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 [] {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>
/// Construct the trainer.
/// </summary>
/// <param name="theAlgorithm">The GLM to train.</param>
/// <param name="theTrainingData">The training data.</param>
public TrainReweightLeastSquares(MultipleLinearRegression theAlgorithm, IList<BasicData> theTrainingData)
{
_algorithm = theAlgorithm;
_trainingData = theTrainingData;
_gradient = new DenseMatrix(theAlgorithm.LongTermMemory.Length, 1);
_hessian = new DenseMatrix(theAlgorithm.LongTermMemory.Length, theAlgorithm.LongTermMemory.Length);
}
/// <summary>
/// Construct the trainer.
/// </summary>
/// <param name="theAlgorithm">The GLM to train.</param>
/// <param name="theTrainingData">The training data.</param>
public TrainReweightLeastSquares(MultipleLinearRegression theAlgorithm, IList <BasicData> theTrainingData)
{
_algorithm = theAlgorithm;
_trainingData = theTrainingData;
_gradient = new DenseMatrix(theAlgorithm.LongTermMemory.Length, 1);
_hessian = new DenseMatrix(theAlgorithm.LongTermMemory.Length, theAlgorithm.LongTermMemory.Length);
}
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);
}
}
public void TestBasic()
{
var reg = new MultipleLinearRegression(1);
Assert.AreEqual(2, reg.LongTermMemory.Length);
var lnk = new LogLinkFunction();
reg.LinkFunction = lnk;
Assert.IsTrue(reg.LinkFunction == lnk);
reg.LongTermMemory[0] = 1;
reg.LongTermMemory[1] = 2;
double[] input = { 1.0 };
double[] output = reg.ComputeRegression(input);
Assert.AreEqual(1, output.Length);
Assert.AreEqual(1.0986122886681098, 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);
}
/// <summary>
/// Construct the trainer.
/// </summary>
/// <param name="theAlgorithm">The algorithm to train.</param>
/// <param name="theTrainingData">The training data.</param>
public TrainLeastSquares(MultipleLinearRegression theAlgorithm, IList <BasicData> theTrainingData)
{
_algorithm = theAlgorithm;
_trainingData = theTrainingData;
}
/// <summary>
/// Construct the trainer.
/// </summary>
/// <param name="theAlgorithm">The algorithm to train.</param>
/// <param name="theTrainingData">The training data.</param>
public TrainLeastSquares(MultipleLinearRegression theAlgorithm, IList<BasicData> theTrainingData)
{
_algorithm = theAlgorithm;
_trainingData = theTrainingData;
}