0.986767 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 6 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 50 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 **************************************************************** * * 6 points. Chose rsnum -1 (thus using 6 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 50 600 1 0 * experiment 329.339 3.88769 398.138 0.544418 48.2903 * est rs center 305.306 26.6379 314.64 0.477613 25.9995 * est rs spread 57.3163 13.8262 170.694 0.316721 14.6755 * * ------------------------------ * att0: (399.496878-202.622100)/200.000000 = 0.984374 * att1: (49.283669-1.404736)/49.000000 = 0.977121 * att2: (598.623460-11.629208)/600.000000 = 0.978324 * att3: (0.986364-0.005961)/0.995000 = 0.985329 * att4: (49.459735-0.302735)/50.000000 = 0.983140 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 7 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 50 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 **************************************************************** * * 7 points. Chose rsnum -1 (thus using 7 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 50 600 1 0 * experiment 216.72 39.6562 352.586 0.0372957 44.2153 * est rs center 304.107 25.6416 310.749 0.51368 24.0441 * est rs spread 54.982 14.9727 167.46 0.296597 14.8669 * * ------------------------------ * att0: (396.496047-201.366717)/200.000000 = 0.975647 * att1: (49.627991-1.009703)/49.000000 = 0.992210 * att2: (596.683466-4.306578)/600.000000 = 0.987295 * att3: (0.991819-0.021187)/0.995000 = 0.975509 * att4: (49.829929-1.173751)/50.000000 = 0.973124 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 7 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 50 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 **************************************************************** * * 7 points. Chose rsnum -1 (thus using 7 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 50 600 1 0 * experiment 224.856 26.0853 584.484 0.577469 36.3518 * est rs center 300.13 25.8694 261.675 0.511957 26.4331 * est rs spread 56.3844 13.512 170.933 0.263078 14.1793 * * ------------------------------ * att0: (395.459560-205.253405)/200.000000 = 0.951031 * att1: (49.756957-1.846479)/49.000000 = 0.977765 * att2: (561.486047-0.440736)/600.000000 = 0.935076 * att3: (0.998866-0.046178)/0.995000 = 0.957474 * att4: (48.622165-0.202530)/50.000000 = 0.968393 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 8 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 50 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 **************************************************************** * * 8 points. Chose rsnum -1 (thus using 8 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 50 600 1 50 * experiment 313.181 26.8695 223.52 0.0546618 4.71817 * est rs center 298.621 27.8436 276.236 0.467758 24.3201 * est rs spread 53.9791 14.35 177.814 0.294083 14.6519 * * ------------------------------ * att0: (398.474500-204.602941)/200.000000 = 0.969358 * att1: (49.913921-1.856906)/49.000000 = 0.980755 * att2: (590.964103-0.046981)/600.000000 = 0.984862 * att3: (0.982304-0.014462)/0.995000 = 0.972706 * att4: (49.837130-0.922166)/50.000000 = 0.978299 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 9 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 **************************************************************** * * 9 points. Chose rsnum -1 (thus using 9 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 33.597 600 0.005 10.9116 * experiment 269.329 6.6941 373.717 0.989899 5.36329 * est rs center 294.612 26.2899 302.594 0.486715 24.5429 * est rs spread 60.2726 14.0561 165.33 0.300102 14.6079 * * ------------------------------ * att0: (398.611022-202.586034)/200.000000 = 0.980125 * att1: (49.671068-2.103366)/49.000000 = 0.970769 * att2: (581.645204-1.113583)/600.000000 = 0.967553 * att3: (0.989484-0.008032)/0.995000 = 0.986384 * att4: (49.612211-0.060147)/50.000000 = 0.991041 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 10 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 **************************************************************** * * 10 points. Chose rsnum -1 (thus using 10 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 1 600 1 40.6756 * experiment 384.35 48.1425 41.7389 0.136149 10.3281 * est rs center 293.013 22.4167 317.568 0.541168 23.8188 * est rs spread 57.2839 12.961 182.401 0.263662 14.0807 * * ------------------------------ * att0: (399.608881-203.411810)/200.000000 = 0.980985 * att1: (48.852848-1.039882)/49.000000 = 0.975775 * att2: (599.885615-7.853271)/600.000000 = 0.986721 * att3: (0.979478-0.024934)/0.995000 = 0.959340 * att4: (49.391974-0.699439)/50.000000 = 0.973851 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 11 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 **************************************************************** * * 11 points. Chose rsnum -1 (thus using 11 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 28.0927 600 0.005 50 * experiment 312.222 35.4855 184.307 0.0134481 16.6734 * est rs center 296.16 25.1528 331.451 0.491119 25.3148 * est rs spread 59.0391 14.2619 165.2 0.275261 15.4271 * * ------------------------------ * att0: (396.926647-200.622517)/200.000000 = 0.981521 * att1: (48.935032-1.204732)/49.000000 = 0.974088 * att2: (599.851330-17.476369)/600.000000 = 0.970625 * att3: (0.991416-0.011164)/0.995000 = 0.985178 * att4: (49.720954-0.043910)/50.000000 = 0.993541 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 12 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 **************************************************************** * * 12 points. Chose rsnum -1 (thus using 12 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 20.4941 571.17 1 0 * experiment 395.713 20.6508 565.153 0.982216 0.312798 * est rs center 301.208 24.8709 289.806 0.512036 26.7348 * est rs spread 56.5386 14.2793 173.295 0.288742 13.2149 * * ------------------------------ * att0: (398.495770-201.295172)/200.000000 = 0.986003 * att1: (49.591819-1.103012)/49.000000 = 0.989567 * att2: (596.998891-5.281168)/600.000000 = 0.986196 * att3: (0.998446-0.008389)/0.995000 = 0.995032 * att4: (49.540927-0.312524)/50.000000 = 0.984568 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 13 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 **************************************************************** * * 13 points. Chose rsnum -1 (thus using 13 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 17.1965 600 0.005 34.8988 * experiment 396.672 17.1998 582.467 0.0186186 34.1555 * est rs center 296.154 25.5573 293.387 0.476715 24.1696 * est rs spread 55.6337 13.4807 173.587 0.279647 14.5561 * * ------------------------------ * att0: (399.105797-202.963664)/200.000000 = 0.980711 * att1: (49.344836-1.840190)/49.000000 = 0.969483 * att2: (599.118599-17.194673)/600.000000 = 0.969873 * att3: (0.983355-0.008446)/0.995000 = 0.979807 * att4: (49.406919-0.580590)/50.000000 = 0.976527 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 14 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 **************************************************************** * * 14 points. Chose rsnum -1 (thus using 14 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 1 414.019 0.005 50 * experiment 202.371 1.95065 414.571 0.0175148 48.7215 * est rs center 305.699 25.0261 275.546 0.486417 25.9108 * est rs spread 56.8867 14.6489 178.02 0.297613 14.1259 * * ------------------------------ * att0: (398.614383-206.859399)/200.000000 = 0.958775 * att1: (49.076422-1.386219)/49.000000 = 0.973269 * att2: (599.276009-5.958916)/600.000000 = 0.988862 * att3: (0.998099-0.005803)/0.995000 = 0.997282 * att4: (49.864703-1.382395)/50.000000 = 0.969646 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 15 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 **************************************************************** * * 15 points. Chose rsnum -1 (thus using 15 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 15.5354 202.902 1 50 * experiment 204.756 16.0488 208.671 0.987171 48.6029 * est rs center 305.058 25.9212 293.755 0.481703 22.2316 * est rs spread 61.1422 14.1544 187.276 0.299417 13.8352 * * ------------------------------ * att0: (395.994267-201.337444)/200.000000 = 0.973284 * att1: (49.514940-1.458696)/49.000000 = 0.980740 * att2: (596.620713-11.726673)/600.000000 = 0.974823 * att3: (0.979347-0.025418)/0.995000 = 0.958723 * att4: (49.910258-0.354814)/50.000000 = 0.991109 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 16 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 **************************************************************** * * 16 points. Chose rsnum -1 (thus using 16 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 50 237.326 1 6.08694 * experiment 399.775 48.6243 240.964 0.971923 5.34177 * est rs center 301.953 26.9517 317.41 0.510041 25.1993 * est rs spread 56.8376 14.9728 160.183 0.289366 14.911 * * ------------------------------ * att0: (396.096703-200.208615)/200.000000 = 0.979440 * att1: (49.930883-1.097005)/49.000000 = 0.996610 * att2: (592.414480-2.454292)/600.000000 = 0.983267 * att3: (0.979537-0.030543)/0.995000 = 0.953763 * att4: (49.653847-1.172325)/50.000000 = 0.969630 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 17 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 80 Output 1 called output1 range: 0 to 8000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 **************************************************************** * * 17 points. Chose rsnum -1 (thus using 17 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 1 75.2823 0.005 29.2024 * experiment 398.143 1.33134 77.2878 0.0174857 29.2779 * est rs center 308.609 25.8771 302.359 0.473619 24.0118 * est rs spread 62.3008 13.2743 179.626 0.283865 14.0238 * * ------------------------------ * att0: (399.887250-200.103473)/200.000000 = 0.998919 * att1: (49.952136-1.133469)/49.000000 = 0.996299 * att2: (594.645324-2.893475)/600.000000 = 0.986253 * att3: (0.994714-0.006398)/0.995000 = 0.993283 * att4: (49.929408-0.727765)/50.000000 = 0.984033 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 18 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 100 Output 1 called output1 range: 0 to 12000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 **************************************************************** * * 18 points. Chose rsnum -1 (thus using 18 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 50 0 0.0268601 50 * experiment 204.777 49.3686 13.3637 0.0342934 49.3622 * est rs center 308.134 25.0255 304.157 0.479445 23.8133 * est rs spread 56.7955 14.1927 156.033 0.285157 15.1041 * * ------------------------------ * att0: (399.698677-202.278861)/200.000000 = 0.987099 * att1: (49.518141-1.127980)/49.000000 = 0.987554 * att2: (594.103990-5.252601)/600.000000 = 0.981419 * att3: (0.980752-0.018480)/0.995000 = 0.967107 * att4: (49.971419-0.191541)/50.000000 = 0.995598 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 19 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 100 Output 1 called output1 range: 0 to 12000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 **************************************************************** * * 19 points. Chose rsnum -1 (thus using 19 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 50 600 0.295409 0 * experiment 397.197 49.8356 598.735 0.308773 1.20531 * est rs center 310.874 26.9791 313 0.568884 23.074 * est rs spread 60.1805 15.7205 177.865 0.300793 14.3844 * * ------------------------------ * att0: (398.700865-206.888977)/200.000000 = 0.959059 * att1: (49.992789-1.682681)/49.000000 = 0.985921 * att2: (593.782499-3.396475)/600.000000 = 0.983977 * att3: (0.999762-0.008849)/0.995000 = 0.995892 * att4: (48.931760-1.718866)/50.000000 = 0.944258 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 20 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 100 Output 1 called output1 range: 0 to 12000 Output 2 called output2 range: 0 to 3 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 **************************************************************** * * 20 points. Chose rsnum -1 (thus using 20 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 400 45.0269 0 0.245203 0 * experiment 205.433 4.26433 151.131 0.0503398 8.99264 * est rs center 293.844 25.1387 301.697 0.485045 24.0853 * est rs spread 59.3214 14.3381 173.207 0.296176 14.6609 * * ------------------------------ * att0: (396.659256-201.681123)/200.000000 = 0.974891 * att1: (49.914059-1.122983)/49.000000 = 0.995736 * att2: (588.788947-5.426247)/600.000000 = 0.972271 * att3: (0.996572-0.011032)/0.995000 = 0.990493 * att4: (49.685740-0.358851)/50.000000 = 0.986538 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 21 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 **************************************************************** * * 21 points. Chose rsnum 0 (thus using 21 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 1 166.401 0.416517 18.8992 * experiment 378.307 48.4286 56.2429 0.892682 40.4936 * est rs center 302.355 25.6259 288.861 0.521018 25.5864 * est rs spread 61.1015 13.3642 172.041 0.292556 14.5281 * * Newtiness: 0.000000 * E(rec): -422.234 -43860 29.267=> 422.233560 * E(expt): 1085.89 21526.6 -44.986=> -1085.888824 * * ------------------------------ * att0: (397.114390-205.562069)/200.000000 = 0.957762 * att1: (48.871361-1.100360)/49.000000 = 0.974918 * att2: (597.430568-2.254161)/600.000000 = 0.991961 * att3: (0.997782-0.013215)/0.995000 = 0.989514 * att4: (49.544093-0.042464)/50.000000 = 0.990033 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 22 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 **************************************************************** * * 22 points. Chose rsnum -1 (thus using 22 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 