public void Creation() { var covariance = new RunningCovariance(); Assert.That(covariance.MeanX, Is.EqualTo(0.0)); Assert.That(covariance.MeanY, Is.EqualTo(0.0)); Assert.That(covariance.Covariance, Is.EqualTo(0.0)); }
public CovarianceBuilder(PhotographData data) { int size = data.NPsf; iMatrix = new RunningCovariance[size, size]; for (int i = 0; i < size; i++) { for (int j = i; j < size; j++) { iMatrix[i, j] = new RunningCovariance(); } } Data = data; }
private void Init(int size) { Size = size; covarianceMatrix = new RunningCovariance[Size, Size]; for (int i = 0; i < Size; i++) { for (int j = i; j < Size; j++) { covarianceMatrix[i, j] = new RunningCovariance(); } } meanVector = new double[Size]; SampleSize = 0; }
public CovarianceBuilder(PhotographData data) { int size = data.NPsf; iMatrix = new RunningCovariance[size, size]; for (int i = 0; i < size; i++) { for (int j = i; j < size; j++) { iMatrix[i, j] = new RunningCovariance(); } } Data = data; }
public void InitialCovariance() { var covariance = new RunningCovariance(); int nPoints = 250; double[] dataX = new double[nPoints]; double[] dataY = new double[nPoints]; for (int i = 0; i < nPoints; i++) { dataX[i] = i + 1; dataY[nPoints - 1 - i] = i + 1; } for (int i = 0; i < dataX.Length; i++) { covariance.Add(dataX[i], dataY[i]); double meanX = 0; double meanY = 0; double covar = 0; for (int j = 0; j <= i; j++) { meanX += dataX[j]; meanY += dataY[j]; } meanX /= (i + 1); meanY /= (i + 1); Assert.That(covariance.MeanX, Is.EqualTo(meanX).Within(0.00001), "Initial meanX i = " + i); Assert.That(covariance.MeanY, Is.EqualTo(meanY).Within(0.00001), "Initial meanY i = " + i); if (i > 1) { for (int j = 0; j <= i; j++) { double termX = dataX[j] - meanX; double termY = dataY[j] - meanY; covar += (termX * termY); } covar /= i; Assert.That(covariance.Covariance, Is.EqualTo(covar).Within(0.0000000001), "Initial variance i = " + i); } } }
private void Init(int size) { Size = size; covarianceMatrix = new RunningCovariance[Size, Size]; for (int i = 0; i < Size; i++) { for (int j = i; j < Size; j++) { covarianceMatrix[i, j] = new RunningCovariance(); } } meanVector = new double[Size]; SampleSize = 0; }
public void InitialCovariance() { var covariance = new RunningCovariance(); int nPoints = 250; double[] dataX = new double[nPoints]; double[] dataY = new double[nPoints]; for (int i = 0; i < nPoints; i++) { dataX[i] = i + 1; dataY[nPoints - 1 - i] = i + 1; } for (int i = 0; i < dataX.Length; i++) { covariance.Add(dataX[i], dataY[i]); double meanX = 0; double meanY = 0; double covar = 0; for (int j = 0; j <= i; j++) { meanX += dataX[j]; meanY += dataY[j]; } meanX /= (i + 1); meanY /= (i + 1); Assert.That(covariance.MeanX, Is.EqualTo(meanX).Within(0.00001), "Initial meanX i = " + i); Assert.That(covariance.MeanY, Is.EqualTo(meanY).Within(0.00001), "Initial meanY i = " + i); if (i > 1) { for (int j = 0; j <= i; j++) { double termX = dataX[j] - meanX; double termY = dataY[j] - meanY; covar += (termX * termY); } covar /= i; Assert.That(covariance.Covariance, Is.EqualTo(covar).Within(0.0000000001), "Initial variance i = " + i); } } }
public void Creation() { var covariance = new RunningCovariance(); Assert.That(covariance.MeanX, Is.EqualTo(0.0)); Assert.That(covariance.MeanY, Is.EqualTo(0.0)); Assert.That(covariance.Covariance, Is.EqualTo(0.0)); }