bool GetLinearRegressionModel(string qualityParam, string inputParam, bool writeModel = true)
{
int inputParameterID = XMLWork.FindIDWithName(inputParam, Properties.Settings.Default.Languages);
int qualityParameterID = XMLWork.FindIDWithName(qualityParam, Properties.Settings.Default.Languages);
try
{
var generalData = (from row in TrainData.Train
select new PointD {
X = (double)row.Input[inputParameterID], Y = (double)row.Output[qualityParameterID]
}).ToList();
if (!generalData.Any(item => Math.Abs(item.X) > DataSensitivityThreshhold && Math.Abs(item.Y) > DataSensitivityThreshhold))
{
throw new Exception("Incorrect input data");
}
DrawMainChart(generalData, writeModel);
DrawErrorsChart(generalData);
return(true);
}
catch (Exception ex)
{
MessageBox.Show($"Error parsing data: \"{ex.Message}\"", "Exception", MessageBoxButtons.OK);
return(false);
}
}
private void PreprocessToWorkTree()
{
var c45 = new DecisionTreeC45();
int parameterID = XMLWork.FindIDWithName(parameterCondition.SelectedItem.ToString(), Properties.Settings.Default.Languages);
_root = c45.MountTree(TrainData.Train, TrainData.nameParameter, parameterID, Convert.ToInt32(textEdit1.Value) + 1);
MessageBox.Show(Localization.MyStrings.Over);
}
private void GetPoints(ref decimal pointX, OneRow data, ref decimal pointY)
{
foreach (var item in ListDelete.Items)
{
int parameterID = XMLWork.FindIDWithName(item.ToString(), Properties.Settings.Default.Languages);
pointX = pointX * data.Input[parameterID]; //234 232
}
foreach (var item in ListDeleteError.Items)
{
int parameterID = XMLWork.FindIDWithName(item.ToString(), Properties.Settings.Default.Languages);
pointY = pointY * data.Input[parameterID]; //234 232
}
}
private void GetBooleanForOutput(List <OneRow> trainData, decimal criterion)
{
int parameterID = XMLWork.FindIDWithName(parameterCondition.SelectedItem.ToString(), Properties.Settings.Default.Languages);
foreach (OneRow oneRow in trainData)
{
if (oneRow.Output[parameterID] >= criterion)
{
oneRow.OutputBool[parameterID] = true;
}
else
{
oneRow.OutputBool[parameterID] = false;
}
}
}
private void PreprocessToWorkTreeRandomForest(TrainData trainedData, TrainData checkData,
Dictionary <int, string> analysParameter)
{
int numberOfForest = Convert.ToInt32(numberOfForests.Value);
RandForests = new List <DM.DecisionTree.TreeNode>();
//double accuracy = 0.0;
//do
//{
//Random rand = new Random((int) (DateTime.Now.Ticks));
//DivideSet(rand, trainData, trainedData, checkData);
List <DM.DecisionTree.TreeNode> rndForests = new List <DM.DecisionTree.TreeNode>();
int parameterID = XMLWork.FindIDWithName(parameterCondition.SelectedItem.ToString(), Properties.Settings.Default.Languages);
int restrictCountOfParameters = Convert.ToInt32(Math.Sqrt(analysParameter.Count)) + 1;
Dictionary <int, string> newParameters = new Dictionary <int, string>();
for (int forestsCount = 0; forestsCount < numberOfForest;)
{
DM.DecisionTree.RandomForest random = new RandomForest();
List <int> parametersName = new List <int>();
foreach (KeyValuePair <int, string> keyValuePair in analysParameter)
{
if (!keyValuePair.Value.StartsWith("Def"))
{
parametersName.Add(keyValuePair.Key);
}
}
