/// <summary>
/// Creates a Multinomial Logistic Regression Analysis.
/// </summary>
///
private void btnCreate_Click(object sender, EventArgs e)
{
if (dgvLearningSource.DataSource == null)
{
MessageBox.Show("Please load some data first.");
return;
}
// Finishes and save any pending changes to the given data
dgvLearningSource.EndEdit();
// Creates a matrix from the entire source data table
double[,] table = (dgvLearningSource.DataSource as DataTable).ToMatrix(out columnNames);
// Get only the input vector values (first two columns)
double[][] inputs = table.GetColumns(new int[] { 0, 1 }).ToArray();
// Get only the output labels (last column)
int[] outputs = table.GetColumn(2).Subtract(1).ToInt32();
// Create and compute a new Simple Descriptive Analysis
var sda = new DescriptiveAnalysis(table, columnNames);
sda.Compute();
// Show the descriptive analysis on the screen
dgvDistributionMeasures.DataSource = sda.Measures;
// Creates the Support Vector Machine for 2 input variables
mlr = new MultinomialLogisticRegressionAnalysis(inputs, outputs);
try
{
// Run
mlr.Compute();
lbStatus.Text = "Analysis complete!";
}
catch (ConvergenceException)
{
lbStatus.Text = "Convergence could not be attained. " +
"The learned machine might still be usable.";
}
createSurface(table);
// Populate details about the fitted model
tbChiSquare.Text = mlr.ChiSquare.Statistic.ToString("N5");
tbPValue.Text = mlr.ChiSquare.PValue.ToString("N5");
checkBox1.Checked = mlr.ChiSquare.Significant;
tbDeviance.Text = mlr.Deviance.ToString("N5");
tbLogLikelihood.Text = mlr.LogLikelihood.ToString("N5");
dgvCoefficients.DataSource = mlr.Coefficients;
}
public void CoefficientsTest1()
{
double[][] inputs;
int[] outputs;
MultinomialLogisticRegressionTest.CreateInputOutputsExample2(out inputs, out outputs);
var analysis = new MultinomialLogisticRegressionAnalysis(inputs, outputs);
int inputCount = 5;
int coeffCount = inputCount + 1;
var mlr = analysis.regression;
analysis.Iterations = 100;
analysis.Tolerance = 1e-6;
analysis.Compute();
foreach (var coefficient in analysis.Coefficients)
{
Assert.IsNotNull(coefficient.Analysis);
Assert.IsNotNull(coefficient.Confidence);
Assert.IsNotNull(coefficient.ConfidenceLower);
Assert.IsNotNull(coefficient.ConfidenceUpper);
Assert.IsNotNull(coefficient.Name);
Assert.IsNotNull(coefficient.Class);
Assert.IsNotNull(coefficient.StandardError);
Assert.IsNotNull(coefficient.Value);
}
Assert.AreEqual(13, analysis.Coefficients.Count);
Assert.AreEqual(2, analysis.CoefficientValues.Length);
var class1 = analysis.CoefficientValues[0];
Assert.AreEqual(-11.774547061739975, class1[0], 1e-10);
Assert.AreEqual(0.523813075806107, class1[1], 1e-10);
Assert.AreEqual(0.36820307277024716, class1[2], 1e-10);
Assert.AreEqual(0, class1[3], 1e-6);
Assert.AreEqual(0, class1[4], 1e-6);
Assert.AreEqual(0, class1[5], 1e-6);
var class2 = analysis.CoefficientValues[1];
Assert.AreEqual(-22.721272157115514, class2[0], 1e-10);
Assert.AreEqual(0.46593949381162203, class2[1], 1e-10);
Assert.AreEqual(0.68590438098052586, class2[2], 1e-10);
Assert.AreEqual(0, class2[3], 1e-6);
Assert.AreEqual(0, class2[4], 1e-6);
Assert.AreEqual(0, class2[5], 1e-6);
}
public void AnalyzeExample1()
{
// Note: results perfectly match the example at
