/* private void button1_Click(object sender, EventArgs e)
* {
* for (int i = 0; i < dataGridView1.ColumnCount; i++)
* {
* for (int j = 0; j < dataGridView1.RowCount; j++)
* {
* double val1 = Convert.ToDouble(dataGridView1.Rows[j].Cells[i].Value);
* if (!nZvalue.Contains(val1))
* {
* nZvalue.Add(val1);
* }
* }
* Ztype.Add(AnalysisClass.getScale(nZvalue.Count));
* nZvalue.Clear();
* }
* for (int i = 0; i < dataGridView1.RowCount; i++)
* {
* double val1 = Convert.ToDouble(dataGridView1.Rows[i].Cells[0].Value);
* nZvalue.Add(val1);
* }
* resTypeReg.Text = AnalysisClass.getReggressionMethod(Ztype, comboBox2.Text, nZvalue);
* nZvalue.Clear();
*
* }*/
private void AnalizMetod_Click(object sender, EventArgs e)
{
Ztype.Clear();
nZvalue.Clear();
if ((dataGridView1.Columns.Count - 1) * 3 <= dataGridView1.RowCount)
{
for (int i = 0; i < dataGridView1.ColumnCount; i++)
{
for (int j = 0; j < dataGridView1.RowCount; j++)
{
double val1 = Convert.ToDouble(dataGridView1.Rows[j].Cells[i].Value);
if (!nZvalue.Contains(val1))
{
nZvalue.Add(val1);
}
}
Ztype.Add(AnalysisClass.getScale(nZvalue.Count));
nZvalue.Clear();
}
for (int i = 0; i < dataGridView1.RowCount; i++)
{
double val1 = Convert.ToDouble(dataGridView1.Rows[i].Cells[0].Value);
nZvalue.Add(val1);
}
string[] words = AnalysisClass.getCurrentReggressionMethod(Ztype, nZvalue).Split(new char[] { ':' }, StringSplitOptions.RemoveEmptyEntries);
richTextBox1.Text = string.Join(Environment.NewLine, words);
nZvalue.Clear();
}
else
{
richTextBox1.Text = "Выборка слишком мала для точного анализа.";
}
}
private void calculateBtn_Click(object sender, EventArgs e)
{
List <Double> xUniqVals = new List <Double>();
List <Double> yUniqVals = new List <Double>();
for (int i = 0; i < dataGV_data.RowCount - 1; i++)
{
Double val1 = Convert.ToDouble(dataGV_data.Rows[i].Cells[0].Value);
Double val2 = Convert.ToDouble(dataGV_data.Rows[i].Cells[1].Value);
if (!xUniqVals.Contains(val1))
{
xUniqVals.Add(val1);
}
if (!yUniqVals.Contains(val2))
{
yUniqVals.Add(val2);
}
xVals.Add(val1);
yVals.Add(val2);
}
volumeTxtBx.Text = (dataGV_data.RowCount - 1).ToString();
xUniqCountTxtBx.Text = xUniqVals.Count.ToString();
yUniqCountTxtBx.Text = yUniqVals.Count.ToString();
xSchaleTxtBx.Text = AnalysisClass.getScale(xUniqVals.Count);
ySchaleTxtBx.Text = AnalysisClass.getScale(yUniqVals.Count);
meanXTxtBx.Text = MathNet.Numerics.Statistics.Statistics.Mean(xVals).ToString();
stddevXTxtBx.Text = MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(xVals).ToString();
meanYTxtBx.Text = MathNet.Numerics.Statistics.Statistics.Mean(yVals).ToString();
stddevYTxtBx.Text = MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(yVals).ToString();
Double corrVal = MathNet.Numerics.Statistics.Correlation.Spearman(xVals, yVals);
corrValTxtBx.Text = corrVal.ToString();
int freedomDegree = xVals.Count - 2;
if (freedomDegree >= 5 && freedomDegree <= 30)
{
critValTxtBx.Text = CorrelationUtils.getSpearmanCriticalValue(freedomDegree, float.Parse("0,05")).ToString();
isCorrelatedTxtBx.Text = CorrelationUtils.getSpearmanSignificant(freedomDegree, corrVal);
}
else
{
critValTxtBx.Text = "не рассчитано";
isCorrelatedTxtBx.Text = CorrelationUtils.getSpearmanSignificant(freedomDegree, corrVal);
}
cl.CreateGraph(zg1, dataGV_data);