for (int i = 0; i < restrictCountOfParameters; i++)
{
int j = rand.Next(0, analysParameter.Count);
if (!newParameters.ContainsKey(parametersName[j]))
{
newParameters.Add(parametersName[j], analysParameter[parametersName[j]]);
}
}
List <OneRow> trainData = new List <OneRow>();
trainData = GetRandomData(TrainData.Train, newParameters, parameterID);
_root = random.MountTree(trainData, newParameters, parameterID, Convert.ToInt32(textEdit1.Value) + 1);
if (_root.attributeName != "False")
{
RandForests.Add(_root);
forestsCount++;
}
newParameters.Clear();
}
}
bool GetDataForRSquaredGraph(string qualityParam)
{
var allData = new Dictionary <string, KeyValuePair <double, double> >();
var inputParametersToCheck = checkedListBoxInputParametersToCheck.CheckedItems.Cast <string>();
int qualityParameterID = XMLWork.FindIDWithName(qualityParam, Properties.Settings.Default.Languages);
try
{
foreach (var inputParam in inputParametersToCheck)
{
int inputParameterID = XMLWork.FindIDWithName(inputParam, Properties.Settings.Default.Languages);
double rSquared, Fisher;
var generalData = (from row in TrainData.Train
select new PointD {
X = (double)row.Input[inputParameterID], Y = (double)row.Output[qualityParameterID]
}).ToList();
if (!generalData.Any(item => Math.Abs(item.X) > DataSensitivityThreshhold && Math.Abs(item.Y) > DataSensitivityThreshhold))
{
throw new Exception("Incorrect input data");
}
Regression.Correlation(generalData, checkBoxNormalizeValuesPage2.Checked, out rSquared, out Fisher);
allData.Add(inputParam, new KeyValuePair <double, double>(rSquared, Fisher));
}
allData = allData.Where(item => !IsNanOrInfinity(item.Value)).OrderByDescending(item => item.Value.Key)
.Take(BestSimpleRegressionsShowingCount).ToDictionary(item => item.Key, item => item.Value);
ShowRSquaredGraph(allData);
return(true);
}
catch (Exception ex)
{
MessageBox.Show($"Error processing data: \"{ex.Message}\"", "Exception", MessageBoxButtons.OK);
return(false);
}
}
private void parameterCondition_SelectedIndexChanged(object sender, EventArgs e)
{
ListAdd.Items.Clear();
ListDelete.Items.Clear();
List <int> keys = new List <int>();
int parameterID = XMLWork.FindIDWithName(parameterCondition.SelectedItem.ToString(), Properties.Settings.Default.Languages);
Dictionary <int, decimal> firstRow = TrainData.Train.First().Input;
foreach (OneRow data in TrainData.Train)
{
foreach (var item in data.Input)
{
if (keys.Contains(item.Key))
{
continue;
}
if (item.Key != firstRow[item.Key])
{
keys.Add(item.Key);
}
}
break;
}
foreach (int key in keys)
{
ListAdd.Items.Add(XMLWork.FindNameWithID(key, Properties.Settings.Default.Languages));
}
if (ListAdd.Items.Count != 0)
{
ListAdd.SelectedIndex = 0;
}
if (ListDelete.Items.Count != 0)
{
ListDelete.SelectedIndex = 0;
}
}
public void LoadFromDB()
{
values = null;
normalise = null;
weights = null;
normaliseRandom = null;
listParameter.Clear();
values = new decimal[ListDelete.Items.Count, TrainData.Train.Count];
normalise = new decimal[ListDelete.Items.Count, TrainData.Train.Count];
normaliseRandom = new decimal[ListDelete.Items.Count, TrainData.Train.Count];
weights = new decimal[ListDelete.Items.Count, Convert.ToInt32(numberOfNeurons.Text)];