// https://web.archive.org/web/20170210050820/http://www.ats.ucla.edu/stat/stata/dae/mlogit.htm
CsvReader reader = CsvReader.FromText(Properties.Resources.hsbdemo, hasHeaders: true);
var table = reader.ToTable();
var codification = new Codification(table);
codification["ses"].VariableType = CodificationVariable.CategoricalWithBaseline;
codification["prog"].VariableType = CodificationVariable.Categorical;
codification["write"].VariableType = CodificationVariable.Continuous;
codification["ses"].Remap("low", 0);
codification["ses"].Remap("middle", 1);
codification["prog"].Remap("academic", 0);
codification["prog"].Remap("general", 1);
var inputs = codification.Apply(table, "write", "ses");
var output = codification.Apply(table, "prog");
// Get inputs
string[] inputNames;
var inputsData = inputs.ToJagged(out inputNames);
// Get outputs
string[] outputNames;
var outputData = output.ToJagged(out outputNames);
var analysis = new MultinomialLogisticRegressionAnalysis(inputsData, outputData, inputNames, outputNames);
analysis.Compute();
testmlr(analysis);
}
public void AnalyzeExample1()
{
// http://www.ats.ucla.edu/stat/stata/dae/mlogit.htm
CsvReader reader = CsvReader.FromText(Properties.Resources.hsbdemo, hasHeaders: true);
var table = reader.ToTable();
var codification = new Codification(table);
codification["ses"].VariableType = CodificationVariable.CategoricalWithBaseline;
codification["prog"].VariableType = CodificationVariable.Categorical;
codification["prog"].Remap("academic", 0);
var inputs = codification.Apply(table, "ses", "write");
var output = codification.Apply(table, "prog");
// Get inputs
string[] inputNames;
var inputsData = inputs.ToArray(out inputNames);
// Get outputs
string[] outputNames;
var outputData = output.ToArray(out outputNames);
var analysis = new MultinomialLogisticRegressionAnalysis(inputsData, outputData, inputNames, outputNames);
analysis.Compute();
Assert.AreEqual(9, analysis.Coefficients.Count);
int i = 0;
Assert.AreEqual("(baseline)", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: academic", analysis.Coefficients[i].Class);
Assert.AreEqual(0, analysis.Coefficients[i].Value);
i++;
Assert.AreEqual("Intercept", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: general", analysis.Coefficients[i].Class);
Assert.AreEqual(1.0302662690579185, analysis.Coefficients[i].Value);
i++;
Assert.AreEqual("write", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: general", analysis.Coefficients[i].Class);
Assert.AreEqual(-0.083689163424126883, analysis.Coefficients[i].Value);
i++;
Assert.AreEqual("ses: middle", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: general", analysis.Coefficients[i].Class);
Assert.AreEqual(-0.58217998138556049, analysis.Coefficients[i].Value);
i++;
Assert.AreEqual("ses: high", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: general", analysis.Coefficients[i].Class);
Assert.AreEqual(-1.1112048569892283, analysis.Coefficients[i].Value);
i++;
Assert.AreEqual("Intercept", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: vocation", analysis.Coefficients[i].Class);
Assert.AreEqual(1.2715455854613191, analysis.Coefficients[i].Value);
i++;
Assert.AreEqual("write", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: vocation", analysis.Coefficients[i].Class);
