Hello Dear UQLab users
I have 35 independent variables and 1 dependent variables, the data matrix is 28x35. Why did I get this meesage I did not figured it out. Can you help?
The code is below:
Best Regards
uqlab;
comp=[
106.00;
182.00;
207.00;
213.00;
271.00;
374.00;
412.00;
468.00;
486.00;
522.00;
615.00;
627.00;
730.00;
734.00;
840.00;
953.00;
1015.00;
1035.00;
1140.00;
1193.00;
1213.00;
1235.25;
1248.46;
1270.08;
1301.30;
1318.12;
1358.95;
1366.16
];
data=[
150.00 184.00 111.70 169.00 174.20 210.50 201.00 184.40 183.60 121.50 171.00 184.00 201.20 209.40 141.40 210.50 191.80 209.00 188.30 183.60 215.20 102.70 200.80 191.00 162.10 210.20 230.90 217.00 169.90 189.80 207.80 210.00 191.00 184.40 182.40;
182.00 215.00 200.80 67.97 289.80 340.20 232.00 221.00 214.80 223.00 232.00 206.30 313.70 235.20 236.00 326.20 225.40 312.10 207.00 205.90 323.40 181.00 296.10 224.20 213.70 320.30 338.30 232.00 230.10 232.00 321.10 242.20 217.60 201.60 207.00;
204.00 235.50 266.80 140.60 378.10 413.30 358.60 275.80 271.90 310.50 325.40 271.50 357.40 313.70 321.10 355.10 280.90 429.70 269.10 269.50 402.70 204.30 351.60 280.50 311.70 350.40 390.20 269.10 257.40 287.50 344.90 340.20 323.40 269.90 365.60;
228.90 319.10 306.30 197.70 414.00 478.50 398.40 385.50 340.20 402.30 425.80 383.60 409.40 414.10 393.00 400.80 354.30 462.90 377.70 377.70 426.60 305.00 387.50 357.80 400.80 397.30 453.90 350.00 332.80 334.40 393.00 374.60 360.50 381.60 479.30;
264.10 397.30 403.50 256.30 516.40 497.70 460.90 406.00 385.20 444.10 471.50 407.00 473.40 456.60 465.20 467.60 402.00 499.00 410.50 406.00 465.20 406.00 413.30 404.70 419.50 417.00 490.00 447.30 370.70 410.20 462.00 435.50 407.80 421.50 605.50;
380.90 428.50 464.50 294.90 589.10 535.00 510.20 455.50 434.40 496.00 487.10 468.40 510.90 495.30 570.30 480.00 450.80 503.90 474.20 490.00 580.10 468.00 459.40 423.00 471.90 463.30 513.30 510.50 459.40 447.30 476.00 474.60 438.30 465.60 639.50;
415.00 464.80 491.80 364.10 607.00 623.40 610.00 494.10 483.60 623.80 524.00 499.60 628.10 534.80 623.00 599.60 482.00 609.00 493.00 607.00 606.00 492.00 472.00 465.20 500.00 580.10 587.50 578.90 492.00 468.80 588.00 529.70 475.00 495.00 716.00;
434.80 499.60 575.40 425.00 644.50 715.20 657.00 523.00 501.60 723.40 573.40 607.00 729.30 606.00 709.80 602.00 505.50 630.10 520.00 637.00 678.90 512.00 525.00 583.20 521.00 654.70 611.70 610.00 543.80 492.20 606.00 584.40 561.70 505.10 736.70;
494.90 569.10 603.50 455.50 733.00 741.40 700.00 541.40 528.00 730.50 604.00 641.40 833.20 671.50 736.70 687.50 605.10 738.30 557.40 725.00 699.20 573.40 534.40 603.00 607.40 697.30 702.30 687.50 571.10 536.00 681.00 607.00 603.00 624.20 789.50;
534.00 606.60 693.00 498.00 799.20 786.30 730.00 612.00 598.80 748.00 726.60 725.00 870.70 719.90 771.50 732.00 654.70 771.90 605.10 775.00 740.00 624.00 602.30 723.00 646.50 725.40 735.90 715.20 664.10 601.00 713.00 711.70 645.70 723.40 817.20;
