public void RegressTest()
{
MultipleLinearRegression target = new MultipleLinearRegression(1, true);
double[][] inputs =
{
new double[] { 80 },
new double[] { 60 },
new double[] { 10 },
new double[] { 20 },
new double[] { 30 },
};
double[] outputs = { 20, 40, 30, 50, 60 };
double error = target.Regress(inputs, outputs);
double slope = target.Coefficients[0];
double intercept = target.Coefficients[1];
Assert.AreEqual(-0.264706, slope, 1e-5);
Assert.AreEqual(50.588235, intercept, 1e-5);
Assert.AreEqual(761.764705, error, 1e-5);
double r = target.CoefficientOfDetermination(inputs, outputs);
Assert.AreEqual(0.23823529, r, 1e-6);
string str = target.ToString(null, System.Globalization.CultureInfo.GetCultureInfo("pt-BR"));
Assert.AreEqual("y(x0) = -0,264705882352941*x0 + 50,5882352941176", str);
}
public void RegressTest2()
{
MultipleLinearRegression target = new MultipleLinearRegression(1, false);
double[][] inputs =
{
new double[] { 80, 1 },
new double[] { 60, 1 },
new double[] { 10, 1 },
new double[] { 20, 1 },
new double[] { 30, 1 },
};
double[] outputs = { 20, 40, 30, 50, 60 };
double error = target.Regress(inputs, outputs);
double slope = target.Coefficients[0];
double intercept = target.Coefficients[1];
Assert.AreEqual(-0.264706, slope, 1e-5);
Assert.AreEqual(50.588235, intercept, 1e-5);
Assert.AreEqual(761.764705, error, 1e-5);
double r = target.CoefficientOfDetermination(inputs, outputs);
Assert.AreEqual(0.23823529, r, 1e-6);
string str = target.ToString();
Assert.AreEqual("y(x0, x1) = -0,264705882352942*x0 + 50,5882352941177*x1", str);
}
public void RegressTest2()
{
var target = new MultipleLinearRegression(2, false);
Assert.IsFalse(target.HasIntercept);
double[][] inputs =
{
new double[] { 80, 1 },
new double[] { 60, 1 },
new double[] { 10, 1 },
new double[] { 20, 1 },
new double[] { 30, 1 },
};
double[] outputs = { 20, 40, 30, 50, 60 };
double error = target.Regress(inputs, outputs);
Assert.AreEqual(2, target.NumberOfInputs);
Assert.AreEqual(1, target.NumberOfOutputs);
double slope = target.Coefficients[0];
double intercept = target.Coefficients[1];
Assert.AreEqual(-0.264706, slope, 1e-5);
Assert.AreEqual(50.588235, intercept, 1e-5);
Assert.AreEqual(761.764705, error, 1e-5);
double r = target.CoefficientOfDetermination(inputs, outputs);
Assert.AreEqual(0.23823529, r, 1e-6);
string str = target.ToString(null, System.Globalization.CultureInfo.GetCultureInfo("en-US"));
Assert.AreEqual("y(x0, x1) = -0.264705882352941*x0 + 50.5882352941176*x1", str);
}
public void RegressTest6()
{
MultipleLinearRegression target = new MultipleLinearRegression(2, false);
double[][] inputs =
{
new double[] { 0, 0 },
new double[] { 0, 0 },
new double[] { 0, 0 },
new double[] { 0, 0 },
new double[] { 0, 0 },
};
double[] outputs = { 20, 40, 30, 50, 60 };
double error = target.Regress(inputs, outputs);
double slope = target.Coefficients[0];
double intercept = target.Coefficients[1];
Assert.AreEqual(0, slope, 1e-5);
Assert.AreEqual(0, intercept, 1e-5);
Assert.AreEqual(9000, error);
double r = target.CoefficientOfDetermination(inputs, outputs);
Assert.AreEqual(-8, r, 1e-6);
string str = target.ToString(null, System.Globalization.CultureInfo.GetCultureInfo("pt-BR"));
Assert.AreEqual("y(x0, x1) = 0*x0 + 0*x1", str);
}
private void btnSampleRunAnalysis_Click(object sender, EventArgs e)
{
// Check requirements
if (sourceTable == null)
{
MessageBox.Show("A sample spreadsheet can be found in the " +
"Resources folder in the same directory as this application.",
"Please load some data before attempting an analysis");
return;
}
if (checkedListBox1.CheckedItems.Count == 0)
{
MessageBox.Show("Please select the dependent input variables to be used in the regression model.",
"Please choose at least one input variable");
}
// Finishes and save any pending changes to the given data
dgvAnalysisSource.EndEdit();
sourceTable.AcceptChanges();
// Gets the column of the dependent variable
String dependentName = (string)comboBox1.SelectedItem;
DataTable dependent = sourceTable.DefaultView.ToTable(false, dependentName);
// Gets the columns of the independent variables
List<string> names = new List<string>();
foreach (string name in checkedListBox1.CheckedItems)
{
names.Add(name);
}
String[] independentNames = names.ToArray();
DataTable independent = sourceTable.DefaultView.ToTable(false, independentNames);
// Creates the input and output matrices from the source data table
double[][] input = independent.ToArray();
double[] output = dependent.Columns[dependentName].ToArray();
String[] sourceColumns;
double[,] sourceMatrix = sourceTable.ToMatrix(out sourceColumns);
// Creates the Simple Descriptive Analysis of the given source
DescriptiveAnalysis sda = new DescriptiveAnalysis(sourceMatrix, sourceColumns);
sda.Compute();
// Populates statistics overview tab with analysis data
dgvDistributionMeasures.DataSource = sda.Measures;
// Creates the Logistic Regression Analysis of the given source
lra = new LogisticRegressionAnalysis(input, output, independentNames, dependentName);
// Compute the Logistic Regression Analysis
lra.Compute();
// Populates coefficient overview with analysis data
dgvLogisticCoefficients.DataSource = lra.Coefficients;
// Populate details about the fitted model
tbChiSquare.Text = lra.ChiSquare.Statistic.ToString("N5");
tbPValue.Text = lra.ChiSquare.PValue.ToString("N5");
checkBox1.Checked = lra.ChiSquare.Significant;
tbDeviance.Text = lra.Deviance.ToString("N5");
tbLogLikelihood.Text = lra.LogLikelihood.ToString("N5");
// Perform linear regression
mlr = new MultipleLinearRegression(independentNames.Length, true);
mlr.Regress(input, output);
tbLinearExpression.Text = mlr.ToString();
tbLinearR.Text = mlr.CoefficientOfDetermination(input, output, false).ToString("N5");
tbLinearAdjustedR.Text = mlr.CoefficientOfDetermination(input, output, true).ToString("N5");
// Populate projection source table
string[] cols = independentNames;
if (!independentNames.Contains(dependentName))
cols = independentNames.Concatenate(dependentName);
DataTable projSource = sourceTable.DefaultView.ToTable(false, cols);
dgvProjectionSource.DataSource = projSource;
}