cl.CreateGraph2(zg2, dataGV_data);
cl.CreateGraph3(zg3, dataGV_data);
xVals.Clear();
yVals.Clear();
xUniqVals.Clear();
yUniqVals.Clear();
}
private void variableanalysisbutton_Click(object sender, EventArgs e)
{
clear();
if (comboBox1.Text.Equals("Зависимая переменная"))
{
number_cols = 0;
}
else
{
string resultString = string.Join(string.Empty, Regex.Matches(comboBox1.Text, @"\d+").OfType <Match>().Select(m => m.Value));
int.TryParse(resultString, out number_cols);
}
for (int i = 0; i < dataGridView1.RowCount; i++)
{
double val1 = Convert.ToDouble(dataGridView1.Rows[i].Cells[number_cols].Value);
if (!valuesUQ.Contains(val1))
{
valuesUQ.Add(val1);
}
values.Add(val1);
}
Numberofvalues.Text = values.Count.ToString();
numberuniquevalues.Text = valuesUQ.Count.ToString();
scaletype.Text = AnalysisClass.getScale(valuesUQ.Count);
Accord.Statistics.Distributions.Univariate.NormalDistribution normDist =
Accord.Statistics.Distributions.Univariate.NormalDistribution.Standard;
if (!scaletype.Text.Equals("дихотомическая"))
{
Accord.Statistics.Testing.KolmogorovSmirnovTest kstX =
new Accord.Statistics.Testing.KolmogorovSmirnovTest(values.ToArray(), normDist);
TypeDistr.Text = AnalysisClass.getDistributionScale(kstX.Significant);
avg.Text = Math.Round(MathNet.Numerics.Statistics.Statistics.Mean(values), 3).ToString();
standarddeviation.Text = Math.Round(MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(values), 3).ToString();
skewness.Text = Math.Round(MathNet.Numerics.Statistics.Statistics.PopulationSkewness(values), 3).ToString();
piko.Text = Math.Round(MathNet.Numerics.Statistics.Statistics.PopulationKurtosis(values), 3).ToString();
plotting.CreateGraphNormDistr(zg1, values, valuesUQ);
}
else
{
TypeDistr.Text = "Невозможно определить определить";
avg.Text = "Невозможно определить определить";
standarddeviation.Text = "Невозможно определить определить";
skewness.Text = "Невозможно определить определить";
piko.Text = "Невозможно определить определить";
zg1.GraphPane.CurveList.Clear();
zg1.GraphPane.GraphObjList.Clear();
zg1.Refresh();
}
}
private void generateBtn_Click(object sender, EventArgs e)
{
double[] xVal = AnalysisClass.generateValues(xScaleCmbBx.Text,
xDistrCmbBx.Text, Convert.ToInt32(numericUpDown1.Value));
double[] yVal = AnalysisClass.generateValues(yScaleCmbBx.Text,
yDistrCmbBx.Text, Convert.ToInt32(numericUpDown1.Value));
dataGV_data.Columns[0].Name = "X";
dataGV_data.Columns[0].HeaderText = "X"; // вывод имен столбцов
dataGV_data.Columns[1].Name = "Y";
dataGV_data.Columns[1].HeaderText = "Y"; // вывод имен столбцов
dataGV_data.Rows.Clear();
for (int i = 0; i < numericUpDown1.Value; i++)
{
dataGV_data.Rows.Add(xVal[i], yVal[i]);
}
}
private void doAnalysisBtn_Click(object sender, EventArgs e)
{
List <Double> xUniqVals = new List <Double>();
List <Double> yUniqVals = new List <Double>();
for (int i = 0; i < dataGV_data.RowCount - 1; i++)
{
Double val1 = Convert.ToDouble(dataGV_data.Rows[i].Cells[0].Value);
Double val2 = Convert.ToDouble(dataGV_data.Rows[i].Cells[1].Value);
if (!xUniqVals.Contains(val1))
{
xUniqVals.Add(val1);
}
if (!yUniqVals.Contains(val2))
{
yUniqVals.Add(val2);
}
xVals.Add(val1);
yVals.Add(val2);
}
volumeTxtBx.Text = (dataGV_data.RowCount - 1).ToString();
