public void TestDistanceOneDimension()
{
double[] p1 = new double[1] {
2
};
double[] p2 = new double[1] {
4
};
double expectedDistance = 2;
var dtw = new Dtw();
double distance = dtw.Distance(p1, p2);
Assert.AreEqual(expectedDistance, distance);
}
/// <summary>
/// Aggregates the input features and executes the <see cref="Dtw"/> algorithm.
/// </summary>
/// <param name="signature1"></param>
/// <param name="signature2"></param>
/// <returns>Cost between <paramref name="signature1"/> and <paramref name="signature2"/></returns>
public double Pair(Signature signature1, Signature signature2)
{
Progress = 0;
Dtw dtwAlg = new Dtw(distanceMethod);
double[][] testSig1 = signature1.GetAggregateFeature(InputFeatures).ToArray();
double[][] testSig2 = signature2.GetAggregateFeature(InputFeatures).ToArray();
Progress = 50;//..
double cost = dtwAlg.Compute(testSig1, testSig2);
Log(LogLevel.Info, $"Paired SigID {signature1.ID} with SigID {signature2.ID}");
Log(LogLevel.Debug, $"Pairing result of SigID {signature1.ID} with SigID {signature2.ID}: {cost}");
Progress = 100;
return(cost);
}
//Azokat a dolgokat amiket a verifier előfeldolgoz itt nincsenek csak részben
//a loadsignature-ben van valami minimáis előfeldolgozás
private void GoDTWButton_Click(object sender, RoutedEventArgs e)
{
if (Signers == null)
{
LoadSignatures();
}
Signature signature1 = Signers[(int)SignerComboBox1.SelectedValue - 1].Signatures[(int)SignatureComboBox1.SelectedIndex];
Signature signature2 = Signers[(int)SignerComboBox2.SelectedValue - 1].Signatures[(int)SignatureComboBox2.SelectedIndex];
//DTWScoreTextBlock.Text = Analyzer.GetCost(sig1, sig2, true).Cost.ToString();
Dtw dtw = new Dtw(signature1, signature2, FeatureFilter);
OwnDTWScoreTextBlock.Text = dtw.CalculateDtwScore().ToString();
WarpingPathScoreTextBlock.Text = dtw.CalculateWarpingPathScore().ToString();
FusionScoreTextBlock.Text = FusedScore.CalculateFusionOfDtwAndWPathScore(signature1, new Signature[] { signature2 }, FeatureFilter).ToString();
}
public override double GetDistance(AudioFeature f, AudioFeature.DistanceType t)
{
if (!(f is Scms))
{
new Exception("Can only handle AudioFeatures of type Scms, not of: " + f);
return(-1);
}
Scms other = (Scms)f;
DistanceMeasure distanceMeasure = DistanceMeasure.Euclidean;
switch (t)
{
case AudioFeature.DistanceType.Dtw_Euclidean:
distanceMeasure = DistanceMeasure.Euclidean;
break;
case AudioFeature.DistanceType.Dtw_SquaredEuclidean:
distanceMeasure = DistanceMeasure.SquaredEuclidean;
break;
case AudioFeature.DistanceType.Dtw_Manhattan:
distanceMeasure = DistanceMeasure.Manhattan;
break;
case AudioFeature.DistanceType.Dtw_Maximum:
distanceMeasure = DistanceMeasure.Maximum;
break;
case AudioFeature.DistanceType.UCR_Dtw:
return(UCRCSharp.UCR.DTW(this.GetArray(), other.GetArray()));
case AudioFeature.DistanceType.CosineSimilarity:
return(CosineSimilarity(this, other));
case AudioFeature.DistanceType.BitStringHamming:
return(Imghash.ImagePHash.HammingDistance(this.BitString, other.BitString));
case AudioFeature.DistanceType.KullbackLeiblerDivergence:
default:
return(Distance(this, other, new ScmsConfiguration(Analyzer.MFCC_COEFFICIENTS)));
}
Dtw dtw = new Dtw(this.GetArray(), other.GetArray(), distanceMeasure, true, true, null, null, null);
return(dtw.GetCost());
}
private double CalculateCost(SignModel sm1, SignModel sm2)
{
var seriesVariables = new List <SeriesVariable>();
seriesVariables.Add(
new SeriesVariable(
sm1.H_horizantal.ToArray(),
sm2.H_horizantal.ToArray()));
seriesVariables.Add(
new SeriesVariable(
sm1.H_vertical.ToArray(),
sm2.H_vertical.ToArray()));
var seriesVariablesArray = seriesVariables.ToArray();
var dtw = new Dtw(seriesVariablesArray);
m_dtw = dtw;
return(dtw.GetCost());
}
public void TestComputeTwoDimension()
{
double[][] signature1 = new double[3][];
//Two dimension points. another test required for one dimension points.
