public void testKL_D(int NumOfItems)
{
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());
// Dichotomous Items
DataFrame it_given_R = engineObj.Evaluate("it_given_R <- DichoItems[c(4, 8),]").AsDataFrame();
engineObj.SetSymbol("it_given_R", it_given_R);
CATItems it_given = itemBank.FindItem(new int[] { 4, 8 });
//Creation of a response pattern
NumericVector x_val = engineObj.Evaluate("x_val <- c(0, 1)").AsNumeric();
engineObj.SetSymbol("x_val", x_val);
int[] x = new int[] { 0, 1 };
// Call "thetaEST" function from R, method = \"ML\"
NumericVector Theta = engineObj.Evaluate("Theta <- thetaEst(it_given_R, x_val, method = \"ML\")").AsNumeric();
// Call "KL" function from R
NumericVector r_KL = engineObj.Evaluate("r_KL <- KL(DichoItems, 1, x_val, it_given_R, theta = Theta)").AsNumeric();
// Call "thetaEST" function from CS
double th = CatRLib.ThetaEst(it_given, x, "", method: "ML");
// Call "KL" function from CS
double cs_KL = CatRLib.KL(itemBank, 1, x, it_given, "", theta: th);
if (r_KL[0] - cs_KL > testEpsilon)
{
resultFlag = false;
}
Assert.IsTrue(resultFlag);
}
public void test_MWI_D(int NumOfItems)
{
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());
// Dichotomous Items
DataFrame it_given_R = engineObj.Evaluate("it_given_R <- DichoItems[c(4, 8),]").AsDataFrame();
engineObj.SetSymbol("it_given_R", it_given_R);
//Creation of a response pattern for theta value
engineObj.Evaluate("set.seed(1)");
NumericVector x_val = engineObj.Evaluate("x_val <- genPattern(0, it_given_R)").AsNumeric();
engineObj.SetSymbol("x_val", x_val);
int[] x = x_val.Select(y => (int)y).ToArray();
CATItems it_given = itemBank.FindItem(new int[] { 4, 8 });
// Call "MWI" function from R
NumericVector r_mwi = engineObj.Evaluate("r_mei <- MWI(DichoItems, 1, x_val, it_given_R, type = \"MPWI\")").AsNumeric();
// Call "MWI" function from CatRCS
double cs_mwi = CatRLib.MWI(itemBank, 1, x, it_given, "", 2);
if (r_mwi[0] - cs_mwi > testEpsilon)
{
resultFlag = false;
}
Assert.IsTrue(resultFlag);
}
public static double MEI_Calc(CATItems itemBank, int item, int[] x, double theta, CATItems it_given, string model = null, string method = "BM", double D = 1, double[] priorPar = null, string priorDist = "norm", double[] range = null, int[] parInt = null, int infoType = 2 /* Observed */)
{
double res = 0;
if (priorPar == null || priorPar.Length < 2)
{
priorPar = new double[2];
priorPar[0] = 0;
priorPar[1] = 1;
}
if (parInt == null || parInt.Length < 3)
{
parInt = new int[3];
parInt[0] = -4;
parInt[1] = 4;
parInt[2] = 33;
}
if (range == null || range.Length < 2)
{
range = new double[2];
range[0] = -4;
range[1] = 4;
}
if ((infoType == (int)ModelNames.InfoType.Observed) || (infoType == (int)ModelNames.InfoType.Fisher)) // type is either "Observed" or "Fisher"
{
double th = theta;
// Finding desired "item" from itembank & Added to given items "it_given".
