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public add ( |
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| acceleration | ||
| Résultat | void | |
private Gesture Circle()
{
var gesture = new Gesture();
var steps = Enumerable.Range(1, 100)
.Select(i => 2 * Math.PI * i / 100.0)
.Select(i => new AccelerationVector(-Math.Sin(i), -Math.Cos(i), -Math.Sin(i)));
foreach (var s in steps)
{
gesture.add(s);
}
return gesture;
}
/**
* Trains the model to a set of motion-sequences, representing
* different evaluations of a gesture
*
* @param trainsequence a vector of gestures
*/
public void Train(IEnumerable <Gesture> trainsequence)
{
// summarize all vectors from the different gestures in one
// gesture called sum.
double maxacc = 0;
double minacc = 0;
Gesture sum = new Gesture();
foreach (var gesture in trainsequence)
{
IEnumerable <AccelerationVector> t = gesture.getData();
// add the max and min acceleration, we later get the average
maxacc += gesture.getMaxAcceleration();
minacc += gesture.getMinAcceleration();
// transfer every single accelerationevent of each gesture to
// the new gesture sum
foreach (var accelerationEvent in t)
{
sum.add(accelerationEvent);
}
}
// get the average and set it to the sum gesture
sum.setMaxAndMinAcceleration(maxacc / trainsequence.Count(), minacc / trainsequence.Count());
// train the centeroids of the quantizer with this master gesture sum.
this.quantizer.trainCenteroids(sum);
// convert gesture vector to a sequence of discrete values
var seqs = new List <int[]>();
foreach (var gesture in trainsequence)
{
seqs.Add(this.quantizer.getObservationSequence(gesture));
}
// train the markov model with this derived discrete sequences
this.markovmodell.train(seqs);
// set the default probability for use with the bayes classifier
this.SetDefaultProbability(trainsequence);
}
/**
* Trains the model to a set of motion-sequences, representing
* different evaluations of a gesture
*
* @param trainsequence a vector of gestures
*/
public void Train(IEnumerable<Gesture> trainsequence)
{
// summarize all vectors from the different gestures in one
// gesture called sum.
double maxacc = 0;
double minacc = 0;
Gesture sum = new Gesture();
foreach (var gesture in trainsequence)
{
IEnumerable<AccelerationVector> t = gesture.getData();
// add the max and min acceleration, we later get the average
maxacc += gesture.getMaxAcceleration();
minacc += gesture.getMinAcceleration();
// transfer every single accelerationevent of each gesture to
// the new gesture sum
foreach (var accelerationEvent in t)
{
sum.add(accelerationEvent);
}
}
// get the average and set it to the sum gesture
sum.setMaxAndMinAcceleration(maxacc / trainsequence.Count(), minacc / trainsequence.Count());
// train the centeroids of the quantizer with this master gesture sum.
this.quantizer.trainCenteroids(sum);
// convert gesture vector to a sequence of discrete values
var seqs = new List<int[]>();
foreach (var gesture in trainsequence)
{
seqs.Add(this.quantizer.getObservationSequence(gesture));
}
// train the markov model with this derived discrete sequences
this.markovmodell.train(seqs);
// set the default probability for use with the bayes classifier
this.SetDefaultProbability(trainsequence);
}