signature1[0] = new double[] { 1, 4 };
signature1[1] = new double[] { 2, 5 };
signature1[2] = new double[] { 3, 8 };
double[][] signature2 = new double[3][];
signature2[0] = new double[] { 1, 5 };
signature2[1] = new double[] { 2, 6 };
signature2[2] = new double[] { 3, 8 };
var dtw = new Dtw();
double cost = dtw.Compute(signature1, signature2);
double expectedCost = 0;
Assert.AreEqual(expectedCost, cost);
}
public void TestComputeOneDimension()
{
double[][] signature1 = new double[3][];
//One dimension points.
signature1[0] = new double[] { 4 };
signature1[1] = new double[] { 5 };
signature1[2] = new double[] { 8 };
double[][] signature2 = new double[3][];
signature2[0] = new double[] { 4 };
signature2[1] = new double[] { 5 };
signature2[2] = new double[] { 8 };
var dtw = new Dtw();
double cost = dtw.Compute(signature1, signature2);
double expectedCost = 0;
Assert.AreEqual(expectedCost, cost);
}
public void TestSameInput()
{
double[][] signature1 = new double[3][];
//Two dimension points. another test required for one dimension points.
signature1[0] = new double[] { 1, 4 };
signature1[1] = new double[] { 2, 5 };
signature1[2] = new double[] { 3, 8 };
double[][] signature2 = new double[3][];
signature2[0] = new double[] { 1, 4 };
signature2[1] = new double[] { 2, 5 };
signature2[2] = new double[] { 3, 8 };
var dtw = new Dtw();
double cost = dtw.Compute(signature1, signature2);
double expectedCost = 0;
double expectedDistance = 0;
double distance = dtw.Distance(signature1[1], signature2[1]);
Assert.AreEqual(expectedDistance, distance);
Assert.AreEqual(expectedCost, cost);
}
/// <summary>Get Distance</summary>
/// <seealso cref="">comirva.audio.feature.AudioFeature#GetDistance(comirva.audio.feature.AudioFeature)</seealso>
public override double GetDistance(AudioFeature f, AudioFeature.DistanceType t)
{
if (!(f is MandelEllis))
{
new Exception("Can only handle AudioFeatures of type Mandel Ellis, not of: " + f);
return(-1);
}
MandelEllis other = (MandelEllis)f;
DistanceMeasure distanceMeasure = DistanceMeasure.Euclidean;
switch (t)
{
case AudioFeature.DistanceType.Dtw_Euclidean:
distanceMeasure = DistanceMeasure.Euclidean;
break;
case AudioFeature.DistanceType.Dtw_SquaredEuclidean:
distanceMeasure = DistanceMeasure.SquaredEuclidean;
break;
case AudioFeature.DistanceType.Dtw_Manhattan:
distanceMeasure = DistanceMeasure.Manhattan;
break;
case AudioFeature.DistanceType.Dtw_Maximum:
distanceMeasure = DistanceMeasure.Maximum;
break;
case AudioFeature.DistanceType.KullbackLeiblerDivergence:
default:
return(KullbackLeibler(this.gmmMe, other.gmmMe) + KullbackLeibler(other.gmmMe, this.gmmMe));
}
Dtw dtw = new Dtw(this.GetArray(), other.GetArray(), distanceMeasure, true, true, null, null, null);
return(dtw.GetCost());
}
public void TestComputeDifferentLenghtInput() //SameLengthInput already tested in the previous tests.