// Instead of "itj", "it_given" will be used.
itemBank.AddItem(it_given, item);
if (String.IsNullOrEmpty(model)) // Dichotomous Items
{
List <int> temp_x = x.ToList();
temp_x.Add(0);
double th0 = CatRcs.ThetaEST.ThetaEst_Calc(it_given, temp_x.ToArray(), D: D, model: model, method: method, priorPar: priorPar, priorDist: priorDist, parInt: parInt, range: range);
temp_x = x.ToList();
temp_x.Add(1);
double th1 = CatRcs.ThetaEST.ThetaEst_Calc(it_given, temp_x.ToArray(), D: D, model: model, method: method, priorPar: priorPar, priorDist: priorDist, parInt: parInt, range: range);
double p1 = CatRcs.Pi.Pi_Calc(th, itemBank.FindItem(item), model, D).Pi.First();
double p0 = 1 - p1;
double Ij0 = 0, Ij1 = 0;
if ((infoType == (int)ModelNames.InfoType.Fisher))
{
Ij0 = CatRcs.Ii.Ii_Calc(th0, itemBank.FindItem(item), model, D).Ii.First();
Ij1 = CatRcs.Ii.Ii_Calc(th1, itemBank.FindItem(item), model, D).Ii.First();
}
else
{
Ij0 = CatRcs.OIi.OIi_Calc(th0, itemBank.FindItem(item), new int[] { 0 }, model, D).OIi.First();
Ij1 = CatRcs.OIi.OIi_Calc(th1, itemBank.FindItem(item), new int[] { 1 }, model, D).OIi.First();
}
res = (p0 * Ij0) + (p1 * Ij1);
}
else // Polytomous Items
{
double[,] probs = Pi.Pi_Poly_Calc(theta, itemBank.FindItem(item), model, D).Pi;
double[] Ii_new = new double[probs.GetLength(1)];
for (int j = 0; j < probs.GetLength(1); j++) // column iteration
{
List <int> temp_x = x.ToList();
temp_x.Add(j);
double th_new = CatRcs.ThetaEST.ThetaEst_Calc(it_given, temp_x.ToArray(), D: D, model: model, method: method, priorPar: priorPar, priorDist: priorDist, parInt: parInt, range: range);
if ((infoType == (int)ModelNames.InfoType.Fisher))
{
Ii_new[j] = CatRcs.Ii.Ii_Calc(th_new, itemBank.FindItem(item), model, D).Ii.First();
}
else
{
Ii_new[j] = CatRcs.OIi.OIi_Calc(th_new, itemBank.FindItem(item), new int[] { j }, model, D).OIi.First();
}
}
res = CatRcs.Utils.RowColumn.Sum(Ii_new.ToList().Select((p, i) => p * probs[0, i]).ToArray());
}
}
else
{
return(res);
}
return(res);
}
public void testKL_P(int NumOfItems, ModelNames.Models paramModel)
{
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());
}
DataFrame it_given_R = engineObj.Evaluate("it_given_R <- PolyItems[c(4, 8),]").AsDataFrame();
engineObj.SetSymbol("it_given_R", it_given_R);
CATItems it_given = itemBank.FindItem(new int[] { 4, 8 });
//Creation of a response pattern for theta value
engineObj.Evaluate("set.seed(1)");
NumericVector x_val = engineObj.Evaluate("x_val <- genPattern(0, it_given_R, model = modelName)").AsNumeric();
engineObj.SetSymbol("x_val", x_val);
int[] x = x_val.Select(y => (int)y).ToArray();
// Call "thetaEST" function from R, method = \"ML\"
NumericVector Theta = engineObj.Evaluate("Theta <- thetaEst(it_given_R, x_val, method = \"ML\", model = modelName)").AsNumeric();
// Call "KL" function from R
NumericVector r_KL = engineObj.Evaluate("r_KL <- KL(PolyItems, 1, x_val, it_given_R, theta = Theta, model = modelName)").AsNumeric();
// Call "thetaEST" function from CS
double th_est = CatRLib.ThetaEst(it_given, x, paramModel.EnumToString(), method: "ML");
// Call "KL" function from CS
double cs_KL = CatRLib.KL(itemBank, 1, x, it_given, paramModel.EnumToString(), theta: th_est);
if (r_KL[0] - cs_KL > testEpsilon)
{
resultFlag = false;
}
Assert.IsTrue(resultFlag);
}
// Optimization needed !!!