dimensionOfVector.Text = Convert.ToString(ListDelete.Items.Count);
int count = 0;
foreach (var item in ListDelete.Items)
{
int parameterID = XMLWork.FindIDWithName(item.ToString(), Properties.Settings.Default.Languages);
OneRow row = TrainData.Train.First();
decimal value = 0.0M;
if (row.Input.ContainsKey(parameterID))
{
value = row.Input[parameterID];
}
else if (row.Output.ContainsKey(parameterID))
{
value = row.Output[parameterID];
}
listParameter.Add(count, new ParameterCharacteristics(item.ToString(), value, value, value, 0.0M, 0.0M));
count++;
}
if (TrainData.Train.Count > 0)
{
int j = 0;
//Вычисление среднего значения каждого атрибута выборки + сумма, минимум и максимум также вычисляются
foreach (OneRow row in TrainData.Train)
{
for (int i = 0; i < listParameter.Count; i++)
{
try
{
int parameterID = XMLWork.FindIDWithName(listParameter[i].ParameterName, Properties.Settings.Default.Languages);
if (row.Input.ContainsKey(parameterID))
{
values[i, j] = row.Input[parameterID];
}
else if (row.Output.ContainsKey(parameterID))
{
values[i, j] = row.Output[parameterID];
}
if (values[i, j] < listParameter[i].Min)
{
listParameter[i].Min = values[i, j];
}
if (values[i, j] > listParameter[i].Max)
{
listParameter[i].Max = values[i, j];
}
listParameter[i].Sum += values[i, j];
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
}
}
j++;
}
//Поиск среднего значения по выборке
for (int i = 0; i < listParameter.Count; i++)
{
listParameter[i].Mean = listParameter[i].Sum / Convert.ToDecimal(TrainData.Train.Count);
j = 0;
decimal differenceSquares = 0.0M;
foreach (OneRow row in TrainData.Train)
{
differenceSquares += ((values[i, j] - listParameter[i].Mean) * (values[i, j] - listParameter[i].Mean));
j++;
}
listParameter[i].Deviation = differenceSquares / (decimal)(TrainData.Train.Count - 1);
}
//Нормализация данных
for (int i = 0; i < listParameter.Count; i++)
{
for (int k = 0; k < TrainData.Train.Count; k++)
{
if (listParameter[i].Max - listParameter[i].Min != 0)
{
normalise[i, k] = Normalisation.NormaliseWithDeviation(values[i, k], listParameter[i].Mean, listParameter[i].Deviation);
}
else
{
normalise[i, k] = 0.0M;
}
}
}
}
else
{
MessageBox.Show(Localization.MyStrings.Absent, Localization.MyStrings.Warning);
}
//
int step = TrainData.Train.Count / Convert.ToInt32(numberOfNeurons.Text);
//Здесь задаются начальные значения весов
/*
* for (int j = 0, n = 0; j < TrainData.Train.Count; j = j + step, n++)
* {
* if (n < Convert.ToInt32(numberOfNeurons.Text))
* {
* for (int i = 0; i < listParameter.Count; i++)
* weights[i,n] = normalise[i,j];
* }
* }*/
Random rand = new Random((int)(DateTime.Now.Ticks));
for (int j = 0; j < Convert.ToInt32(numberOfNeurons.Text); j++)
{
for (int k = 0; k < listParameter.Count; k++)
{
weights[k, j] = Randomise.RandomDecimal(rand);
}
}
}
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);
}
}
void ShowParameterData(string parameterName)
{
if (Calculation.stringName != parameterName)
{
Calculation.stringName = parameterName;
TextBoxColumnCount.Text = "";
TextBoxMax.Text = "";
TextBoxMin.Text = "";
}
List <KeyValuePair <DateTime, double> > data;
int parameterID = XMLWork.FindIDWithName(parameterName, Properties.Settings.Default.Languages);
