private void openRegressionModelsDatabaseToolStripMenuItem_Click(object sender, EventArgs e)
{
var allModels = _databaseHelper.GetAllRegressionModels();
var loginResult = new AuthorizationRegressionAnalysis().ShowDialog();
RegressionModelsDatabase databaseForm;
switch (loginResult)
{
case DialogResult.Yes:
databaseForm = new RegressionModelsDatabase(_databaseHelper, allModels, true);
break;
case DialogResult.No:
databaseForm = new RegressionModelsDatabase(_databaseHelper, allModels, false);
break;
default: return;
}
var result = databaseForm.ShowDialog();
if (result == DialogResult.OK)
{
var selectedModel = databaseForm.SelectedEquation;
if (selectedModel.IsLinearRegression)
{
_linearEquation = selectedModel;
ShowLinearRegressionModel();
}
else
{
_multipleRegressionEquation = selectedModel;
ShowMultipleRegressionModel();
}
}
}
private void SaveRegressionModel(RegressionModelEquation model)
{
if (model == null)
{
MessageBox.Show("There is no model to save!");
return;
}
ModelNameDateSelection.GetRegressionModelSaveInfo(out string name, out DateTime creationDate);
_databaseHelper.AddRegressionModel(model, name, creationDate);
MessageBox.Show($"Regression model {name} successfully saved!");
}
void DrawMainChart(List <PointD> points, bool writeModel)
{
double[] linearRegression = Regression.LinearRegression(points, checkBoxNormalizeValuesPage1.Checked);
var rmse = Regression.RMSE(points, linearRegression[1], linearRegression[0]);
var series = chartRegressionModel.Series["SourceData"];
series.Points.Clear();
foreach (var point in points)
{
series.Points.AddXY(point.X, point.Y, point.Y, linearRegression[0] + linearRegression[1] * point.X);
}
double minX = (int)points.Min(item => item.X);
double maxX = (int)points.Max(item => item.X);
double minY = (int)points.Min(item => item.Y);
double maxY = (int)points.Max(item => item.Y);
if (writeModel)
{
_linearEquation = new RegressionModelEquation("DefaultName", linearRegression, rmse, minX, maxX, minY,
maxY,
comboBoxInputParams.SelectedItem.ToString(), comboBoxQualityParams.SelectedItem.ToString(), checkBoxNormalizeValuesPage1.Checked);
}
_linearEquation.GetLinearRegressionModelInfo(out string modelText, out string xAxisTitle, out string yAxisTitle, out double[] coefficients, out minX, out maxX, out string rmseString, out bool normalizeValues);
labelEquasion.Text = modelText;
labelRMSE.Text = rmseString;
checkBoxNormalizeValuesPage1.Checked = normalizeValues;
chartRegressionModel.ChartAreas["ChartAreaMain"].AxisX.Title = xAxisTitle;
chartRegressionModel.ChartAreas["ChartAreaMain"].AxisY.Title = yAxisTitle;
chartRegressionModel.ChartAreas["ChartAreaMain"].AxisX.Minimum = minX - 1;
chartRegressionModel.ChartAreas["ChartAreaMain"].AxisX.Maximum = maxX + 1;
chartRegressionModel.ChartAreas["ChartAreaMain"].AxisY.Minimum = minY - 1;
chartRegressionModel.ChartAreas["ChartAreaMain"].AxisY.Maximum = maxY + 1;
chartRegressionModel.ChartAreas["ChartAreaMain"].AxisX.Interval = (maxX - minX + 2) / LineChartXAxisIntervalsCount;
chartRegressionModel.ChartAreas["ChartAreaMain"].AxisY.Interval = (maxY - minY + 2) / LineChartYAxisIntervalsCount;
DrawMainChartLine(linearRegression, minX - 1, maxX + 1, minY - 1, maxY + 1);
}
private void CreateInputParametersTable(RegressionModelEquation model)
{
DataTable table = new DataTable();
table.Columns.Add("ParamId", typeof(string));
table.Columns.Add("ParamName", typeof(string));
table.Columns.Add("ParamCoefficient", typeof(double));
table.Columns.Add("ParamUpperBound", typeof(string));
table.Columns.Add("ParamLowerBound", typeof(string));
foreach (var parameter in model.InputParameters)
{