50 600 0.005 50 * experiment 202.126 49.3375 592.974 0.0172184 49.0474 * est rs center 307.189 23.5255 252.697 0.496477 24.6578 * est rs spread 56.8356 14.84 180.203 0.280245 14.0437 * * ------------------------------ * att0: (393.344137-201.552374)/200.000000 = 0.958959 * att1: (49.808037-1.058793)/49.000000 = 0.994883 * att2: (598.059209-7.551020)/600.000000 = 0.984180 * att3: (0.987907-0.011082)/0.995000 = 0.981733 * att4: (49.966876-0.063317)/50.000000 = 0.998071 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 23 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 **************************************************************** * * 23 points. Chose rsnum -1 (thus using 23 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 1 335.669 1 1.8062 * experiment 216.926 34.3737 235.234 0.552275 11.5974 * est rs center 291.126 26.4327 282.199 0.541068 26.1547 * est rs spread 60.3958 13.6457 181.779 0.292393 15.173 * * ------------------------------ * att0: (399.647685-201.884895)/200.000000 = 0.988814 * att1: (49.629381-2.083996)/49.000000 = 0.970314 * att2: (585.657350-2.127696)/600.000000 = 0.972549 * att3: (0.988644-0.005195)/0.995000 = 0.988391 * att4: (49.603102-0.484324)/50.000000 = 0.982376 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 24 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 **************************************************************** * * 24 points. Chose rsnum 0 (thus using 24 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 1 301.342 1 0.0994419 * experiment 200.718 2.29 295.082 0.990097 0.919712 * est rs center 294.915 25.9717 304.659 0.493362 26.8446 * est rs spread 60.4344 14.1449 178.965 0.293396 13.6422 * * Newtiness: 0.845740 * E(rec): -100.656 -4775.08 -2.67398=> 100.655653 * E(expt): -99.2449 -4619.21 -2.7828=> 99.244903 * * ------------------------------ * att0: (397.999714-201.794566)/200.000000 = 0.981026 * att1: (49.724139-1.040194)/49.000000 = 0.993550 * att2: (599.820350-8.551403)/600.000000 = 0.985448 * att3: (0.997167-0.011297)/0.995000 = 0.990824 * att4: (49.735858-0.022441)/50.000000 = 0.994268 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 25 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 **************************************************************** * * 25 points. Chose rsnum -1 (thus using 25 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 50 507.597 1 27.1778 * experiment 339.968 24.2856 107.171 0.408875 45.2882 * est rs center 305.089 24.2048 300.242 0.581589 23.9769 * est rs spread 59.1112 13.5058 174.493 0.280494 14.8527 * * ------------------------------ * att0: (396.572673-201.042933)/200.000000 = 0.977649 * att1: (48.927708-1.038481)/49.000000 = 0.977331 * att2: (596.588408-3.395482)/600.000000 = 0.988655 * att3: (0.994082-0.006104)/0.995000 = 0.992943 * att4: (49.826114-0.458827)/50.000000 = 0.987346 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 26 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 **************************************************************** * * 26 points. Chose rsnum 0 (thus using 26 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 50 505.956 1 27.2977 * experiment 202.4 49.5206 511.787 0.979872 27.937 * est rs center 303.219 25.862 307.836 0.525204 25.7923 * est rs spread 56.1355 14.8867 179.24 0.27856 13.6607 * * Newtiness: 0.703380 * E(rec): -81.0823 -11965.3 -3.31978=> 81.082280 * E(expt): -78.6063 -11413.8 -3.21638=> 78.606258 * * ------------------------------ * att0: (399.703508-205.267297)/200.000000 = 0.972181 * att1: (48.958670-1.177631)/49.000000 = 0.975123 * att2: (595.251497-13.656490)/600.000000 = 0.969325 * att3: (0.998478-0.006271)/0.995000 = 0.997194 * att4: (49.474152-0.061088)/50.000000 = 0.988261 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 27 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 **************************************************************** * * 27 points. Chose rsnum 1 (thus using 26 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 1 400.907 0.005 19.72 * experiment 204.134 1.17113 406.892 0.0225535 20.3829 * est rs center 302.539 25.3059 306.631 0.512459 26.0769 * est rs spread 59.1378 14.6647 179.515 0.2818 14.625 * * Newtiness: 0.735796 * E(rec): -70.9843 -7180.62 0.863314=> 70.984251 * E(expt): -71.197 -7289.01 0.905951=> 71.196960 * * ------------------------------ * att0: (399.589198-203.425432)/200.000000 = 0.980819 * att1: (49.597244-1.124729)/49.000000 = 0.989235 * att2: (597.493850-11.009141)/600.000000 = 0.977475 * att3: (0.998059-0.015298)/0.995000 = 0.987700 * att4: (49.718031-0.505245)/50.000000 = 0.984256 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 28 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 **************************************************************** * * 28 points. Chose rsnum 2 (thus using 26 points) * Of 100 random points, 100 were inside rstat * Estimated rstat fractional volume is 1.0000 * base 200 50 600 1 28.6545 * experiment 202.345 48.6022 596.041 0.979576 28.0507 * est rs center 306.812 23.327 306.83 0.469775 23.6686 * est rs spread 57.4267 13.3356 180.879 0.243218 14.7904 * * Newtiness: 0.938072 * E(rec): -92.4832 -11361.2 4.31557=> 92.483211 * E(expt): -90.7398 -11226.5 4.24337=> 90.739817 * * ------------------------------ * att0: (398.212494-201.989454)/200.000000 = 0.981115 * att1: (49.940758-1.867732)/49.000000 = 0.981082 * att2: (593.816695-6.980309)/600.000000 = 0.978061 * att3: (0.953026-0.041951)/0.995000 = 0.915653 * att4: (49.793403-0.068096)/50.000000 = 0.994506 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 29 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 **************************************************************** * * 29 points. Chose rsnum 3 (thus using 26 points) * Of 102 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9804 * base 200 9.4046 600 0.005 40.7977 * experiment 307.007 33.9724 511.108 0.0985526 12.7276 * est rs center 298.885 28.4407 336.483 0.497401 25.6482 * est rs spread 55.4598 13.8078 172.057 0.270731 14.2227 * * Newtiness: 0.000000 * E(rec): -55.3687 322.01 2.59456=> 55.368653 * E(expt): -97.2009 -2825.41 1.05918=> 97.200883 * * ------------------------------ * att0: (396.646388-202.691445)/200.000000 = 0.969775 * att1: (49.248683-2.255818)/49.000000 = 0.959038 * att2: (598.291311-9.581811)/600.000000 = 0.981183 * att3: (0.974779-0.017420)/0.995000 = 0.962170 * att4: (49.907092-1.358617)/50.000000 = 0.970970 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 30 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 **************************************************************** * * 30 points. Chose rsnum 4 (thus using 26 points) * Of 107 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9346 * base 200 24.5383 600 0.005 50 * experiment 200.612 24.5877 584.984 0.00859166 49.5782 * est rs center 294.72 23.4222 309.412 0.498293 23.7167 * est rs spread 55.3501 13.6422 