public void TestTrain()
{
double[][] x = {
new [] {1.0},
new [] {3.0},
new [] {2.0},
new [] {200.0},
new [] {230.0}};
double[][] y = {
new [] {1.0},
new [] {1.0},
new [] {1.0},
new [] {0.0},
new [] {0.0}
};
var trainingData = BasicData.ConvertArrays(x, y);
var regression = new MultipleLinearRegression(1) {LinkFunction = new LogitLinkFunction()};
var train = new TrainReweightLeastSquares(regression, trainingData);
train.Iteration();
double[] input = { 0 };
double[] output = regression.ComputeRegression(input);
Assert.AreEqual(0.883301730269988, output[0], AIFH.DefaultPrecision);
}
/// <summary>
/// Run the example.
/// </summary>
public void Process()
{
// read the iris data from the resources
Assembly assembly = Assembly.GetExecutingAssembly();
var res = assembly.GetManifestResourceStream("AIFH_Vol1.Resources.breast-cancer-wisconsin.csv");
// did we fail to read the resouce
if (res == null)
{
Console.WriteLine("Can't read iris data from embedded resources.");
return;
}
// load the data
var istream = new StreamReader(res);
DataSet ds = DataSet.Load(istream);
istream.Close();
ds.DeleteUnknowns();
ds.DeleteColumn(0);
ds.ReplaceColumn(9, 4, 1, 0);
var trainingData = ds.ExtractSupervised(0, 9, 9, 1);
var reg = new MultipleLinearRegression(9) {LinkFunction = new LogitLinkFunction()};
var train = new TrainReweightLeastSquares(reg, trainingData);
int iteration = 0;
do
{
iteration++;
train.Iteration();
Console.WriteLine("Iteration #" + iteration + ", Error: " + train.Error);
} while (iteration < 1000 && train.Error > 0.01);
Query(reg, trainingData);
Console.WriteLine("Error: " + train.Error);
}