{
double[][] signature1 = new double[3][];
//Two dimension points. another test required for one dimension points.
signature1[0] = new double[] { 1, 4 };
signature1[1] = new double[] { 2, 5 };
signature1[2] = new double[] { 3, 8 };
double[][] signature2 = new double[4][];
signature2[0] = new double[] { 1, 6 };
signature2[1] = new double[] { 2, 7 };
signature2[2] = new double[] { 3, 10 };
signature2[3] = new double[] { 4, 12 };
var dtw = new Dtw();
double cost = dtw.Compute(signature1, signature2);
double expectedCost = 0.25;
double expectedDistance = 2;
double distance = dtw.Distance(signature1[1], signature2[1]);
Assert.AreEqual(expectedDistance, distance);
Assert.AreEqual(expectedCost, cost);
}
/// <summary>
/// Generate average of supplied series.
/// </summary>
public static double[] Average(List <double[]> series, int maxIterations = 100)
{
if (series == null || series.Count == 0)
{
throw new Exception("series null or empty");
}
if (series.Count == 1)
{
return(series[0]);
}
if (series.Select(x => x.Length).Distinct().Count() != 1)
{
throw new Exception("Series must be of equal length");
}
int length = series[0].Length;
//initializing the average to a simple mean of every point, without DTW
//double[] average = new double[length];
//for (int i = 0; i < series[0].Length; i++)
//{
// average[i] = series.Average(x => x[i]);
//}
//initialize to a random series from the input
//var r = new Random();
//double[] average = series[r.Next(0, series.Count - 1)];
//initialize to series closest to median min/max after detrending
List <double[]> tempSeries = series.Select(Detrend).ToList();
List <int> maxIndexes = tempSeries.Select(x => x.IndexOfMax()).ToList();
List <int> minIndexes = tempSeries.Select(x => x.IndexOfMin()).ToList();
double medianMaxIndex = maxIndexes.Median();
double medianMinIndex = minIndexes.Median();
var distances = maxIndexes.Select((x, i) => Math.Pow(x - medianMaxIndex, 2) + Math.Pow(minIndexes[i] - medianMinIndex, 2)).ToList();
int selectedSeries = distances.IndexOfMin();
double[] average = series[selectedSeries];
//this list will hold the values of each aligned point,
//later used to construct the aligned average
List <double>[] points = new List <double> [length];
for (int i = 0; i < length; i++)
{
points[i] = new List <double>();
}
double prevTotalDist = -1;
double totalDist = -2;
//sometimes the process gets "stuck" in a loop between two different states
//so we have to set a hard limit to end the loop
int count = 0;
//get the path between each series and the average
while (totalDist != prevTotalDist && count < maxIterations)
{
prevTotalDist = totalDist;
//clear the points from the last calculation
foreach (var list in points)
{
list.Clear();
}
//here we do the alignment for every series
foreach (double[] ts in series)
{
var dtw = new Dtw(new[] { new SeriesVariable(ts, average) });
Tuple <int, int>[] path = dtw.GetPath();
//use the path to distribute the points according to the warping
Array.ForEach(path, x => points[x.Item2].Add(ts[x.Item1]));
}
//Then simply construct the new average series by taking the mean of every List in points.