var scadaName = XMLWork.FindScadaNameWithID(parameterID);
if (scadaName.StartsWith("Def"))
{
data = (from row in TrainData.Train
select new KeyValuePair <DateTime, double>(row.Date, (double)row.Output[parameterID])).ToList();
}
else
{
data = (from row in TrainData.Train
select new KeyValuePair <DateTime, double>(row.Date, (double)row.Input[parameterID])).ToList();
}
/* double min, max;
* min = String.IsNullOrWhiteSpace(TextBoxMin.Text) ? data.Min(item => item.Value) : double.Parse(TextBoxMin.Text);
* TextBoxMin.Text = min.ToString();
* max = String.IsNullOrWhiteSpace(TextBoxMax.Text) ? data.Max(item => item.Value) : double.Parse(TextBoxMax.Text);
* TextBoxMax.Text = max.ToString();*/
if (options.log)
{
data = data.Where(item => item.Value != 0).ToList();
List <KeyValuePair <DateTime, double> > keyValuePairs = new List <KeyValuePair <DateTime, double> >();
foreach (var d in data)
{
keyValuePairs.Add(new KeyValuePair <DateTime, double>(d.Key, Math.Round(Math.Log(d.Value), 2)));
}
data = keyValuePairs;
if (!String.IsNullOrWhiteSpace(TextBoxMin.Text) || !String.IsNullOrWhiteSpace(TextBoxMin.Text))
{
if (data.Max(n => n.Value) < Convert.ToDouble(TextBoxMax.Text) || data.Min(n => n.Value) > Convert.ToDouble(TextBoxMin.Text))
{
TextBoxMin.Text = "";
TextBoxMax.Text = "";
}
}
}
if (options.unlog)
{
List <KeyValuePair <DateTime, double> > keyValuePairs = new List <KeyValuePair <DateTime, double> >();
foreach (var d in data)
{
keyValuePairs.Add(new KeyValuePair <DateTime, double>(d.Key, Math.Round(Math.Exp(d.Value), 2)));
}
data = keyValuePairs;
TextBoxMin.Text = "";
TextBoxMax.Text = "";
}
if (options.sigma)
{
List <KeyValuePair <DateTime, double> > dataControl;
double expectedValue = data.Sum(item => item.Value) / data.Count;
double standartDeviation = Math.Sqrt(data.Sum(item => Math.Pow(item.Value - expectedValue, 2)) / (data.Count - 1));
do
{
data = data.Where(item => item.Value > (expectedValue - 3 * standartDeviation) && item.Value < (expectedValue + 3 * standartDeviation)).ToList();
expectedValue = data.Sum(item => item.Value) / data.Count;
standartDeviation = Math.Sqrt(data.Sum(item => Math.Pow(item.Value - expectedValue, 2)) / (data.Count - 1));
double a = expectedValue - 3 * standartDeviation;
double b = expectedValue + 3 * standartDeviation;
dataControl = data.Where(item => item.Value <= a || item.Value >= b).ToList();
} while (dataControl.Count() > 0);
}
double min, max;
if (options.language)
{
TextBoxMin.Text = TextBoxMin.Text.Replace(".", ",");
TextBoxMax.Text = TextBoxMax.Text.Replace(".", ",");
}
else
{
TextBoxMin.Text = TextBoxMin.Text.Replace(",", ".");
TextBoxMax.Text = TextBoxMax.Text.Replace(",", ".");
}
min = String.IsNullOrWhiteSpace(TextBoxMin.Text) ? data.Min(item => item.Value) : double.Parse(TextBoxMin.Text);
TextBoxMin.Text = min.ToString();
max = String.IsNullOrWhiteSpace(TextBoxMax.Text) ? data.Max(item => item.Value) : double.Parse(TextBoxMax.Text);
TextBoxMax.Text = max.ToString();
if (options.minmax)
{
data = data.Where(item => item.Value >= min && item.Value <= max).ToList();
}
dataResult = data;
ShowChart1(data, parameterName);
ShowChart2(data, parameterName);
}
void ShowThrends2()
{