public static double KL_Calc(CATItems itemBank, int item, int[] x, CATItems it_given, string model = null, double theta = 0, double[] priorPar = null, double[] X = null, double[] lik = null, int type = 1, double D = 1, string priorDist = "norm", double lower = -4, double upper = 4, int nqp = 33)
{
double res = 0;
if ((type == (int)ModelNames.KLTypes.KL) || (type == (int)ModelNames.KLTypes.KLP)) // type is either "KL" or "KLP"
{
if (X != null && lik != null)
{
if (X.Length > 0 && lik.Length > 0)
{
if (X.Length != lik.Length)
{
return(res); // Error handling
}
}
}
if (theta == 0)
{
theta = CatRcs.ThetaEST.ThetaEst_Calc(it_given, x, D: D, model: model, method: CatRcs.Models.ModelNames.EstimaatorMethods.ML.EnumToString());
}
double[] KLF = new double[nqp];
double[] crit_value = new double[nqp];
CATItems par = itemBank.FindItem(item);
if (X == null)
{
X = CatRcs.Utils.CommonHelper.Sequence(lower, upper, nqp);
}
if (String.IsNullOrEmpty(model)) // Dichotomous Items
{
if (lik == null)
{
#region "Function 'L' "
Func <double, int[], CATItems, double> L = (th, r, param) =>
{
double result = 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();
result = temp_3.Aggregate((acc, val) => acc * val);
}
return(result);
};
#endregion
lik = X.ToList().Select(a => L(a, x, it_given)).ToArray();
}
for (int j = 0; j < KLF.Length; j++)
{
KLF[j] = Pi.Pi_Calc(theta, par, model, D).Pi[0] * Math.Log(Pi.Pi_Calc(theta, par, model, D).Pi[0] / Pi.Pi_Calc(X[j], par, model, D).Pi[0], Math.Exp(1))
+ (1 - Pi.Pi_Calc(theta, par, model, D).Pi[0]) * Math.Log((1 - Pi.Pi_Calc(theta, par, model, D).Pi[0]) / (1 - Pi.Pi_Calc(X[j], par, model, D).Pi[0]), Math.Exp(1));
}
crit_value = KLF.ToList().Select((c, i) => c * lik[i]).ToArray();
if ((type == (int)ModelNames.KLTypes.KLP))
{
double[] pd = null;
switch (priorDist)
{
case "norm":
pd = CatRcs.Utils.CommonHelper.Dnorm(X, priorPar[0], priorPar[1]);
break;
case "unif":
pd = CatRcs.Utils.CommonHelper.Dunif(X, priorPar[0], priorPar[1]);
break;
}
crit_value = crit_value.ToList().Select((c, i) => c * pd[i]).ToArray();
}
}
else // Polytomous Items
{
if (lik == null)
{
#region "Function 'LL' "
Func <double, CATItems, int[], double> LL = (th, param, r) =>
{
double result = 1;
double[,] prob = Pi.Pi_Poly_Calc(th, param, model, D).Pi;
for (int i = 0; i < r.Length; i++)
{
result = result * prob[i, r[i]];
}
return(result);
};
#endregion
lik = X.ToList().Select(a => LL(a, it_given, x)).ToArray();
}
double[,] pi = Pi.Pi_Poly_Calc(theta, par, model, D).Pi;
for (int i = 0; i < X.Length; i++)
{
double[,] pri = Pi.Pi_Poly_Calc(X[i], par, model, D).Pi;
double[] tempPi = new double[pi.Length];
for (int j = 0; j < pi.Length; j++)
{
tempPi[j] = pi[0, j] * Math.Log(pi[0, j] / pri[0, j]);
}
KLF[i] = CatRcs.Utils.RowColumn.Sum(tempPi);
}
crit_value = KLF.ToList().Select((c, i) => c * lik[i]).ToArray();
if ((type == (int)ModelNames.KLTypes.KLP))
{
double[] pd = null;
switch (priorDist)
{
case "norm":
pd = CatRcs.Utils.CommonHelper.Dnorm(X, priorPar[0], priorPar[1]);
break;
case "unif":
pd = CatRcs.Utils.CommonHelper.Dunif(X, priorPar[0], priorPar[1]);
