private LogisticRegression compute(double[][] input, double[] output, double[] weights)
{
if (regression == null)
{
initialize(input, output);
this.regression = new LogisticRegression()
{
NumberOfInputs = input.Columns()
};
}
var learning = new IterativeReweightedLeastSquares(regression)
{
Regularization = regularization,
Iterations = iterations,
Tolerance = tolerance,
Token = Token
};
learning.Learn(input, output, weights);
this.InformationMatrix = learning.GetInformationMatrix();
computeInformation(input, output, weights);
innerComputed = false;
if (ComputeInnerModels)
{
computeInner(input, output, weights);
}
return(regression);
}
public void prediction_interval()
{
CsvReader reader = CsvReader.FromText(Properties.Resources.logreg, true);
DataTable data = reader.ToTable();
double[][] inputs = data.ToArray("AGE");
double[] output = data.Columns["CHD"].ToArray();
var learner = new IterativeReweightedLeastSquares <LogisticRegression>();
var lr = learner.Learn(inputs, output);
Assert.AreEqual(0.111, lr.Weights[0], 5e-4);
Assert.AreEqual(-5.309, lr.Intercept, 5e-4);
Assert.AreEqual(1.1337, lr.StandardErrors[0], 5e-5);
Assert.AreEqual(0.0241, lr.StandardErrors[1], 5e-5);
double ll = lr.GetLogLikelihood(inputs, output);
Assert.AreEqual(-53.6765, ll, 1e-4);
double[] point = new double[] { 50 };
double y = lr.Score(point);
double[][] im = learner.GetInformationMatrix();
//double se = lr.GetStandardError(inputs, im);
var ci = lr.GetConfidenceInterval(point, inputs.Length, im);
Assert.AreEqual(0.435, ci.Min, 5e-3);
Assert.AreEqual(0.677, ci.Max, 5e-3);
var pi = lr.GetPredictionInterval(point, inputs.Length, im);
Assert.AreEqual(0.1405, pi.Min, 5e-3);
Assert.AreEqual(0.9075, pi.Max, 5e-3);
}