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(); }