static void Main(string[] args)
{
var trainingset = new double[100, 4];
var testset = new double[20, 4];
var testset_noans = new double[20, 3];
for (int i = 0; i < 100; i++)
{
Random r = new Random();
trainingset[i, 0] = r.Next(1, 3000);
Random r1 = new Random();
if (i % 2 == 1)
{
trainingset[i, 3] = 1;
if (r1.Next(0, 100) < 80)
{
trainingset[i, 1] = 1;
}
else
{
trainingset[i, 1] = 0;
}
}
else
{
trainingset[i, 3] = 0;
if (r1.Next(0, 100) < 50)
{
trainingset[i, 1] = 0;
}
else
{
trainingset[i, 1] = 1;
}
}
Random r2 = new Random();
if (i % 2 == 1)
{
trainingset[i, 2] = r2.Next(60, 100);
}
else
{
trainingset[i, 2] = r2.Next(0, 60);
}
}
for (int i = 0; i < 20; i++)
{
Random r = new Random();
testset[i, 0] = r.Next(1, 3000);
testset_noans[i, 0] = testset[i, 0];
Random r1 = new Random();
if (i % 2 == 1)
{
testset[i, 3] = 1;
if (r1.Next(0, 100) < 80)
{
testset[i, 1] = 1;
testset_noans[i, 1] = testset[i, 1];
}
else
{
testset[i, 1] = 0;
testset_noans[i, 1] = testset[i, 1];
}
}
else
{
testset[i, 3] = 0;
if (r1.Next(0, 100) < 50)
{
testset[i, 1] = 0;
testset_noans[i, 1] = testset[i, 1];
}
else
{
testset[i, 1] = 1;
testset_noans[i, 1] = testset[i, 1];
}
}
Random r2 = new Random();
if (i % 2 == 1)
{
testset[i, 2] = r2.Next(60, 100);
testset_noans[i, 2] = testset[i, 2];
}
else
{
testset[i, 2] = r2.Next(0, 60);
testset_noans[i, 2] = testset[i, 2];
}
}
int NPoints = 70; // - training set size, NPoints>=1
int NVars = 3; // - number of independent variables, NVars>=1
int NClasses = 2;
int NTrees = 20; // - number of trees in a forest, NTrees>=1.
// recommended values: 50-100.
double R = 0.5;
int info = 0;
var forest = new alglib.dforest.decisionforest();
var report = new alglib.dforest.dfreport();
var prediction = new double[20, 2];
var prediction1 = new double[2];
alglib.dforest.dfbuildrandomdecisionforest(trainingset, NPoints, NVars, NClasses, NTrees, R, ref info, forest, report);
Console.WriteLine(info);
for (int i = 0; i < 20; i++)
{
var test1 = new double[3];
test1[0] = testset_noans[i, 0];
test1[1] = testset_noans[i, 1];
test1[2] = testset_noans[i, 2];
alglib.dforest.dfprocess(forest, test1, ref prediction1);
prediction[i, 0] = prediction1[0];
prediction[i, 1] = prediction1[1];
}
Console.WriteLine("break");
for (int i = 0; i < 20; i++)
{
Console.WriteLine("Result was " + testset[i, 3]);
Console.WriteLine("Prediction is - zero: " + prediction[i, 0] + " one: " + prediction[i, 1]);
}
Console.Write("");
//int size = 6;
//int[,] matrix = new int[size,size];
//Random r = new Random();
//for (int i = 0; i < size; i++)
//{
// for (int j = 0; j < size; j++)
// {
// matrix[i, j] = r.Next(1,3000);
// }
//}
//Console.WriteLine("Initial cost");
//for (int i = 0; i < size; i++)
//{
// Console.Write("[");
// for (int j = 0; j < size -1; j++)
// {
// Console.Write(matrix[i,j] + ", ");
// }
// Console.Write(matrix[i, size-1]);
// Console.Write("]\n");
//}
//MatchingAlgorithm Algo = new MatchingAlgorithm(matrix);
//int[,] mat = Algo.Run();
//Console.WriteLine("Final assignment");
//for (int i = 0; i < size; i++)
//{
// Console.Write("[");
// for (int j = 0; j < size - 1; j++)
// {
// Console.Write(mat[i, j] + ", ");
// }
// Console.Write(mat[i, size - 1]);
// Console.Write("]\n");
//}
//Console.Read();
}
