private void OpenLearn_Click(object sender, RoutedEventArgs e)
{
OpenFileDialog dlg = new OpenFileDialog();
dlg.Filter = "txt files (.txt)|*.txt";
dlg.DefaultExt = ".csv";
dlg.RestoreDirectory = true;
dlg.CheckFileExists = true;
dlg.CheckPathExists = true;
if (dlg.ShowDialog() != true)
{
return;
}
FileStream stream = null;
StreamReader file = null;
try
{
stream = new FileStream(dlg.FileName, FileMode.Open, FileAccess.Read, FileShare.None);
file = new StreamReader(stream, Encoding.Default);
List <RegCoef> Buffer = new List <RegCoef>();
while (!file.EndOfStream)
{
RegCoef cur = new RegCoef()
{
Name = file.ReadLine()
};
cur.coef = double.Parse(file.ReadLine());
cur.standCoef = double.Parse(file.ReadLine());
Buffer.Add(cur);
}
if (Buffer.Count < 3)
{
throw new FormatException();
}
if (Buffer[Buffer.Count - 2].Name != "Intercept")
{
throw new FormatException();
}
regression = new MultipleLinearRegression(Buffer.Count - 2, Buffer[Buffer.Count - 2].coef);
List <double> Weights = new List <double>();
for (int i = 0; i < Buffer.Count - 2; i++)
{
Weights.Add(Buffer[i].coef);
}
regression.Weights = Weights.ToArray();
CoefficentOfDetermination.Text = $"{Buffer[Buffer.Count - 1].coef:F4}";
Buffer.RemoveAt(Buffer.Count - 1);
Buffer.RemoveAt(Buffer.Count - 1);
TrainModel = Buffer;
SortedTrainModel = new List <RegCoef>(TrainModel);
CoefGrid.ItemsSource = null;
CoefGrid.ItemsSource = SortedTrainModel;
}
catch (FormatException)
{
MessageBox.Show("В файле некорректные данные");
}
catch (Exception)
{
MessageBox.Show("Открыть файл не удалось");
}
finally
{
file?.Dispose();
stream?.Dispose();
}
}
private void Train_Click(object sender, RoutedEventArgs e)
{
List <RegCoef> OldTrainModel = TrainModel;
if (TrainData.Rows.Count < 1)
{
return;
}
try
{
double[][] inputs = new double[TrainData.Rows.Count][];
List <int> IndicesOfActive = new List <int>();
for (int i = 1; i < NamesOfParameters.Count - 1; i++)
{
if (!NamesOfParameters[i].Hidden)
{
IndicesOfActive.Add(i);
}
}
if (IndicesOfActive.Count < 1)
{
return;
}
for (int i = 0; i < inputs.Length; i++)
{
inputs[i] = new double[IndicesOfActive.Count];
for (int j = 0; j < inputs[i].Length; j++)
{
inputs[i][j] = double.Parse(TrainData.Rows[i][IndicesOfActive[j]].ToString(), CultureInfo.InvariantCulture);
}
}
double[] outputs = new double[inputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
outputs[i] = double.Parse(TrainData.Rows[i][TrainData.Columns.Count - 1].ToString(), CultureInfo.InvariantCulture);
}
var ord = new OrdinaryLeastSquares()
{
UseIntercept = true
};
regression = ord.Learn(inputs, outputs);
CoefficentOfDetermination.Text = $"{regression.CoefficientOfDetermination(inputs, outputs, adjust: true):F4}";
TrainModel = new List <RegCoef>();
for (int i = 0; i < IndicesOfActive.Count; i++)
{
double[] x = new double[outputs.Length];
for (int j = 0; j < inputs.Length; j++)
{
x[j] = inputs[j][i];
}
double scoef = regression.Weights[i] * (StandardDeviation(x) / StandardDeviation(outputs));
var cur = new RegCoef();
cur.Name = NamesOfParameters[IndicesOfActive[i]].Name;
cur.StandCoef = $"{scoef:F4}";
cur.Coef = $"{regression.Weights[i]:F4}";
TrainModel.Add(cur);
}
SortedTrainModel = new List <RegCoef>(TrainModel);
CoefGrid.ItemsSource = null;
CoefGrid.ItemsSource = SortedTrainModel;
}
catch (FormatException)
{
MessageBox.Show("Данные должны представляться числами");
TrainModel = OldTrainModel;
}
catch (Exception)
{
MessageBox.Show("Неизвестная ошибка");
TrainModel = OldTrainModel;
}
}
