private void Button_Click_1(object sender, RoutedEventArgs e)
{
int firstItem = comboValues1.SelectedIndex;
int secondItem = comboValues2.SelectedIndex;
for (int i = 2; i < 302; i++)
{
// if (Convert.ToDouble((range.Cells[i, 1] as Excel.Range).Value2.ToString().Split(',')[secondItem].CompareTo(".")))
yLine[i - 2] = Convert.ToDouble((range.Cells[i, 1] as Excel.Range).Value2.ToString().Split(',')[secondItem]);
// if(Convert.ToDouble((range.Cells[i, 1] as Excel.Range).Value2.ToString().Split(',')[firstItem].CompareTo(".")))
xLine[i - 2] = Convert.ToDouble((range.Cells[i, 1] as Excel.Range).Value2.ToString().Split(',')[firstItem].Replace('.', ','));
}
KeyValuePair <double, double>[] valueList = new KeyValuePair <double, double> [xLine.Length];
LinearRegression LR = new LinearRegression(xLine, yLine);
labelEq.Content = LR.GetEquation();
labelR.Content = LR.GetDetermination();
for (int i = 0; i < xLine.Length; i++)
{
valueList[i] = new KeyValuePair <double, double>(xLine[i], yLine[i]);
}
KeyValuePair <double, double>[] regressionLine = new KeyValuePair <double, double>[2] {
new KeyValuePair <double, double>(xLine.Min(), LR.Predict(xLine.Min())), new KeyValuePair <double, double>(xLine.Max(), LR.Predict(xLine.Max()))
};
((LineSeries)Graph.Series[0]).ItemsSource = valueList;
((LineSeries)Graph.Series[1]).ItemsSource = regressionLine;
Gr2.Title = "Regression Line";
Gr1.Title = "Data points";
}
/// <summary>
/// a simple method to test the algorithm with ternary linear regression.
/// </summary>
static void Test()
{
List <List <double> > X = new List <List <double> >()
{
new List <double>()
{
1, 0, 0, 0
},
new List <double>()
{
1, 13, 169, 2197
},
new List <double>()
{
1, 34, 1156, 39304
},
new List <double>()
{
1, 47, 2209, 103823
},
new List <double>()
{
1, 67, 4489, 300763
},
new List <double>()
{
1, 97, 9409, 912673
},
new List <double>()
{
1, 122, 14884, 1815848
},
new List <double>()
{
1, 150, 22500, 3375000
},
new List <double>()
{
1, 164, 26896, 4410944
},
new List <double>()
{
1, 229, 52441, 12008989
},
new List <double>()
{
1, 291, 84681, 24642171
},
new List <double>()
{
1, 351, 123201, 43243551
},
new List <double>()
{
1, 364, 132496, 48228544
},
new List <double>()
{
1, 422, 178084, 75151448
},
new List <double>()
{
1, 493, 243049, 119823157
}
};
List <double> y = new List <double> {
54, 75, 97, 108, 120, 136, 145, 161, 164, 185, 211, 243, 271, 296, 320
};
// init
//for (int i = 0; i < x.Count; i++)
//{
// var example = new List<double> { 1, x[i], Math.Pow(x[i], 2), Math.Pow(x[i], 3) };
// X.Add(example);
//}
// call function
var liner = new LinearRegression(1, 100000);
var theta = liner.GradeDecent(X, y);
// write result
for (int i = 0; i < theta.Count; i++)
{
Console.Write("{0}, ", theta[i]);
}
Console.WriteLine();
}