static void Main(string[] args)
{
//create Students (objects)
Student s1 = new Student("Nick", 19);
Student s2 = new Student("Bella", 18);
Student s3 = new Student("Victor", 21);
Student s4 = new Student("Susie", 20);
s1.Level = "Sophomore";
s3.Level = "Graduate";
s2.Major = "English";
s4.Major = "History";
//create filestream to save the info from ToString() into the file.txt
FileStream f = new FileStream("file.text", FileMode.OpenOrCreate, FileAccess.ReadWrite);
// streamWriter is used to write into the file.txt
StreamWriter s = new StreamWriter(f);
//create an array of students (or list)- to applay methods to each of them
Student[] stud_array = new Student[] { s1, s2, s3, s4 };
//use foreach
foreach (var Stud in stud_array)
{
//Prints out info as a list / from array
Console.WriteLine(Stud.DisplayInformation());
Console.WriteLine("");
Console.WriteLine("---------------------------------------------------------------------------------------------------------------------");
//saves info in the file.txt
Stud.ToString();
//method to write into streamWriter object (s) - after that the file.txt is created with saved info:
s.WriteLine(Stud.ToString());
}
// always close the objects: streamwriter and filestream - after being done with them
s.Close();
f.Close();
Console.WriteLine("Press any key to exit.");
Console.ReadKey();
}
public string CreateGraph(Stud dataA, Stud dataB, bool kmeans, bool dbscan, bool linearregression,
bool polynomialregression, bool pearsoncorrelation, bool spearmancorrelation, bool knearest, bool naivebayes)
{
var gradedStudents = Students.StudentsGraded;
var classifyungraded = knearest || naivebayes;
if (classifyungraded)
{
var grades = new Dictionary <int, double>();
var clusters = new Dbscan(210, 3,
Students.StudentsGraded.Select(x => x.ToGenericVector(Stud.Attempts, Stud.Class, Stud.FailRatio,
Stud.Fails, Stud.Succeeds, Stud.SuccessRatio, Stud.Grade)));
foreach (var cluster in clusters.DataClusters)
{
grades[cluster.Key] = cluster.Value.Sum(x => x[6]) / cluster.Value.Count();
}
Classification classification = new NaiveBayesClassification(clusters.DataClusters, 50000);
if (knearest)
{
classification = new KnearestClassification(clusters.DataClusters, 5);
}
foreach (var student in Students.StudentsUngraded)
{
var cluster = classification.ClassifyPoint(student.ToGenericVector(Stud.Attempts, Stud.Class,
Stud.FailRatio, Stud.Fails, Stud.Succeeds, Stud.SuccessRatio, Stud.Grade));
student.Grade = (int)grades[cluster];
gradedStudents.Add(student);
}
}
var list = new List <int>();
var a = list
.GroupBy(x => x)
.Select(x => x.OrderBy(y => y))
.Select(x => x.First());
foreach (var student in gradedStudents)
{
student.Filter();
}
var data = new Dataset(gradedStudents.Select(x => x.ToGenericVector(dataA, dataB)));
var highChart = new HighchartsAdapter(Highchart.Scatterplot);
//Dbscan removes outliers, so we have to change are dataset afterwards
if (dbscan)
{
var newData = new List <GenericVector>();
var dBscan = new Dbscan(50, 3, data);
foreach (var cluster in dBscan.DataClusters)
{
newData.AddRange(cluster.Value);
}
data = new Dataset(newData);
highChart.AddClusters(dBscan);
}
if (kmeans)
{
highChart.AddClusters(new Kmeans(4, 100, data));
}
if (linearregression)
{
highChart.AddRegression(new LinearRegression(data.Select(x => x.ToVector2())));
}
if (polynomialregression)
{
highChart.AddRegression(new PolynomialRegression(data.Select(x => x.ToVector2()), 3));
}
if (pearsoncorrelation)
{
highChart.AddCorrelation(new PearsonCorrelation(data.Select(x => x.ToVector2())));
}
if (spearmancorrelation)
{
highChart.AddCorrelation(new SpearmanCorrelation(data.Select(x => x.ToVector2())));
}
highChart.SetDivId("plotkmeans");
highChart.SetTitle($"{dataA} vs {dataB}");
highChart.SetXlabel(dataA.ToString());
highChart.SetYlabel(dataB.ToString());
return(highChart.CreateTemplate());
}
