private void Button_Click_1(object sender, RoutedEventArgs e)
{
string collectionName = CollectionName.Text;
int featureCount = int.Parse(FeatureCount.Text);
//int featureToSelectCount = int.Parse(FeatureToSelectCount.Text);
DatabaseService databaseService = new DatabaseService();
var dataSet = databaseService.ConvertMongoColectionToListOfLists(featureCount, collectionName);
SpearmanCorrelation spearmanCorrelation = new SpearmanCorrelation();
var wynik = spearmanCorrelation.MakeCorelationTable(featureCount, collectionName, dataSet);
////before your loop
var csv = new StringBuilder();
for (int i = 0; i < featureCount + 1; i++)
{
var newLine = string.Empty;
for (int j = 0; j < featureCount + 1; j++)
{
newLine += wynik[i, j].ToString() + ";";
}
csv.AppendLine(newLine);
}
File.WriteAllText($"E://cos//wynikiDobreDoMGR//Spearman_{collectionName}_corelationTable_{DateTime.Now.ToString("yyyy-dd-M--HH-mm-ss")}.txt", csv.ToString());
//FiltersHelper filtersHelper = new FiltersHelper();
//filtersHelper.SelectFeaturesAndWriteToFile(wynik, featureToSelectCount, "Spearman_" + collectionName, featureCount);
FeatureSelection featureSelection = new FeatureSelection();
featureSelection.SelectFeaturesAndCheckClasification(wynik, featureCount + 1, dataSet, collectionName, "Spearman");
MessageBox.Show("Wykonano obliczenia.");
}
public void TestSimple()
{
User user1 = GetUser("test1", 1.0, 2.0, 3.0);
User user2 = GetUser("test2", 2.0, 3.0, 1.0);
DataModel dataModel = GetDataModel(user1, user2);
double correlation = new SpearmanCorrelation(dataModel).GetUserCorrelation(user1, user2);
AssertCorrelationEquals(-0.5, correlation);
}
public void TestFullCorrelation2()
{
User user1 = GetUser("test1", 1.0, 2.0, 3.0);
User user2 = GetUser("test2", 4.0, 5.0, 6.0);
DataModel dataModel = GetDataModel(user1, user2);
double correlation = new SpearmanCorrelation(dataModel).GetUserCorrelation(user1, user2);
AssertCorrelationEquals(1.0, correlation);
}
public void TestHeightAgeCorrelation()
{
const double epsilon = 0.0000000005;
var fileLoader = new FileLoader();
Matrix values = fileLoader.LoadFile(@"Data\Data.csv", new string[] { "Height", "Age" });
var evaluator = new SpearmanCorrelation();
double correlation = evaluator.Evaluate(values);
Assert.IsTrue(correlation - 0.103759398496241 < epsilon);
}
public void TestSpearman()
{
var sampleVectors = new List <GenericVector>();
for (int i = 0; i < 100; i++)
{
sampleVectors.Add(new GenericVector(i, i + 5));
}
var correlation = new SpearmanCorrelation(sampleVectors.Select(x => x.ToVector2()));
Assert.Equal(1, correlation.GetCorrelationCoefficient());
}
public void TestName()
{
var evaluator = new SpearmanCorrelation();
Assert.AreEqual("Spearman Correlation", evaluator.Name);
}
