public List<Vector<double>> AddNoise(List<Vector<double>> points,
double variance, int seed = 0)
{
List<Vector<double>> noisedPoints = new List<Vector<double>>(points.Count);
int pointSize = points[0].Count;
GaussianNoiseGenerator noise = new GaussianNoiseGenerator();
noise.Variance = variance;
noise.Mean = 0.0;
noise.RandomSeed = seed != 0;
noise.Seed = seed;
noise.UpdateDistribution();
for(int i = 0; i < _pointsCount; ++i)
{
Vector<double> cpoint = new DenseVector(pointSize);
for(int p = 0; p < pointSize - 1; ++p)
{
cpoint[p] = points[i][p] + noise.GetSample();
}
cpoint[pointSize - 1] = 1.0f;
noisedPoints.Add(cpoint);
}
return noisedPoints;
}
public List<CalibrationPoint> AddNoise(List<CalibrationPoint> points,
double varReal, double varImg, int seed = 0)
{
List<CalibrationPoint> noisedPoints = new List<CalibrationPoint>(points.Count);
GaussianNoiseGenerator noiseReal = new GaussianNoiseGenerator();
noiseReal.Variance = varReal;
noiseReal.Mean = 0.0;
noiseReal.RandomSeed = seed != 0;
noiseReal.Seed = seed;
noiseReal.UpdateDistribution();
GaussianNoiseGenerator noiseImage = new GaussianNoiseGenerator();
noiseImage.Variance = varImg;
noiseImage.Mean = 0.0;
noiseImage.RandomSeed = seed != 0;
noiseImage.Seed = seed;
noiseImage.UpdateDistribution();
for(int i = 0; i < _pointsCount; ++i)
{
CalibrationPoint cpoint = new CalibrationPoint();
cpoint.RealX = points[i].RealX + noiseReal.GetSample();
cpoint.RealY = points[i].RealY + noiseReal.GetSample();
cpoint.RealZ = points[i].RealZ + noiseReal.GetSample();
cpoint.ImgX = points[i].ImgX + noiseImage.GetSample();
cpoint.ImgY = points[i].ImgY + noiseImage.GetSample();
noisedPoints.Add(cpoint);
}
return noisedPoints;
}
public Matrix<double> AddNoise(Matrix<double> matrix)
{
Matrix<double> noisedMat = matrix.Clone();
GaussianNoiseGenerator noise = new GaussianNoiseGenerator();
noise.Mean = 0.0;
noise.RandomSeed = false;
Random rand = new Random(_seed);
for(int r = 0; r < noisedMat.RowCount; ++r)
{
for(int c = 0; c < noisedMat.ColumnCount; ++c)
{
noise.Seed = rand.Next();
noise.Variance = Math.Abs(noisedMat[r, c]) < 1e-12 ?
1e-12 : Math.Abs(noisedMat[r, c]) / 100.0;
noise.UpdateDistribution();
noisedMat[r, c] = noisedMat[r, c] + noise.GetSample();
}
}
return noisedMat;
}