Assert.AreEqual(-0.13231057837059781, analysis.Coefficients[i].Value);
i++;
Assert.AreEqual("ses: middle", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: vocation", analysis.Coefficients[i].Class);
Assert.AreEqual(0.20451187629162043, analysis.Coefficients[i].Value);
i++;
Assert.AreEqual("ses: high", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: vocation", analysis.Coefficients[i].Class);
Assert.AreEqual(-0.93207938490449849, analysis.Coefficients[i].Value);
}
public void CoefficientsTest1()
{
double[][] inputs;
int[] outputs;
MultinomialLogisticRegressionTest.CreateInputOutputsExample2(out inputs, out outputs);
var analysis = new MultinomialLogisticRegressionAnalysis(inputs, outputs);
int inputCount = 5;
int coeffCount = inputCount + 1;
var mlr = analysis.regression;
analysis.Iterations = 100;
analysis.Tolerance = 1e-6;
analysis.Compute();
foreach (var coefficient in analysis.Coefficients)
{
Assert.IsNotNull(coefficient.Analysis);
Assert.IsNotNull(coefficient.Confidence);
Assert.IsNotNull(coefficient.ConfidenceLower);
Assert.IsNotNull(coefficient.ConfidenceUpper);
Assert.IsNotNull(coefficient.Name);
Assert.IsNotNull(coefficient.Class);
Assert.IsNotNull(coefficient.StandardError);
Assert.IsNotNull(coefficient.Value);
}
Assert.AreEqual(13, analysis.Coefficients.Count);
Assert.AreEqual(2, analysis.CoefficientValues.Length);
var class1 = analysis.CoefficientValues[0];
Assert.AreEqual(-11.774547061739975, class1[0]);
Assert.AreEqual(0.523813075806107, class1[1]);
Assert.AreEqual(0.36820307277024716, class1[2]);
Assert.AreEqual(0, class1[3], 1e-6);
Assert.AreEqual(0, class1[4], 1e-6);
Assert.AreEqual(0, class1[5], 1e-6);
var class2 = analysis.CoefficientValues[1];
Assert.AreEqual(-22.721272157115514, class2[0]);
Assert.AreEqual(0.46593949381162203, class2[1]);
Assert.AreEqual(0.68590438098052586, class2[2]);
Assert.AreEqual(0, class2[3], 1e-6);
Assert.AreEqual(0, class2[4], 1e-6);
Assert.AreEqual(0, class2[5], 1e-6);
}
public void ComputeTest2()
{
double[][] inputs;
int[] outputs;
MultinomialLogisticRegressionTest.CreateInputOutputsExample2(out inputs, out outputs);
var analysis = new MultinomialLogisticRegressionAnalysis(inputs, outputs);
int inputCount = 5;
int outputCount = 3;
int coeffCount = inputCount + 1;
var mlr = analysis.regression;
Assert.AreEqual(inputCount, mlr.Inputs);
Assert.AreEqual(outputCount, mlr.Categories);
Assert.AreEqual(inputCount, analysis.Inputs.Length);
Assert.AreEqual(outputCount, analysis.OutputNames.Length);
analysis.Iterations = 100;
analysis.Tolerance = 1e-6;
analysis.Compute();
Assert.AreEqual(outputCount - 1, analysis.CoefficientValues.Length);
Assert.AreEqual(outputCount - 1, analysis.StandardErrors.Length);
Assert.AreEqual(outputCount - 1, analysis.WaldTests.Length);
Assert.AreEqual(outputCount - 1, analysis.Confidences.Length);
for (int i = 0; i < analysis.CoefficientValues.Length; i++)
{
Assert.AreEqual(coeffCount, analysis.CoefficientValues[i].Length);
Assert.AreEqual(coeffCount, analysis.StandardErrors[i].Length);
Assert.AreEqual(coeffCount, analysis.WaldTests[i].Length);
Assert.AreEqual(coeffCount, analysis.Confidences[i].Length);
for (int j = 0; j < analysis.CoefficientValues[i].Length; j++)
