//---------------------------------------------------------------------------------------------------//
private void PopulateDistributions()
{
foreach (var point in Dataset.DataPoints)
{
point.X = (int)(HistoX.Left + HistoX.Width * (double.Parse(point.rawX) - double.Parse(Dataset.Min.Item1)) / double.Parse(Dataset.Range.Item1));
point.Y = (int)(HistoY.Bottom - HistoY.Height * (double.Parse(point.rawY) - double.Parse(Dataset.Min.Item2)) / double.Parse(Dataset.Range.Item2));
// point.X = X.X_viewport(point.rawX, Scatterplot.Left, Scatterplot.Width);
// point.Y = Y.Y_viewport(point.rawY, Scatterplot.Bottom, Scatterplot.Height);
// Dataset.DataPoints.Add(point);
}
int nx = Dataset.Distribution.Item1.Count;
int ny = Dataset.Distribution.Item2.Count;
Dataset.CalculateDistributions(HistoX.Left, HistoX.Width / nx + 1, HistoY.Top, HistoY.Height / ny + 1);
}
//---------------------------------------------------------------------------------------------------//
//
//
//
private void PopulateDatabase()
{
Database = new BivariateDataset();
int count = Dataset.GetVariableCount(SelectVarX.Text);
HistoXRect = new List <Rectangle>();
HistoYRect = new List <Rectangle>();
// Min
Database.Min = new Tuple <string, string>(Dataset.GetMinAsString(SelectVarX.Text), Dataset.GetMinAsString(SelectVarY.Text));
var item = this.DataTable.Items[0];
item.SubItems.Clear();
item.Text = LVStatistics[0];
item.SubItems.Add(Database.Min.Item1);
item.SubItems.Add(Database.Min.Item2);
// Max
Database.Max = new Tuple <string, string>(Dataset.GetMaxAsString(SelectVarX.Text), Dataset.GetMaxAsString(SelectVarY.Text));
item = this.DataTable.Items[1];
item.SubItems.Clear();
item.Text = LVStatistics[1];
item.SubItems.Add(Database.Max.Item1);
item.SubItems.Add(Database.Max.Item2);
// Range
Database.Range = new Tuple <string, string>(Dataset.GetRangeAsString(SelectVarX.Text), Dataset.GetRangeAsString(SelectVarY.Text));
item = this.DataTable.Items[2];
item.SubItems.Clear();
item.Text = LVStatistics[2];
item.SubItems.Add(Database.Range.Item1);
item.SubItems.Add(Database.Range.Item2);
// arithmetic mean
Database.AM = new Tuple <string, string>(Dataset.GetAMAsString(SelectVarX.Text), Dataset.GetAMAsString(SelectVarY.Text));
item = this.DataTable.Items[3];
item.SubItems.Clear();
item.Text = LVStatistics[3];
item.SubItems.Add(Database.AM.Item1);
item.SubItems.Add(Database.AM.Item2);
// var tempX = Dataset.UnivariateDatasets[SelectVarX.Text];
// foreach(var x in X.ANO)
// {
// Values.Add(X.X_viewport(x, v_left, v_width));
// }
// // var X = Dataset.getX(SelectVarX.Text, Scatterplot.Left, Scatterplot.Width);
// var X = Dataset.getStringValues(SelectVarX.Text);
// // var Y = Dataset.getY(SelectVarY.Text, Scatterplot.Bottom, Scatterplot.Height);
// var Y = Dataset.getStringValues(SelectVarY.Text);
var X = Dataset.UnivariateDatasets[SelectVarX.Text];
var Y = Dataset.UnivariateDatasets[SelectVarY.Text];
var Label = fetchLabels();
(double X, double Y)var_comp = (0, 0);
double covar_comp = 0;
// MedianX.Init();
// MedianY.Init();
ordX = new List <string>();
ordY = new List <string>();
// Points
foreach (var i in Enumerable.Range(0, count))
{
var point = new MyPoint();
point.rawX = X.ANO[i];
point.rawY = Y.ANO[i];
ordX.Add(point.rawX);
ordY.Add(point.rawY);
point.X = X.X_viewport(point.rawX, Scatterplot.Left, Scatterplot.Width);
point.Y = Y.Y_viewport(point.rawY, Scatterplot.Bottom, Scatterplot.Height);
point.Label = Label[i];
Database.DataPoints.Add(point);
var_comp.X += Math.Pow(double.Parse(point.rawX), 2) / (double)count;
var_comp.Y += Math.Pow(double.Parse(point.rawY), 2) / (double)count;
covar_comp += double.Parse(point.rawX) * double.Parse(point.rawY) / (double)count;
// Console.WriteLine("{0}, {1}", point.X, point.Y);
}
ordX = ordX.OrderBy(x => double.Parse(x)).ToList();