xUniqCountTxtBx.Text = xUniqVals.Count.ToString();
yUniqCountTxtBx.Text = yUniqVals.Count.ToString();
xSchaleTxtBx.Text = AnalysisClass.getScale(xUniqVals.Count);
ySchaleTxtBx.Text = AnalysisClass.getScale(yUniqVals.Count);
Accord.Statistics.Distributions.Univariate.NormalDistribution normDist =
Accord.Statistics.Distributions.Univariate.NormalDistribution.Standard;
cl.CreateGraph(zg1, dataGV_data);
if (!xSchaleTxtBx.Text.Equals("дихотомическая"))
{
Accord.Statistics.Testing.KolmogorovSmirnovTest kstX =
new Accord.Statistics.Testing.KolmogorovSmirnovTest(xVals.ToArray(), normDist);
distrXTxtBx.Text = AnalysisClass.getDistributionScale(kstX.Significant);
meanXTxtBx.Text = MathNet.Numerics.Statistics.Statistics.Mean(xVals).ToString();
stddevXTxtBx.Text = MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(xVals).ToString();
skewXTxtBx.Text = MathNet.Numerics.Statistics.Statistics.PopulationSkewness(xVals).ToString();
kurtXTxtBx.Text = MathNet.Numerics.Statistics.Statistics.PopulationKurtosis(xVals).ToString();
cl.CreateGraph3(zg3, dataGV_data);
}
if (!ySchaleTxtBx.Text.Equals("дихотомическая"))
{
Accord.Statistics.Testing.KolmogorovSmirnovTest kstY =
new Accord.Statistics.Testing.KolmogorovSmirnovTest(yVals.ToArray(), normDist);
distrYTxtBx.Text = AnalysisClass.getDistributionScale(kstY.Significant);
meanYTxtBx.Text = MathNet.Numerics.Statistics.Statistics.Mean(yVals).ToString();
stddevYTxtBx.Text = MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(yVals).ToString();
skewYTxtBx.Text = MathNet.Numerics.Statistics.Statistics.PopulationSkewness(yVals).ToString();
kurtYTxtBx.Text = MathNet.Numerics.Statistics.Statistics.PopulationKurtosis(yVals).ToString();
cl.CreateGraph2(zg2, dataGV_data);
}
coefficientsTxtBx.Text = AnalysisClass.getCorrelationCoefficients(
xSchaleTxtBx.Text, distrXTxtBx.Text, ySchaleTxtBx.Text, distrYTxtBx.Text, dataGV_data.RowCount - 1);
xVals.Clear();
yVals.Clear();
xUniqVals.Clear();
yUniqVals.Clear();
}
private void button2_Click(object sender, EventArgs e)
{
SaveRegression saveRegression = new SaveRegression();
if (!string.IsNullOrEmpty(richTextBox1.Text))
{
string NumberOfVariable = "", NumberOfUqVariable = "", TypeOfScale = "", DistType = "", AGV = "", Slope = "", Mechanics = "", StandardDeviation = "";
List <double> listtemp = new List <double>();
int temp = 0;
Accord.Statistics.Distributions.Univariate.NormalDistribution normDist =
Accord.Statistics.Distributions.Univariate.NormalDistribution.Standard;
double[,] matr = new double[dataGridView1.RowCount, dataGridView1.ColumnCount];
for (int j = 0; j < dataGridView1.ColumnCount; j++)
{
for (int i = 0; i < dataGridView1.RowCount; i++)
{
matr[i, j] = Convert.ToDouble(dataGridView1.Rows[i].Cells[j].Value);
if (!listtemp.Contains(matr[i, j]))
{
temp++;
}
if (dataGridView1.Rows[i].Cells[j].Value != null)
{
listtemp.Add(matr[i, j]);
}
}
TypeOfScale += AnalysisClass.getScale(temp) + "!";
NumberOfUqVariable += temp.ToString() + "!";
NumberOfVariable += listtemp.Count().ToString() + "!";
if (AnalysisClass.getScale(temp) != "дихотомическая")
{
Accord.Statistics.Testing.KolmogorovSmirnovTest kstX =
new Accord.Statistics.Testing.KolmogorovSmirnovTest(listtemp.ToArray(), normDist);
DistType += AnalysisClass.getDistributionScale(kstX.Significant) + "!";