{
Assert.IsFalse(double.IsNaN(analysis.CoefficientValues[i][j]));
Assert.IsFalse(double.IsNaN(analysis.StandardErrors[i][j]));
}
}
var coefficients = analysis.CoefficientValues;
// brand 2
Assert.AreEqual(-11.774655, coefficients[0][0], 1e-3); // intercept
Assert.AreEqual(0.523814, coefficients[0][1], 1e-3); // female
Assert.AreEqual(0.368206, coefficients[0][2], 1e-3); // age
// brand 3
Assert.AreEqual(-22.721396, coefficients[1][0], 1e-3); // intercept
Assert.AreEqual(0.465941, coefficients[1][1], 1e-3); // female
Assert.AreEqual(0.685908, coefficients[1][2], 1e-3); // age
var standard = analysis.StandardErrors;
Assert.AreEqual(-702.97, analysis.LogLikelihood, 1e-2);
Assert.AreEqual(185.85, analysis.ChiSquare.Statistic, 1e-2);
Assert.AreEqual(1405.9414080469473, analysis.Deviance, 1e-5);
}
public void ComputeTest1()
{
double[][] inputs;
int[] outputs;
MultinomialLogisticRegressionTest.CreateInputOutputsExample1(out inputs, out outputs);
var analysis = new MultinomialLogisticRegressionAnalysis(inputs, outputs);
int inputCount = 2;
int outputCount = 3;
int coeffCount = inputCount + 1;
var mlr = analysis.regression;
Assert.AreEqual(inputCount, mlr.Inputs);
Assert.AreEqual(outputCount, mlr.Categories);
Assert.AreEqual(inputCount, analysis.Inputs.Length);
Assert.AreEqual(outputCount, analysis.OutputNames.Length);
analysis.Iterations = 100;
analysis.Tolerance = 1e-6;
analysis.Compute();
Assert.AreEqual(outputCount - 1, analysis.CoefficientValues.Length);
Assert.AreEqual(outputCount - 1, analysis.StandardErrors.Length);
Assert.AreEqual(outputCount - 1, analysis.WaldTests.Length);
Assert.AreEqual(outputCount - 1, analysis.Confidences.Length);
for (int i = 0; i < analysis.CoefficientValues.Length; i++)
{
Assert.AreEqual(coeffCount, analysis.CoefficientValues[i].Length);
Assert.AreEqual(coeffCount, analysis.StandardErrors[i].Length);
Assert.AreEqual(coeffCount, analysis.WaldTests[i].Length);
Assert.AreEqual(coeffCount, analysis.Confidences[i].Length);
for (int j = 0; j < analysis.CoefficientValues[i].Length; j++)
{
Assert.IsFalse(double.IsNaN(analysis.CoefficientValues[i][j]));
Assert.IsFalse(double.IsNaN(analysis.StandardErrors[i][j]));
}
}
var coefficients = analysis.CoefficientValues;
// brand 2
Assert.AreEqual(-11.774655, coefficients[0][0], 1e-4); // intercept
Assert.AreEqual(0.523814, coefficients[0][1], 1e-4); // female
Assert.AreEqual(0.368206, coefficients[0][2], 1e-4); // age
// brand 3
Assert.AreEqual(-22.721396, coefficients[1][0], 1e-4); // intercept
Assert.AreEqual(0.465941, coefficients[1][1], 1e-4); // female
Assert.AreEqual(0.685908, coefficients[1][2], 1e-4); // age
var standard = analysis.StandardErrors;
// Using the lower-bound approximation
Assert.AreEqual(1.047378039787443, standard[0][0], 1e-6);
Assert.AreEqual(0.153150051082552, standard[0][1], 1e-6);
Assert.AreEqual(0.031640507386863, standard[0][2], 1e-6);
Assert.AreEqual(1.047378039787443, standard[1][0], 1e-6);
Assert.AreEqual(0.153150051082552, standard[1][1], 1e-6);
Assert.AreEqual(0.031640507386863, standard[1][2], 1e-6);
Assert.AreEqual(-702.97, analysis.LogLikelihood, 1e-2);
Assert.AreEqual(185.85, analysis.ChiSquare.Statistic, 1e-2);
Assert.AreEqual(1405.9414080469473, analysis.Deviance, 1e-10);