603.10 703.10 770.70 523.40 849.20 807.80 785.90 714.80 633.20 772.70 737.90 793.00 1027.00 744.90 807.00 799.60 708.60 800.80 707.00 839.00 760.50 694.90 646.10 794.90 723.40 769.10 775.00 733.20 726.20 638.70 764.50 732.00 706.30 759.80 865.60;
668.80 748.40 807.40 594.50 878.90 834.40 838.30 734.00 691.80 807.40 791.80 849.20 1046.00 764.80 847.30 826.00 733.00 838.70 728.00 878.00 813.70 727.00 719.90 835.20 730.10 827.00 829.00 768.00 791.80 718.80 804.70 785.90 732.40 805.50 957.80;
723.00 777.70 838.70 631.30 905.50 881.60 907.00 769.50 728.90 851.20 813.30 899.60 1159.00 796.50 921.10 876.60 773.00 971.90 782.40 959.00 836.00 773.00 743.00 891.40 747.70 888.70 898.80 785.90 828.50 737.10 849.60 805.90 800.00 831.00 1021.00;
753.90 854.30 890.60 721.00 975.00 911.70 960.20 818.40 749.60 877.70 896.50 958.20 1191.00 820.30 957.40 919.50 789.10 1002.00 815.00 1024.00 861.70 808.60 799.20 957.40 773.00 1021.00 953.10 850.80 951.60 758.60 909.40 842.60 856.60 879.30 1073.00;
775.00 888.70 1018.00 771.10 1027.00 960.50 1023.00 952.30 804.70 895.70 955.50 1026.00 1210.00 879.30 1018.00 1027.00 866.40 1039.00 868.80 1040.00 903.90 845.30 831.30 1023.00 808.60 1068.00 992.20 887.50 1054.00 802.00 957.80 877.70 896.50 957.40 1111.00;
840.00 1025.00 1064.00 798.80 1092.00 1019.00 1066.00 1029.00 843.00 953.90 1022.00 1079.00 1242.00 956.60 1060.00 1079.00 957.80 1127.00 941.40 1063.00 957.00 903.50 874.20 1065.00 851.60 1093.00 1031.00 945.70 1093.00 831.60 1023.00 1091.00 958.20 1024.00 1198.00;
874.60 1075.00 1106.00 845.70 1119.00 1041.00 1093.00 1071.00 942.60 996.10 1080.00 1116.00 1367.64 989.50 1121.00 1098.00 1024.00 1139.00 962.10 1111.00 1022.00 1020.00 957.00 1110.00 891.80 1109.00 1108.00 956.60 1122.00 923.00 1077.00 1123.00 1024.00 1071.00 1251.95;
889.80 1095.00 1145.00 876.60 1200.00 1096.00 1111.00 1139.00 964.50 1036.00 1116.00 1181.00 1390.85 1031.00 1157.00 1117.00 1079.00 1161.00 1024.00 1147.00 1062.00 1069.00 1019.00 1182.00 977.70 1121.00 1145.00 1026.00 1131.00 986.00 1145.00 1147.00 1069.00 1111.00 1271.76;
944.50 1112.00 1190.00 971.50 1211.00 1102.00 1198.00 1164.00 1014.00 1068.00 1126.00 1211.00 1474.38 1089.00 1172.00 1162.00 1105.00 1193.00 1049.00 1177.00 1091.00 1109.00 1058.00 1209.00 1026.00 1145.00 1201.00 1068.00 1195.00 1034.00 1200.00 1158.00 1095.00 1153.00 1343.03;
1028.00 1125.00 1222.00 1027.00 1237.00 1153.00 1285.72 1205.00 1043.00 1110.00 1180.00 1321.10 1601.88 1120.00 1203.00 1202.00 1122.00 1211.00 1129.00 1208.00 1131.00 1150.00 1085.00 1312.42 1077.00 1174.00 1261.86 1086.00 1295.27 1091.00 1246.78 1211.00 1112.00 1192.00 1451.83;
1069.00 1198.00 1340.28 1075.00 1351.48 1166.00 1333.08 1259.00 1089.00 1165.00 1201.00 1372.93 1664.49 1195.00 1335.06 1297.41 1180.00 1339.03 1174.00 1359.30 1160.00 1199.00 1103.00 1364.33 1112.00 1201.00 1306.51 1097.00 1347.26 1146.00 1292.21 1276.05 1129.00 1224.00 1505.26;
