public void test_NextItem_P(int numofItems, ModelNames.Models paramModel, ModelNames.CriterionTypes paramCriterion) { resultFlag = true; REngine.SetEnvironmentVariables(); REngine engineObj = REngine.GetInstance(); // Loading a library from R engineObj.Evaluate("library(catR)"); // Polytomous Items CharacterVector modelName = engineObj.CreateCharacterVector(new string[] { paramModel.EnumToString() }); engineObj.SetSymbol("modelName", modelName); DataFrame PolyItems = engineObj.Evaluate("PolyItems <- genPolyMatrix(" + numofItems + ", 5, model = modelName, same.nrCat = TRUE)").AsDataFrame(); engineObj.SetSymbol("PolyItems", PolyItems); // Adapting with the existing "CAT-Items" type (Wrapper) Console.WriteLine("*******************************************"); Console.WriteLine("Polytomous Items, Model : " + paramModel.EnumToString()); Console.WriteLine("*******************************************"); CATItems itemBank = new CATItems(PolyItems[0].Length, paramModel.EnumToString(), 5); Tuple <CATItems.ColumnNames, int>[] cols = itemBank.GetKeys(); for (int i = 0; i < cols.Length; i++) { itemBank.SetItemParamter(cols[i], PolyItems[i].Select(y => (double)y).ToArray()); } // Call "NextItem" function from R GenericVector r_NextItem = engineObj.Evaluate("r_NextItem <- nextItem(PolyItems, model = modelName, theta = 0, criterion = \"" + paramCriterion.ToString() + "\")").AsList(); // Selected item NumericVector item = r_NextItem[0].AsNumeric(); DataFrame par = r_NextItem[1].AsDataFrame(); // Value of "info" NumericVector thStart = r_NextItem[2].AsNumeric(); // Criterion CharacterVector startSelect = r_NextItem[3].AsCharacter(); // Call "NextItem" function from CS NextItemModel cs_NextItem = CatRLib.NextItem(itemBank, model: paramModel.EnumToString(), theta: 0, criterion: (int)paramCriterion); //Test passed for "MFI" if (item[0] != cs_NextItem.item) { resultFlag = false; } Assert.IsTrue(resultFlag); }
public static NextItemModel NextItem(CATItems itemBank, string model = null, double theta = 0, int[] Out = null, int[] x = null, int criterion = 5, /* MFI */ string method = "BM", string priorDist = "norm", double[] priorPar = null, double D = 1, double[] range = null, int[] parInt = null, int infoType = 2, /* observed */ int randomesque = 1, int rule = 1, /* Length */ double thr = 20, double?SETH = null, double AP = 1, int[] nAvailable = null, int maxItems = 50, CBControlList cbControl = null, string[] cbGroup = null) { NextItemModel result = CatRcs.NextItem.NextItem_Calc(itemBank, model, theta, Out, x, criterion, method, priorDist, priorPar, D, range, parInt, infoType, randomesque, rule, thr, SETH, AP, nAvailable, maxItems, cbControl, cbGroup); return(result); }
// Function for calculating Next Item public static NextItemModel NextItem_Calc(CATItems itemBank, string model = null, double theta = 0, int[] Out = null, int[] x = null, int criterion = 5, /* MFI */ string method = "BM", string priorDist = "norm", double[] priorPar = null, double D = 1, double[] range = null, int[] parInt = null, int infoType = 2, /* observed */ int randomesque = 1, int rule = 1, /* Length */ double thr = 20, double?SETH = null, double AP = 1, int[] nAvailable = null, int maxItems = 50, CBControlList cbControl = null, string[] cbGroup = null) { CATItems par = null; NextItemModel result = null; #region "Parameter Validation" if (priorPar == null || priorPar.Length < 2) { priorPar = new double[2]; priorPar[0] = 0; priorPar[1] = 1; } if (range == null || range.Length < 2) { range = new double[2]; range[0] = -4; range[1] = 4; } if (parInt == null || parInt.Length < 3) { parInt = new int[3]; parInt[0] = -4; parInt[1] = 4; parInt[2] = 33; } ModelNames.CriterionTypes?crit = null; switch (criterion) { case (int)ModelNames.CriterionTypes.bOpt: crit = ModelNames.CriterionTypes.bOpt; break; case (int)ModelNames.CriterionTypes.thOpt: crit = ModelNames.CriterionTypes.thOpt; break; case (int)ModelNames.CriterionTypes.KL: crit = ModelNames.CriterionTypes.KL; break; case (int)ModelNames.CriterionTypes.KLP: crit = ModelNames.CriterionTypes.KLP; break; case (int)ModelNames.CriterionTypes.MEI: crit = ModelNames.CriterionTypes.MEI; break; case (int)ModelNames.CriterionTypes.MEPV: crit = ModelNames.CriterionTypes.MEPV; break; case (int)ModelNames.CriterionTypes.MFI: crit = ModelNames.CriterionTypes.MFI; break; case (int)ModelNames.CriterionTypes.MLWI: crit = ModelNames.CriterionTypes.MLWI; break; case (int)ModelNames.CriterionTypes.MPWI: crit = ModelNames.CriterionTypes.MPWI; break; case (int)ModelNames.CriterionTypes.progressive: crit = ModelNames.CriterionTypes.progressive; break; case (int)ModelNames.CriterionTypes.proportional: crit = ModelNames.CriterionTypes.proportional; break; case (int)ModelNames.CriterionTypes.random: crit = ModelNames.CriterionTypes.random; break; } if (crit == null) { result = new NextItemModel(true, "Invalid 'criterion' name!"); return(result); } int mod = 0; ModelNames.Models modelEnum = ModelNames.StringToEnum(model); if (!String.IsNullOrEmpty(model)) { switch (modelEnum) { case ModelNames.Models.GRM: mod = 1; break; case ModelNames.Models.MGRM: mod = 2; break; case ModelNames.Models.PCM: mod = 3; break; case ModelNames.Models.GPCM: mod = 4; break; case ModelNames.Models.RSM: mod = 5; break; case ModelNames.Models.NRM: mod = 6; break; } if (mod == 0) { result = new NextItemModel(true, "Invalid 'model' type!"); return(result); } } #endregion int[] OUT = null; double[] empProp = null; int nrGroup = 0; double[] thProp = null; #region Handling "cbControl" Parameter if (cbControl == null) { OUT = Out; } else { if (cbGroup == null) { result = new NextItemModel(true, "'cbGroup' argument must be provided for content balancing!"); return(result); } if (RowColumn.Sum(cbControl.Props) != 1) { double temp_sum = RowColumn.Sum(cbControl.Props); for (int i = 0; i < cbControl.Props.Length; i++) { cbControl.Props[i] = cbControl.Props[i] / temp_sum; } } nrGroup = cbControl.Names.Length; empProp = new double[nrGroup]; if (Out == null) { empProp = CatRcs.Utils.CommonHelper.Replicate(new double[] { 0 }, nrGroup); } else { string[] temp_grp = new string[Out.Length]; for (int i = 0; i < Out.Length; i++) { temp_grp[i] = cbGroup[Out[i]]; } for (int j = 0; j < nrGroup; j++) { string[] values = temp_grp.Where(m => m == cbControl.Names[j]).ToArray(); if (values != null && values.Length > 0) { empProp[j] = values.Length; } else { empProp[j] = 0; } } empProp = empProp.Select(n => n / CatRcs.Utils.RowColumn.Sum(empProp)).ToArray(); // Functional Testing needed !! } thProp = cbControl.Props; List <int> indGroup = new List <int>(); int selGroup = 0; // Array of Group Numbers ex: "1 2 3 4 5" for nrGroup = 5. int[] GrpIndValues = new int[nrGroup]; GrpIndValues = GrpIndValues.Select((a, i) => i + 1).ToArray(); if (empProp.Min() == 0) { for (int m = 0; m < empProp.Length; m++) { if (empProp[m] == 0) { indGroup.Add(GrpIndValues[m]); } } } else { double[] tempProp = thProp.Select((a, i) => a - empProp[i]).ToArray(); for (int n = 0; n < tempProp.Length; n++) { if (tempProp[n] == tempProp.Max()) { indGroup.Add(GrpIndValues[n]); } } } if (indGroup.Count == 1) { selGroup = indGroup[0]; } else { selGroup = CatRcs.Utils.RandomNumberHandler.Sample(indGroup.ToArray(), 1, false)[0]; } // Populating the OUT array string[] tempGrp = cbGroup.Where(c => c != cbControl.Names[selGroup]).ToArray(); OUT = tempGrp.Select((a, i) => i + 1).ToArray(); OUT = Out.Concat(OUT).ToArray(); OUT = CatRcs.Utils.CommonHelper.Unique(OUT); } #endregion #region Handling "nAvalilable Parameter" if (nAvailable != null) { List <int> ind_temp = new List <int>(); for (int k = 0; k < nAvailable.Length; k++) { if (nAvailable[k] == 0) { ind_temp.Add(k); } } OUT = OUT.Concat(ind_temp.ToArray()).ToArray(); OUT = CatRcs.Utils.CommonHelper.Unique(OUT); } #endregion int select = 0; #region Criterion Type "MFI" if (crit == ModelNames.CriterionTypes.MFI) { int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a] - 1] = 0; } } double[] info = Ii.Ii_Calc(theta, itemBank, model, D).Ii; double[] ranks = CatRcs.Utils.CommonHelper.Rank(info).Select(m => m).ToArray(); int nrIt = new int[] { randomesque, (int)CatRcs.Utils.RowColumn.Sum(items) }.Min(); List <double> tempRanks = new List <double>(); for (int j = 0; j < items.Length; j++) { if (items[j] == 1) { tempRanks.Add(ranks[j]); } } tempRanks = tempRanks.OrderByDescending(n => n).ToList(); double[] keepRank = tempRanks.GetRange(0, nrIt).ToArray(); List <int> keep = new List <int>(); if (ranks.Length == items.Length) { for (int m = 0; m < keepRank.Length; m++) { for (int n = 0; n < ranks.Length; n++) { if ((items[n] == 1) && (ranks[n] == keepRank[m])) { keep.Add(n + 1); } } } } if (keep.Count == 1) { select = keep[0]; } else { select = CatRcs.Utils.RandomNumberHandler.Sample(keep.ToArray(), 1, false)[0]; } result = new NextItemModel(select, itemBank.FindItem(select), info[select - 1], criterion, randomesque); } #endregion #region Criterion Type "bOpt" if (crit == ModelNames.CriterionTypes.bOpt) { if (string.IsNullOrEmpty(model)) { int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a] - 1] = 0; } } double[] distance = itemBank.GetItemParamter(CATItems.ColumnNames.b).Select(a => Math.Abs(a - theta)).ToArray(); double[] ranks = CatRcs.Utils.CommonHelper.Rank(distance).Select(m => m).ToArray(); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { ranks[OUT[a] - 1] = -1; } } int nrIt = new int[] { randomesque, (int)CatRcs.Utils.RowColumn.Sum(items) }.Min(); List <double> tempRanks = new List <double>(); for (int j = 0; j < items.Length; j++) { if (items[j] == 1) { tempRanks.Add(ranks[j]); } } tempRanks = tempRanks.OrderBy(n => n).ToList(); double[] keepRank = tempRanks.GetRange(0, nrIt).ToArray(); keepRank = keepRank.Distinct().ToArray(); List <int> keep = new List <int>(); if (ranks.Length == items.Length) { for (int m = 0; m < keepRank.Length; m++) { for (int n = 0; n < ranks.Length; n++) { if ((items[n] == 1) && (ranks[n] == keepRank[m])) { keep.Add(n + 1); } } } } if (keep.Count == 1) { select = keep[0]; } else { select = CatRcs.Utils.RandomNumberHandler.Sample(keep.ToArray(), 1, false)[0]; } result = new NextItemModel(select, itemBank.FindItem(select), distance[select - 1], criterion, randomesque); } else { result = new NextItemModel(true, "bOpt's rule cannot be considered with polytomous items!"); return(result); } } #endregion #region Criterion Type "MLWI" OR "MPWI" if (crit == ModelNames.CriterionTypes.MLWI || crit == ModelNames.CriterionTypes.MPWI) { if (Out != null) { if (Out.Length == 1) { par = itemBank.FindItem(Out[0]); } else { par = itemBank.FindItem(Out); } } else { result = new NextItemModel(true, "Out parameter can't be empty!"); return(result); } int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a] - 1] = 0; } } double[] likInfo = CatRcs.Utils.CommonHelper.Replicate(new double[] { 0 }, itemBank.NumOfItems); int mwiType = 0; if (criterion == (int)ModelNames.CriterionTypes.MLWI) { mwiType = (int)ModelNames.MWI_Type.MLWI; } if (criterion == (int)ModelNames.CriterionTypes.MPWI) { mwiType = (int)ModelNames.MWI_Type.MPWI; } if (x != null) { for (int j = 0; j < itemBank.NumOfItems; j++) { if (items[j] == 1) { likInfo[j] = MWI.MWI_Calc(itemBank, j + 1, x, par, model, mwiType, priorPar, D, priorDist, parInt[0], parInt[1], parInt[2]); /* item number is always 1 greater than the index */ } } } int nrIt = new int[] { randomesque, (int)CatRcs.Utils.RowColumn.Sum(items) }.Min(); double likVal = likInfo.ToList().OrderByDescending(n => n).ToArray()[nrIt - 1]; // First value with index 0 List <int> keep = new List <int>(); for (int k = 0; k < items.Length; k++) { if (likInfo[k] >= likVal) { keep.Add(k + 1); // Converting from index to item number } } if (keep.Count == 1) { select = keep[0]; } else { select = CatRcs.Utils.RandomNumberHandler.Sample(keep.ToArray(), 1, false)[0]; } result = new NextItemModel(select, itemBank.FindItem(select), likInfo[select - 1], criterion, randomesque); } #endregion #region Criterion Type "KL" OR "KLP" if (crit == ModelNames.CriterionTypes.KL || crit == ModelNames.CriterionTypes.KLP) { if (Out != null) { if (Out.Length == 1) { par = itemBank.FindItem(Out[0]); } else { par = itemBank.FindItem(Out); } } else { result = new NextItemModel(true, "Out parameter can't be empty!"); return(result); } int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a] - 1] = 0; } } double[] klValue = CatRcs.Utils.CommonHelper.Replicate(new double[] { 0 }, itemBank.NumOfItems); double[] X = CatRcs.Utils.CommonHelper.Sequence(parInt[0], parInt[1], parInt[2]); #region "Function 'L' " Func <double, int[], CATItems, double> L = (th, r, param) => { double res = 0; var temp_1 = Pi.Pi_Calc(th, param, model, D).Pi.Select((p, i) => Math.Pow(p, r[i])).ToArray(); var temp_2 = Pi.Pi_Calc(th, param, model, D).Pi.Select((p, i) => Math.Pow(1 - p, 1 - r[i])).ToArray(); if (temp_1.Length == temp_2.Length) { var temp_3 = temp_1.Select((p, i) => p * temp_2[i]).ToArray(); res = temp_3.Aggregate((acc, val) => acc * val); } return(res); }; #endregion #region "Function 'LL' " Func <double, int[], CATItems, double> LL = (th, r, param) => { double res = 0; if (param.NumOfItems == 0) { res = 1; } else { double[,] prob = Pi.Pi_Poly_Calc(th, param, model, D).Pi; for (int i = 0; i < r.Length; i++) { res = res * prob[i, r[i] + 1]; } } return(res); }; #endregion double[] LF = null; if (string.IsNullOrEmpty(model)) { LF = X.Select(p => L(p, x, par)).ToArray(); } else { LF = X.Select(p => LL(p, x, par)).ToArray(); } int kType = 0; if (criterion == (int)ModelNames.CriterionTypes.KL) { kType = (int)ModelNames.KLTypes.KL; } if (criterion == (int)ModelNames.CriterionTypes.KLP) { kType = (int)ModelNames.KLTypes.KLP; } for (int j = 0; j < itemBank.NumOfItems; j++) { if (items[j] == 1) { klValue[j] = KL.KL_Calc(itemBank, j + 1, x, par, model, theta, priorPar, X, LF, kType, D, priorDist, parInt[0], parInt[1], parInt[2]); /* item number is always 1 greater than the index */ } } int nrIt = new int[] { randomesque, (int)CatRcs.Utils.RowColumn.Sum(items) }.Min(); double klVal = klValue.ToList().OrderByDescending(n => n).ToArray()[nrIt - 1]; List <int> keep = new List <int>(); for (int k = 0; k < items.Length; k++) { if (klValue[k] >= klVal) { keep.Add(k + 1); } } if (keep.Count == 1) { select = keep[0]; } else { select = CatRcs.Utils.RandomNumberHandler.Sample(keep.ToArray(), 1, false)[0]; } result = new NextItemModel(select, itemBank.FindItem(select), klValue[select - 1], criterion, randomesque); } #endregion #region Criterion Type "MEI" if (crit == ModelNames.CriterionTypes.MEI) { int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a] - 1] = 0; } } double[] infos = CatRcs.Utils.CommonHelper.Replicate(new double[] { 0 }, itemBank.NumOfItems); for (int j = 0; j < items.Length; j++) { if (items[j] > 0) { infos[j] = MEI.MEI_Calc(itemBank, j + 1, x, theta, itemBank.FindItem(Out), model, method, D, priorPar, priorDist, range, parInt, infoType); } } int nrIt = new int[] { randomesque, (int)CatRcs.Utils.RowColumn.Sum(items) }.Min(); double infoVal = infos.ToList().OrderByDescending(n => n).ToArray()[nrIt - 1]; List <int> keep = new List <int>(); for (int k = 0; k < items.Length; k++) { if (infos[k] >= infoVal) { keep.Add(k + 1); } } if (keep.Count == 1) { select = keep[0]; } else { select = CatRcs.Utils.RandomNumberHandler.Sample(keep.ToArray(), 1, false)[0]; } result = new NextItemModel(select, itemBank.FindItem(select), infos[select - 1], criterion, randomesque); } #endregion #region Criterion Type "MEPV" if (crit == ModelNames.CriterionTypes.MEPV) { int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a] - 1] = 0; } } double[] epvs = CatRcs.Utils.CommonHelper.Replicate(new double[] { 1000 }, itemBank.NumOfItems); for (int j = 0; j < items.Length; j++) { if (items[j] > 0) { epvs[j] = EPV.EPV_Calc(itemBank, j + 1, x, theta, itemBank.FindItem(Out), model, priorPar, parInt, D, priorDist); } } var tempVal = new int[] { randomesque, items.Sum() }.Min(); double epVal = epvs.ToList().OrderBy(n => n).ToArray()[tempVal - 1]; List <int> keep = new List <int>(); for (int k = 0; k < itemBank.NumOfItems; k++) { if (epvs[k] <= epVal) { keep.Add(k + 1); } } if (keep.Count == 1) { select = keep[0]; } else { select = CatRcs.Utils.RandomNumberHandler.Sample(keep.ToArray(), 1, false)[0]; } result = new NextItemModel(select, itemBank.FindItem(select), epvs[select - 1], criterion, randomesque); } #endregion #region Criterion Type "random" if (crit == ModelNames.CriterionTypes.random) { int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a]] = 0; } } int gen = Convert.ToInt32(Convert.ToInt32(CatRcs.Utils.RandomNumberHandler.Runif(1, 0, 1)[0]) * items.Sum()) + 1; List <int> indexs = new List <int>(); for (int k = 0; k < itemBank.NumOfItems; k++) { if (items[k] > 0) { indexs.Add(k + 1); } } select = indexs.ElementAt(gen); result = new NextItemModel(select, itemBank.FindItem(select), double.NaN, criterion, randomesque); } #endregion #region Criterion Type "progressive" if (crit == ModelNames.CriterionTypes.progressive) { int item_administered = Out.Length; int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a] - 1] = 0; } } double[] info = Ii.Ii_Calc(theta, itemBank, model, D).Ii; List <double> tempItems = new List <double>(); for (int j = 0; j < items.Length; j++) { if (items[j] == 1) { tempItems.Add(info[j]); } } double wq = 0; double itemMaxInfo = tempItems.Max(); double[] randomValues = CatRcs.Utils.RandomNumberHandler.Runif(items.Length, 0, itemMaxInfo); if (rule == (int)ModelNames.RuleType.Precision) { double infostop = Math.Pow((1 / thr), 2); double cuminfo = Math.Pow(double.Parse((1 / SETH).ToString()), 2); if (item_administered > 0) { wq = Math.Pow(new double[] { cuminfo / infostop, item_administered / (maxItems - 1) }.Max(), AP); } } if (rule == (int)ModelNames.RuleType.Length) { if (item_administered > 0) { List <double> tempNum = new List <double>(); for (int i = 1; i <= item_administered; i++) { tempNum.Add(Math.Pow(i, AP)); } double numerador = tempNum.Sum(); List <double> tempDenom = new List <double>(); for (int j = 1; j <= thr - 1; j++) { tempDenom.Add(Math.Pow(j, AP)); } double denominador = tempDenom.Sum(); wq = numerador / denominador; } } double[] funcPR = info.Select((d, i) => d * wq + randomValues[i] * (1 - wq)).ToArray(); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { funcPR[OUT[a] - 1] = 0; } } List <int> keep = new List <int>(); for (int k = 0; k < funcPR.Length; k++) { if (funcPR[k] == funcPR.Max()) { keep.Add(k + 1); } } if (keep.Count == 1) { select = keep[0]; } else { select = CatRcs.Utils.RandomNumberHandler.Sample(keep.ToArray(), 1, false)[0]; } result = new NextItemModel(select, itemBank.FindItem(select), info[select - 1], criterion, randomesque); } #endregion #region Criterion Type "proportional" if (crit == ModelNames.CriterionTypes.proportional) { int item_administered = Out.Length; int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a] - 1] = 0; } } double wq = 0; if (rule == (int)ModelNames.RuleType.Precision) { double infostop = Math.Pow((1 / thr), 2); double cuminfo = Math.Pow(double.Parse((1 / SETH).ToString()), 2); if (item_administered > 0) { wq = infostop * Math.Pow(new double[] { cuminfo / infostop, item_administered / (maxItems - 1) }.Max(), AP); } } if (rule == (int)ModelNames.RuleType.Length) { if (item_administered > 0) { List <double> tempNum = new List <double>(); for (int i = 1; i <= item_administered; i++) { tempNum.Add(Math.Pow(i, AP)); } double numerador = tempNum.Sum(); List <double> tempDenom = new List <double>(); for (int j = 1; j <= thr - 1; j++) { tempDenom.Add(Math.Pow(j, AP)); } double denominador = tempDenom.Sum(); wq = thr * numerador / denominador; } } double[] info = Ii.Ii_Calc(theta, itemBank, model, D).Ii; double[] infoPR = info.Select(s => Math.Pow(s, wq)).ToArray(); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { infoPR[OUT[a] - 1] = 0; } } List <double> tempInfoPR = new List <double>(); for (int k = 0; k < items.Length; k++) { if (items[k] == 1) { tempInfoPR.Add(infoPR[k]); } } double totalInfoPR = tempInfoPR.Sum(); double[] probSelect = infoPR.Select(m => m / totalInfoPR).ToArray(); int[] selectItems = items.Select((n, i) => i + 1).ToArray(); select = CatRcs.Utils.RandomNumberHandler.Sample(selectItems, 1, false)[0]; // prob parameter will be added after Sample functiom modification result = new NextItemModel(select, itemBank.FindItem(select), info[select], criterion, randomesque); } #endregion #region Criterion Type "thOpt" if (crit == ModelNames.CriterionTypes.thOpt) { if (string.IsNullOrEmpty(model)) // Only for Dichotomous Items { int[] items = CatRcs.Utils.CommonHelper.Replicate(new int[] { 1 }, itemBank.NumOfItems); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { items[OUT[a] - 1] = 0; } } double[] u = itemBank.GetItemParamter(CATItems.ColumnNames.c).Select((s, i) => - 0.75 + (s + itemBank.GetItemParamter(CATItems.ColumnNames.d)[i] + -2 * s * itemBank.GetItemParamter(CATItems.ColumnNames.d)[i]) / 2).ToArray(); double[] v = itemBank.GetItemParamter(CATItems.ColumnNames.c).Select((n, i) => (n + itemBank.GetItemParamter(CATItems.ColumnNames.d)[i] - 1) / 4).ToArray(); double[] xstar = u.Select((m, i) => 2 * Math.Sqrt(-m / 3) * Math.Cos(Math.Acos(-v[i] * Math.Sqrt(-27 / Math.Pow(m, 3)) / 2) / 3 + 4 * (Math.PI / 3)) + 0.5).ToArray(); double[] thstar = itemBank.GetItemParamter(CATItems.ColumnNames.b).Select((o, i) => o + Math.Log((xstar[i] - itemBank.GetItemParamter(CATItems.ColumnNames.c)[i]) / (itemBank.GetItemParamter(CATItems.ColumnNames.d)[i] - xstar[i])) / (D * itemBank.GetItemParamter(CATItems.ColumnNames.a)[i])).ToArray(); double[] distance = thstar.Select(p => Math.Abs(p - theta)).ToArray(); double[] ranks = CatRcs.Utils.CommonHelper.Rank(distance).Select(m => m).ToArray(); if (OUT != null) { for (int a = 0; a < OUT.Length; a++) { ranks[OUT[a] - 1] = -1; } } int nrIt = new int[] { randomesque, (int)CatRcs.Utils.RowColumn.Sum(items) }.Min(); List <double> tempRanks = new List <double>(); for (int j = 0; j < items.Length; j++) { if (items[j] == 1) { tempRanks.Add(ranks[j]); } } tempRanks = tempRanks.OrderBy(n => n).ToList(); double[] keepRank = tempRanks.GetRange(0, nrIt).ToArray(); keepRank = keepRank.Distinct().ToArray(); List <int> keep = new List <int>(); if (ranks.Length == items.Length) { for (int m = 0; m < keepRank.Length; m++) { for (int n = 0; n < ranks.Length; n++) { if ((items[n] == 1) && (ranks[n] == keepRank[m])) { keep.Add(n + 1); } } } } if (keep.Count == 1) { select = keep[0]; } else { select = CatRcs.Utils.RandomNumberHandler.Sample(keep.ToArray(), 1, false)[0]; } result = new NextItemModel(select, itemBank.FindItem(select), distance[select - 1], criterion, randomesque); } else { result = new NextItemModel(true, "thOpt's rule cannot be considered with polytomous items!"); return(result); } } #endregion #region Handling "cbControl" Parameter if (cbControl == null) { if (result != null) { result.prior_prop = null; result.post_prop = null; result.cb_prop = null; } } else { result.prior_prop = empProp; double[] postProp = new double[nrGroup]; string[] temp_grp = new string[Out.Length + 1]; for (int i = 0; i < temp_grp.Length; i++) { if (i == 0) { temp_grp[i] = cbGroup[result.item]; } temp_grp[i] = cbGroup[Out[i]]; } for (int j = 0; j < postProp.Length; j++) { string[] values = temp_grp.Where(m => m == cbControl.Names[j]).ToArray(); if (values != null && values.Length > 0) { postProp[j] = values.Length; } else { postProp[j] = 0; } } result.post_prop = postProp.Select(n => n / CatRcs.Utils.RowColumn.Sum(postProp)).ToArray(); result.cb_prop = thProp; } #endregion return(result); }