var wald = analysis.WaldTests;
Assert.AreEqual(-11.241995503283842, wald[0][0].Statistic, 1e-4);
Assert.AreEqual(3.4202662152119889, wald[0][1].Statistic, 1e-4);
Assert.AreEqual(11.637150673342207, wald[0][2].Statistic, 1e-4);
Assert.AreEqual(-21.693553825772664, wald[1][0].Statistic, 1e-4);
Assert.AreEqual(3.0423802097069097, wald[1][1].Statistic, 1e-4);
Assert.AreEqual(21.678124991086548, wald[1][2].Statistic, 1e-4);
}
public void ComputeTest2()
{
double[][] inputs;
int[] outputs;
MultinomialLogisticRegressionTest.CreateInputOutputsExample2(out inputs, out outputs);
var analysis = new MultinomialLogisticRegressionAnalysis(inputs, outputs);
int inputCount = 5;
int outputCount = 3;
int coeffCount = inputCount + 1;
var mlr = analysis.regression;
Assert.AreEqual(inputCount, mlr.Inputs);
Assert.AreEqual(outputCount, mlr.Categories);
Assert.AreEqual(inputCount, analysis.Inputs.Length);
Assert.AreEqual(outputCount, analysis.OutputNames.Length);
analysis.Iterations = 100;
analysis.Tolerance = 1e-6;
analysis.Compute();
Assert.AreEqual(outputCount - 1, analysis.CoefficientValues.Length);
Assert.AreEqual(outputCount - 1, analysis.StandardErrors.Length);
Assert.AreEqual(outputCount - 1, analysis.WaldTests.Length);
Assert.AreEqual(outputCount - 1, analysis.Confidences.Length);
for (int i = 0; i < analysis.CoefficientValues.Length; i++)
{
Assert.AreEqual(coeffCount, analysis.CoefficientValues[i].Length);
Assert.AreEqual(coeffCount, analysis.StandardErrors[i].Length);
Assert.AreEqual(coeffCount, analysis.WaldTests[i].Length);
Assert.AreEqual(coeffCount, analysis.Confidences[i].Length);
for (int j = 0; j < analysis.CoefficientValues[i].Length; j++)
{
Assert.IsFalse(double.IsNaN(analysis.CoefficientValues[i][j]));
Assert.IsFalse(double.IsNaN(analysis.StandardErrors[i][j]));
}
}
var coefficients = analysis.CoefficientValues;
// brand 2
Assert.AreEqual(-11.774655, coefficients[0][0], 1e-3); // intercept
Assert.AreEqual(0.523814, coefficients[0][1], 1e-3); // female
Assert.AreEqual(0.368206, coefficients[0][2], 1e-3); // age
// brand 3
Assert.AreEqual(-22.721396, coefficients[1][0], 1e-3); // intercept
Assert.AreEqual(0.465941, coefficients[1][1], 1e-3); // female
Assert.AreEqual(0.685908, coefficients[1][2], 1e-3); // age
var standard = analysis.StandardErrors;
Assert.AreEqual(-702.97, analysis.LogLikelihood, 1e-2);
Assert.AreEqual(185.85, analysis.ChiSquare.Statistic, 1e-2);
Assert.AreEqual(1405.9414080469473, analysis.Deviance, 1e-5);
}
public void AnalyzeExample1()
{
// http://www.ats.ucla.edu/stat/stata/dae/mlogit.htm
CsvReader reader = CsvReader.FromText(Properties.Resources.hsbdemo, hasHeaders: true);
var table = reader.ToTable();
var codification = new Codification(table);
codification["ses"].VariableType = CodificationVariable.CategoricalWithBaseline;
codification["prog"].VariableType = CodificationVariable.Categorical;
codification["prog"].Remap("academic", 0);
var inputs = codification.Apply(table, "ses", "write");
var output = codification.Apply(table, "prog");
// Get inputs
string[] inputNames;
var inputsData = inputs.ToArray(out inputNames);
// Get outputs
string[] outputNames;
var outputData = output.ToArray(out outputNames);