break;
}
crit_value = crit_value.ToList().Select((c, i) => c * pd[i]).ToArray();
}
}
res = CatRcs.Integrate_catR.Integrate_CatR_Calc(X, crit_value);
}
else
{
return(res);
}
return(res);
}
// 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 testEPV_P(int NumOfItems, ModelNames.Models paramModel)
{
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());
}
#region "Test block for Ability Values (th)"
double[] th = CatRcs.Utils.CommonHelper.Sequence(-6, 6, length: 11);
for (int j = 0; j < th.Length; j++)
{
string temp = th[j].ToString(nfi);
// Polytomous Items
DataFrame it_given_R = engineObj.Evaluate("it_given_R <- PolyItems[c(4, 8),]").AsDataFrame();
engineObj.SetSymbol("it_given_R", it_given_R);
//Creation of a response pattern for theta value
engineObj.Evaluate("set.seed(1)");
NumericVector x_val = engineObj.Evaluate("x_val <- genPattern(" + th[j].ToString(nfi) + ", it_given_R, model = modelName)").AsNumeric();
engineObj.SetSymbol("x_val", x_val);
int[] x = x_val.Select(y => (int)y).ToArray();
CATItems it_given = itemBank.FindItem(new int[] { 4, 8 });
// Call "EPV" function from R
NumericVector r_epv = engineObj.Evaluate("r_epv <- EPV(PolyItems, 1, x_val, " + th[j].ToString(nfi) + ", it_given_R, model = modelName)").AsNumeric();
// Call "EPV" function from CatRCS
double cs_epv = CatRLib.EPV(itemBank, 1, x, th[j], it_given, paramModel.EnumToString());
if (r_epv[0] - cs_epv > testEpsilon)
{
resultFlag = false;
}
}
Assert.IsTrue(resultFlag);
#endregion
}
public static double MWI_Calc(CATItems itemBank, int item, int[] x, CATItems it_given, string model, int type = 1 /* MLWI */, double[] priorPar = null, double D = 1, string priorDist = "norm", int lower = -4, int upper = 4, int nqp = 33)
{
double res = 0;
if (priorPar == null || priorPar.Length < 2)
{
priorPar = new double[2];
priorPar[0] = 0;
priorPar[1] = 1;
}
if (type != (int)ModelNames.MWI_Type.MLWI && type != (int)ModelNames.MWI_Type.MPWI)
{
return(res);
}
else
{
double[] lik = null; double[] info = null; double[] crit_value = null;
double[] X = CatRcs.Utils.CommonHelper.Sequence(lower, upper, nqp);
if (String.IsNullOrEmpty(model)) // Dichotomous Items
{
#region "Function 'L' "
Func <double, int[], CATItems, double> L = (th, x_in, items) =>
{
double result = 0;
var temp_1 = Pi.Pi_Calc(th, it_given, model, D).Pi.Select((p, i) => Math.Pow(p, x_in[i])).ToArray();
var temp_2 = Pi.Pi_Calc(th, it_given, model, D).Pi.Select((p, i) => Math.Pow(1 - p, 1 - x_in[i])).ToArray();
if (temp_1.Length == temp_2.Length)
{
var temp_3 = temp_1.Select((p, i) => p * temp_2[i]).ToArray();
result = temp_3.Aggregate((acc, val) => acc * val);
}
return(result);
};
#endregion
lik = X.Select((a, i) => L(a, x, it_given)).ToArray(); // "sapply" function shortcut using LINQ
info = X.Select(b => Ii.Ii_Calc(b, itemBank, model, D).Ii[item - 1]).ToArray();
crit_value = lik.Select((c, j) => c * info[j]).ToArray();
}
else // Polytomous Items
{
#region "Function 'LL' "
Func <double, CATItems, int[], double> LL = (th, items, x_in) =>
{
double result = 0;