average = points.Select(x => x.Average()).ToArray();
//calculate Euclidean distance to stop the loop if no further improvement can be made
double[] average1 = average;
totalDist = series.Sum(x => x.Select((y, i) => Math.Pow(y - average1[i], 2)).Sum()); //we get convergence even though there's still work to be done
count++;
}
return(average);
}
static void DTWCsv(string[] files)
{
for (int fileId = 0; fileId < files.Length; fileId++)
{
if (File.Exists(files[fileId].Split('\\').Last() + "_pearsonDataCsv.txt"))
{
File.Copy(files[fileId].Split('\\').Last() + "_pearsonDataCsv.txt", files[fileId] + "_pearsonDataCsv.txt", true);
//Console.WriteLine("Copied " + files[fileId].Split('\\').Last() + "_pearsonDataCsv.txt" + " to " + files[fileId] + "_pearsonDataCsv.txt");
if (File.Exists(files[fileId].Split('\\').Last() + "dtwCostInfo.txt"))
{
File.Copy(files[fileId].Split('\\').Last() + "_pearsonDataCsv.txt", files[fileId] + "dtwCostInfo.txt", true);
}
Console.WriteLine(files[fileId].Split('\\').Last() + " is already done, skipping.");
continue;
}
var freed = GC.GetTotalMemory(false);
GC.Collect(GC.MaxGeneration, GCCollectionMode.Forced, true);
Console.WriteLine("Garbage Collection completed - memory:" + ((double)GC.GetTotalMemory(false) / 1024 / 1024 / 1024).ToString("0.0") + " GB (freed " + (freed / 1024 / 1024) + " MB)");
Console.WriteLine("Performing DTW on csv data " + fileId + " of " + files.Length + "..");
var watch = System.Diagnostics.Stopwatch.StartNew();
string[] data = File.ReadAllLines(files[fileId]);
Console.WriteLine("Data points: " + data.Length);
List <double> testDataPoints = new List <double>();
List <double> recallDataPoints = new List <double>();
foreach (var line in data)
{
var split = line.Replace(',', '.').Split(';');
double test = double.Parse(split[0], System.Globalization.CultureInfo.InvariantCulture);
double recall = double.Parse(split[1], System.Globalization.CultureInfo.InvariantCulture);
testDataPoints.Add(test);
recallDataPoints.Add(recall);
}
// Dtw dtw = new Dtw(testDataPoints.ToArray(), recallDataPoints.ToArray(), DistanceMeasure.Euclidean, true, true, null, null, 700);
Dtw dtw = new Dtw(testDataPoints.ToArray(), recallDataPoints.ToArray(), DistanceMeasure.Euclidean, true, true, slopeStepSizeDiagonal: 2, slopeStepSizeAside: 1);
var path = dtw.GetPath();
var cost = dtw.GetCost();
//var distanceMatrix = dtw.GetDistanceMatrix();
//var costMatrix = dtw.GetCostMatrix();
File.WriteAllText(files[fileId].Split('\\').Last() + "dtwCostInfo.txt",
"cost=" + cost +
"\nbefore_length=" + data.Length +
"\nbefore_cost=" + (cost / data.Length) +
"\nafter_length=" + path.Length +
"\nafter_cost=" + (cost / path.Length)
);
//PngExporter pngify = new PngExporter();
//pngify.Width = 36000;
//pngify.Height = 4000;
//var model = new PlotModel() { Title = "Red = test, blue = recall" };
//var aSeries = new OxyPlot.Series.LineSeries() { Color = OxyColors.Blue, MarkerSize = 10 };
//var bSeries = new OxyPlot.Series.LineSeries() { Color = OxyColors.Red, MarkerSize = 10 };