var analysis = new MultinomialLogisticRegressionAnalysis(inputsData, outputData, inputNames, outputNames);
analysis.Compute();
Assert.AreEqual(9, analysis.Coefficients.Count);
int i = 0;
Assert.AreEqual("(baseline)", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: academic", analysis.Coefficients[i].Class);
Assert.AreEqual(0, analysis.Coefficients[i].Value);
i++;
Assert.AreEqual("Intercept", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: general", analysis.Coefficients[i].Class);
Assert.AreEqual(1.0302662690579185, analysis.Coefficients[i].Value, 1e-10);
i++;
Assert.AreEqual("write", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: general", analysis.Coefficients[i].Class);
Assert.AreEqual(-0.083689163424126883, analysis.Coefficients[i].Value, 1e-10);
i++;
Assert.AreEqual("ses: middle", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: general", analysis.Coefficients[i].Class);
Assert.AreEqual(-0.58217998138556049, analysis.Coefficients[i].Value, 1e-10);
i++;
Assert.AreEqual("ses: high", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: general", analysis.Coefficients[i].Class);
Assert.AreEqual(-1.1112048569892283, analysis.Coefficients[i].Value, 1e-10);
i++;
Assert.AreEqual("Intercept", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: vocation", analysis.Coefficients[i].Class);
Assert.AreEqual(1.2715455854613191, analysis.Coefficients[i].Value, 1e-10);
i++;
Assert.AreEqual("write", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: vocation", analysis.Coefficients[i].Class);
Assert.AreEqual(-0.13231057837059781, analysis.Coefficients[i].Value, 1e-10);
i++;
Assert.AreEqual("ses: middle", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: vocation", analysis.Coefficients[i].Class);
Assert.AreEqual(0.20451187629162043, analysis.Coefficients[i].Value, 1e-10);
i++;
Assert.AreEqual("ses: high", analysis.Coefficients[i].Name);
Assert.AreEqual("prog: vocation", analysis.Coefficients[i].Class);
Assert.AreEqual(-0.93207938490449849, analysis.Coefficients[i].Value, 1e-10);
}
public void ComputeTest1()
{
double[][] inputs;
int[] outputs;
MultinomialLogisticRegressionTest.CreateInputOutputsExample1(out inputs, out outputs);
var analysis = new MultinomialLogisticRegressionAnalysis(inputs, outputs);
int inputCount = 2;
int outputCount = 3;
int coeffCount = inputCount + 1;
var mlr = analysis.regression;
Assert.AreEqual(inputCount, mlr.Inputs);
Assert.AreEqual(outputCount, mlr.Categories);
Assert.AreEqual(inputCount, analysis.Inputs.Length);
Assert.AreEqual(outputCount, analysis.OutputNames.Length);
analysis.Iterations = 100;
analysis.Tolerance = 1e-6;
analysis.Compute();
Assert.AreEqual(outputCount - 1, analysis.CoefficientValues.Length);
Assert.AreEqual(outputCount - 1, analysis.StandardErrors.Length);
Assert.AreEqual(outputCount - 1, analysis.WaldTests.Length);
Assert.AreEqual(outputCount - 1, analysis.Confidences.Length);
for (int i = 0; i < analysis.CoefficientValues.Length; i++)
{
Assert.AreEqual(coeffCount, analysis.CoefficientValues[i].Length);
Assert.AreEqual(coeffCount, analysis.StandardErrors[i].Length);
Assert.AreEqual(coeffCount, analysis.WaldTests[i].Length);
Assert.AreEqual(coeffCount, analysis.Confidences[i].Length);
for (int j = 0; j < analysis.CoefficientValues[i].Length; j++)
{