table.Rows.Add(parameter.Id.HasValue ? parameter.Id.Value.ToString() : string.Empty,
parameter.Id.HasValue
? XMLWork.FindNameWithID(parameter.Id.Value, Properties.Settings.Default.Languages)
: "free term", Math.Round(parameter.Coefficient, EquationSignificantDigitsCount),
parameter.UpperBound.HasValue ? Math.Round(parameter.UpperBound.Value, EquationSignificantDigitsCount).ToString() : string.Empty,
parameter.LowerBound.HasValue ? Math.Round(parameter.LowerBound.Value, EquationSignificantDigitsCount).ToString() : string.Empty);
}
dataGridViewInputParametersInfo.DataSource = table;
}
bool GetMultipleRegressionModel(string qualityParam)
{
double[,] processParametersData;
double[] qualityParameterData;
var inputParametersToCheck = checkedListBoxInputParametersToCheckPage3.CheckedItems.Cast <string>().ToList();
int qualityParameterID = XMLWork.FindIDWithName(qualityParam, Properties.Settings.Default.Languages);
processParametersData = new double[TrainData.Train.Count, inputParametersToCheck.Count + 1];
qualityParameterData = new double[TrainData.Train.Count];
//Setting first column of input values of regression matrix X with "1" values
for (int i = 0; i < processParametersData.GetLength(0); i++)
{
processParametersData[i, 0] = 1;
}
List <double> parametersLowerBounds = new List <double>(), parametersUpperBounds = new List <double>();
//Setting all input data
for (int paramNumber = 0; paramNumber < inputParametersToCheck.Count; paramNumber++)
{
double minValue = double.MaxValue, maxValue = double.MinValue;
int inputParameterID = XMLWork.FindIDWithName(inputParametersToCheck[paramNumber], Properties.Settings.Default.Languages);
for (int rowNumber = 0; rowNumber < TrainData.Train.Count; rowNumber++)
{
processParametersData[rowNumber, paramNumber + 1] =
(double)TrainData.Train[rowNumber].Input[inputParameterID];
if (processParametersData[rowNumber, paramNumber + 1] < minValue)
{
minValue = processParametersData[rowNumber, paramNumber + 1];
}
if (processParametersData[rowNumber, paramNumber + 1] > maxValue)
{
maxValue = processParametersData[rowNumber, paramNumber + 1];
}
}
parametersLowerBounds.Add(minValue);
parametersUpperBounds.Add(maxValue);
}
//Setting output data
for (int i = 0; i < TrainData.Train.Count; i++)
{
qualityParameterData[i] = (double)TrainData.Train[i].Output[qualityParameterID];
}
//Call for multiple regression calculation method
try
{
for (int j = 1; j < processParametersData.GetLength(1); j++)
{
bool throwData = true;
for (int i = 0; i < processParametersData.GetLength(0); i++)
{
if (Math.Abs(processParametersData[i, j]) > DataSensitivityThreshhold)
{
throwData = false;
break;
}
}
if (throwData)
{
throw new Exception("Incorrect input data");
}
}
if (!qualityParameterData.Any(item => Math.Abs(item) > DataSensitivityThreshhold))
{
throw new Exception("Incorrect input data");
}
var coefficients = Regression.MultipleRegression(processParametersData, qualityParameterData, checkBoxNormalizeValuesPage3.Checked);
double rmse = Regression.MultipleRMSE(processParametersData, qualityParameterData, coefficients);
labelRMSEPage3.Text = $"RMSE = {Math.Round(rmse, RMSESignificantDigitsCount)}";
_multipleRegressionEquation = new RegressionModelEquation("DefaultName", qualityParam,
inputParametersToCheck, rmse, coefficients,
inputParametersToCheck.ToArray(), parametersLowerBounds.ToArray(), parametersUpperBounds.ToArray(), checkBoxNormalizeValuesPage3.Checked);
ShowRegressionEquasion(qualityParam, coefficients);
ShowRegressionEquasionHelp(inputParametersToCheck.ToArray());
return(true);
}
catch (Exception ex)
{
MessageBox.Show($"Exception thrown in multiple regression calculation: \"{ex.Message}\"!");
return(false);
}
}