ordY = ordY.OrderBy(y => double.Parse(y)).ToList();
Database.Median = new Tuple <string, string>(ordX[ordX.Count / 2], ordY[ordY.Count / 2]);
item = this.DataTable.Items[4];
item.SubItems.Clear();
item.Text = LVStatistics[4];
item.SubItems.Add(Database.Median.Item1);
item.SubItems.Add(Database.Median.Item2);
// var(x), var(y)
Database.Var = new Tuple <string, string>(
(var_comp.X - Math.Pow(double.Parse(Database.AM.Item1), 2)).ToString(),
(var_comp.Y - Math.Pow(double.Parse(Database.AM.Item2), 2)).ToString()
);
item = this.DataTable.Items[5];
item.SubItems.Clear();
item.Text = LVStatistics[5];
item.SubItems.Add(Database.Var.Item1);
item.SubItems.Add(Database.Var.Item2);
// covar
Database.Cov = new Tuple <string, string>(
(covar_comp - double.Parse(Database.AM.Item1) * double.Parse(Database.AM.Item2)).ToString(),
(covar_comp - double.Parse(Database.AM.Item1) * double.Parse(Database.AM.Item2)).ToString()
);
item = this.DataTable.Items[6];
item.SubItems.Clear();
item.Text = LVStatistics[6];
item.SubItems.Add(Database.Cov.Item1);
item.SubItems.Add(Database.Cov.Item2);
// -1 <= r <= 1
var sigma_x = Math.Pow(double.Parse(Database.Var.Item1), 0.5);
var sigma_y = Math.Pow(double.Parse(Database.Var.Item2), 0.5);
var sigma_xy = double.Parse(Database.Cov.Item1);
var r = (sigma_xy / (sigma_x * sigma_y)).ToString();
item = this.DataTable.Items[7];
item.SubItems.Clear();
item.Text = LVStatistics[7];
item.SubItems.Add(r);
item.SubItems.Add(r);
// 0 <= R^2 <= 1
var R_2 = Math.Pow(double.Parse(r), 2).ToString();
item = this.DataTable.Items[8];
item.SubItems.Clear();
item.Text = LVStatistics[8];
item.SubItems.Add(R_2);
item.SubItems.Add(R_2);
// Regression Line X
Database.RegrX = (
(sigma_xy * (double.Parse(Database.Min.Item2) - double.Parse(Database.AM.Item2)) / Math.Pow(sigma_y, 2) + double.Parse(Database.AM.Item1)).ToString(),
Database.Min.Item2,
(sigma_xy * (double.Parse(Database.Max.Item2) - double.Parse(Database.AM.Item2)) / Math.Pow(sigma_y, 2) + double.Parse(Database.AM.Item1)).ToString(),
Database.Max.Item2
);
if ((double.Parse(Database.RegrX.x0) < double.Parse(Database.Min.Item1)))
{
Database.RegrX.x0 = Database.Min.Item1;
Database.RegrX.y0 = (
double.Parse(Database.AM.Item2) + (double.Parse(Database.Min.Item1) - double.Parse(Database.AM.Item1)) * Math.Pow(sigma_y, 2) / sigma_xy
).ToString();
}
else if ((double.Parse(Database.RegrX.x0) > double.Parse(Database.Max.Item1)))
{
Database.RegrX.x0 = Database.Max.Item1;
Database.RegrX.y0 = (
double.Parse(Database.AM.Item2) + (double.Parse(Database.Max.Item1) - double.Parse(Database.AM.Item1)) * Math.Pow(sigma_y, 2) / sigma_xy
).ToString();
}
if ((double.Parse(Database.RegrX.x1) < double.Parse(Database.Min.Item1)))
{
Database.RegrX.x1 = Database.Min.Item1;
Database.RegrX.y1 = (
double.Parse(Database.AM.Item2) + (double.Parse(Database.Min.Item1) - double.Parse(Database.AM.Item1)) * Math.Pow(sigma_y, 2) / sigma_xy
).ToString();
}
else if ((double.Parse(Database.RegrX.x1) > double.Parse(Database.Max.Item1)))
{
Database.RegrX.x1 = Database.Max.Item1;
Database.RegrX.y1 = (
double.Parse(Database.AM.Item2) + (double.Parse(Database.Max.Item1) - double.Parse(Database.AM.Item1)) * Math.Pow(sigma_y, 2) / sigma_xy
).ToString();
}
// Regression Line Y
Database.RegrY = (
Database.Min.Item1,
(sigma_xy * (double.Parse(Database.Min.Item1) - double.Parse(Database.AM.Item1)) / Math.Pow(sigma_x, 2) + double.Parse(Database.AM.Item2)).ToString(),
Database.Max.Item1,
(sigma_xy * (double.Parse(Database.Max.Item1) - double.Parse(Database.AM.Item1)) / Math.Pow(sigma_x, 2) + double.Parse(Database.AM.Item2)).ToString()
);
if ((double.Parse(Database.RegrY.y0) < double.Parse(Database.Min.Item2)))
{
Database.RegrY.y0 = Database.Min.Item2;
Database.RegrY.x0 = (