if ((ListDelete.Items.Count > 10) || (ListDeleteError.Items.Count > 10))
{
MessageBox.Show(
Localization.MyStrings.ParametersSelected);
return;
}
if (parameterCondition.SelectedIndex == -1)
{
MessageBox.Show(Localization.MyStrings.SelectCriterion);
parameterCondition.BackColor = Color.Red;
return;
}
int parameterID = XMLWork.FindIDWithName(parameterCondition.SelectedItem.ToString(), Properties.Settings.Default.Languages);
Chart.plt.Clear();
List <OneRow> trainData = TrainData.Train;
List <double> xs1 = new List <double>(), ys1 = new List <double>(), xs2 = new List <double>(), ys2 = new List <double>();
decimal minX = 0.0M;
decimal maxX = 0.0M;
decimal minY = 0.0M;
decimal maxY = 0.0M;
foreach (OneRow data in trainData)
{
decimal pointX = 1.0M;
decimal pointY = 1.0M;
if (data.Output[parameterID] > (Convert.ToDecimal(uppLimit.Value) * 0.9M))
{
GetPoints(ref pointX, data, ref pointY);
maxX = pointX;
maxY = pointY;
minX = pointX;
minY = pointY;
}
else
{
GetPoints(ref pointX, data, ref pointY);
maxX = pointX;
maxY = pointY;
minX = pointX;
minY = pointY;
}
break;
}
foreach (OneRow data in trainData)
{
decimal pointX = 1.0M;
decimal pointY = 1.0M;
if (data.Output[parameterID] > (Convert.ToDecimal(uppLimit.Value) * 0.9M))
{
GetPoints(ref pointX, data, ref pointY);
if (pointX > maxX)
{
maxX = pointX;
}
if (pointY > maxY)
{
maxY = pointY;
}
if (pointX < minX)
{
minX = pointX;
}
if (pointY < minY)
{
minY = pointY;
}
xs1.Add(Convert.ToDouble(pointX));
ys1.Add(Convert.ToDouble(pointY));
}
else
{
GetPoints(ref pointX, data, ref pointY);
if (pointX > maxX)
{
maxX = pointX;
}
if (pointY > maxY)
{
maxY = pointY;
}
if (pointX < minX)
{
minX = pointX;
}
if (pointY < minY)
{
minY = pointY;
}
xs2.Add(Convert.ToDouble(pointX));
ys2.Add(Convert.ToDouble(pointY));
}
}
Chart.plt.PlotScatter(xs1.ToArray(), ys1.ToArray(), lineWidth: 0, markerShape: MarkerShape.filledCircle, label: Localization.MyStrings.WithD);
Chart.plt.PlotScatter(xs2.ToArray(), ys2.ToArray(), lineWidth: 0, markerShape: MarkerShape.filledCircle, label: Localization.MyStrings.WithoutD);
Chart.plt.Legend(location: legendLocation.upperRight, shadowDirection: shadowDirection.none);
Chart.plt.AxisAuto();
decimal deltaX = Math.Abs(maxX - minX);
decimal deltaY = Math.Abs(maxY - minY);
Chart.plt.Axis(Convert.ToDouble(minX - deltaX * 0.05m), Convert.ToDouble(maxX + deltaX * 0.05m),
Convert.ToDouble(minY - deltaY * 0.05m), Convert.ToDouble(maxY + deltaY * 0.05m));
Chart.Render();
}
/// <summary>
/// Selects parameters in orange
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
/*private void DrawItem(object sender, ListBoxDrawItemEventArgs e)
* {
* ListBoxControl control = (ListBoxControl)sender;
* if (e.Index == (control.SelectedIndex))
* {
* e.Appearance.BackColor = Color.Orange;
* }
* }*/
#endregion
private void parameterCondition_SelectedIndexChanged(object sender, EventArgs e)
{
ListAdd.Items.Clear();
ListDelete.Items.Clear();
ListAddError.Items.Clear();
ListDeleteError.Items.Clear();
List <int> keys = new List <int>();
int parameterID = XMLWork.FindIDWithName(parameterCondition.SelectedItem.ToString(), Properties.Settings.Default.Languages);