Assert.IsFalse(double.IsNaN(analysis.CoefficientValues[i][j]));
Assert.IsFalse(double.IsNaN(analysis.StandardErrors[i][j]));
}
}
var coefficients = analysis.CoefficientValues;
// brand 2
Assert.AreEqual(-11.774655, coefficients[0][0], 1e-4); // intercept
Assert.AreEqual(0.523814, coefficients[0][1], 1e-4); // female
Assert.AreEqual(0.368206, coefficients[0][2], 1e-4); // age
// brand 3
Assert.AreEqual(-22.721396, coefficients[1][0], 1e-4); // intercept
Assert.AreEqual(0.465941, coefficients[1][1], 1e-4); // female
Assert.AreEqual(0.685908, coefficients[1][2], 1e-4); // age
var standard = analysis.StandardErrors;
// Using the lower-bound approximation
Assert.AreEqual(1.047378039787443, standard[0][0], 1e-6);
Assert.AreEqual(0.153150051082552, standard[0][1], 1e-6);
Assert.AreEqual(0.031640507386863, standard[0][2], 1e-6);
Assert.AreEqual(1.047378039787443, standard[1][0], 1e-6);
Assert.AreEqual(0.153150051082552, standard[1][1], 1e-6);
Assert.AreEqual(0.031640507386863, standard[1][2], 1e-6);
Assert.AreEqual(-702.97, analysis.LogLikelihood, 1e-2);
Assert.AreEqual(185.85, analysis.ChiSquare.Statistic, 1e-2);
Assert.AreEqual(1405.9414080469473, analysis.Deviance, 1e-10);
var wald = analysis.WaldTests;
Assert.AreEqual(-11.241995503283842, wald[0][0].Statistic, 1e-4);
Assert.AreEqual(3.4202662152119889, wald[0][1].Statistic, 1e-4);
Assert.AreEqual(11.637150673342207, wald[0][2].Statistic, 1e-4);
Assert.AreEqual(-21.693553825772664, wald[1][0].Statistic, 1e-4);
Assert.AreEqual(3.0423802097069097, wald[1][1].Statistic, 1e-4);
Assert.AreEqual(21.678124991086548, wald[1][2].Statistic, 1e-4);
}
/// <summary>
/// Creates a Multinomial Logistic Regression Analysis.
/// </summary>
///
private void btnCreate_Click(object sender, EventArgs e)
{
if (dgvLearningSource.DataSource == null)
{
MessageBox.Show("Please load some data first.");
return;
}
// Finishes and save any pending changes to the given data
dgvLearningSource.EndEdit();
// Creates a matrix from the entire source data table
double[,] table = (dgvLearningSource.DataSource as DataTable).ToMatrix(out columnNames);
// Get only the input vector values (first two columns)
double[][] inputs = table.GetColumns(0, 1).ToArray();
// Get only the output labels (last column)
int[] outputs = table.GetColumn(2).Subtract(1).ToInt32();
// Create and compute a new Simple Descriptive Analysis
var sda = new DescriptiveAnalysis(table, columnNames);
sda.Compute();
// Show the descriptive analysis on the screen
dgvDistributionMeasures.DataSource = sda.Measures;
// Creates the Support Vector Machine for 2 input variables
mlr = new MultinomialLogisticRegressionAnalysis(inputs, outputs);
try
{
// Run
mlr.Compute();
lbStatus.Text = "Analysis complete!";
}
catch (ConvergenceException)
{
lbStatus.Text = "Convergence could not be attained. " +
"The learned machine might still be usable.";
}
createSurface(table);
// Populate details about the fitted model
tbChiSquare.Text = mlr.ChiSquare.Statistic.ToString("N5");
tbPValue.Text = mlr.ChiSquare.PValue.ToString("N5");
checkBox1.Checked = mlr.ChiSquare.Significant;
tbDeviance.Text = mlr.Deviance.ToString("N5");
tbLogLikelihood.Text = mlr.LogLikelihood.ToString("N5");
dgvCoefficients.DataSource = mlr.Coefficients;
}