private void InitializeComponent(BivariateDataset ds, int width, int height)
{
this.Dataset = ds;
this.Width = width;
int sidebarWidth = (int)(this.Width * 0.3);
MyPlotDataView view = new MyPlotDataView();
this.DisplayAbsoluteButton = new System.Windows.Forms.Button();
this.ControlGroupBox = new System.Windows.Forms.GroupBox();
this.DisplayAbsoluteButton = new System.Windows.Forms.Button();
this.DisplayRelativeButton = new System.Windows.Forms.Button();
this.DisplayAbsoluteButton.Click += new System.EventHandler(this.onDisplayAbsoluteButtonClick);
this.DisplayRelativeButton.Click += new System.EventHandler(this.onDisplayRelativeButtonClick);
this.DisplayDataSettings = new System.Windows.Forms.GroupBox();
this.DataTable = new System.Windows.Forms.ListView();
this.DataTable.View = View.Details;
MyImportCSV ctr = new MyImportCSV();
var temp = ctr.DisplayData(0, 0, 600, 200, this.DisplayDataSettings, this.DataTable);
// this.DisplayDataSettings.Anchor = (
// (System.Windows.Forms.AnchorStyles)(
// (System.Windows.Forms.AnchorStyles.Top) | (System.Windows.Forms.AnchorStyles.Left)
// ));
MyButton b = new MyButton();
b.Init(DisplayDataSettings.Right, temp, 200, ControlGroupBox, DisplayRelativeButton, "Display Options", "Relative Values");
b.Add(ControlGroupBox, DisplayAbsoluteButton, "Absolute Values");
this.ControlGroupBox.Anchor = (
(System.Windows.Forms.AnchorStyles)(
(System.Windows.Forms.AnchorStyles.Right) | (System.Windows.Forms.AnchorStyles.Bottom)
));
this.ChartGroupBox1 = new System.Windows.Forms.GroupBox();
this.Chart1 = new System.Windows.Forms.PictureBox();
view.ChartView(DisplayDataSettings.Right, 0, 200, 200, this.ChartGroupBox1, this.Chart1);
this.ChartGroupBox1.Anchor = (
(System.Windows.Forms.AnchorStyles)(
(System.Windows.Forms.AnchorStyles.Right) | (System.Windows.Forms.AnchorStyles.Top) |
(System.Windows.Forms.AnchorStyles.Bottom)
));
this.ChartGroupBox2 = new System.Windows.Forms.GroupBox();
this.Chart2 = new System.Windows.Forms.PictureBox();
view.ChartView(15, DisplayDataSettings.Bottom, 600, 200, this.ChartGroupBox2, this.Chart2);
this.ChartGroupBox2.Anchor = (
(System.Windows.Forms.AnchorStyles)(
(System.Windows.Forms.AnchorStyles.Left) | (System.Windows.Forms.AnchorStyles.Bottom) |
(System.Windows.Forms.AnchorStyles.Right)
));
this.Height = height;
DisplayDataSettings.Text = "Calculated Statistics";
this.Controls.Add(this.DisplayDataSettings);
this.Controls.Add(this.ControlGroupBox);
this.Controls.Add(this.ChartGroupBox1);
this.Controls.Add(this.ChartGroupBox2);
this.ClientSize = new System.Drawing.Size(this.Width, this.Height);
this.Text = "Contigency Table";
InitializeListView();
}
public FormCT(BivariateDataset ds, int w, int h)
{
InitializeComponent(ds, w, h);
}
public FormCT(BivariateDataset ds)
{
InitializeComponent(ds, 800, 400);
}
//---------------------------------------------------------------------------------------------------//
//
//
//
private void draw()
{
InitializeGraphics();
int j = 0;
var D1 = new Dictionary <Interval, FrequenciesForValues>();
var D2 = new Dictionary <Interval, FrequenciesForValues>();
double size = range / distN;
double start = max;
List <Interval> ListOfIntervals = new List <Interval>();
var ListOfRects = new List <Rectangle>();
//Interval Interval_0 = new Interval(start, size);
// ListOfIntervals.Add(Interval_0);
// D.Add(Interval_0, new FrequenciesForValues());
var bv = new BivariateDataset();
double t = 0;
foreach (var kvp in myds.UnivariateDatasets)
{
var path = kvp.Value;
double index = 0;
foreach (var item in path.ANO)
{
double val = double.Parse(path.getObservationAt((int)index));
int X_device = Calc.Viewport(index, 0, n - 1, Viewport.Left, Viewport.Width);
int Y_device = Calc.Viewport(val, min, range, Viewport.Bottom, -Viewport.Height);
if (X_device > 0)
{
g.DrawLine(MyPen[j], prev_X[j], prev_Y[j], X_device, Y_device);
}
if (index == check)
{
bv.Add(D1, ListOfIntervals, start, size, (index / (double)n), val, m);
}
else if (index == n - 1)
{
bv.Add(D2, ListOfIntervals, start, size, (index / (double)n), val, m);
}
index += 1;
prev_X[j] = X_device;
prev_Y[j] = Y_device;
}
j++;
}
//
foreach (var kvp in D1)
{
var rect = distToRect(kvp, Viewport.Left + (int)(Viewport.Width * check / (double)n));
g.FillRectangle(new SolidBrush(Color.FromArgb(200, 0, 0, 0)), rect);
g.DrawRectangle(new Pen(Color.FromArgb(255, 255, 255)), rect);
}
double dist_index = 1;
foreach (var kvp in D2)
{
var x_i = (kvp.Key.getMin() + (kvp.Key.getMax() - kvp.Key.getMin()) * 0.5);
var val = kvp.Value.RelativeFrequency * x_i;
variance += val * x_i;
mean += val;
//mean += (val - mean)/(double)dist_index;
var rect = distToRect(kvp, Viewport.Right);
g.FillRectangle(new SolidBrush(Color.FromArgb(200, 0, 0, 0)), rect);
g.DrawRectangle(new Pen(Color.FromArgb(255, 255, 255)), rect);
dist_index++;
}
variance = variance - (mean * mean);
// g.DrawLine(Pens.Red, Viewport.Left, Calc.Viewport(mean, min, range, Viewport.Bottom, -Viewport.Height) , Viewport.Right, Calc.Viewport(mean, min, range, Viewport.Bottom, -Viewport.Height));
Statistics = new List <string>();
Statistics.Add(min.ToString());
Statistics.Add(max.ToString());
Statistics.Add(range.ToString());
Statistics.Add(mean.ToString());
Statistics.Add(variance.ToString());
InitializeListView(Statistics);
Chart.Image = b;
}
//---------------------------------------------------------------------------------------------------//
//
//
//
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;
}
}
