RSM Surrogate model methods examples

RSM Surrogate model methods examples：

// Modulevar  RSM = require ("../Build/Release/rsm").RSM;// Module Object var rsm = new RSM;// Data Setvar data = [    [[37.5, 37.5], [803.78]],    [[230.101, 805.777], [815.73]],    [[447.21, 557.351], [813.86]],    [[520.839, 354.306], [813.8]],    [[999.713, 801.97], [819.05]],    [[77.3747, 506.866], [815.59]],    [[916.19, 211.731], [821.18]],    [[170.566, 918.607], [816.67]],    [[703.838, 975.497], [818.97]],    [[685.363, 189.721], [819.41]],    [[43.4607, 987.401], [817.23]],    [[181.685, 359.548], [816.67]],    [[516.911, 12.7494], [814]],    [[520.597, 354.396], [813.8]],    [[221.671, 554.194], [816.45]],    [[38.4605, 535.462], [817.28]],    [[628.684, 584.226], [812.84]],    [[419.248, 428.101], [813.87]],    [[358.279, 319.41], [814.88]],    [[163.466, 185.673], [817.64]],    [[147.272, 948.653], [816.74]],    [[852.848, 922.912], [819.05]],    [[669.124, 345.997], [819.6]],    [[853.498, 636.962], [819.05]],    [[369.065, 493.693], [814.87]],    [[719.035, 248.153], [819.14]],    [[572.292, 452.362], [813.12]],    [[740.381, 25.9373], [819.41]], ]; // Methodsfunction relation( x1, x2 ){      //var res = x1*x1*x1 +x2*x2*x2 + 2.0*x1*x1*x2;      var res =  x1*x1 +x2*x2  + 2.0*x1*x2 + x1*x1*x1 +x2*x2*x2;      // var res =  x1*x1 + 3.0*x2*x2  + 2.0*x1*x2 +100.0;       // var res =  100.0 + x1 + x2       return res;}function reformData() {    var len = data.length;    for( var i = 0; i < len; i++ )    {      var x1 = data[i][0][0];      var x2 = data[i][0][1];      var res = relation( x1, x2 );              data[i][1][0] = res ;    }    //console.log("data:", data );} reformData();var rsm =  new RSM();rsm.setRankNumber( 3 );rsm.setExperimentData( data );var RSMModel = rsm.getModelExpression()console.log("RSM Model:", RSMModel );var response =  relation( 740.381, 25.9373 );var result = rsm.evaluate( [740.381, 25.9373] );console.log("Response:", response );console.log("RSM:", result ); 

1-order RSM Test Results:

         Response Surface Method Report         ==============================     input variable number: 2  response variable number: 1               rank number: 1             require cross: 1   require standardization: 0  minimize data set number: 3     input data set number: 28       RSM experiment data result Experiment data set number: 3          Response average: 805.146    RSM prediction average: 805.146       Error value average: 0                  Error(%): 0              Testing data result    Testing data set number: 25          Response average: 1077.78    RSM prediction average: 1077.78       Error value average: 0.000113525                  Error(%): 1.05333e-05            Total data set statistics      Total data set number: 28          Response average: 1048.57    RSM prediction average: 1048.57       Error value average: 0.000101362                  Error(%): 9.66673e-06RSM Model: 100 + 1.000000*varX_0 + 1.000000*varX_1Response: 866.3183RSM: 866.3184566497803

2-order RSM Test Results:

         Response Surface Method Report         ==============================     input variable number: 2  response variable number: 1               rank number: 2             require cross: 1   require standardization: 0  minimize data set number: 6     input data set number: 28       RSM experiment data result Experiment data set number: 6          Response average: 1.73592e+06    RSM prediction average: 1.73592e+06       Error value average: 0.0208333                  Error(%): 1.20013e-06              Testing data result    Testing data set number: 22          Response average: 1.69869e+06    RSM prediction average: 1.69869e+06       Error value average: 0.214489                  Error(%): 1.26267e-05            Total data set statistics      Total data set number: 28          Response average: 1.70667e+06    RSM prediction average: 1.70667e+06       Error value average: 0.172991                  Error(%): 1.01362e-05RSM Model: 100.015 + 0.000105*varX_0 -0.000537*varX_1 + 1.000001*varX_0*varX_0 + 1.999999*varX_1*varX_0 + 3.000001*varX_1*varX_1Response: 588689.2239774701RSM: 588689.6932533896

3-order RSM Test Results:

         Response Surface Method Report         ==============================     input variable number: 2  response variable number: 1               rank number: 3             require cross: 1   require standardization: 0  minimize data set number: 10     input data set number: 28       RSM experiment data result Experiment data set number: 10          Response average: 5.80628e+08    RSM prediction average: 5.80628e+08       Error value average: 26.4                  Error(%): 4.5468e-06              Testing data result    Testing data set number: 18          Response average: 3.94894e+08    RSM prediction average: 3.94894e+08       Error value average: 111.278                  Error(%): 2.81792e-05            Total data set statistics      Total data set number: 28          Response average: 4.61227e+08    RSM prediction average: 4.61227e+08       Error value average: 80.9643                  Error(%): 1.75541e-05RSM Model: -85.2369 + 2.659389*varX_0 -0.107714*varX_1 + 0.994998*varX_0*varX_0 + 1.997093*varX_1*varX_0 + 1.000277*varX_1*varX_1 + 1.000003*varX_0*varX_0*varX_0 + 0.000001*varX_1*varX_0*varX_0 + 0.000002*varX_1*varX_1*varX_0 + 1.000000*varX_1*varX_1*varX_1Response: 406454922.00043535RSM: 406455147.7619492