static void Main(string[] args)
{
string filename = "REGRESSION-";
filename += DateTime.Now.Ticks;
filename += ".txt";
StreamWriter sw = new StreamWriter(filename);
Console.SetOut(sw);
sw.AutoFlush = true;
HouseDataMgr dataMgr = new HouseDataMgr(@"house.csv");
dataMgr.Load();
Console.WriteLine("Data Loaded");
DistributedOneDimDataTrainer trainer = new DistributedOneDimDataTrainer();
trainer.PrintAdjacency();
trainer.TrainCenterMachine();
trainer.TrainDistributedMachines();
trainer.TestCenterMachine();
trainer.TestDistributedMachines();
/*
* KernelRegression kernelMachine = new KernelRegression(KernelRegression.KernelType.LINEAR);
* //KernelSimpleTester tester = new KernelSimpleTester(kernelMachine);
* //KernelRegressionTester tester = new KernelRegressionTester(dataMgr);
* //OneDimDataTester tester = new OneDimDataTester();
* TwoDimDataTester tester = new TwoDimDataTester();
* tester.Train();
* tester.Test();
* //tester.Test2();
*/
sw.Flush();
sw.Close();
return;
}
public KernelRegressionTester(HouseDataMgr dataMgr, KernelRegression machine = null)
{
_dataMgr = dataMgr;
_input = new double[_dataMgr.count][];
_output = new double[_dataMgr.count];
for (int i = 0; i < _dataMgr.count; i++)
{
_input[i] = _dataMgr.GetInputData(i);
_output[i] = _dataMgr.GetLabelData(i);
}
if (null == machine)
{
_regressionMachine = new KernelRegression();
}
else
{
_regressionMachine = machine;
}
_rnd = new Random();
}
