// Function to make linear least squares estimation.
private void LeastSquares()
{
// Arrays for datagrid's values. Makes matrix and vector creating easier.
double[] realtempArray = new double[dgCalibration.Rows.Count];
double[] sensorTempArray = new double[dgCalibration.Rows.Count];
for (int i = 0; i < dgCalibration.Rows.Count; i++)
{
if (dgCalibration.Rows[i].Cells[1].Value != null) // Includes only values that has been set by user.
{
double sensorTemp = Convert.ToDouble(dgCalibration.Rows[i].Cells[0].Value);
double realTemp = Convert.ToDouble(dgCalibration.Rows[i].Cells[1].Value);
realtempArray[i] = realTemp;
sensorTempArray[i] = sensorTemp;
}
}
// Fitting a straight on Data Points with LS-method.
DoubleVector y = new DoubleVector(realtempArray);
DoubleMatrix m = new DoubleMatrix(dgCalibration.Rows.Count, 1, sensorTempArray, StorageType.ColumnMajor);
var lsq = new DoubleLeastSquares(m, y, true);
_intercept = lsq.X[0];
_slope = lsq.X[1];
nudSensorCalA.Value = (decimal)lsq.X[1];
nudSensorCalB.Value = (decimal)lsq.X[0];
}
private static double[] GSolve(Pixel[][] imagesSamples, double[] exposureTime, int smoothFactor, int ch)
{
int z_max = 256;
int p = imagesSamples.Length;
int n = imagesSamples[0].Length;
double[,] A = new double[n * p + z_max + 1, z_max + n];
double[] b = new double[A.GetLength(0)];
int k = 0;
for (int i = 0; i < n; ++i)
{
for (int j = 0; j < p; ++j)
{
int z = imagesSamples[j][i].getChannel(ch);
double wij = W(z);
A[k, z] = wij;
A[k, z_max + i] = -wij;
b[k] = wij * Math.Log(exposureTime[j]);
k += 1;
}
}
// Limit middle value
A[k, 128] = 1;
k += 1;
// Smoothing
for (int i = 0; i < z_max - 1; ++i)
{
double w_k = W(i + 1);
A[k, i] = smoothFactor * W(w_k);
A[k, i + 1] = -2 * smoothFactor * w_k;
A[k, i + 2] = smoothFactor * w_k;
k += 1;
}
// Solve SVD
var AA = new DoubleMatrix(A);
var bb = new DoubleVector(b);
var lsq = new DoubleLeastSquares(AA, bb, true);
double[] g = new double[256];
for (int i = 0; i < 256; ++i)
{
g[i] = lsq.X[i];
}
return(g);
}