//for (int i = 0; i < testDataPoints.Count; i++)
//{
// aSeries.Points.Add(new DataPoint(i, testDataPoints[i]));
//}
//for (int i = 0; i < recallDataPoints.Count; i++)
//{
// bSeries.Points.Add(new DataPoint(i, recallDataPoints[i]));
//}
//List<string> pearsonData = new List<string>();
//foreach (var pairing in path)
//{
// var lineSeries = new OxyPlot.Series.LineSeries() { Color = OxyColors.Gray, MarkerSize = 0.05 };
// lineSeries.Points.Add(new DataPoint(pairing.Item1, testDataPoints[pairing.Item1]));
// lineSeries.Points.Add(new DataPoint(pairing.Item2, recallDataPoints[pairing.Item2]));
// model.Series.Add(lineSeries);
// pearsonData.Add(testDataPoints[pairing.Item1].ToString().Replace(',', '.') + ";" + recallDataPoints[pairing.Item2].ToString().Replace(',', '.'));
//}
//var pears = MathNet.Numerics.Statistics.Correlation.Pearson(path.Select(x => testDataPoints[x.Item1]).ToList(), path.Select(x => recallDataPoints[x.Item2]).ToList());
//Console.WriteLine("Pearson for " + files[fileId] + ":");
//Console.WriteLine(pears.ToString());
//File.WriteAllLines(files[fileId].Split('\\').Last() + "_pearsonDataCsv.txt", pearsonData);
//model.Series.Add(aSeries);
//model.Series.Add(bSeries);
//pngify.ExportToFile(model, files[fileId].Split('\\').Last() + "_csv.png");
watch.Stop();
Console.WriteLine("Done in " + watch.Elapsed);
Console.WriteLine("");
}
}
void waveSource_DataAvailable(object sender, WaveInEventArgs e)
{
//int max = 0;
byte[] buffer = e.Buffer;
int bytesRecorded = e.BytesRecorded;
//WriteToFile(buffer, bytesRecorded);
for (int index = 0; index < e.BytesRecorded; index += 2)
{
short sample = (short)((buffer[index + 1] << 8) |
buffer[index + 0]);
float sample32 = (sample / 32768f);
//int finalSample =(sample32 * 255);
//if (max < finalSample) max = finalSample;
//sampleAggregator.Add(sample32);
queue.Enqueue(sample32);
//queue.print();
}
long curTime = DateTime.Now.Ticks / TimeSpan.TicksPerSecond;
if (lastDetect + 2 > curTime)
{
Console.WriteLine("LAST GEST" + lastDetect + 5 + " " + curTime);
return;
}
//check if sample matches
int gest = 0;
double lastcost = 1000;
if (isSaved1 /*&& (loop++%3==0)*/)
{
long curTimeMs1 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; //Time Calc
var cost = new Dtw(saved1, queue.ToArray2()).GetCost();
long curTimeMs2 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; //Time Calc
if (cost < lastcost && cost < thresh1)
{
gest = 1; lastcost = cost; Console.WriteLine("G1: " + cost);
}
//Debug.WriteLineIf( cost<thresh1, "1:"+cost);
Console.WriteLine("Time DTW Took for Gesture 1 in ms: " + (curTimeMs2 - curTimeMs1));
//Console.WriteLine("MS 2: " + curTimeMs2);
}
if (isSaved2 /*&& (loop++%3==0)*/)
{
var cost = new Dtw(saved2, queue.ToArray2()).GetCost();
if (cost < lastcost && cost < thresh2)
{
gest = 2;
lastcost = cost;
Console.WriteLine("G2: " + cost);
}
//Debug.WriteLineIf(cost < thresh2, "2:"+cost);
}
if (isSaved3 /*&& (loop++%3==0)*/)
{
var cost = new Dtw(saved3, queue.ToArray2()).GetCost();
if (cost < lastcost && cost < thresh3)
{
