/**
* Turn sample into template.
*/
public void AddTemplate(Sample sample)
{
JackknifeTemplate t = new JackknifeTemplate(Blades, sample);
templates.Add(t);
int newlen = sample.Trajectory.Count;
if (!templateLenghts.ContainsKey(sample.GestureId))
{
templateLenghts.Add(sample.GestureId, newlen);
}
try {
if (templateLenghts[sample.GestureId] < newlen)
{
templateLenghts[sample.GestureId] = newlen;
}
}
catch (KeyNotFoundException) { }
if (newlen > maxTemplateLen)
{
maxTemplateLen = newlen;
}
if (newlen < minTemplateLen)
{
minTemplateLen = newlen;
}
}
/**
* Hardcode the rejection thresholds
*/
public void SetRejectionThresholds(double rt)
{
for (int t = 0; t < templates.Count; t++)
{
JackknifeTemplate temp = templates[t];
temp.RejectionThreshold = rt;
templates[t] = temp;
}
}
/**
* Check if buffer matches a certain template
*/
public bool IsMatch(
List <Vector> trajectory,
int gid,
out double score)
{
JackknifeFeatures features = new JackknifeFeatures(Blades, trajectory);
double best_score = double.PositiveInfinity;
bool ret = false;
for (int tid = 0; tid < templates.Count; tid++)
{
if (templates[tid].Sample.GestureId != gid)
{
continue;
}
double cf = 1;
if (Blades.CFAbsDistance)
{
cf *= 1.0 / Math.Max(0.01, features.Abs.Dot(templates[tid].Features.Abs));
}
if (Blades.CFBbWidths)
{
cf *= 1.0 / Math.Max(0.01, features.Bb.Dot(templates[tid].Features.Bb));
}
JackknifeTemplate temp = templates[tid];
temp.CF = cf;
templates[tid] = temp;
if (Blades.LowerBound)
{
JackknifeTemplate tempLB = templates[tid];
tempLB.LB = cf * LowerBound(features.Vecs, templates[tid]);
templates[tid] = tempLB;
}
double d = templates[tid].CF;
d *= DTW(features.Vecs, templates[tid].Features.Vecs);
if (d < templates[tid].RejectionThreshold)
{
ret = true;
}
if (d < best_score)
{
best_score = d;
}
}
score = best_score;
return(ret);
}
/**
* Find the lower bound score for a candidate
* against a given template.
*/
private double LowerBound(List <Vector> vecs, JackknifeTemplate t)
{
double lb = 0.0; // lower bound
int component_cnt = vecs[0].Size;
for (int i = 0; i < vecs.Count; i++)
{
double cost = 0.0;
for (int j = 0; j < component_cnt; j++)
{
if (Blades.InnerProduct)
{
if (vecs[i][j] < 0.0)
{
cost += vecs[i][j] * t.Lower[i][j];
}
else
{
cost += vecs[i][j] * t.Upper[i][j];
}
}
else if (Blades.EuclideanDistance)
{
double diff = 0.0;
if (vecs[i][j] < t.Lower[i][j])
{
diff = vecs[i][j] - t.Lower[i][j];
}
else if (vecs[i][j] > t.Upper[i][j])
{
diff = vecs[i][j] - t.Upper[i][j];
}
cost += (diff * diff);
}
else
{
throw new Exception("Should not get here!");
}
}
// inner products are bounded
if (Blades.InnerProduct)
{
cost = 1.0 - Math.Min(1.0, Math.Max(-1.0, cost));
}
lb += cost;
}
return(lb);
}
/**
* Check if buffer matches a certain template
*/
public List <Machete.ContinuousResult> IsMatch(
List <Vector> trajectory, int start, int end)
{
List <Machete.ContinuousResult> res = new List <Machete.ContinuousResult>();
JackknifeFeatures features = new JackknifeFeatures(Blades, trajectory);
for (int tid = 0; tid < templates.Count; tid++)
{
double cf = 1;
if (Blades.CFAbsDistance)
{
cf *= 1.0 / Math.Max(0.01, features.Abs.Dot(templates[tid].Features.Abs));
}
if (Blades.CFBbWidths)
{
cf *= 1.0 / Math.Max(0.01, features.Bb.Dot(templates[tid].Features.Bb));
}
//JackknifeTemplate temp = templates[tid];
//temp.CF = cf;
//templates[tid] = temp;
templates[tid].CF = cf;
if (Blades.LowerBound)
{
JackknifeTemplate tempLB = templates[tid];
tempLB.LB = cf * LowerBound(features.Vecs, templates[tid]);
templates[tid] = tempLB;
}
double d = templates[tid].CF;
d *= DTW(features.Vecs, templates[tid].Features.Vecs);
if (d < templates[tid].RejectionThreshold)
{
// Add to the Continuous Results
Machete.ContinuousResult cr = new Machete.ContinuousResult();
cr.startFrameNo = start;
cr.endFrameNo = end;
cr.score = d;
cr.gid = templates[tid].GestureId;
res.Add(cr);
}
}
return(res);
}
/**
* Learn a rejection threshold for each template.
