public void example_learn()
{
#region doc_learn_part1
// Suppose we have the following data about some patients.
// The first variable is continuous and represent patient
// age. The second variable is dichotomic and give whether
// they smoke or not (this is completely fictional data).
double[][] inputs =
{
// Age Smoking
new double[] { 55, 0 },
new double[] { 28, 0 },
new double[] { 65, 1 },
new double[] { 46, 0 },
new double[] { 86, 1 },
new double[] { 56, 1 },
new double[] { 85, 0 },
new double[] { 33, 0 },
new double[] { 21, 1 },
new double[] { 42, 1 },
};
// Additionally, we also have information about whether
// or not they those patients had lung cancer. The array
// below gives 0 for those who did not, and 1 for those
// who did.
double[] output =
{
0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};
// Create a Logistic Regression analysis
var lra = new LogisticRegressionAnalysis()
{
Regularization = 0
};
// compute the analysis
LogisticRegression regression = lra.Learn(inputs, output);
// Now we can show a summary of the analysis
// Accord.Controls.DataGridBox.Show(regression.Coefficients);
#endregion
#region doc_learn_part2
// We can also investigate all parameters individually. For
// example the coefficients values will be available at the
// vector
double[] coef = lra.CoefficientValues;
// The first value refers to the model's intercept term. We
// can also retrieve the odds ratios and standard errors:
double[] odds = lra.OddsRatios;
double[] stde = lra.StandardErrors;
// We can use the analysis to predict a score for a new patient:
double y = lra.Regression.Score(new double[] { 87, 1 }); // 0.75
// For those inputs, the answer probability is approximately 75%.
// We can also obtain confidence intervals for the probability:
DoubleRange ci = lra.GetConfidenceInterval(new double[] { 87, 1 });
#endregion
Assert.AreEqual(0.085627701183146374, odds[0], 1e-8);
Assert.AreEqual(1.0208597029292648, odds[1], 1e-8);
Assert.AreEqual(5.8584748981777919, odds[2], 1e-8);
Assert.AreEqual(2.1590686019473897, stde[0], 1e-8);
Assert.AreEqual(0.033790422321041035, stde[1], 1e-8);
Assert.AreEqual(1.4729903935788211, stde[2], 1e-8);
Assert.AreEqual(0.75143272858389798, y, 1e-8);
Assert.AreEqual(0.079591541770048527, ci.Min, 1e-8);
Assert.AreEqual(0.99062645401700389, ci.Max, 1e-8);
}