171.807 0.249038 14.0269 * * Newtiness: 0.353322 * E(rec): 2.90701 -3248.47 9.45167=> -2.907006 * E(expt): 2.53229 -3215.75 9.24156=> -2.532293 * * ------------------------------ * att0: (397.294318-200.551883)/200.000000 = 0.983712 * att1: (48.711409-1.295144)/49.000000 = 0.967679 * att2: (599.127388-9.024434)/600.000000 = 0.983505 * att3: (0.964009-0.020766)/0.995000 = 0.947983 * att4: (49.259341-0.264401)/50.000000 = 0.979899 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 31 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 **************************************************************** * * 31 points. Chose rsnum 3 (thus using 28 points) * Of 104 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9615 * base 200 22.8279 600 0.005 27.5094 * experiment 338.374 21.2383 445.965 0.612318 13.0899 * est rs center 291.701 28.4767 322.924 0.535527 26.8746 * est rs spread 58.2302 13.2096 166.09 0.293737 14.7563 * * Newtiness: 0.000000 * E(rec): -27.493 7.6958 2.33751=> 27.492968 * E(expt): -49.5156 -7621.94 5.50492=> 49.515639 * * ------------------------------ * att0: (399.442536-200.968512)/200.000000 = 0.992370 * att1: (49.953983-2.373455)/49.000000 = 0.971031 * att2: (599.075533-20.917296)/600.000000 = 0.963597 * att3: (0.997683-0.014935)/0.995000 = 0.987686 * att4: (49.665685-0.043441)/50.000000 = 0.992445 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 31 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 **************************************************************** * * 31 points. Chose rsnum 5 (thus using 26 points) * Of 115 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.8696 * base 200 25.3323 447.448 1 9.79596 * experiment 205.454 25.6138 450.784 0.971469 10.4582 * est rs center 300.707 25.493 310.121 0.54368 25.6047 * est rs spread 54.3981 14.7393 153.009 0.300535 14.4604 * * Newtiness: 0.682701 * E(rec): 36.6097 -3157.66 5.16095=> -36.609664 * E(expt): 40.3361 -2850.77 5.10823=> -40.336117 * * ------------------------------ * att0: (397.994429-200.311188)/200.000000 = 0.988416 * att1: (49.974094-1.073894)/49.000000 = 0.997963 * att2: (599.204791-35.605169)/600.000000 = 0.939333 * att3: (0.995544-0.006504)/0.995000 = 0.994010 * att4: (49.101866-0.280162)/50.000000 = 0.976434 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 32 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 **************************************************************** * * 32 points. Chose rsnum 4 (thus using 28 points) * Of 107 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9346 * base 200 25.5424 545.223 0.005 50 * experiment 204.631 25.5978 553.975 0.00509269 48.8903 * est rs center 297.298 26.1568 328.107 0.546122 24.0611 * est rs spread 55.8518 14.9401 166.823 0.294062 13.5403 * * Newtiness: 0.450156 * E(rec): 9.50454 -1380.47 9.61943=> -9.504540 * E(expt): 8.94901 -1347.79 9.62206=> -8.949007 * * ------------------------------ * att0: (398.190646-203.759522)/200.000000 = 0.972156 * att1: (49.576889-1.151957)/49.000000 = 0.988264 * att2: (595.404236-35.239174)/600.000000 = 0.933608 * att3: (0.980311-0.022210)/0.995000 = 0.962916 * att4: (49.307552-1.353431)/50.000000 = 0.959082 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 33 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 **************************************************************** * * 33 points. Chose rsnum 7 (thus using 26 points) * Of 140 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.7143 * base 200 50 600 1 28.6545 * experiment 259.09 43.4388 291.204 0.250046 41.9736 * est rs center 296.815 26.3579 384.09 0.501907 25.2517 * est rs spread 58.3751 14.2538 129.845 0.297881 12.8903 * * Newtiness: 0.000000 * E(rec): -56.769 -10933 9.05373=> 56.769024 * E(expt): 174.7 11478.1 6.92256=> -174.700303 * * ------------------------------ * att0: (399.090809-201.239210)/200.000000 = 0.989258 * att1: (49.574055-1.310550)/49.000000 = 0.984969 * att2: (599.175920-155.159831)/600.000000 = 0.740027 * att3: (0.976896-0.031313)/0.995000 = 0.950335 * att4: (49.632348-0.049349)/50.000000 = 0.991660 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 34 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 **************************************************************** * * 34 points. Chose rsnum 6 (thus using 28 points) * Of 107 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9346 * base 274.52 38.4589 600 0.005 50 * experiment 272.327 38.6885 589.401 0.0296994 49.4019 * est rs center 284.967 25.4036 362.859 0.467744 25.2476 * est rs spread 51.7517 13.5103 154.052 0.274086 15.2665 * * Newtiness: 0.377588 * E(rec): -29.4071 -7426.35 10.5122=> 29.407104 * E(expt): -26.7798 -7046.35 10.2764=> 26.779834 * * ------------------------------ * att0: (390.152252-203.769255)/200.000000 = 0.931915 * att1: (49.620234-1.456168)/49.000000 = 0.982940 * att2: (596.948750-38.541969)/600.000000 = 0.930678 * att3: (0.986494-0.008288)/0.995000 = 0.983121 * att4: (49.791136-0.235276)/50.000000 = 0.991117 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 35 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 **************************************************************** * * 35 points. Chose rsnum 4 (thus using 31 points) * Of 105 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9524 * base 200 26.255 472.298 1 21.7655 * experiment 205.211 26.0013 479.025 0.981964 22.0328 * est rs center 292.578 27.0131 304.403 0.447792 25.8391 * est rs spread 57.9123 14.1486 170.478 0.297823 13.154 * * Newtiness: 0.982729 * E(rec): -6.95829 -6326.77 7.94492=> 6.958290 * E(expt): -5.09423 -6102.75 8.01616=> 5.094226 * * ------------------------------ * att0: (399.695417-201.384069)/200.000000 = 0.991557 * att1: (49.917336-1.211117)/49.000000 = 0.994004 * att2: (587.611265-5.405603)/600.000000 = 0.970343 * att3: (0.999206-0.022865)/0.995000 = 0.981247 * att4: (49.599589-0.196822)/50.000000 = 0.988055 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 36 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 **************************************************************** * * 36 points. Chose rsnum 8 (thus using 28 points) * Of 142 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.7042 * base 200 24.5383 600 0.005 50 * experiment 393.203 13.3403 399.573 0.0881315 1.61318 * est rs center 292.596 23.7014 385.618 0.523728 26.0153 * est rs spread 56.0616 15.1318 122.885 0.278641 15.7241 * * Newtiness: 0.313962 * E(rec): 49.092 -2967.68 9.71715=> -49.091962 * E(expt): 42.2984 1373.72 8.50987=> -42.298431 * * ------------------------------ * att0: (399.517867-202.974882)/200.000000 = 0.982715 * att1: (49.863946-1.372367)/49.000000 = 0.989624 * att2: (596.818494-166.320593)/600.000000 = 0.717497 * att3: (0.992552-0.008745)/0.995000 = 0.988751 * att4: (49.872118-0.296555)/50.000000 = 0.991511 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 37 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 **************************************************************** * * 37 points. Chose rsnum 6 (thus using 31 points) * Of 112 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.8929 * base 200 23.749 513.562 0.005 45.2235 * experiment 339.485 18.5645 281.011 0.934014 31.334 * est rs center 295.477 26.0269 338.617 0.461507 24.4902 * est rs spread 57.6783 15.2583 163.115 0.293624 14.6842 * * Newtiness: 0.000000 * E(rec): -6.47024 -3979.57 5.00084=> 6.470244 * E(expt): 28.0498 1638.98 8.30029=> -28.049848 * * ------------------------------ * att0: (398.067895-200.182192)/200.000000 = 0.989429 * att1: (49.970795-1.282346)/49.000000 = 0.993642 * att2: (598.159846-19.171933)/600.000000 = 0.964980 * att3: (0.994759-0.008221)/0.995000 = 0.991496 * att4: (49.066085-0.100690)/50.000000 = 0.979308 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 38 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 **************************************************************** * * 38 points. Chose rsnum 4 (thus using 34 points) * Of 103 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9709 * base 200 21.0377 521.37 0.005 41.7979 * experiment 201.118 20.3601 529.19 0.0247117 42.4327 * est rs center 303.822 25.0542 329.226 0.500209 25.8631 * est rs spread 61.9548 13.1223 155.86 0.296667 15.2702 * * Newtiness: 0.797288 * E(rec): -12.9457 -5220.02 3.43699=> 12.945733 * E(expt): -11.1175 -5191.76 3.55772=> 11.117518 * * ------------------------------ * att0: (398.105265-203.417121)/200.000000 = 0.973441 * att1: (49.304594-1.433820)/49.000000 = 0.976955 * att2: (599.963285-20.183567)/600.000000 = 0.966300 * att3: (0.999860-0.009687)/0.995000 = 0.995148 * att4: (49.669611-0.035111)/50.000000 = 0.992690 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 39 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 **************************************************************** * * 39 points. Chose rsnum 5 (thus using 34 points) * Of 106 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9434 * base 200 22.0957 539.04 0.005 41.9476 * experiment 207.34 16.4605 134.06 0.307814 40.0846 * est rs center 304.635 25.7342 329.537 0.476624 23.7439 * est rs spread 53.9957 14.3508 171.813 0.29035 13.8162 * * Newtiness: 0.000000 * E(rec): -10.7844 -4181.03 3.64385=> 10.784438 * E(expt): 54.3602 3227.78 5.32121=> -54.360222 * * ------------------------------ * att0: (395.175958-201.020209)/200.000000 = 0.970779 * att1: (49.949440-1.037763)/49.000000 = 0.998197 * att2: (598.945124-22.247799)/600.000000 = 0.961162 * att3: (0.992886-0.013902)/0.995000 = 0.983903 * att4: (48.799002-0.402440)/50.000000 = 0.967931 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 40 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 **************************************************************** * * 40 points. Chose rsnum 6 (thus using 34 points) * Of 110 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9091 * base 200 23.2971 513.785 0.005 44.7596 * experiment 201.019 23.4836 504.978 0.0103008 44.4028 * est rs center 287.363 25.9271 308.344 0.446728 26.9039 * est rs spread 54.8918 14.6852 157.895 0.272039 13.5913 * * Newtiness: 0.165169 * E(rec): -6.77748 -4110.34 4.8564=> 6.777482 * E(expt): -7.08952 -4017.97 4.78032=> 7.089520 * * ------------------------------ * att0: (392.786113-201.508379)/200.000000 = 0.956389 * att1: (49.956641-2.149962)/49.000000 = 0.975647 * att2: (596.719542-24.769379)/600.000000 = 0.953250 * att3: (0.997913-0.007358)/0.995000 = 0.995532 * att4: (49.524151-0.348607)/50.000000 = 0.983511 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 41 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 **************************************************************** * * 41 points. Chose rsnum 3 (thus using 38 points) * Of 104 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9615 * base 200 22.3723 600 0.005 28.2788 * experiment 205.717 21.6897 583.158 0.00767722 27.7532 * est rs center 295.36 26.0669 311.169 0.470888 25.3512 * est rs spread 56.5669 13.6635 165.044 0.293101 14.1306 * * Newtiness: 0.891689 * E(rec): -44.8574 -5881.33 0.719728=> 44.857390 * E(expt): -46.0559 -6206.03 0.840911=> 46.055858 * * ------------------------------ * att0: (399.778847-208.671031)/200.000000 = 0.955539 * att1: (49.871905-1.397530)/49.000000 = 0.989273 * att2: (580.661907-9.261679)/600.000000 = 0.952334 * att3: (0.994642-0.034568)/0.995000 = 0.964899 * att4: (49.873818-0.061769)/50.000000 = 0.996241 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 42 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 **************************************************************** * * 42 points. Chose rsnum 4 (thus using 38 points) * Of 107 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9346 * base 200 22.1861 527.391 0.005 38.946 * experiment 204.3 22.4654 527.258 0.0230879 39.6802 * est rs center 299 25.9732 314.426 0.496874 25.7859 * est rs spread 56.1444 13.8707 173.527 0.290911 14.6897 * * Newtiness: 0.099422 * E(rec): -18.3511 -6069.16 2.42835=> 18.351115 * E(expt): -17.718 -6168.98 2.59375=> 17.717985 * * ------------------------------ * att0: (396.623250-201.452379)/200.000000 = 0.975854 * att1: (47.472196-1.775953)/49.000000 = 0.932576 * att2: (598.852107-7.805327)/600.000000 = 0.985078 * att3: (0.991769-0.008060)/0.995000 = 0.988652 * att4: (48.520148-0.739705)/50.000000 = 0.955609 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 43 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 **************************************************************** * * 43 points. Chose rsnum 5 (thus using 38 points) * Of 104 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9615 * base 200 22.5573 542.004 0.005 39.8943 * experiment 201.415 3.0745 53.7786 0.0143324 1.19275 * est rs center 289.655 23.236 296.912 0.498588 21.9832 * est rs spread 55.7004 13.9118 172.189 0.248257 14.0852 * * Newtiness: 0.000000 * E(rec): -13.7441 -4691.03 2.67369=> 13.744127 * E(expt): 22.3658 1273.61 4.25415=> -22.365796 * * ------------------------------ * att0: (398.581945-200.129264)/200.000000 = 0.992263 * att1: (49.672111-2.069980)/49.000000 = 0.971472 * att2: (589.774094-8.553103)/600.000000 = 0.968702 * att3: (0.963006-0.006906)/0.995000 = 0.960904 * att4: (48.960218-0.017685)/50.000000 = 0.978851 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 44 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 **************************************************************** * * 44 points. Chose rsnum 6 (thus using 38 points) * Of 111 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9009 * base 200 22.7503 517.009 0.005 42.7318 * experiment 200.671 23.1748 517.045 0.0241465 42.0249 * est rs center 287.553 24.6979 332.696 0.50503 23.8626 * est rs spread 56.9954 14.3028 167.921 0.295872 13.7526 * * Newtiness: 0.393315 * E(rec): -11.5912 -4614.33 3.63071=> 11.591195 * E(expt): -11.2054 -4611.64 3.54466=> 11.205420 * * ------------------------------ * att0: (398.576791-202.165139)/200.000000 = 0.982058 * att1: (49.377709-2.361456)/49.000000 = 0.959515 * att2: (596.881835-5.944752)/600.000000 = 0.984895 * att3: (0.985601-0.009778)/0.995000 = 0.980727 * att4: (49.789068-0.616279)/50.000000 = 0.983456 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 45 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 **************************************************************** * * 45 points. Chose rsnum 14 (thus using 31 points) * Of 291 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.3436 * base 201.019 23.4836 504.978 0.0103008 44.4028 * experiment 304.889 47.5676 478.046 0.464138 33.6758 * est rs center 293.901 28.4006 484.645 0.493421 25.3944 * est rs spread 55.2605 14.2531 67.6772 0.261497 13.4083 * * Newtiness: 0.000000 * E(rec): -766.728 -63553.2 15.0634=> 766.728464 * E(expt): -573.669 -49798.5 15.4645=> 573.668554 * * ------------------------------ * att0: (397.592635-201.274339)/200.000000 = 0.981591 * att1: (49.225925-1.584193)/49.000000 = 0.972280 * att2: (599.177597-350.320309)/600.000000 = 0.414762 * att3: (0.990616-0.032232)/0.995000 = 0.963200 * att4: (48.442583-0.969555)/50.000000 = 0.949461 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 46 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 **************************************************************** * * 46 points. Chose rsnum 15 (thus using 31 points) * Of 299 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.3344 * base 201.019 23.4836 504.978 0.0103008 44.4028 * experiment 240.986 47.3888 540.61 0.0711942 6.02942 * est rs center 298.089 25.208 496.785 0.524128 24.6369 * est rs spread 54.1974 12.6283 66.9016 0.2874 12.833 * * Newtiness: 0.678727 * E(rec): -610.43 -51461.5 -9.45878=> 610.429813 * E(expt): -631.277 -51869.6 -13.3679=> 631.276513 * * ------------------------------ * att0: (397.363164-203.814789)/200.000000 = 0.967742 * att1: (48.388600-2.860609)/49.000000 = 0.929143 * att2: (599.926027-357.233300)/600.000000 = 0.404488 * att3: (0.991715-0.020890)/0.995000 = 0.975704 * att4: (49.302371-0.527278)/50.000000 = 0.975502 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 47 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 **************************************************************** * * 47 points. Chose rsnum 6 (thus using 41 points) * Of 113 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.8850 * base 200 26.0897 496.611 0.005 35.1375 * experiment 201.875 26.6337 504.294 0.00928346 35.466 * est rs center 291.976 24.826 351.63 0.481291 23.8585 * est rs spread 56.2285 14.2907 160.426 0.294803 14.7399 * * Newtiness: 0.524106 * E(rec): -16.2663 -4957.03 2.46318=> 16.266260 * E(expt): -16.1244 -5063.21 2.50347=> 16.124418 * * ------------------------------ * att0: (393.666388-200.830438)/200.000000 = 0.964180 * att1: (49.789003-1.261974)/49.000000 = 0.990348 * att2: (599.868337-23.214348)/600.000000 = 0.961090 * att3: (0.990446-0.006080)/0.995000 = 0.989312 * att4: (48.744616-0.151637)/50.000000 = 0.971860 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 48 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 **************************************************************** * * 48 points. Chose rsnum 20 (thus using 28 points) * Of 316 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.3165 * base 201.019 23.4836 504.978 0.0103008 44.4028 * experiment 360.534 9.12953 504.247 0.700046 21.9795 * est rs center 282.689 24.8631 481.718 0.48693 20.1587 * est rs spread 51.5805 14.6977 68.8428 0.285418 13.7287 * * Newtiness: 0.000000 * E(rec): 8195.9 446010 -585.839=> -8195.895476 * E(expt): 16100.4 891169 -1070.14=> -16100.404619 * * ------------------------------ * att0: (382.949688-200.392761)/200.000000 = 0.912785 * att1: (49.888228-1.862067)/49.000000 = 0.980126 * att2: (597.745599-341.643052)/600.000000 = 0.426838 * att3: (0.996466-0.013953)/0.995000 = 0.987451 * att4: (48.321488-1.070314)/50.000000 = 0.945023 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 49 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 **************************************************************** * * 49 points. Chose rsnum 21 (thus using 28 points) * Of 346 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.2890 * base 201.019 23.4836 504.978 0.0103008 44.4028 * experiment 345.392 26.8552 386.158 0.58201 33.9929 * est rs center 275.493 26.4122 495.617 0.512432 25.1241 * est rs spread 49.8458 13.3125 66.9868 0.279426 14.0695 * * Newtiness: 0.845972 * E(rec): 8195.9 446010 -585.839=> -8195.895473 * E(expt): 6008.49 355076 -360.12=> -6008.491723 * * ------------------------------ * att0: (363.124823-202.246047)/200.000000 = 0.804394 * att1: (49.909973-2.534049)/49.000000 = 0.966856 * att2: (599.920605-354.368275)/600.000000 = 0.409254 * att3: (0.967146-0.029556)/0.995000 = 0.942302 * att4: (47.608624-0.023392)/50.000000 = 0.951705 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 50 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 140 Output 1 called output1 range: 0 to 20000 Output 2 called output2 range: 0 to 5 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.7300 0.9650 0.2199 **************************************************************** * * 50 points. Chose rsnum 4 (thus using 46 points) * Of 108 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.9259 * base 200 27.9047 551.482 0.005 41.1979 * experiment 357.704 39.4886 544.394 0.952575 36.5051 * est rs center 294.718 24.2895 318.074 0.476867 26.5048 * est rs spread 62.3641 14.2104 154.87 0.295933 14.4605 * * Newtiness: 0.000000 * E(rec): -27.8893 -6944.05 0.832752=> 27.889337 * E(expt): -77.1359 -15795.2 0.310778=> 77.135931 * * ------------------------------ * att0: (397.841543-200.754530)/200.000000 = 0.985435 * att1: (47.098814-1.004119)/49.000000 = 0.940708 * att2: (595.279835-13.062102)/600.000000 = 0.970363 * att3: (0.991393-0.011384)/0.995000 = 0.984933 * att4: (49.140787-0.167723)/50.000000 = 0.979461 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 51 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 120 Output 1 called output1 range: 0 to 14000 Output 2 called output2 range: 0 to 4 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.7300 0.9650 0.2199 357.7035 39.4886 544.3939 0.9526 36.5051 -> 12.7600 1010.9220 0.2789 **************************************************************** * * 51 points. Chose rsnum 20 (thus using 31 points) * Of 310 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.3226 * base 204.631 25.5978 553.975 0.00509269 48.8903 * experiment 300.552 1.5968 414.083 0.937325 42.8014 * est rs center 278.561 25.0366 483.274 0.482611 24.0976 * est rs spread 54.6741 13.5105 69.9447 0.289101 14.1287 * * Newtiness: 0.820622 * E(rec): 1210.61 84035.6 -72.6803=> -1210.608329 * E(expt): 1422.9 107087 -74.7809=> -1422.903976 * * ------------------------------ * att0: (394.744788-200.379069)/200.000000 = 0.971829 * att1: (49.587479-1.389214)/49.000000 = 0.983638 * att2: (598.281960-354.261161)/600.000000 = 0.406701 * att3: (0.982223-0.009480)/0.995000 = 0.977632 * att4: (49.963102-0.023407)/50.000000 = 0.998794 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 52 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 120 Output 1 called output1 range: 0 to 14000 Output 2 called output2 range: 0 to 4 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.7300 0.9650 0.2199 357.7035 39.4886 544.3939 0.9526 36.5051 -> 12.7600 1010.9220 0.2789 204.6310 25.5978 553.9748 0.0051 48.8903 -> 2.1500 5.3470 0.1870 **************************************************************** * * 52 points. Chose rsnum 11 (thus using 41 points) * Of 172 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.5814 * base 200 1 414.019 0.005 50 * experiment 290.334 13.1632 234.95 0.827797 40.1254 * est rs center 282.882 25.728 430.691 0.461179 22.8054 * est rs spread 54.7957 13.8339 107.901 0.301549 13.8551 * * Newtiness: 0.419891 * E(rec): 1.58542 -2921.25 1.59842=> -1.585425 * E(expt): -10.1232 385.793 6.67523=> 10.123186 * * ------------------------------ * att0: (392.305041-200.049656)/200.000000 = 0.961277 * att1: (49.462333-1.770194)/49.000000 = 0.973309 * att2: (597.958652-215.630541)/600.000000 = 0.637214 * att3: (0.994220-0.006349)/0.995000 = 0.992835 * att4: (47.407764-0.619087)/50.000000 = 0.935774 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 53 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 120 Output 1 called output1 range: 0 to 14000 Output 2 called output2 range: 0 to 4 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.7300 0.9650 0.2199 357.7035 39.4886 544.3939 0.9526 36.5051 -> 12.7600 1010.9220 0.2789 204.6310 25.5978 553.9748 0.0051 48.8903 -> 2.1500 5.3470 0.1870 290.3335 13.1632 234.9498 0.8278 40.1254 -> 63.1800 5152.9720 0.3143 **************************************************************** * * 53 points. Chose rsnum 15 (thus using 38 points) * Of 184 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.5435 * base 200 1 414.019 0.005 50 * experiment 223.65 10.9478 252.39 0.378734 30.0411 * est rs center 284.904 23.539 443.079 0.501567 22.8923 * est rs spread 47.8296 14.8281 108.333 0.279157 14.4368 * * Newtiness: 0.000000 * E(rec): 20.3592 1602.11 1.52533=> -20.359217 * E(expt): 19.9295 2410.48 3.05706=> -19.929478 * * ------------------------------ * att0: (392.804569-204.112753)/200.000000 = 0.943459 * att1: (49.850871-1.076331)/49.000000 = 0.995399 * att2: (597.607287-226.441452)/600.000000 = 0.618610 * att3: (0.998180-0.012033)/0.995000 = 0.991102 * att4: (49.328951-0.296432)/50.000000 = 0.980650 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 54 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 120 Output 1 called output1 range: 0 to 14000 Output 2 called output2 range: 0 to 4 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.7300 0.9650 0.2199 357.7035 39.4886 544.3939 0.9526 36.5051 -> 12.7600 1010.9220 0.2789 204.6310 25.5978 553.9748 0.0051 48.8903 -> 2.1500 5.3470 0.1870 290.3335 13.1632 234.9498 0.8278 40.1254 -> 63.1800 5152.9720 0.3143 200.0000 1.0000 414.0192 0.0050 50.0000 -> 14.7300 1972.8110 0.2142 **************************************************************** * * 54 points. Chose rsnum 23 (thus using 31 points) * Of 366 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.2732 * base 201.019 23.4836 504.978 0.0103008 44.4028 * experiment 272.483 18.6791 573.962 0.64962 2.05513 * est rs center 279.45 24.6562 496.898 0.543816 24.7199 * est rs spread 41.8855 14.4447 67.24 0.269428 12.7383 * * Newtiness: 0.000000 * E(rec): 8946.41 601449 -589.648=> -8946.407224 * E(expt): 16247.6 1.09111e+006 -1079.02=> -16247.593271 * * ------------------------------ * att0: (353.562424-203.801060)/200.000000 = 0.748807 * att1: (49.683173-1.177945)/49.000000 = 0.989903 * att2: (598.357559-371.638351)/600.000000 = 0.377865 * att3: (0.970471-0.020936)/0.995000 = 0.954306 * att4: (49.846709-0.149665)/50.000000 = 0.993941 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 55 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 120 Output 1 called output1 range: 0 to 14000 Output 2 called output2 range: 0 to 4 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.7300 0.9650 0.2199 357.7035 39.4886 544.3939 0.9526 36.5051 -> 12.7600 1010.9220 0.2789 204.6310 25.5978 553.9748 0.0051 48.8903 -> 2.1500 5.3470 0.1870 290.3335 13.1632 234.9498 0.8278 40.1254 -> 63.1800 5152.9720 0.3143 200.0000 1.0000 414.0192 0.0050 50.0000 -> 14.7300 1972.8110 0.2142 272.4827 18.6791 573.9617 0.6496 2.0551 -> 2.3600 5.9520 0.9945 **************************************************************** * * 55 points. Chose rsnum 24 (thus using 31 points) * Of 336 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.2976 * base 204.3 22.4654 527.258 0.0230879 39.6802 * experiment 233.022 47.9486 401.35 0.391765 45.8367 * est rs center 267.824 27.2749 473.42 0.463565 25.3748 * est rs spread 40.2899 12.1733 63.1552 0.28612 13.8165 * * Newtiness: 0.000000 * E(rec): -1635.26 -97249.5 32.5309=> 1635.259871 * E(expt): 607.024 60063.7 -34.2684=> -607.024208 * * ------------------------------ * att0: (351.544581-200.160819)/200.000000 = 0.756919 * att1: (49.871383-1.532343)/49.000000 = 0.986511 * att2: (598.818477-365.839070)/600.000000 = 0.388299 * att3: (0.977314-0.010930)/0.995000 = 0.971240 * att4: (49.901897-1.060794)/50.000000 = 0.976822 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 56 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 120 Output 1 called output1 range: 0 to 14000 Output 2 called output2 range: 0 to 4 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.7300 0.9650 0.2199 357.7035 39.4886 544.3939 0.9526 36.5051 -> 12.7600 1010.9220 0.2789 204.6310 25.5978 553.9748 0.0051 48.8903 -> 2.1500 5.3470 0.1870 290.3335 13.1632 234.9498 0.8278 40.1254 -> 63.1800 5152.9720 0.3143 200.0000 1.0000 414.0192 0.0050 50.0000 -> 14.7300 1972.8110 0.2142 272.4827 18.6791 573.9617 0.6496 2.0551 -> 2.3600 5.9520 0.9945 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5300 0.5970 0.2373 **************************************************************** * * 56 points. Chose rsnum 25 (thus using 31 points) * Of 521 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.1919 * base 200.671 23.1748 517.045 0.0241465 42.0249 * experiment 232.057 43.0188 501.05 0.514192 9.73202 * est rs center 276.09 25.9304 487.168 0.589513 27.2778 * est rs spread 39.1498 14.2691 56.6961 0.274997 14.5615 * * Newtiness: 0.000000 * E(rec): -2536.72 -192282 60.3627=> 2536.722131 * E(expt): -1483.07 -110983 19.382=> 1483.071800 * * ------------------------------ * att0: (356.305962-200.787384)/200.000000 = 0.777593 * att1: (49.210278-1.782637)/49.000000 = 0.967911 * att2: (593.917986-398.596985)/600.000000 = 0.325535 * att3: (0.994001-0.011697)/0.995000 = 0.987240 * att4: (49.194536-0.613747)/50.000000 = 0.971616 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 57 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 120 Output 1 called output1 range: 0 to 14000 Output 2 called output2 range: 0 to 4 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.7300 0.9650 0.2199 357.7035 39.4886 544.3939 0.9526 36.5051 -> 12.7600 1010.9220 0.2789 204.6310 25.5978 553.9748 0.0051 48.8903 -> 2.1500 5.3470 0.1870 290.3335 13.1632 234.9498 0.8278 40.1254 -> 63.1800 5152.9720 0.3143 200.0000 1.0000 414.0192 0.0050 50.0000 -> 14.7300 1972.8110 0.2142 272.4827 18.6791 573.9617 0.6496 2.0551 -> 2.3600 5.9520 0.9945 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5300 0.5970 0.2373 232.0565 43.0188 501.0496 0.5142 9.7320 -> 11.2800 216.4160 0.6455 **************************************************************** * * 57 points. Chose rsnum 26 (thus using 31 points) * Of 452 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.2212 * base 201.019 23.4836 504.978 0.0103008 44.4028 * experiment 248.445 2.37839 437.482 0.389454 11.2759 * est rs center 269.322 22.9458 488.399 0.463979 26.3622 * est rs spread 43.7418 14.42 54.7353 0.27716 13.9502 * * Newtiness: 0.616996 * E(rec): 1986.82 127873 -92.4288=> -1986.817379 * E(expt): 1853.11 120297 -75.7068=> -1853.114441 * * ------------------------------ * att0: (352.845921-201.614787)/200.000000 = 0.756156 * att1: (49.983954-1.062911)/49.000000 = 0.998389 * att2: (598.332368-405.070165)/600.000000 = 0.322104 * att3: (0.994346-0.017581)/0.995000 = 0.981674 * att4: (49.731289-0.311608)/50.000000 = 0.988394 * *************************************************************** A dataset with the following dimensions: Number of inputs = 5 Number of outputs = 3 Number of datapoints = 58 Input 0 called attribute0 range: 200 to 400 Input 1 called attribute1 range: 0 to 50 Input 2 called attribute2 range: 0 to 600 Input 3 called attribute3 range: 0 to 1 Input 4 called attribute4 range: 0 to 60 Output 0 called output0 range: 0 to 120 Output 1 called output1 range: 0 to 14000 Output 2 called output2 range: 0 to 4 394.2555 43.1513 352.8510 0.1846 48.8325 -> 57.5100 5640.7090 0.3045 282.3176 19.8323 16.1830 0.6322 20.6292 -> 70.7100 6065.7750 0.4575 360.4345 4.9008 588.8372 0.1879 1.1174 -> 29.2100 1659.6730 1.0794 400.0000 1.0000 600.0000 0.0050 0.0000 -> 1.9300 4.8090 2.7453 272.6072 42.4309 597.8018 0.9188 6.6573 -> 9.2700 224.8450 0.7282 200.0000 50.0000 600.0000 1.0000 3.4623 -> 7.0500 56.8370 2.0029 329.3389 3.8877 398.1383 0.5444 48.2903 -> 54.9000 4305.6840 0.4955 224.8564 26.0853 584.4842 0.5775 36.3518 -> 8.7900 124.0190 0.2515 200.0000 50.0000 600.0000 1.0000 50.0000 -> 4.1100 19.7630 0.2334 269.3285 6.6941 373.7171 0.9899 5.3633 -> 4.4200 68.6400 0.8460 200.0000 1.0000 600.0000 1.0000 40.6756 -> 12.1900 415.1250 0.5846 312.2223 35.4855 184.3070 0.0134 16.6734 -> 58.8900 4003.1730 0.5832 400.0000 20.4941 571.1699 1.0000 0.0000 -> 12.0100 495.7790 0.9715 396.6723 17.1998 582.4674 0.0186 34.1555 -> 12.2900 677.6950 0.3030 200.0000 1.0000 414.0192 0.0050 50.0000 -> 3.0400 62.2600 0.1888 204.7564 16.0488 208.6712 0.9872 48.6029 -> 30.3000 1133.6180 0.2340 400.0000 50.0000 237.3263 1.0000 6.0869 -> 66.5900 6321.6330 1.1187 398.1432 1.3313 77.2878 0.0175 29.2779 -> 95.3400 11053.4600 0.4587 200.0000 50.0000 0.0000 0.0269 50.0000 -> 46.3000 2361.8940 0.1537 397.1973 49.8356 598.7346 0.3088 1.2053 -> 14.1600 1004.4680 0.9770 400.0000 45.0269 0.0000 0.2452 0.0000 -> 129.5300 17843.7300 4.4401 378.3072 48.4286 56.2429 0.8927 40.4936 -> 109.3800 13979.5020 0.2222 200.0000 50.0000 600.0000 0.0050 50.0000 -> 6.0500 38.4790 0.1917 216.9264 34.3737 235.2341 0.5523 11.5974 -> 21.0200 717.5120 0.4909 200.0000 1.0000 301.3416 1.0000 0.0994 -> 10.0800 184.7500 2.1483 339.9675 24.2856 107.1706 0.4089 45.2882 -> 105.0200 12936.8800 0.2518 200.0000 50.0000 505.9559 1.0000 27.2977 -> 8.1000 77.9740 0.1997 204.1340 1.1711 406.8918 0.0226 20.3829 -> 8.1400 80.7460 1.3153 200.0000 50.0000 600.0000 1.0000 28.6545 -> 6.7900 47.1030 0.2501 307.0068 33.9724 511.1081 0.0986 12.7276 -> 4.6900 23.4010 0.3965 200.0000 24.5383 600.0000 0.0050 50.0000 -> 1.1500 1.8830 0.1964 200.0000 25.3323 447.4477 1.0000 9.7960 -> 13.0700 642.3570 0.4176 204.6310 25.5978 553.9748 0.0051 48.8903 -> 1.9500 4.6050 0.1808 200.0000 50.0000 600.0000 1.0000 28.6545 -> 5.7400 35.3260 0.2313 272.3273 38.6885 589.4008 0.0297 49.4019 -> 14.8900 405.1350 0.1692 200.0000 26.2550 472.2981 1.0000 21.7655 -> 4.1800 39.7720 0.2566 393.2033 13.3403 399.5735 0.0881 1.6132 -> 31.4100 2475.4550 3.7860 200.0000 23.7490 513.5621 0.0050 45.2235 -> 2.5700 7.2150 0.2364 201.1180 20.3601 529.1903 0.0247 42.4327 -> 1.1100 2.0250 0.2411 200.0000 22.0957 539.0402 0.0050 41.9476 -> 2.2400 5.6520 0.1913 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4600 0.4620 0.2390 200.0000 22.3723 600.0000 0.0050 28.2788 -> 2.4500 6.6710 0.4087 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5800 0.5600 0.7753 200.0000 22.5573 542.0040 0.0050 39.8943 -> 13.0000 1168.0400 0.2558 200.6714 23.1748 517.0448 0.0241 42.0249 -> 0.5500 0.3650 0.2710 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.5800 0.9120 0.2388 240.9855 47.3888 540.6103 0.0712 6.0294 -> 8.2300 90.8810 0.3766 200.0000 26.0897 496.6108 0.0050 35.1375 -> 4.2700 18.7130 0.1773 360.5340 9.1295 504.2472 0.7000 21.9795 -> 33.4000 2826.6660 0.5361 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.7300 0.9650 0.2199 357.7035 39.4886 544.3939 0.9526 36.5051 -> 12.7600 1010.9220 0.2789 204.6310 25.5978 553.9748 0.0051 48.8903 -> 2.1500 5.3470 0.1870 290.3335 13.1632 234.9498 0.8278 40.1254 -> 63.1800 5152.9720 0.3143 200.0000 1.0000 414.0192 0.0050 50.0000 -> 14.7300 1972.8110 0.2142 272.4827 18.6791 573.9617 0.6496 2.0551 -> 2.3600 5.9520 0.9945 204.2995 22.4654 527.2579 0.0231 39.6802 -> 0.5300 0.5970 0.2373 232.0565 43.0188 501.0496 0.5142 9.7320 -> 11.2800 216.4160 0.6455 201.0189 23.4836 504.9776 0.0103 44.4028 -> 0.4100 0.2950 0.2592 **************************************************************** * * 58 points. Chose rsnum 27 (thus using 31 points) * Of 395 random points, 100 were inside rstat * Estimated rstat fractional volume is 0.2532 * base 204.3 22.4654 527.258 0.0230879 39.6802 * experiment 276.873 1.52672 434.607 0.117529 6.66481 * est rs center 262.066 25.7284 492.288 0.499358 23.2578 * est rs spread 40.2411 14.3461 51.565 0.29691 15.4107 * * Newtiness: 0.000000 * E(rec): 1991.94 130879 -95.5442=> -1991.939456 * E(expt): 1629.12 107905 -67.927=> -1629.115200 * * ------------------------------ * att0: (344.323701-200.024630)/200.000000 = 0.721495 * att1: (49.962524-1.000204)/49.000000 = 0.999231 * att2: (595.415949-404.766321)/600.000000 = 0.317749 * att3: (0.992088-0.007257)/0.995000 = 0.989780 * att4: (49.882156-0.073439)/50.000000 = 0.996174 * ***************************************************************