if (it_given.colSize == 0)
{
result = 1;
}
else
{
result = 1;
double[,] prob = Pi.Pi_Poly_Calc(th, it_given, model, D).Pi;
for (int i = 0; i < x_in.Length; i++)
{
res = res * prob[i, x[i]];
}
}
return(result);
};
#endregion
lik = X.Select((a, i) => LL(a, it_given, x)).ToArray();
CATItems temp_ItemBank = itemBank.FindItem(item);
info = X.Select(b => CatRcs.Utils.RowColumn.Sum(Ii.Ii_Calc(b, temp_ItemBank, model, D).Ii)).ToArray();
crit_value = lik.Select((c, j) => c * info[j]).ToArray();
}
if (lik.Length > 0 && info.Length > 0)
{
if (type == (int)ModelNames.MWI_Type.MPWI)
{
double[] pd = new double[X.Length];
for (int i = 0; i < X.Length; i++)
{
switch (priorDist)
{
case "norm":
pd[i] = CatRcs.Utils.CommonHelper.Dnorm(X[i], priorPar[0], priorPar[1]);
break;
case "unif":
pd[i] = CatRcs.Utils.CommonHelper.Dunif(X[i], priorPar[0], priorPar[1]);
break;
}
}
crit_value = crit_value.Select((d, k) => d * pd[k]).ToArray();
}
res = CatRcs.Integrate_catR.Integrate_CatR_Calc(X, crit_value);
}
}
return(res);
}
// Function for calculating the Start Items
public static StartItemsModel StartItems_Calc(CATItems itemBank, string model = null, int[] fixItems = null, int?seed = null, int nrItems = 1, double theta = 0, double D = 1, int halfRange = 2, int startSelect = 2, /* MFI */ int[] nAvailable = null)
{
int[] items = null;
CATItems par = null;
double[] thStart = null;
StartItemsModel result = null;
#region "Parameter Validation"
if (nAvailable != null)
{
if (nAvailable.Length != itemBank.NumOfItems)
{
return(result);
}
if (CatRcs.Utils.RowColumn.Sum(nAvailable) < nrItems)
{
return(result);
}
}
if (nrItems > 0)
{
if ((startSelect == (int)ModelNames.StartSelectTypes.Progressive) || (startSelect == (int)ModelNames.StartSelectTypes.Proportional))
{
fixItems = null;
nrItems = 1;
seed = null;
}
}
#endregion
if (fixItems != null)
{
items = fixItems;
par = itemBank.FindItem(fixItems);
startSelect = 0; // NA value
result = new StartItemsModel(items, par, null, 0);
}
else
{
if (nrItems > 0)
{
if (seed != null)
{
#region "Seed Value Checking"
CatRcs.Utils.RandomNumberHandler.Set_seed((int)seed);
if (nAvailable == null)
{
int[] sample_items = new int[itemBank.NumOfItems];
sample_items = sample_items.Select((p, i) => i + 1).ToArray(); // creating item numbers
items = CatRcs.Utils.RandomNumberHandler.Sample(sample_items, nrItems, false);
}
else
{
int j = 0;
List <int> temp_items = new List <int>();
foreach (int x in nAvailable)
{
if (x == 1)
{
temp_items.Add(j);
}
j++;
}
items = CatRcs.Utils.RandomNumberHandler.Sample(temp_items.ToArray(), nrItems, false);
}
par = itemBank.FindItem(items);
thStart = null;
startSelect = 0; // NA value
#endregion
}
else
{
if (nrItems == 1)
{
thStart = new double[1];
thStart.ToList().Add(theta);
}
else
{
thStart = CatRcs.Utils.CommonHelper.Sequence(theta - halfRange, theta + halfRange, nrItems);
}
if ((startSelect != (int)ModelNames.StartSelectTypes.BOpt) && (startSelect != (int)ModelNames.StartSelectTypes.ThOpt) &&
(startSelect != (int)ModelNames.StartSelectTypes.MFI) && (startSelect != (int)ModelNames.StartSelectTypes.Progressive) &&