Dictionary <int, decimal> firstRow = TrainData.Train.First(o => o.Output[parameterID] > (Convert.ToDecimal(uppLimit.Value) * 0.9M)).Input;
foreach (OneRow data in TrainData.Train)
{
if (data.Output[parameterID] > 1.0M)
{
foreach (var item in data.Input)
{
if (keys.Contains(item.Key))
{
continue;
}
if (item.Key != firstRow[item.Key])
{
keys.Add(item.Key);
}
}
}
}
foreach (int key in keys)
{
ListAdd.Items.Add(XMLWork.FindNameWithID(key, Properties.Settings.Default.Languages));
ListAddError.Items.Add(XMLWork.FindNameWithID(key, Properties.Settings.Default.Languages));
}
addAllToFirstCoordinate.Enabled = true;
addAllToSecondCoordinate.Enabled = true;
addOneCoordinate.Enabled = true;
addOneToSecondCoordinate.Enabled = true;
deleteAllToFirstCoordinate.Enabled = true;
deleteAllToSecondCoordinate.Enabled = true;
deleteOneToSecondCoordinate.Enabled = true;
deleteToFirstCoordinate.Enabled = true;
if (parameterCondition.SelectedIndex != -1)
{
parameterCondition.BackColor = Color.White;
}
if (ListAdd.Items.Count != 0)
{
ListAdd.SelectedIndex = 0;
}
if (ListDelete.Items.Count != 0)
{
ListDelete.SelectedIndex = 0;
}
if (ListAddError.Items.Count != 0)
{
ListAddError.SelectedIndex = 0;
}
if (ListDeleteError.Items.Count != 0)
{
ListDeleteError.SelectedIndex = 0;
}
}
/// <summary>
/// Implement FisherCritery
/// </summary>
private void FisherCritery()
{
if (parameterCondition.Items.Count != 0)
{
int countElements = 10;
List <OneRow> trainData = TrainData.Train;
Dictionary <int, decimal> bestValues = new Dictionary <int, decimal>();
List <FisherCritery> fishers = new List <FisherCritery>();
try
{
int parameterID = XMLWork.FindIDWithName((string)parameterCondition.SelectedItem, Properties.Settings.Default.Languages);
decimal averageY = AverageOutput(trainData, parameterID);
//Fisher Critery
foreach (var item in trainData[0].Input)
{
Fisher fisher = new Fisher(trainData, item.Key, parameterID, averageY);
fisher.CalculateFisher();
fishers.Add(new FisherCritery(item.Key, fisher.Critery));
}
//Sort of data
ListOfElements fisherElements = new ListOfElements(fishers);
fisherElements.InsertionSort();
//Give Best Value
for (int i = 0; i < countElements; i++)
{
bestValues.Add(fisherElements.Fisher[i].Key, fisherElements.Fisher[i].Value);
}
//Clear Palette
this.chart1.Series.Clear();
this.chart1.Titles.Clear();
this.chart1.ChartAreas[0].AxisY.Maximum = Math.Round(Convert.ToDouble(bestValues.First().Value), 0) + 25;
this.chart1.ChartAreas[0].AxisY.Minimum = Math.Round(Convert.ToDouble(bestValues.Last().Value), 0) - 25;
// Set palette.
this.chart1.Palette = ChartColorPalette.SeaGreen;
// Set title.
this.chart1.Titles.Add("Critery F-Test");
double numberOfParameters = 1.0;
foreach (var item in bestValues)
{
// Add series.
Series series = this.chart1.Series.Add(XMLWork.FindNameWithID(item.Key, Properties.Settings.Default.Languages));
// Add point.
//series.Points.Add(Convert.ToDouble(item.Value));
series.Points.AddXY(numberOfParameters, item.Value);
numberOfParameters += 0.1;
}
}
catch (InvalidOperationException ex)
{
MessageBox.Show(Localization.MyStrings.SelectCriterion);
}
catch (Exception excep)
{
MessageBox.Show(excep.ToString());
}
}
else
{
MessageBox.Show(Localization.MyStrings.WithoutDefect);
}
// Add series.
