public override void Train(IForecastingDataSets datasets)
{
OnStartRunning(new ComponentRunEventArgs(datasets));
AnnModelParameter para = mParameter as AnnModelParameter;
LinearLayer inputLayer = new LinearLayer(datasets.InputData[0].Length);
SigmoidLayer hiddenLayer = new SigmoidLayer(para.HiddenNeuronsCount[0]);
SigmoidLayer outputLayer = new SigmoidLayer(1);
new BackpropagationConnector(inputLayer, hiddenLayer).Initializer = new RandomFunction(0d, 0.3d);
new BackpropagationConnector(hiddenLayer, outputLayer).Initializer = new RandomFunction(0d, 0.3d);
network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(para.LearningRate);
network.JitterEpoch = para.JitterEpoch;
network.JitterNoiseLimit = para.JitterNoiseLimit;
network.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs args)
{
// TODO: trainning error needs to be calculated
OnRunningEpoch(new AnnModelRunEpochEventArgs(args.TrainingIteration + 1, 0));
});
network.Learn(ForecastingDataSets.ConvertToTrainingSet(datasets), para.Iterations);
datasets.ForecastedData = new double[datasets.InputData.Length][];
for (int i = 0; i < datasets.InputData.Length; i++)
{
datasets.ForecastedData[i] = new double[1];
datasets.ForecastedData[i][0] = Forecast(datasets.InputData[i]);
}
OnFinishRunning(new ComponentRunEventArgs(datasets));
}
public void Analyze(IForecastingDataSets datasets)
{
if (ModelStartRunning != null)
{
ModelStartRunning(this, new ComponentRunEventArgs(datasets));
}
int learningRadius = Math.Max(mSOMParameter.LayerWidth, mSOMParameter.LayerHeight) / 2;
KohonenLayer inputLayer = new KohonenLayer(datasets.InputData[0].Length);
KohonenLayer outputLayer = new KohonenLayer(new Size(mSOMParameter.LayerWidth, mSOMParameter.LayerHeight),
mSOMParameter.NeighborhoodFunction, mSOMParameter.Topology);
KohonenConnector connector = new KohonenConnector(inputLayer, outputLayer);
connector.Initializer = new RandomFunction(0, 100);
outputLayer.SetLearningRate(mSOMParameter.LearningRate, mSOMParameter.FinalLearningRate);
outputLayer.IsRowCircular = mSOMParameter.IsRowCircular;
outputLayer.IsColumnCircular = mSOMParameter.IsColumnCircular;
mNetwork = new KohonenNetwork(inputLayer, outputLayer);
mNetwork.EndEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs args)
{
if (ModelRunningEpoch != null)
{
ModelRunningEpoch(this, new ComponentRunEpochEventArgs(args.TrainingIteration));
}
});
mTrainingSet = ForecastingDataSets.ConvertToUnSupervisedTrainingSet(datasets);
mNetwork.Learn(mTrainingSet, mSOMParameter.Iterations);
if (ModelFinishRunning != null)
{
ModelFinishRunning(this, new ComponentRunEventArgs(datasets));
}
}
public override void Train(IForecastingDataSets datasets)
{
OnStartRunning(new ComponentRunEventArgs(datasets));
ForecastingDataSets fds = datasets as ForecastingDataSets;
//MultipleLinearRegression mlineRegrsn = new MultipleLinearRegression(datasets.InputData, fds.GetOutputDataColumn(0), true);
//Matrix result = new Matrix();
//mlineRegrsn.ComputeFactorCoref(result);
//RegressionCoefficients = result[0, Matrix.mCol];
int solutionSize = datasets.Length;
datasets.ForecastedData = new double[solutionSize][];
for (int i = 0; i < solutionSize; i++)
{
datasets.ForecastedData[i] = new double[1];
datasets.ForecastedData[i][0] = Forecast(fds.InputData[i]);
}
OnFinishRunning(new ComponentRunEventArgs(datasets));
}
private IForecastingDataSets ExtractSubSets(int[] splitLocations, double[] splitValues, IForecastingDataSets datasets, RuleType type)
{
List <double[]> listInput = new List <double[]>();
List <double[]> listOutput = new List <double[]>();
List <DateTime> dates = new List <DateTime>();
for (int i = 0; i < datasets.Length; i++)
{
double[] vector = datasets.InputData[i];
bool isCase = true;
int j = 0;
foreach (int sl in splitLocations)
{
if (type == RuleType.LeftLeaf)
{
if (vector[sl] > splitValues[j])
{
isCase = false;
break;
}
}
else if (type == RuleType.RightLeaf)
{
if (vector[sl] <= splitValues[j])
{
isCase = false;
break;
}
}
j++;
}
if (isCase)
{
listInput.Add(vector);
listOutput.Add(datasets.OutputData[i]);
dates.Add(datasets.Date[i]);
}
}
IForecastingDataSets extractedSets = new ForecastingDataSets(listInput.ToArray(), listOutput.ToArray());
extractedSets.Date = dates.ToArray();
return(extractedSets);
}
