public void TestTable()
{
var args = new SqlConnectionStringBuilder("Server=192.168.100.12;Initial Catalog=InSysProMaxLive20181126;User ID=sa;Password=dev123sql$%^;");
using (var dfg = new ZTable(args, "tSellerHRIS", false))
{
dfg.Load(100);
}
}
public IActionResult GetSubjectData(string subject, string faculty)
{
var sheets = ExamSheetManager.Get(faculty, subject, true);
var model = new SubjectChartJsonModel();
if (!sheets?.Any() ?? true)
{
return(Json(model));
}
var ids = sheets.Select(x => x.Id);
var groupedByYears = sheets.GroupBy(x => x.Year).OrderBy(x => x.Key);
model.AverageRatings = new Dictionary <string, float>();
model.RatingFrequency = new Dictionary <short, short>();
var ratings = new List <RatingModel>();
foreach (var year in groupedByYears)
{
var groupedBySemesters = year.GroupBy(x => x.Semester).OrderByDescending(x => x.Key);
foreach (var semester in groupedBySemesters)
{
var sum = 0;
var count = 0;
foreach (var item in semester)
{
ratings.AddRange(item.Ratings);
sum += item.Ratings.Sum(x => x.Mark);
count += item.Ratings.Count;
}
var average = sum / (float)count;
var title = string.Format("{0} {1}", year.Key, semester.Key);
model.AverageRatings.Add(title, average);
}
}
foreach (var rating in ratings)
{
if (model.RatingFrequency.ContainsKey(rating.Mark))
{
model.RatingFrequency[rating.Mark]++;
}
else
{
model.RatingFrequency.Add(rating.Mark, 1);
}
}
var M = ratings.Sum(x => x.Mark) / ratings.Count;
var D = ratings.Sum(x => Math.Pow(x.Mark - M, 2)) / (ratings.Count - 1);
var sigma = Math.Sqrt(D);
var size = 4;
model.NormalDistributions = new Dictionary <short, double> [size];
for (int i = 0; i < size; ++i)
{
model.NormalDistributions[i] = new Dictionary <short, double>();
}
var eScore = calcZScore(60, M, sigma);
var cScore = calcZScore(74, M, sigma);
var aScore = calcZScore(90, M, sigma);
var eProbability = ZTable.GetProbability(eScore);
var cProbability = ZTable.GetProbability(cScore);
var aProbability = ZTable.GetProbability(aScore);
var probFail = eProbability * 100;
var probSatisfactorily = (cProbability - eProbability) * 100;
var probGood = (aProbability - cProbability) * 100;
var probExcellent = (1 - aProbability) * 100;
model.NormalDistributionLabels = new List <string>()
{
string.Format("Ймовірність не здати предмет: {0}%", probFail.ToString("0.0")),
string.Format("Ймовірність здати на 60-74: {0}%", probSatisfactorily.ToString("0.0")),
string.Format("Ймовірність здати на 74-90: {0}%", probGood.ToString("0.0")),
string.Format("Ймовірність здати на 90+: {0}%", probExcellent.ToString("0.0"))
};
for (short i = 0; i < 100; ++i)
{
double func = (1 / (sigma * Math.Sqrt(2 * Math.PI))) * Math.Exp(-(Math.Pow((i - M), 2)) / (2 * sigma * sigma));
if (i < 60)
{
model.NormalDistributions[0].Add(i, func * 1000);
}
else if (i < 74)
{
model.NormalDistributions[1].Add(i, func * 1000);
}
else if (i < 90)
{
model.NormalDistributions[2].Add(i, func * 1000);
}
else
{
model.NormalDistributions[3].Add(i, func * 1000);
}
}
return(Json(model));
}
