protected override void Train(GestureData gestureData)
{
MultivariateAlgorithmInput multivariateInput = new MultivariateAlgorithmInput(gestureData);
CreateUnivariateAlgorithms(multivariateInput);
FindBestGestures(gestureData);
TrainAlgorithms(multivariateInput);
}
private void TrainAlgorithms(MultivariateAlgorithmInput multivariateInput)
{
foreach (string key in Gesture.parameterKeys)
{
for (int i = 0; i < parametersToAlgorithms[key].Length; i++)
{
parametersToAlgorithms[key][i].Train(multivariateInput[key][i]);
}
}
}
private void CreateUnivariateAlgorithms(MultivariateAlgorithmInput multivariateInput)
{
foreach (string key in Gesture.parameterKeys)
{
UnivariateAlgorithmInput[] value = multivariateInput[key];
int dimension = value.Length;
UnivariateAlgorithm[] algorithms = new UnivariateAlgorithm[dimension];
for (int i = 0; i < dimension; i++)
{
object[] args = { key, i, value[i] };
algorithms[i] = (UnivariateAlgorithm)Activator.CreateInstance(typeof(Algorithm), args);
}
parametersToAlgorithms[key] = algorithms;
}
}
public override double ComputeError(GestureData data)
{
MultivariateAlgorithmInput multivariateInput = new MultivariateAlgorithmInput(data);
List <double> results = new List <double>();
foreach (string key in Gesture.parameterKeys)
{
UnivariateAlgorithmInput[] value = multivariateInput[key];
int dimension = value.Length;
for (int i = 0; i < dimension; i++)
{
results.Add(parametersToAlgorithms[key][i].ComputeError(value[i].data));
}
}
return(results.ToArray().Max());
}
