public PNormMinClass(floatLinalg.floatMatrix A, floatLinalg.floatVector b, floatLinalg.floatVector w,
floatLinalg.floatVector lambda, floatLinalg.floatVector CLp, floatLinalg.floatVector CLq)
{
this.A = A;
this.b = b;
this.w = w;
this.lambda = lambda;
this.CLp = CLp;
this.CLq = CLq;
//Temporary buffers
tempX = new floatLinalg.floatVector(new float[A.Cols]);
tempdX = new floatLinalg.floatVector(new float[A.Cols]);
tempAxmb = new floatLinalg.floatVector(new float[A.Rows]);
Axmb = new floatLinalg.floatVector(new float[A.Rows]);
Diagw = new floatLinalg.floatDiag(w);
Diaglambda = new floatLinalg.floatDiag(lambda);
tempdX = new floatLinalg.floatVector(new float[lambda.Length]);
tempd2x = new floatLinalg.floatVector(new float[lambda.Length]);
tempAtz = new floatLinalg.floatVector(new float[lambda.Length]);
dtempAtz = new floatLinalg.floatVector(new float[w.Length]);
d2tempAtz = new floatLinalg.floatVector(new float[w.Length]);
Diagd2Atz = new floatLinalg.floatDiag(d2tempAtz);
float[] id = new float[A.Rows];
for (int i = 0; i < A.Rows; i++) id[i] = 1;
Identity = new floatLinalg.floatDiag(id);
}
/// <summary>Computes least squares fitting of Ax = b weighted by W and returns x</summary>
/// <param name="A">Dependent variables measurements</param>
/// <param name="b">Independent variables measurements</param>
/// <param name="W">Weights</param>
/// <param name="lambda">Regularization term</param>
public static float[] LeastSquares(float[,] A, float[] b, float[] W, float lambda)
{
floatLinalg.floatMatrix CLA = new floatLinalg.floatMatrix(A);
floatLinalg.floatVector CLb = new floatLinalg.floatVector(b);
if (W != null && W.Length != CLA.Rows) throw new Exception("Incompatible Weight dimensions");
floatLinalg.floatDiag CLW;
if (W == null)
{
float[] ww = new float[CLA.Rows];
for (int i = 0; i < ww.Length; i++) ww[i] = 1;
CLW = new floatLinalg.floatDiag(ww);
}
else CLW = new floatLinalg.floatDiag(W);
float[] lambdas = new float[CLA.Cols];
for (int i = 0; i < lambdas.Length; i++) lambdas[i] = lambda;
floatLinalg.floatVector CLlambda = new floatLinalg.floatVector(lambdas);
floatLinalg.floatSymPosDefMatrix AtA = null;
AtA = floatLinalg.BLAS.MatrTranspMatrProd(CLA, CLW, CLlambda, ref AtA);
//CLA.CLValues.ReadFromDeviceTo(CLA.Values);
//AtA.CLValues.ReadFromDeviceTo(AtA.Values);
floatLinalg.floatVector Atb = null;
Atb = floatLinalg.BLAS.MatrTraspVecMult(CLA, CLW, CLb, ref Atb);
//Atb.CLValues.WriteToDevice(Atb.Values);
GC.Collect();
floatLinalg.floatVector resp = null;
AtA.LinearSolve(Atb, true, ref resp);
if (CLCalc.CLAcceleration == CLCalc.CLAccelerationType.UsingCL) resp.CLValues.ReadFromDeviceTo(resp.Values);
return resp.Values;
}