double.Parse(Database.AM.Item1) + (double.Parse(Database.Min.Item2) - double.Parse(Database.AM.Item2)) * Math.Pow(sigma_x, 2) / sigma_xy
).ToString();
}
else if ((double.Parse(Database.RegrY.y0) > double.Parse(Database.Max.Item2)))
{
Database.RegrY.y0 = Database.Max.Item2;
Database.RegrY.x0 = (
double.Parse(Database.AM.Item1) + (double.Parse(Database.Max.Item2) - double.Parse(Database.AM.Item2)) * Math.Pow(sigma_x, 2) / sigma_xy
).ToString();
}
if ((double.Parse(Database.RegrY.y1) < double.Parse(Database.Min.Item2)))
{
Database.RegrY.y1 = Database.Min.Item2;
Database.RegrY.x1 = (
double.Parse(Database.AM.Item1) + (double.Parse(Database.Min.Item2) - double.Parse(Database.AM.Item2)) * Math.Pow(sigma_x, 2) / sigma_xy
).ToString();
}
else if ((double.Parse(Database.RegrY.y1) > double.Parse(Database.Max.Item2)))
{
Database.RegrY.y1 = Database.Max.Item2;
Database.RegrY.x1 = (
double.Parse(Database.AM.Item1) + (double.Parse(Database.Max.Item2) - double.Parse(Database.AM.Item2)) * Math.Pow(sigma_x, 2) / sigma_xy
).ToString();
}
int nx = (int)IntervalX.Value;
if (nx == 0)
{
nx = 1;
}
int interval_x = (Scatterplot.Width / nx);
int ny = (int)IntervalY.Value;
if (ny == 0)
{
ny = 1;
}
int interval_y = (Scatterplot.Height / ny);
int start1 = Scatterplot.Left;
int start2 = Scatterplot.Bottom;
Database.CalculateDistributions(start1, interval_x + 1, start2, interval_y + 1);
}
//---------------------------------------------------------------------------------------------------//
//
//
//
private void PopulateUnivariateDatabase()
{
Database = new BivariateDataset();
var X = new UnivariateDataset <int>();
string variableName = (SelectVar.SelectedIndex < 0)? SelectVar.Items[0].ToString() :SelectVar.Text;
int count = Dataset.GetVariableCount(variableName);
HistoRect = new List <Rectangle>();
// Min
X.Min = 1;
Database.Min = new Tuple <string, string>(Dataset.GetMinAsString(variableName), X.Min.ToString());
var item = this.DataTable.Items[0];
item.SubItems.Clear();
item.Text = LVStatistics[0];
item.SubItems.Add(Database.Min.Item2);
item.SubItems.Add(Database.Min.Item1);
// Max
X.Max = count;
Database.Max = new Tuple <string, string>(Dataset.GetMaxAsString(variableName), X.Max.ToString());
item = this.DataTable.Items[1];
item.SubItems.Clear();
item.Text = LVStatistics[1];
item.SubItems.Add(Database.Max.Item2);
item.SubItems.Add(Database.Max.Item1);
// Range
X.Range = X.Max - X.Min;
Database.Range = new Tuple <string, string>(Dataset.GetRangeAsString(variableName), X.Range.ToString());
item = this.DataTable.Items[2];
item.SubItems.Clear();
item.Text = LVStatistics[2];
item.SubItems.Add(Database.Range.Item2);
item.SubItems.Add(Database.Range.Item1);
// arithmetic mean
X.ArithmeticMean = count / 2;
Tuple <string, string> tuple = new Tuple <string, string>(Dataset.GetAMAsString(variableName), X.ArithmeticMean.ToString());
Database.AM = tuple;
item = this.DataTable.Items[3];
item.SubItems.Clear();
item.Text = LVStatistics[3];
item.SubItems.Add(Database.AM.Item2);
item.SubItems.Add(Database.AM.Item1);
var YR = Dataset.getY(variableName, Riemann.Bottom, Riemann.Height);
// Points
foreach (var i in Enumerable.Range(0, YR.Count))
{
var point = new MyPoint();
point.X = X.X_viewport((i + 1).ToString(), Riemann.Left, Riemann.Width);
point.Y = YR[i];
Database.DataPoints.Add(point);
}
int nx = (int)Interval.Value;
if (nx == 0)
{
nx = 1;
}
int interval_x = (Riemann.Width / nx) + 1;
int ny = (int)Interval.Value;
if (ny == 0)
{
ny = 1;
}
int interval_y = (Lebesgue.Height / ny) + 1;
int start1 = Riemann.Left;
int start2 = Lebesgue.Top - Riemann.Top + 1;
Database.CalculateDistributions(start1, interval_x, start2, interval_y);
if (populated == false)
{
populated = true;
}
if (SelectVar.SelectedIndex < 0)
{
SelectVar.SelectedIndex = 0;
}
}
