public void PushTest2()
{
double[] values = { 0.24, 1.61, 2.22, 5.82 };
int windowSize = values.Length;
MovingNormalStatistics target = new MovingNormalStatistics(windowSize);
target.Push(0.29);
target.Push(1.11);
for (int i = 0; i < values.Length; i++)
{
target.Push(values[i]);
}
double actualMean = target.Mean;
double expectedMean = Tools.Mean(values);
Assert.AreEqual(expectedMean, actualMean);
double actualVariance = target.Variance;
double expectedVariance = Tools.Variance(values);
Assert.AreEqual(expectedVariance, actualVariance);
}
public void PushTest2()
{
double[] values = { 0.24, 1.61, 2.22, 5.82 };
int windowSize = values.Length;
MovingNormalStatistics target = new MovingNormalStatistics(windowSize);
target.Push(0.29);
target.Push(1.11);
for (int i = 0; i < values.Length; i++)
target.Push(values[i]);
double actualMean = target.Mean;
double expectedMean = values.Mean();
Assert.AreEqual(expectedMean, actualMean);
double actualVariance = target.Variance;
double expectedVariance = values.Variance();
Assert.AreEqual(expectedVariance, actualVariance);
}
public void doc_example()
{
#region doc_example
// Moving Normal Statistics can be used to keep track of the mean, variance and standard
// deviation of the last N samples that have been fed to it. It is possible to feed one
// value at a time, and the class will keep track of the mean and variance of the samples
// without registering the samples themselves.
// An example where this class can be useful is when trying to filter the (x,y) trajectories
// of a movement tracker (i.e. to show the average point on the past 10 frames) and monitor
// the quality of the tracking (i.e. if the variance becomes too high, tracking might have been lost).
// Fix seed for reproducible results
Accord.Math.Random.Generator.Seed = 0;
Random rnd = Accord.Math.Random.Generator.Random;
// Create a estimator that will keep the mean and standard deviation of the past 5 samples:
var normal = new MovingNormalStatistics(windowSize: 5);
// Read 50 samples
for (int i = 0; i < 50; i++)
{
normal.Push(rnd.Next(1, 10));
}
// The properties of the 5 last samples are:
double sum = normal.Sum; // 21
double mean = normal.Mean; // 4.2
double stdDev = normal.StandardDeviation; // 2.4899799195977463
double var = normal.Variance; // 6.2
#endregion
Assert.AreEqual(21, sum, 1e-10);
Assert.AreEqual(4.2, mean, 1e-10);
Assert.AreEqual(2.4899799195977463, stdDev, 1e-10);
Assert.AreEqual(6.2, var, 1e-10);
}
private void computeCurrentPosition(int width, int height)
{
TrackingObject obj = tracker.TrackingObject;
DoubleRange scaleX = new DoubleRange(0, width);
DoubleRange scaleY = new DoubleRange(0, height);
DoubleRange unit = new DoubleRange(-1, 1);
this.rawX = (float)BestCS.Math.Tools.Scale(scaleX, unit, obj.Center.X);
this.rawY = (float)BestCS.Math.Tools.Scale(scaleY, unit, obj.Center.Y);
double newPositionX = BestCS.Math.Tools.Scale(xaxisRange, unit, rawX);
double newPositionY = BestCS.Math.Tools.Scale(yaxisRange, unit, rawY);
xsmooth.Push(newPositionX);
ysmooth.Push(newPositionY);
newPositionX = xsmooth.Mean;
newPositionY = ysmooth.Mean;
this.currentX = (float)(Math.Round(newPositionX, 1));
this.currentY = (float)(Math.Round(newPositionY, 1));
}
protected override void ProcessInputObject(double value)
{
_state.Push(value);
WriteObject(_state.Variance);
}
protected override void ProcessInputObject(double value)
{
_state.Push(value);
WriteObject(_state.StandardDeviation);
}
private void OnTrade(Trade trade)
{
MovingAverage.Push((double)trade.Price);
OnUpdated();
}