private RemoveChannels RemoveEmptyChannels(Matrix <double> input)
{
//Scan for and store all non-zero channels (i.e. only use loaded channels)
//Return an updated matrix with empty channels removed, and a bool array identifying which channels are used.
double[,] InputArray = input.ToArray();
bool[] usedCol = new bool[InputArray.GetLength(1)];
int count = 0;
for (int i = InputArray.GetLength(1) - 1; i >= 0; i--)
{
if (InputArray[2, i] == 0)
{
usedCol[i] = false;
input = input.RemoveColumn(i);
}
else
{
usedCol[i] = true;
count++;
}
}
RemoveChannels result = new RemoveChannels();
result.ReducedMatrix = input;
result.usedCol = usedCol;
return(result);
}
//Called after gathering training data
public Vector <double>[] Solve(double[,] X, double[,] yArray)
{
//Solve matrix to generate weights for runtime calibration
double[] y = new double[yArray.GetLength(0)];
Vector <double>[] theta = new Vector <double> [yArray.GetLength(1)];
//Process each collum one by one
for (int i = 0; i < yArray.GetLength(1); i++)
{
//Clone raw data to manipulate
double[,] Xclone = X.Clone() as double[, ];
//Disable channels not used for this solve
Xclone = DisableChannels(Xclone, ActiveSensors, i);
Matrix <double> XMatrix = CreateMatrix.DenseOfArray(Xclone);
RemoveChannels temp = RemoveEmptyChannels(XMatrix);
XMatrix = temp.ReducedMatrix;
//Get a solution vector
y = getCollumn(yArray, i);
Matrix <double> tempMat = XMatrix;
//Perform Regression fit
Vector <double> yVector = CreateVector.DenseOfArray(y);
theta[i] = MultipleRegression.Svd <double>(tempMat, yVector);
//Add removed collumns
theta[i] = PadArray(temp.usedCol, theta[i]);
}
return(theta);
}