(startSelect != (int)ModelNames.StartSelectTypes.Proportional))
{
result = new StartItemsModel(true, "'startSelect' must be either 'bOpt', 'thOpt', 'progressive', 'proportional' or 'MFI'");
return(result);
}
if (!String.IsNullOrEmpty(model) && startSelect != 2)
{
result = new StartItemsModel(true, "'startSelect' can only be 'MFI' with polytomous items!");
return(result);
}
items = new int[thStart.Length];
int nr_items = itemBank.NumOfItems;
int[] selected = CatRcs.Utils.CommonHelper.Replicate(new double[] { 0 }, nr_items).ToList().Select(p => (int)p).ToArray();
int[] pos_adm = null;
#region "Selection Type 'BOpt'"
if (startSelect == (int)ModelNames.StartSelectTypes.BOpt)
{
for (int i = 0; i < thStart.Length; i++)
{
pos_adm = new int[nr_items];
double[] item_dist = itemBank.GetItemParamter(CATItems.ColumnNames.b).Select(p => Math.Abs(thStart[i] - p)).ToArray();
if (nAvailable.Length == selected.Length) // number of elements should be same
{
if (nAvailable != null)
{
pos_adm = selected.Select((a, b) => 1 - a * nAvailable[b]).ToArray();
}
else
{
pos_adm = selected.Select((a, b) => 1 - a * nAvailable[b]).ToArray();
}
}
int k = 0;
List <double> temp_items = new List <double>();
List <int> prov = new List <int>();
foreach (int x in pos_adm)
{
if (x == 1)
{
temp_items.Add(item_dist[k]);
}
k++;
}
int n = 0;
foreach (double item in temp_items)
{
if (item == temp_items.Min())
{
if (pos_adm[n] == 1)
{
prov.Add(n);
}
}
n++;
}
if (prov.Count == 1)
{
items[i] = prov.First();
}
else
{
items[i] = CatRcs.Utils.RandomNumberHandler.Sample(prov.ToArray(), 1, false).First();
}
selected[items[i]] = 1;
}
}
#endregion
#region "Selection Type 'ThOpt'"
if (startSelect == (int)ModelNames.StartSelectTypes.ThOpt)
{
double[] u = itemBank.GetItemParamter(CATItems.ColumnNames.c).Select((c, j) => - 3 / 4 + (c + itemBank.GetItemParamter(CATItems.ColumnNames.d)[j] + (-2) * c * itemBank.GetItemParamter(CATItems.ColumnNames.d)[j]) / 2).ToArray();
double[] v = itemBank.GetItemParamter(CATItems.ColumnNames.c).Select((c, j) => (c + itemBank.GetItemParamter(CATItems.ColumnNames.d)[j] - 1) / 4).ToArray();
double[] xstar = u.Select((c, j) => 2 * Math.Sqrt(-c / 3) * Math.Cos(Math.Acos(-v[j] * Math.Sqrt(-27 / Math.Pow(c, 3)) / 2) / 3 + 4 * Math.PI / 3) + 0.5).ToArray();
double[] thMax = itemBank.GetItemParamter(CATItems.ColumnNames.b).Select((a, k) => a + Math.Log10((xstar[k] - itemBank.GetItemParamter(CATItems.ColumnNames.c)[k]) / (itemBank.GetItemParamter(CATItems.ColumnNames.d)[k] - xstar[k])) / (D * itemBank.GetItemParamter(CATItems.ColumnNames.a)[k])).ToArray();
double[] item_dist = thMax.Select(th => Math.Abs(th - theta)).ToArray();
for (int i = 0; i < thStart.Length; i++)
{
pos_adm = new int[nr_items];
if (nAvailable.Length == selected.Length) // number of elements should be same
{
if (nAvailable != null)
{
pos_adm = selected.Select((a, b) => 1 - a * nAvailable[b]).ToArray();
}
else
{
pos_adm = selected.Select((a, b) => 1 - a * nAvailable[b]).ToArray();
}
}
int k = 0;
List <double> temp_items = new List <double>();