* Call after all templates have been added.
*
* See explanation in jackknife_train to understand
* the input parameters.
*/
public void Train(int gpsrN, int gpsrR, double beta)
{
int template_cnt = templates.Count;
List <Distributions> distributions = new List <Distributions>();
List <Vector> synthetic = new List <Vector>();
double worst_score = 0.0;
Random rand = new Random();
//
// Create negative samples.
//
for (int i = 0; i < 1000; i++)
{
synthetic.Clear();
// Splice two samples together
// to create one negative sample.
for (int j = 0; j < 2; j++)
{
int t = rand.Next(template_cnt);
Sample s = templates[t].Sample;
int len = s.Trajectory.Count;
int start = rand.Next(len / 2);
for (int kk = 0; kk < len / 2; kk++)
{
synthetic.Add(s.Trajectory[start + kk]);
}
}
JackknifeFeatures features = new JackknifeFeatures(Blades, synthetic);
// and score it
for (int t = 0; t < template_cnt; t++)
{
double score = DTW(
features.Vecs,
templates[t].Features.Vecs);
if (worst_score < score)
{
worst_score = score;
}
if (i > 50)
{
distributions[t].AddNegativeScore(score);
}
}
// Generate a few samples to get an estimate
// of worst possible score.
if (i != 50)
{
continue;
}
// allocate distributions
for (int t = 0; t < template_cnt; t++)
{
distributions.Add(new Distributions(worst_score, 1000));
}
}
//
// Create positive examples.
//
for (int t = 0; t < template_cnt; t++)
{
for (int i = 0; i < 1000; i++)
{
synthetic.Clear();
// Create a synthetic variation of the sample.
synthetic = Mathematics.GPSR(templates[t].Sample.Trajectory, gpsrN, 0.25, gpsrR);
JackknifeFeatures features = new JackknifeFeatures(Blades, synthetic);
// and score it
double score = DTW(features.Vecs, templates[t].Features.Vecs);
distributions[t].AddPositiveScore(score);
}
}
//
// Now extract the rejection thresholds.
//
for (int t = 0; t < template_cnt; t++)
{
double threshold = distributions[t].RejectionThreshold(beta);
JackknifeTemplate temp = templates[t];
temp.RejectionThreshold = threshold;
templates[t] = temp;
}
}
/**
* Turn sample into template.
*/
public void AddTemplate(Sample sample)
{
JackknifeTemplate t = new JackknifeTemplate(Blades, sample);
templates.Add(t);
}
/**
* Determine gesture class of the given sample.
* The gesture id is returned.
*
* This function can be modified to return an n-best list, but
* because of early rejection, such a list may not make sense.
*
*/
public int Classify(List <Vector> trajectory)
{
JackknifeFeatures features = new JackknifeFeatures(Blades, trajectory);
int templateCnt = templates.Count;
for (int t = 0; t < templateCnt; t++)
{
double cf = 1;
if (Blades.CFAbsDistance)
{
cf *= 1 / Math.Max(0.01, features.Abs.Dot(templates[t].Features.Abs));
}
if (Blades.CFBbWidths)
{
cf *= 1 / Math.Max(0.01, features.Bb.Dot(templates[t].Features.Bb));
}
JackknifeTemplate temp = templates[t];
temp.CF = cf;
templates[t] = temp;
if (Blades.LowerBound)
{
JackknifeTemplate tempLB = templates[t];
tempLB.LB = cf * LowerBound(features.Vecs, templates[t]);
templates[t] = tempLB;
}
}
templates.Sort(new SortTemplate());
double best = double.PositiveInfinity;
int ret = -1;
for (int t = 0; t < templateCnt; t++)
{
if (templates[t].LB > templates[t].RejectionThreshold)
{
continue;
}
if (templates[t].LB > best)
{
continue;
}
double score = templates[t].CF;
score *= DTW(features.Vecs, templates[t].Features.Vecs);
if (score > templates[t].RejectionThreshold)
{
continue;
}
if (score < best)
{
best = score;
ret = templates[t].GestureId;
}
}
return(ret);
}
