public void Process()
{
// read the iris data from the resources
Assembly assembly = Assembly.GetExecutingAssembly();
var res = assembly.GetManifestResourceStream("AIFH_Vol1.Resources.abalone.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();
// The following ranges are setup for the Abalone data set. If you wish to normalize other files you will
// need to modify the below function calls other files.
ds.EncodeOneOfN(0, 0, 1);
istream.Close();
var trainingData = ds.ExtractSupervised(0, 10, 10, 1);
var reg = new MultipleLinearRegression(10);
var train = new TrainLeastSquares(reg, trainingData);
train.Iteration();
Query(reg, trainingData);
Console.WriteLine("Error: " + train.Error);
}
public void TestTrain()
{
double[][] x =
{
new [] { 5.0, 10.0, 2.0 },
new [] { 10.0, 20.0, 4.0 },
new [] { 15.0, 30.0, 6.0 },
new [] { 20.0, 40.0, 8.0 },
new [] { 25.0, 50.0, 10.0 }
};
double[][] y =
{
new [] { 70.0 },
new [] { 132.0 },
new [] { 194.0 },
new [] { 256.0 },
new [] { 318.0 }
};
var trainingData = BasicData.ConvertArrays(x, y);
var regression = new MultipleLinearRegression(3);
var train = new TrainLeastSquares(regression, trainingData);
train.Iteration();
Assert.AreEqual(8, regression.LongTermMemory[0], 0.0001);
Assert.AreEqual(-54.8, regression.LongTermMemory[1], 0.0001);
Assert.AreEqual(8, regression.LongTermMemory[2], 0.0001);
Assert.AreEqual(1, train.R2, 0.0001);
Assert.AreEqual(0, train.Error, AIFH.DefaultPrecision);
for (int i = 0; i < x.Length; i++)
{
double[] output = regression.ComputeRegression(x[i]);
Assert.AreEqual(y[i][0], output[0], 0.0001);
}
}