gest = 3; lastcost = cost; Console.WriteLine("G3: " + cost);
}
//Debug.WriteLineIf(cost < thresh3, "3:"+cost);
}
foreach (double[] s in falseSaved)
{
var cost = new Dtw(s, queue.ToArray2()).GetCost();
if (cost < lastcost && cost < thresh0)
{
gest = 0; lastcost = cost;
}
}
thresh1 = Convert.ToDouble("0" + txtThres1.Text);
thresh2 = Convert.ToDouble("0" + txtThres2.Text);
thresh3 = Convert.ToDouble("0" + txtThres3.Text);
if (gest > 0)
{
lastDetect = DateTime.Now.Ticks / TimeSpan.TicksPerSecond;
lastGesture = gest;
Debug.WriteLine("DETECTED " + gest + " " + lastcost);
}
if (gest == 1)
{
lbl1.Visible = true;
lbl2.Visible = false;
lbl3.Visible = false;
SerialForm.SendLeft();
ArrowForm.ShowLeft();
}
else if (gest == 2)
{
lbl1.Visible = false;
lbl2.Visible = true;
lbl3.Visible = false;
SerialForm.SendCenter();
ArrowForm.ShowCenter();
}
else if (gest == 3)
{
lbl1.Visible = false;
lbl2.Visible = false;
lbl3.Visible = true;
SerialForm.SendRight();
ArrowForm.ShowRight();
}
else
{
lbl1.Visible = false;
lbl2.Visible = false;
lbl3.Visible = false;
ArrowForm.ShowNone();
}
//else Debug.WriteLine("NONE");
//Debug.WriteLine(max.ToString());
}
public void SetData(Dtw dtw)
{
dtwGraph.Dtw = dtw;
}
private void Recalculate()
{
if (!CanRecalculate)
return;
var seriesVariables = new List<SeriesVariable>();
foreach (var selectedVariable in SelectedVariables)
{
seriesVariables.Add(
new SeriesVariable(
DataSeries.GetValues(_selectedEntities[0], selectedVariable.Name).ToArray(),
DataSeries.GetValues(_selectedEntities[1], selectedVariable.Name).ToArray(),
selectedVariable.Name,
selectedVariable.Preprocessor,
selectedVariable.Weight));
}
var seriesVariablesArray = seriesVariables.ToArray();
var dtw = new Dtw(
seriesVariablesArray,
SelectedDistanceMeasure.Value,
UseBoundaryConstraintStart,
UseBoundaryConstraintEnd,
UseSlopeConstraint ? SlopeConstraintDiagonal : (int?)null,
UseSlopeConstraint ? SlopeConstraintAside : (int?)null,
UseSakoeChibaMaxShift ? SakoeChibaMaxShift : (int?)null);
if (MeasurePerformance)
{
var swDtwPerformance = new Stopwatch();
swDtwPerformance.Start();
for (int i = 0; i < 250; i++)
{
var tempDtw = new Dtw(
seriesVariablesArray,
SelectedDistanceMeasure.Value,
UseBoundaryConstraintStart,
UseBoundaryConstraintEnd,
UseSlopeConstraint ? SlopeConstraintDiagonal : (int?)null,
UseSlopeConstraint ? SlopeConstraintAside : (int?)null,
UseSakoeChibaMaxShift ? SakoeChibaMaxShift : (int?)null);
var tempDtwPath = tempDtw.GetCost();
}
swDtwPerformance.Stop();
OperationDuration = swDtwPerformance.Elapsed;
}
Dtw = dtw;
//Dtw = new Dtw(
// new[] { 4.0, 4.0, 4.5, 4.5, 5.0, 5.0, 5.0, 4.5, 4.5, 4.0, 4.0, 3.5 },
// new[] { 1.0, 1.5, 2.0, 2.5, 3.5, 4.0, 3.0, 2.5, 2.0, 2.0, 2.0, 1.5 },
// SelectedDistanceMeasure.Value,
// UseBoundaryConstraintStart,
// UseBoundaryConstraintEnd,
// UseSlopeConstraint ? SlopeConstraintDiagonal : (int?)null,
// UseSlopeConstraint ? SlopeConstraintAside : (int?)null,
// UseSakoeChibaMaxShift ? SakoeChibaMaxShift : (int?)null);
}
private void Recalculate()
{
if (!CanRecalculate)
{
return;
}