}
private void DecisionTreeRandomForest()
{
var checkData = new TrainData();
var analysParameter = new Dictionary <int, string>();
var trainData = new TrainData();
var trainedData = new TrainData();
if ((parameterCondition.Items.Count != 0) && (ListDelete.Items.Count != 0) &&
(parameterCondition.SelectedIndex != -1))
{
try
{
GetBooleanForOutput(TrainData.Train, Convert.ToDecimal(criterion.Text));
analysParameter = TrainData.nameParameter.Where(o => !o.Value.StartsWith("Def"))
.ToDictionary(o => o.Key, o => o.Value);
bool falseData = false;
bool trueData = false;
int parameterID = XMLWork.FindIDWithName(parameterCondition.SelectedItem.ToString(), Properties.Settings.Default.Languages);
foreach (OneRow oneRow in TrainData.Train)
{
if ((oneRow.OutputBool[parameterID] == true) && (trueData == false))
{
trueData = true;
}
else if ((oneRow.OutputBool[parameterID] == false) && (falseData == false))
{
falseData = true;
}
}
if ((falseData) && (trueData))
{
PreprocessToWorkTreeRandomForest(trainedData, checkData, analysParameter);
System.Windows.Forms.MessageBox.Show(Localization.MyStrings.Over);
}
else
{
MessageBox.Show(Localization.MyStrings.AnotherCriterion);
}
}
catch (FormatException formatException)
{
MessageBox.Show(Localization.MyStrings.SelectPartitioninCriterion);
criterion.BackColor = Color.Red;
ListDelete.BackColor = Color.White;
parameterCondition.BackColor = Color.White;
}
catch
(InvalidOperationException ex)
{
MessageBox.Show(Localization.MyStrings.SelectCriterion);
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.White;
parameterCondition.BackColor = Color.Red;
}
catch (Exception excep)
{
if (ListDelete.Items.Count == 0)
{
MessageBox.Show(Localization.MyStrings.AddParameter);
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.Red;
parameterCondition.BackColor = Color.White;
}
else
{
MessageBox.Show(excep.ToString());
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.White;
parameterCondition.BackColor = Color.White;
}
}
}
else
{
if (parameterCondition.Items.Count == 0)
{
MessageBox.Show(Localization.MyStrings.WithoutDefect);
}
else if ((parameterCondition.SelectedIndex == -1))
{
MessageBox.Show(Localization.MyStrings.SelectCriterion);
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.White;
parameterCondition.BackColor = Color.Red;
}
if (ListDelete.Items.Count == 0)
{
MessageBox.Show(Localization.MyStrings.AddParameter);
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.Red;
parameterCondition.BackColor = Color.White;
}
}
}
private void trainButton_Click(object sender, EventArgs e)
{
if ((parameterCondition.Items.Count != 0) && (ListDelete.Items.Count != 0) && (parameterCondition.SelectedIndex != -1))
{
try
{
GetBooleanForOutput(TrainData.Train, criterion.Value);
bool falseData = false;
bool trueData = false;
int parameterID = XMLWork.FindIDWithName(parameterCondition.SelectedItem.ToString(), Properties.Settings.Default.Languages);
foreach (OneRow oneRow in TrainData.Train)
{
if ((oneRow.OutputBool[parameterID] == true) && (trueData == false))
{
trueData = true;
}
else if ((oneRow.OutputBool[parameterID] == false) && (falseData == false))
{
falseData = true;
}
}
if ((falseData) && (trueData))
{
PreprocessToWorkTree();
}
else
{
MessageBox.Show(Localization.MyStrings.AnotherCriterion);
}
}
catch
(InvalidOperationException ex)
{
MessageBox.Show(Localization.MyStrings.SelectCriterion);
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.White;
parameterCondition.BackColor = Color.Red;
}
catch
(Exception excep)
{
if (criterion.Text.Trim() == "")
{
MessageBox.Show(Localization.MyStrings.SelectPartitioninCriterion);
criterion.BackColor = Color.Red;
ListDelete.BackColor = Color.White;
parameterCondition.BackColor = Color.White;
}
else if (ListDelete.Items.Count == 0)
{
MessageBox.Show(Localization.MyStrings.AddParameter);
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.Red;
parameterCondition.BackColor = Color.White;
}
else
{
MessageBox.Show(excep.ToString());
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.White;
parameterCondition.BackColor = Color.White;
}
}
}
else
{
if (parameterCondition.Items.Count == 0)
{
MessageBox.Show(Localization.MyStrings.WithoutDefect);
}
else if ((parameterCondition.SelectedIndex == -1))
{
MessageBox.Show(Localization.MyStrings.SelectCriterion);
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.White;
parameterCondition.BackColor = Color.Red;
}
if (ListDelete.Items.Count == 0)
{
MessageBox.Show(Localization.MyStrings.AddParameter);
criterion.BackColor = Color.White;
ListDelete.BackColor = Color.Red;
parameterCondition.BackColor = Color.White;
}
}
// Add series.
}
