public OneDimDataTester(KernelRegression machine = null)
{
_dataGnr = new OneDimDataGenerator();
_rnd = new Random();
_trainDataX = new double[_trainDataSize][];
_trainDataY = new double[_trainDataSize];
for (int i = 0; i < _trainDataSize; i++)
{
_trainDataX[i] = new double[1];
_trainDataX[i][0] = 2 * _rnd.NextDouble() - 1.0;
}
_trainDataY = _dataGnr.Calc(_trainDataX);
if (machine == null)
{
_regressionMachine = new KernelRegression();
}
else
{
_regressionMachine = machine;
}
}
public DistributedOneDimDataTrainer()
{
_agentNum = _trainDataSize;
_adjacency = new int[_agentNum, _agentNum];
_dataGnr = new OneDimDataGenerator();
_rnd = new Random();
_trainDataX = new double[_trainDataSize][];
_trainDataY = new double[_trainDataSize];
_agents = new List <KernelAgent>();
_centerMachine = new KernelRegression();
for (int i = 0; i < _agentNum; i++)
{
_trainDataX[i] = new double[1];
_trainDataX[i][0] = 2 * _rnd.NextDouble() - 1.0;
_agents.Add(new KernelAgent(_trainDataX[i]));
}
_trainDataY = _dataGnr.Calc(_trainDataX);
Init();
}
public KernelSimpleTester(KernelRegression machine = null)
{
if (null == machine)
{
_regressionMachine = new KernelRegression();
}
else
{
_regressionMachine = machine;
}
}
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();
}
public KernelAgent(double[] pos)
{
_machine = new KernelRegression();
_pos = pos;
_neighbors = new List <KernelAgent>();
}