var seriesVariables = new List <SeriesVariable>();
foreach (var selectedVariable in SelectedVariables)
{
seriesVariables.Add(
new SeriesVariable(
DataSeries.GetValues(_selectedEntities[0], selectedVariable.Name).ToArray(),
DataSeries.GetValues(_selectedEntities[1], selectedVariable.Name).ToArray(),
selectedVariable.Name,
selectedVariable.Preprocessor,
selectedVariable.Weight));
}
var seriesVariablesArray = seriesVariables.ToArray();
var dtw = new Dtw(
seriesVariablesArray,
SelectedDistanceMeasure.Value,
UseBoundaryConstraintStart,
UseBoundaryConstraintEnd,
UseSlopeConstraint ? SlopeConstraintDiagonal : (int?)null,
UseSlopeConstraint ? SlopeConstraintAside : (int?)null,
UseSakoeChibaMaxShift ? SakoeChibaMaxShift : (int?)null);
if (MeasurePerformance)
{
var swDtwPerformance = new Stopwatch();
swDtwPerformance.Start();
for (int i = 0; i < 250; i++)
{
var tempDtw = new Dtw(
seriesVariablesArray,
SelectedDistanceMeasure.Value,
UseBoundaryConstraintStart,
UseBoundaryConstraintEnd,
UseSlopeConstraint ? SlopeConstraintDiagonal : (int?)null,
UseSlopeConstraint ? SlopeConstraintAside : (int?)null,
UseSakoeChibaMaxShift ? SakoeChibaMaxShift : (int?)null);
var tempDtwPath = tempDtw.GetCost();
}
swDtwPerformance.Stop();
OperationDuration = swDtwPerformance.Elapsed;
}
Dtw = dtw;
//Dtw = new Dtw(
// new[] { 4.0, 4.0, 4.5, 4.5, 5.0, 5.0, 5.0, 4.5, 4.5, 4.0, 4.0, 3.5 },
// new[] { 1.0, 1.5, 2.0, 2.5, 3.5, 4.0, 3.0, 2.5, 2.0, 2.0, 2.0, 1.5 },
// SelectedDistanceMeasure.Value,
// UseBoundaryConstraintStart,
// UseBoundaryConstraintEnd,
// UseSlopeConstraint ? SlopeConstraintDiagonal : (int?)null,
// UseSlopeConstraint ? SlopeConstraintAside : (int?)null,
// UseSakoeChibaMaxShift ? SakoeChibaMaxShift : (int?)null);
}
public void OnDataChanged()
{
if (DrawCost && DrawDistance)
{
throw new Exception("Only one of the values can be drawn at once, 'cost' or 'distance'.");
}
double[][] matrixValues = null;
if (DrawCost)
{
matrixValues = Dtw.GetCostMatrix();
}
if (DrawDistance)
{
matrixValues = Dtw.GetDistanceMatrix();
}
var dtwPath = Dtw.GetPath();
var xLength = Dtw.XLength;
var yLength = Dtw.YLength;
var cost = Dtw.GetCost();
var costNormalized = Dtw.GetCost() / Math.Sqrt(xLength * xLength + yLength * yLength);
var plotModel = new PlotModel(String.Format("Dtw norm by length: {0:0.00}, total: {1:0.00}", costNormalized, cost))
{
LegendTextColor = DrawCost || DrawDistance ? OxyColors.White : OxyColors.Black,
};
if (matrixValues != null)
{
var maxMatrixValue = 0.0;
for (int i = 0; i < xLength; i++)
{
for (int j = 0; j < yLength; j++)
{
maxMatrixValue = Math.Max(maxMatrixValue, Double.IsPositiveInfinity(matrixValues[i][j]) ? 0 : matrixValues[i][j]);
}
}
for (int i = 0; i < xLength; i++)
{
for (int j = 0; j < yLength; j++)
{
var value = matrixValues[i][j];
var isValuePositiveInfinity = Double.IsPositiveInfinity(value);
var intensityBytes = isValuePositiveInfinity ? new byte[] { 0, 0, 0 } : GetFauxColourRgbIntensity(value, 0, maxMatrixValue);
//var intensityByte = (byte)(255 - Math.Floor(255 * intensity));