public void test_NextItem_D(int numofItems, ModelNames.CriterionTypes paramCriterion) { resultFlag = true; REngine.SetEnvironmentVariables(); REngine engineObj = REngine.GetInstance(); // Loading a library from R engineObj.Evaluate("library(catR)"); // Dichotomous Items DataFrame DichoItems = engineObj.Evaluate("DichoItems <- genDichoMatrix(items = " + numofItems + ")").AsDataFrame(); engineObj.SetSymbol("DichoItems", DichoItems); // Adapting with the existing "CAT-Items" type (Wrapper) CATItems itemBank = new CATItems(DichoItems[0].Length, false); // New Dictionary itemBank.SetItemParamter(CATItems.ColumnNames.a, DichoItems[0].Select(y => (double)y).ToArray()); itemBank.SetItemParamter(CATItems.ColumnNames.b, DichoItems[1].Select(y => (double)y).ToArray()); itemBank.SetItemParamter(CATItems.ColumnNames.c, DichoItems[2].Select(y => (double)y).ToArray()); itemBank.SetItemParamter(CATItems.ColumnNames.d, DichoItems[3].Select(y => (double)y).ToArray()); // Call "thetaEst" function //NumericVector r_th = engineObj.Evaluate("r_th <- thetaEst(rbind(DichoItems[3,], DichoItems[13,]), c(0, 1), method = \"WL\")").AsNumeric(); //engineObj.SetSymbol("r_th", r_th); //double cs_th = CatRLib.ThetaEst(itemBank.FindItem(new int[] { 3, 13 }), x: new int[] { 0, 1 }, model: "", method: ModelNames.EstimaatorMethods.WL.EnumToString()); // Call "NextItem" function from R with parameter "out" GenericVector r_NextItem = engineObj.Evaluate("r_NextItem <- nextItem(DichoItems, theta = 0, out = c(3, 13), criterion = \"" + paramCriterion.ToString() + "\")").AsList(); // Call "NextItem" function from R with response pattern "x" //GenericVector r_NextItem = engineObj.Evaluate("r_NextItem <- nextItem(DichoItems, x = c(0, 1), out = c(3, 13), theta = r_th, criterion = \"" + paramCriterion.ToString() + "\")").AsList(); // Selected item NumericVector item = r_NextItem[0].AsNumeric(); DataFrame par = r_NextItem[1].AsDataFrame(); // Item parameter CATItems parItems = new CATItems(par[0].Length, false); parItems.SetItemParamter(CATItems.ColumnNames.a, par[0].Select(y => (double)y).ToArray()); parItems.SetItemParamter(CATItems.ColumnNames.b, par[1].Select(y => (double)y).ToArray()); parItems.SetItemParamter(CATItems.ColumnNames.c, par[2].Select(y => (double)y).ToArray()); parItems.SetItemParamter(CATItems.ColumnNames.d, par[3].Select(y => (double)y).ToArray()); // Value of "info" NumericVector info = r_NextItem[2].AsNumeric(); // Criterion CharacterVector startSelect = r_NextItem[3].AsCharacter(); // Call "NextItem" function from CS with parameter "out" NextItemModel cs_NextItem = CatRLib.NextItem(itemBank, theta: 0, Out: new int[] { 3, 13 }, criterion: (int)paramCriterion); // Call "NextItem" function from CS with response pattern "x" //NextItemModel cs_NextItem = CatRLib.NextItem(itemBank, theta: cs_th, Out: new int[] { 3, 13 }, x: new int[] { 0, 1 }, criterion: (int)paramCriterion); // Checking item & other values if (item[0] != cs_NextItem.item) { resultFlag = false; } if (decimal.Round((decimal)info[0], 3) != decimal.Round((decimal)cs_NextItem.info, 3)) { resultFlag = false; } Assert.IsTrue(resultFlag); }