1093.00 1253.96 1370.38 1114.00 1378.84 1201.00 1360.80 1286.60 1161.00 1196.00 1213.00 1403.26 1701.14 1201.00 1362.61 1323.79 1206.00 1365.76 1180.00 1388.30 1201.00 1220.00 1151.00 1394.71 1150.00 1319.38 1332.65 1125.00 1377.69 1188.00 1318.80 1303.05 1185.00 1296.61 1536.53;
1104.00 1266.35 1384.17 1132.00 1391.38 1251.76 1373.50 1299.25 1195.00 1214.00 1276.90 1417.17 1717.93 1218.85 1375.24 1335.88 1213.00 1378.02 1205.00 1401.58 1222.58 1244.26 1200.00 1408.64 1197.00 1331.40 1344.63 1168.00 1391.64 1197.00 1330.99 1315.43 1220.00 1309.14 1550.86;
1122.00 1286.61 1406.74 1193.00 1411.90 1268.57 1394.29 1319.95 1159.14 1247.98 1296.05 1439.92 1745.42 1236.58 1395.90 1355.66 1244.71 1398.07 1221.79 1423.33 1239.26 1264.34 1175.49 1431.43 1205.00 1351.08 1364.23 1202.00 1414.46 1178.72 1350.93 1335.68 1238.27 1329.64 1574.32;
1148.00 1315.89 1439.35 1223.00 1441.54 1292.85 1424.32 1349.85 1184.47 1273.51 1323.72 1472.78 1785.12 1262.19 1425.75 1384.23 1272.04 1427.03 1248.44 1454.74 1263.37 1293.36 1200.21 1464.34 1197.97 1379.49 1392.54 1224.70 1447.43 1204.52 1379.74 1364.93 1265.02 1359.26 1608.19;
1162.00 1331.65 1456.90 1181.20 1457.50 1305.93 1440.49 1365.95 1198.10 1287.26 1338.61 1490.48 1806.50 1275.98 1441.82 1399.62 1286.75 1442.63 1262.79 1471.65 1276.34 1308.98 1213.53 1482.07 1211.32 1394.80 1407.79 1237.88 1465.18 1218.40 1395.25 1380.68 1279.43 1375.20 1626.44;
1196.00 1369.94 1499.54 1218.12 1496.26 1337.68 1479.76 1405.05 1231.22 1320.65 1374.79 1533.46 1858.42 1309.47 1480.86 1436.98 1322.49 1480.51 1297.64 1512.72 1307.86 1346.93 1245.86 1525.11 1243.76 1431.96 1444.82 1269.87 1508.29 1252.13 1432.92 1418.93 1314.41 1413.93 1670.74;
1202.00 1376.69 1507.06 1224.64 1503.10 1343.29 1486.69 1411.95 1237.06 1326.54 1381.17 1541.04 1867.58 1315.38 1487.74 1443.58 1328.79 1487.19 1303.79 1519.97 1313.42 1353.62 1251.57 1532.71 1249.48 1438.52 1451.35 1275.52 1515.90 1258.08 1439.57 1425.68 1320.59 1420.76 1678.56
];
inputOpts.Marginals(1).Type = 'Gaussian';
inputOpts.Marginals(1).Parameters = data(:,1);
inputOpts.Marginals(2).Type = 'Gaussian';
inputOpts.Marginals(2).Parameters = data(:,2);
inputOpts.Marginals(3).Type = 'Gaussian';
inputOpts.Marginals(3).Parameters = data(:,3);
inputOpts.Marginals(4).Type = 'Gaussian';
inputOpts.Marginals(4).Parameters = data(:,4);
inputOpts.Marginals(5).Type = 'Gaussian';
inputOpts.Marginals(5).Parameters = data(:,5);
inputOpts.Marginals(6).Type = 'Gaussian';
inputOpts.Marginals(6).Parameters = data(:,6);
inputOpts.Marginals(7).Type = 'Gaussian';
inputOpts.Marginals(7).Parameters = data(:,7);
inputOpts.Marginals(8).Type = 'Gaussian';
inputOpts.Marginals(8).Parameters = data(:,8);
inputOpts.Marginals(9).Type = 'Gaussian';
inputOpts.Marginals(9).Parameters = data(:,9);
inputOpts.Marginals(10).Type = 'Gaussian';
inputOpts.Marginals(10).Parameters = data(:,10);
inputOpts.Marginals(11).Type = 'Gaussian';
inputOpts.Marginals(11).Parameters = data(:,11);
inputOpts.Marginals(12).Type = 'Gaussian';
inputOpts.Marginals(12).Parameters = data(:,12);
inputOpts.Marginals(13).Type = 'Gaussian';