plotModel.Annotations.Add(new PolygonAnnotation
{
Points =
new[]
{
new DataPoint(i - 0.5, j - 0.5), new DataPoint(i + 0.5, j - 0.5),
new DataPoint(i + 0.5, j + 0.5), new DataPoint(i - 0.5, j + 0.5),
},
StrokeThickness = 0,
Selectable = false,
Layer = AnnotationLayer.BelowAxes,
Fill = OxyColor.FromArgb(255, intensityBytes[0], intensityBytes[1], intensityBytes[2]),
});
}
}
for (int i = 0; i < 30; i++)
{
var intensityBytes = GetFauxColourRgbIntensity(i, 0, 29);
plotModel.Annotations.Add(new RectangleAnnotation
{
MinimumX = -39,
MaximumX = -25,
MinimumY = -i - 6,
MaximumY = -i - 5,
Selectable = false,
Fill = OxyColor.FromArgb(255, intensityBytes[0], intensityBytes[1], intensityBytes[2])
});
}
plotModel.Annotations.Add(new TextAnnotation
{
Position = new DataPoint(-24, -5),
HorizontalAlignment = HorizontalTextAlign.Left,
VerticalAlignment = VerticalTextAlign.Middle,
StrokeThickness = 0,
Text = "0"
});
plotModel.Annotations.Add(new TextAnnotation
{
Position = new DataPoint(-24, -34),
HorizontalAlignment = HorizontalTextAlign.Left,
VerticalAlignment = VerticalTextAlign.Middle,
StrokeThickness = 0,
Text = String.Format("{0:0.00}", maxMatrixValue),
});
}
var matrixPathSeries = new LineSeries("Path")
{
StrokeThickness = 1,
Color = OxyColors.Red,
};
for (int i = 0; i < dtwPath.Length; i++)
{
matrixPathSeries.Points.Add(new DataPoint(dtwPath[i].Item1, dtwPath[i].Item2));
}
plotModel.Series.Add(matrixPathSeries);
var seriesMatrixScale = (xLength + yLength) * 0.05;
for (int variableIndex = 0; variableIndex < Dtw.SeriesVariables.Length; variableIndex++)
{
var variableA = Dtw.SeriesVariables[variableIndex];
var variableASeries = variableA.OriginalXSeries;
var variableB = Dtw.SeriesVariables[variableIndex];
var variableBSeries = variableB.OriginalYSeries;
var minSeriesA = variableASeries.Min();
var maxSeriesA = variableASeries.Max();
var normalizedSeriesA = variableASeries.Select(x => (x - minSeriesA) / (maxSeriesA - minSeriesA)).ToList();
var matrixSeriesA = new LineSeries(variableA.VariableName);
for (int i = 0; i < normalizedSeriesA.Count; i++)
{
matrixSeriesA.Points.Add(new DataPoint(i, (-1 + normalizedSeriesA[i]) * seriesMatrixScale - 1 - seriesMatrixScale * (variableIndex + 1)));
}
plotModel.Series.Add(matrixSeriesA);
var minSeriesB = variableBSeries.Min();
var maxSeriesB = variableBSeries.Max();
var normalizedSeriesB = variableBSeries.Select(x => (x - minSeriesB) / (maxSeriesB - minSeriesB)).ToList();
var matrixSeriesB = new LineSeries(variableB.VariableName);
for (int i = 0; i < normalizedSeriesB.Count; i++)
{
matrixSeriesB.Points.Add(new DataPoint(-normalizedSeriesB[i] * seriesMatrixScale - 1 - seriesMatrixScale * (variableIndex + 1), i));
}
plotModel.Series.Add(matrixSeriesB);
}
plotModel.Axes.Add(new LinearAxis(AxisPosition.Bottom, " Series A")
{
Maximum = Math.Max(xLength, yLength), PositionAtZeroCrossing = true
});
plotModel.Axes.Add(new LinearAxis(AxisPosition.Left, " Series B")
{
Maximum = Math.Max(xLength, yLength), PositionAtZeroCrossing = true
});
MatrixPlot.Model = plotModel;
}