public void Predict()
{
var predictor = new AggregateKNNCorrelationPredictor(2, 10, 0.9, new EEGDataType[] { EEGDataType.ALPHA_ABSOLUTE });
double[] values = { 0, 100, -100 };
int dataLength = 100;
var trainingData = new List <EEGEvent[]> [values.Length];
for (int i = 0; i < values.Length; i++)
{
trainingData[i] = new List <EEGEvent[]>();
for (int j = 0; j < dataLength; j++)
{
trainingData[i].Add(new EEGEvent[] { new EEGEvent(DateTime.UtcNow, EEGDataType.ALPHA_ABSOLUTE, new double[] { values[i], values[i] }) });
}
}
for (int i = 0; i < trainingData.Length; i++)
{
foreach (var pt in trainingData[i])
{
predictor.AddTrainingData(i, pt);
}
}
for (int i = 0; i < trainingData.Length; i++)
{
foreach (var pt in trainingData[i])
{
predictor.Predict(pt);
// TODO
//Assert.AreEqual(i, predictor.Predict(pt), "Did not predict correct value");
}
}
}
public void AddTrainingData()
{
var predictor = new AggregateKNNCorrelationPredictor(2, 3, 0.1, new EEGDataType[] { EEGDataType.ALPHA_ABSOLUTE });
var trainingData = new EEGEvent[][] {
new EEGEvent[] { new EEGEvent(DateTime.UtcNow, EEGDataType.ALPHA_ABSOLUTE, new double[] { 1, 1 }) },
};
predictor.AddTrainingData(1, trainingData[0]);
}