AGV += Math.Round(MathNet.Numerics.Statistics.Statistics.Mean(listtemp), 3).ToString() + "!";
StandardDeviation += Math.Round(MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(listtemp), 3).ToString() + "!";
Mechanics += Math.Round(MathNet.Numerics.Statistics.Statistics.PopulationSkewness(listtemp), 3).ToString() + "!";
Slope += Math.Round(MathNet.Numerics.Statistics.Statistics.PopulationKurtosis(listtemp), 3).ToString() + "!";
}
else
{
DistType += "Нет!";
AGV += "Нет!";
StandardDeviation += "Нет!";
Mechanics += "Нет!";
Slope += "Нет!";
}
temp = 0;
listtemp.Clear();
}
DateRegression.TypeMethod = richTextBox1.Text;
DateRegression.NumberOfScale = dataGridView1.ColumnCount;
DateRegression.NumberOfVariable = NumberOfVariable;
DateRegression.NumberOfUqVariable = NumberOfUqVariable;
DateRegression.TypeOfScale = TypeOfScale;
DateRegression.DistType = DistType;
DateRegression.StandardDeviation = StandardDeviation;
DateRegression.Mechanics = Mechanics;
DateRegression.AGV = AGV;
DateRegression.Slope = Slope;
saveRegression.Show();
}
else
{
MessageBox.Show("Сначало нужно нажать кнопку 'Предложить метод исследования'");
}
}
public static string getReggressionMethod(List <string> typeScale, string typeMethod, List <double> vs)
{
Accord.Statistics.Distributions.Univariate.NormalDistribution normDist =
Accord.Statistics.Distributions.Univariate.NormalDistribution.Standard;
Accord.Statistics.Testing.KolmogorovSmirnovTest kstX =
new Accord.Statistics.Testing.KolmogorovSmirnovTest(vs.ToArray(), normDist);
AnalysisClass.getDistributionScale(kstX.Significant);
switch (typeMethod)
{
case "Простая линейная регрессия":
if ((typeScale[0].Equals("ранговая") || typeScale[0].Equals("количественная")) && typeScale.Count == 2)
{
return("Метод применим к данным");
}
else
{
return("Метод не применим к данным");
}
case "Множественная линейная регрессия":
if ((typeScale[0].Equals("ранговая") || typeScale[0].Equals("количественная")) && typeScale.Count > 2)
{
return("Метод применим к данным");
}
else
{
return("Метод не применим к данным");
}
case "Нелинейная регрессия":
if ((typeScale[0].Equals("ранговая") || typeScale[0].Equals("количественная")) && typeScale.Count >= 2 && AnalysisClass.getDistributionScale(kstX.Significant) == "нормальное")
{
return("Метод применим к данным");
}
else
{
return("Метод не применим к данным");
}
case "Бинарная логистическая регрессия":
if ((typeScale[0].Equals("дихотомическая")) && typeScale.Count > 2)
{
return("Метод применим к данным");
}
else
{
return("Метод не применим к данным");
}
case "Мультиномиальная логистическая регрессия":
if (typeScale[0].Equals("ранговая") && typeScale.Count > 2 && AnalysisClass.getDistributionScale(kstX.Significant) == "нормальное")
{
return("Метод применим к данным");
}
else
{
return("Метод не применим к данным");
}
case "Порядковая регрессия":
if (typeScale[0].Equals("ранговая") && typeScale.Count > 2 && AnalysisClass.getDistributionScale(kstX.Significant) == "нормальное")
{
return("Метод применим к данным");
}
else
{
return("Метод не применим к данным");
}
case "Пробит-анализ":
if ((typeScale[0].Equals("дихотомическая")) && typeScale.Count > 2)
{
return("Метод применим к данным");
}
else
{
return("Метод не применим к данным");
}
default:
return("Нет");
}
}