/// <summary>
/// Performs the common tasks of TaskInitial and TaskIntermediate
/// </summary>
/// <param name="window"></param>
private void Process(DataWindow window)
{
foreach (Feature feature in _featureObjects.Values)
feature.frameBlockSize = window.frames.Count;
foreach (Frame frame in window.frames)
{
foreach (Feature feature in _featureObjects.Values)
feature.Process(frame);
}
foreach (string key in _featureObjects.Keys)
{
Feature feature;
if (_featureObjects.TryGetValue(key, out feature))
{
window.features.Add(key, feature.output);
feature.Clear();
}
}
}
/// <summary>
/// Used to remove elements from the feature vectors belonging to channels that are not active.
/// Frames stored in each DataWindow are also discarded.
/// </summary>
/// <param name="inputDataSet"></param>
/// <returns>A DataSet object containing no frames and only features for the active channels.</returns>
private DataSet CreateLightDataSet(DataSet inputDataSet, TrainingPackage trainigPackage)
{
DataSet outputDataSet = new DataSet(inputDataSet.movementCode, inputDataSet.movementComposition);
foreach (DataWindow window in inputDataSet.set)
{
DataWindow lightWindow = new DataWindow();
foreach (string featureName in window.features.Keys)
{
double[] channelVector;
object placeholder;
double[] cleanVector = new double[trainingPackage.recordingConfig.activeChannels];
window.features.TryGetValue(featureName,out placeholder);
channelVector = (double[])placeholder;
int pos = 0;
for (int i = 0; i < channelVector.Length; i++)
{
if(trainingPackage.recordingConfig.channelMask[i])
{
cleanVector[pos] = channelVector[i];
pos++;
}
}
lightWindow.features.Add(featureName,cleanVector);
}
outputDataSet.set.Add(lightWindow);
}
return outputDataSet;
}
/// <summary>
/// Called when isOnline is set to false, which is NOT the default.
/// </summary>
/// <param name="outputItem"></param>
protected override void TaskInitial(out object outputItem)
{
int frameLimit = _startFrame + config.windowLength;
if (_startFrame < recording.data.Count())
{
DataWindow dataWindow = new DataWindow();
for (int i = _startFrame; ((i < frameLimit) && (i < recording.data.Count())); i++)
dataWindow.frames.Add((Frame)recording.data.ElementAt(i));
_startFrame += _stride;
outputItem = dataWindow;
}
else //We have to stop the acquisition, but we cannot do it from the windowmaker thread
{
outputItem = null;
_myPipeline.stopPending = true;
if (!lockingCollection.IsAddingCompleted) lockingCollection.CompleteAdding();
}
}
/// <summary>
/// Called when isOnline is set to true, which is its default value.
/// </summary>
/// <param name="inputItem"></param>
/// <param name="outputItem"></param>
protected override void TaskIntermediate(object inputItem, out object outputItem)
{
outputItem=null;
Frame inputFrame = (Frame)inputItem;
_outputBuffer.Add(inputFrame);
_offsetCounter = (_offsetCounter + 1) % _stride;
if (_outputBuffer.Count == config.windowLength)
{
if (_offsetCounter == 0)
{
_dataWindow = new DataWindow();
foreach (Frame item in _outputBuffer) _dataWindow.frames.Add(item);
outputItem = _dataWindow;
}
_outputBuffer.RemoveAt(0);
}
}
/// <summary>
///Prepare the training package with a training set based on the example solved in
///http://people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html
///One set of vectors will be assigned to movement code 1, the other to movement code 2.
/// </summary>
/// <returns></returns>
private TrainingPackage MakeTrainingPackage()
{
DataWindow tempWindow;
TrainingPackage trainingPackage;
List<DataWindow> windowList1 = new List<DataWindow>();
List<DataWindow> windowList2 = new List<DataWindow>();
//Filling the window lists with feature vectors
//List 1
tempWindow = new DataWindow();
tempWindow.features.Add("curvature", 2.95);
tempWindow.features.Add("diameter", 6.63);
windowList1.Add(tempWindow);
tempWindow = new DataWindow();
tempWindow.features.Add("curvature", 2.53);
tempWindow.features.Add("diameter", 7.79);
windowList1.Add(tempWindow);
tempWindow = new DataWindow();
tempWindow.features.Add("curvature", 3.57);
tempWindow.features.Add("diameter", 5.65);
windowList1.Add(tempWindow);
tempWindow = new DataWindow();
tempWindow.features.Add("curvature", 3.16);
tempWindow.features.Add("diameter", 5.47);
windowList1.Add(tempWindow);
//List 2
tempWindow = new DataWindow();
tempWindow.features.Add("curvature", 2.58);
tempWindow.features.Add("diameter", 4.46);
windowList2.Add(tempWindow);
tempWindow = new DataWindow();
tempWindow.features.Add("curvature", 2.16);
tempWindow.features.Add("diameter", 6.22);
windowList2.Add(tempWindow);
tempWindow = new DataWindow();
tempWindow.features.Add("curvature", 3.27);
tempWindow.features.Add("diameter", 3.52);
windowList2.Add(tempWindow);
trainingPackage = new TrainingPackage();
trainingPackage.trainingSets.Add(new DataSet(1, windowList1));
trainingPackage.trainingSets.Add(new DataSet(2, windowList2));
return trainingPackage;
}