/**
* Extract statistics from the confusion matrices.
*/
public ResultT Results()
{
// aggregate confusion matrix
Vector cm = new Vector(0.0, 4);
for (int ii = 0; ii < confusion_matrices.Count; ii++)
{
cm += confusion_matrices[ii];
}
ResultT ret = new ResultT();
ret.Recall = cm[(int)ConfusionMatrixEntry.TRUE_POSITIVE];
ret.Recall /= (cm[(int)ConfusionMatrixEntry.TRUE_POSITIVE] + cm[(int)ConfusionMatrixEntry.FALSE_NEGATIVE]);
ret.Precision = cm[(int)ConfusionMatrixEntry.TRUE_POSITIVE];
ret.Precision /= (cm[(int)ConfusionMatrixEntry.TRUE_POSITIVE] + cm[(int)ConfusionMatrixEntry.FALSE_POSITIVE]);
ret.FallOut = cm[(int)ConfusionMatrixEntry.FALSE_POSITIVE];
ret.FallOut /= (cm[(int)ConfusionMatrixEntry.FALSE_POSITIVE] + cm[(int)ConfusionMatrixEntry.TRUE_NEGATIVE]);
ret.F1 = 2.0 * cm[(int)ConfusionMatrixEntry.TRUE_POSITIVE];
ret.F1 /= (2.0 * cm[(int)ConfusionMatrixEntry.TRUE_POSITIVE] + cm[(int)ConfusionMatrixEntry.FALSE_POSITIVE] + cm[(int)ConfusionMatrixEntry.FALSE_NEGATIVE]);
return(ret);
}
/**
* Evaluate all user study sessions for a given device type.
*/
public static void evaluate_sessions(DeviceType device)
{
List <ConfusionMatrices> confusion_matrices = new List <ConfusionMatrices>();
int[] participants = get_participant_list(device);
for (int i = 0; i < participants.Length - 1; i++)
{
ConfusionMatrices cm = evaluate_session(
device,
participants[i]);
confusion_matrices.Add(cm);
int idx = confusion_matrices.Count - 1;
Console.WriteLine("Participant: " + participants[i]);
ResultT resultss = confusion_matrices[idx].Results();
resultss.Print();
Console.WriteLine();
}
// put all results into first confusion
// matrix
for (int ii = 1; ii < confusion_matrices.Count; ii++)
{
confusion_matrices[0].AddResult(confusion_matrices[ii]);
}
Console.WriteLine("Aggregate results:");
ResultT result = confusion_matrices[0].Results();
result.Print();
Console.WriteLine();
}
/**
* A simple user independent test.
*/
public static void user_indepedent_test(DeviceType device)
{
// First, load all training data for one device type.
string path = "";
if (device == DeviceType.KINECT)
{
path = Global.GetRootDirectory() + "datasets/jk2017/kinect/training/";
}
else if (device == DeviceType.LEAP_MOTION)
{
path = Global.GetRootDirectory() + "datasets/jk2017/leap_motion/training/";
}
Dataset ds = Dataset.LoadDataset(path);
int subject_cnt = ds.Subjects.Count;
int sample_cnt = ds.Samples.Count;
ConfusionMatrices cm = new ConfusionMatrices(ds);
// iterate through all subjects
for (int subject_id = 0; subject_id < subject_cnt; subject_id++)
{
ConfusionMatrices cm_individual = new ConfusionMatrices(ds);
Console.WriteLine("Participant: " + subject_id);
// train a recognizer with the selected subject
JackknifeBlades blades = new JackknifeBlades();
blades.SetIPDefaults();
Jackknife jk = new Jackknife(blades);
for (int sample_id = 0; sample_id < sample_cnt; sample_id++)
{
Sample sample = ds.Samples[sample_id];
if (sample.SubjectId != subject_id)
{
continue;
}
jk.AddTemplate(ds.Samples[sample_id]);
}
// only train the recognize if you need
// test the recognizer with all other samples
for (int sample_id = 0; sample_id < sample_cnt; sample_id++)
{
Sample sample = ds.Samples[sample_id];
if (sample.SubjectId == subject_id)
{
continue;
}
int gid = jk.Classify(sample);
cm_individual.AddResult(
sample.GestureId,
gid);
}
cm.AddResult(cm_individual);
ResultT resultss = cm_individual.Results();
resultss.Print();
}
Console.WriteLine("Aggregate Results: ");
ResultT results = cm.Results();
results.Print();
}