inputOpts.Marginals(13).Parameters = data(:,13);
inputOpts.Marginals(14).Type = 'Gaussian';
inputOpts.Marginals(14).Parameters = data(:,14);
inputOpts.Marginals(15).Type = 'Gaussian';
inputOpts.Marginals(15).Parameters = data(:,15);
inputOpts.Marginals(16).Type = 'Gaussian';
inputOpts.Marginals(16).Parameters = data(:,16);
inputOpts.Marginals(17).Type = 'Gaussian';
inputOpts.Marginals(17).Parameters = data(:,17);
inputOpts.Marginals(18).Type = 'Gaussian';
inputOpts.Marginals(18).Parameters = data(:,18);
inputOpts.Marginals(19).Type = 'Gaussian';
inputOpts.Marginals(19).Parameters = data(:,19);
inputOpts.Marginals(20).Type = 'Gaussian';
inputOpts.Marginals(20).Parameters = data(:,20);
inputOpts.Marginals(21).Type = 'Gaussian';
inputOpts.Marginals(21).Parameters = data(:,21);
inputOpts.Marginals(22).Type = 'Gaussian';
inputOpts.Marginals(22).Parameters = data(:,22);
inputOpts.Marginals(23).Type = 'Gaussian';
inputOpts.Marginals(23).Parameters = data(:,23);
inputOpts.Marginals(24).Type = 'Gaussian';
inputOpts.Marginals(24).Parameters = data(:,24);
inputOpts.Marginals(25).Type = 'Gaussian';
inputOpts.Marginals(25).Parameters = data(:,25);
inputOpts.Marginals(26).Type = 'Gaussian';
inputOpts.Marginals(26).Parameters = data(:,26);
inputOpts.Marginals(27).Type = 'Gaussian';
inputOpts.Marginals(27).Parameters = data(:,27);
inputOpts.Marginals(28).Type = 'Gaussian';
inputOpts.Marginals(28).Parameters = data(:,28);
inputOpts.Marginals(29).Type = 'Gaussian';
inputOpts.Marginals(29).Parameters = data(:,29);
inputOpts.Marginals(30).Type = 'Gaussian';
inputOpts.Marginals(30).Parameters = data(:,30);
inputOpts.Marginals(31).Type = 'Gaussian';
inputOpts.Marginals(31).Parameters = data(:,31);
inputOpts.Marginals(32).Type = 'Gaussian';
inputOpts.Marginals(32).Parameters = data(:,32);
inputOpts.Marginals(33).Type = 'Gaussian';
inputOpts.Marginals(33).Parameters = data(:,33);
inputOpts.Marginals(34).Type = 'Gaussian';
inputOpts.Marginals(34).Parameters = data(:,34);
inputOpts.Marginals(35).Type = 'Gaussian';
inputOpts.Marginals(35).Parameters = data(:,35);
myInput =uq_createInput(inputOpts);
uq_display(myInput)
uq_print(myInput)
MetaOpts.ExpDesign.X = data;
MetaOpts.ExpDesign.Y = comp;
MetaOpts.Type = 'Metamodel';
MetaOpts.MetaType = 'PCE';
MetaOpts.Method = 'LARS';
MetaOpts.Degree = 1:2;
MetaOpts.TruncOptions.qNorm = 0.5:0.1:1;
MetaOpts.PolyTypes = {'arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary'
'arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary'
'arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary'
'arbitrary','arbitrary','arbitrary','arbitrary','arbitrary'
'arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary','arbitrary'};
myModel = uq_createModel(MetaOpts);
uq_print(myModel)
X = uq_getSample(28);
Y = uq_evalModel(myModel,data(:,:));
m = comp-Y;
figure
scatter(comp, Y)
hold on
plot([min(min(comp), min(Y)), max(max(comp), max(Y))], [ min(min(comp), min(Y)), max(max(comp), max(Y))])