public StatusModel <List <TraineeModel> > GeneratePrediction(Area?area)
{
var status = new StatusModel <List <TraineeModel> >(false, String.Empty, new List <TraineeModel>());
try
{
using (var unitOfWork = new UnitOfWork <BelibaHomaDBEntities>())
{
var traineeRepository = unitOfWork.GetRepository <ITraineeRepository>();
var predictionTrainingRepository = unitOfWork.GetRepository <IPredictionTrainingRepository>();
var alertRepository = unitOfWork.GetRepository <IAlertRepository>();
var trainingRawData = predictionTrainingRepository.GetAll().ToList();
int NPoints = trainingRawData.Count();
const int NVars = 7;
const int NClasses = 2;
const int NTrees = 50;
const double R = 0.6;
int info = 0;
var forest = new alglib.dforest.decisionforest();
var report = new alglib.dforest.dfreport();
var trainingSet = new double[NPoints, 8];
for (int i = 0; i < NPoints; i++)
{
//Gender0,AcademicInstitution1,AcademicMajor2,AcademicMinor3,SemesterNumber4,SemesterGrade5,AlertCount6,IsDroppedOut7
trainingSet[i, 0] = trainingRawData[i].Gender;
trainingSet[i, 1] = trainingRawData[i].AcademicInstitution;
trainingSet[i, 2] = trainingRawData[i].AcademicMajor;
trainingSet[i, 3] = trainingRawData[i].AcademicMinor;
trainingSet[i, 4] = trainingRawData[i].SemesterNumber;
trainingSet[i, 5] = trainingRawData[i].LastSemesterGrade;
trainingSet[i, 6] = trainingRawData[i].AlertCount;
trainingSet[i, 7] = trainingRawData[i].IsDroppedOut;
}
alglib.dforest.dfbuildrandomdecisionforest(trainingSet, NPoints, NVars, NClasses, NTrees, R, ref info, forest, report);
if (info == 1)
{
var testRawData =
traineeRepository.GetQuery(
t => t.User.IsActive && (area == null || t.User.Area == (int?)area)).ToList();
int traineeCount = testRawData.Count();
var endangeredTrainees = new List <TraineeModel>();
for (int i = 0; i < traineeCount; i++)
{
var trainee = testRawData[i];
//Gender0,AcademicInstitution1,AcademicMajor2,AcademicMinor3,SemesterNumber4,SemesterGrade5,AlertCount6
var testParameters = new double[7];
testParameters[0] = trainee.Gender;
testParameters[1] = trainee.AcademicInstitutionId;
testParameters[2] = trainee.AcademicMajorId;
testParameters[3] = (double)(trainee.AcademicMajor1 != null ? trainee.AcademicMinorId : -1);
testParameters[4] = trainee.SemesterNumber;
testParameters[5] = trainee.Grade.OrderByDescending(g => g.SemesterNumber).Select(g => g.Grade1).FirstOrDefault();
//testParameters[6] = (double) alertRepository.GetQuery(a => a.AlertType == (int)AlertType.Intervention && a.TutorReport.TutorTrainee.TraineeId == testRawData[i].UserId).Count();
testParameters[6] =
alertRepository.GetAll().Count(a => a.AlertType == (int)AlertType.Intervention &&
a.TutorReport.TutorTrainee.TraineeId == trainee.UserId);
var prediction = new double[NClasses];
alglib.dforest.dfprocess(forest, testParameters, ref prediction);
if (prediction[1] > 0.65)
{
endangeredTrainees.Add(new TraineeModel(trainee));
}
}
//If we got here - Yay! :)
status.Data = endangeredTrainees;
status.Success = true;
status.Message = String.Format("חיזוי בוצע בהצלחה");
return(status);
}
}
}
catch (Exception ex)
{
if (status.Message == String.Empty)
{
status.Message = String.Format("שגיאה במהלך חיזוי חניכים בסיכון לנשירה");
}
LogService.Logger.Error(status.Message, ex);
}
return(status);
}