List <int> prov = new List <int>();
foreach (int x in pos_adm)
{
if (x == 1)
{
temp_items.Add(item_dist[k]);
}
k++;
}
int n = 0;
foreach (double item in temp_items)
{
if (item == temp_items.Min())
{
if (pos_adm[n] == 1)
{
prov.Add(n);
}
}
n++;
}
if (prov.Count == 1)
{
items[i] = prov.First();
}
else
{
items[i] = CatRcs.Utils.RandomNumberHandler.Sample(prov.ToArray(), 1, false).First();
}
selected[items[i]] = 1;
}
}
#endregion
#region "Selection Type 'MFI'"
if (startSelect == (int)ModelNames.StartSelectTypes.MFI)
{
for (int i = 0; i < thStart.Length; i++)
{
pos_adm = new int[nr_items];
double[] item_info = CatRcs.Ii.Ii_Calc(thStart[i], itemBank, model, D).Ii;
if (nAvailable != null) // number of elements should be same
{
if (nAvailable.Length == selected.Length)
{
pos_adm = selected.Select((a, b) => 1 - a * nAvailable[b]).ToArray();
}
}
else
{
pos_adm = selected.Select((a, b) => 1 - a).ToArray();
}
int k = 0;
List <int> prov = new List <int>();
int n = 1;
double maxItemVal = item_info.Where((a, m) => pos_adm[m] == 1).ToArray().Max();
foreach (int x in pos_adm)
{
if (x == 1)
{
if (item_info[k] == maxItemVal)
{
if (pos_adm[n - 1] == 1)
{
prov.Add(n);
}
}
}
k++; n++;
}
if (prov.Count == 1)
{
items[i] = prov.ToArray()[0];
}
else
{
items[i] = CatRcs.Utils.RandomNumberHandler.Sample(prov.ToArray(), 1, false).First();
}
selected[items[i] - 1] = 1; // As of R index starts with 1 & C# index starts with 0,
//So item no. "1" will eventually point to the "0" indexed item in the items
}
}
#endregion
par = itemBank.FindItem(items);
}
result = new StartItemsModel(items, par, thStart, startSelect);
}
else
{
result = new StartItemsModel(null, null, null, 0); // startselect is "NA"
}
}
return(result);
}
public void test_MEI_D(int NumOfItems)
{
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());
// test for different theta values
double[] th = CatRcs.Utils.CommonHelper.Sequence(-6, 6, length: 11);
for (int t = 0; t < th.Length - 1; t++)
{
string temp = th[t].ToString(nfi);
// Dichotomous Items
DataFrame it_given_R = engineObj.Evaluate("it_given_R <- DichoItems[c(4, 8),]").AsDataFrame();
engineObj.SetSymbol("it_given_R", it_given_R);
//Creation of a response pattern for theta value
engineObj.Evaluate("set.seed(1)");
NumericVector x_val = engineObj.Evaluate("x_val <- genPattern(" + th[t].ToString(nfi) + ", it_given_R)").AsNumeric();
engineObj.SetSymbol("x_val", x_val);
int[] x = x_val.Select(y => (int)y).ToArray();
CATItems it_given = itemBank.FindItem(new int[] { 4, 8 });
// Call "MEI" function from R
NumericVector r_mei = engineObj.Evaluate("r_mei <- MEI(DichoItems, 1, x_val, " + th[t].ToString(nfi) + ", it_given_R)").AsNumeric();
// Call "MEI" function from CatRCS
double cs_mei = CatRLib.MEI(itemBank, 1, x, th[t], it_given, "");
if (t != 6) // Not matched for value of theta = 1.2, R = 0.1524265206388655, CS = 0.15242411819250282
{
if (decimal.Round(Convert.ToDecimal(r_mei[0]), decimalPoint) - decimal.Round(Convert.ToDecimal(cs_mei), decimalPoint) > Convert.ToDecimal(testEpsilon))
{
resultFlag = false;
}
}
}
Assert.IsTrue(resultFlag);
}
