private void Predict()
{
DataGridRow row = DataGridExtension.GetRow(dataGridPredict, 0);
List <double> values = new List <double>();
for (int i = 0; i < dataGridPredict.Columns.Count; i++)
{
TextBlock cellContent = dataGridPredict.Columns[i].GetCellContent(row) as TextBlock;
if (cellContent != null)
{
values.Add(double.Parse(cellContent.Text, CultureInfo.InvariantCulture));
}
}
if (values.Count != AttrCount - 1)
{
throw new Exception("Entered incorrect values");
}
Matrix x = new Matrix(AttrCount, 1);
x[0, 0] = 1;
for (int i = 0; i < values.Count; i++)
{
x[i + 1, 0] = values[i];
}
Matrix transp = Matrix.Transpose(theta);
Matrix h0 = transp * x;
labelResult.Content = "Results: " + h0[0, 0].ToString();
}
private void Predict()
{
DataGridRow row = DataGridExtension.GetRow(dataGridPredict, 0);
List <double> x = new List <double>();
x.Add(1);
for (int i = 0; i < dataGridPredict.Columns.Count; i++)
{
TextBlock cellContent = dataGridPredict.Columns[i].GetCellContent(row) as TextBlock;
if (cellContent != null)
{
x.Add(double.Parse(cellContent.Text, CultureInfo.InvariantCulture));
}
}
if (x.Count != AttrCount)
{
throw new Exception("Entered incorrect values");
}
double result = h0(thetas, x);
if (result >= 0.5)
{
labelResult.Content = "Result: 1 = Good quality of wine";
}
else
{
labelResult.Content = "Result: 0 = Bad quality of wine";
}
}
