public static void Test02()
{
int NR = 100;
int NC = 5;
// erzeuge Basisfunktionen
var X = new DoubleMatrix(NR, NC);
for (int c = 0; c < 5; ++c)
{
double rt = (c + 1) * 4;
for (int r = 0; r < NR; ++r)
{
X[r, c] = Math.Exp(-r / rt);
}
}
var y = new DoubleMatrix(NR, 1);
for (int r = 0; r < NR; ++r)
{
double sum = 0;
for (int c = 0; c < 5; ++c)
{
double amp = 1 - 0.4 * Math.Abs(c - 2);
sum += amp * X[r, c];
}
y[r, 0] = sum;
}
var XtX = new DoubleMatrix(5, 5);
MatrixMath.MultiplyFirstTransposed(X, X, XtX);
var Xty = new DoubleMatrix(5, 1);
MatrixMath.MultiplyFirstTransposed(X, y, Xty);
FastNonnegativeLeastSquares.Execution(XtX, Xty, null, out var x, out var w);
Assert.AreEqual(0.2, x[0, 0], 1e-6);
Assert.AreEqual(0.6, x[1, 0], 1e-6);
Assert.AreEqual(1.0, x[2, 0], 1e-6);
Assert.AreEqual(0.6, x[3, 0], 1e-6);
Assert.AreEqual(0.2, x[4, 0], 1e-6);
}
/// <summary>Solves system of linear equations Ax = b using Gaussian elimination with partial pivoting.</summary>
/// <param name="A">Elements of matrix 'A'. This array is modified during solution!</param>
/// <param name="b">Right part 'b'. This array is also modified during solution!</param>
/// <param name="x">Vector to store the result, i.e. the solution to the problem a x = b.</param>
public void Solve(IROMatrix <double> A, IReadOnlyList <double> b, IVector <double> x)
{
if (_temp_A.RowCount != A.RowCount || _temp_A.ColumnCount != A.ColumnCount)
{
_temp_A = new MatrixWrapperStructForLeftSpineJaggedArray <double>(A.RowCount, A.ColumnCount);
}
if (b.Count != _temp_b?.Length)
{
_temp_b = new double[b.Count];
}
if (b.Count != _temp_x?.Length)
{
_temp_x = new double[b.Count];
}
MatrixMath.Copy(A, _temp_A);
VectorMath.Copy(b, _temp_b);
SolveDestructive(_temp_A, _temp_b, _temp_x);
VectorMath.Copy(_temp_x, x);
}
/// <summary>
/// For a given set of spectra, predicts the y-values and stores them in the matrix <c>predictedY</c>
/// </summary>
/// <param name="mcalib">The calibration model of the analysis.</param>
/// <param name="preprocessOptions">The information how to preprocess the spectra.</param>
/// <param name="matrixX">The matrix of spectra to predict. Each spectrum is a row in the matrix.</param>
/// <param name="numberOfFactors">The number of factors used for prediction.</param>
/// <param name="predictedY">On return, this matrix holds the predicted y-values. Each row in this matrix corresponds to the same row (spectrum) in matrixX.</param>
/// <param name="spectralResiduals">If you set this parameter to a appropriate matrix, the spectral residuals will be stored in this matrix. Set this parameter to null if you don't need the residuals.</param>
public virtual void CalculatePredictedY(
IMultivariateCalibrationModel mcalib,
SpectralPreprocessingOptions preprocessOptions,
IMatrix matrixX,
int numberOfFactors,
MatrixMath.BEMatrix predictedY,
IMatrix spectralResiduals)
{
MultivariateRegression.PreprocessSpectraForPrediction(mcalib, preprocessOptions, matrixX);
MultivariateRegression regress = this.CreateNewRegressionObject();
regress.SetCalibrationModel(mcalib);
regress.PredictedYAndSpectralResidualsFromPreprocessed(matrixX, numberOfFactors, predictedY, spectralResiduals);
MultivariateRegression.PostprocessY(mcalib.PreprocessingModel, predictedY);
}
/** Least squares solution of A*X = B
* @param B A Matrix with as many rows as A and any number of columns.
* @return X that minimizes the two norm of Q*R*X-B.
* @exception IllegalArgumentException Matrix row dimensions must agree.
* @exception RuntimeException Matrix is rank deficient.
*/
public void Solve(IROMatrix <double> B, IMatrix <double> result)
{
if (B.RowCount != m)
{
throw new ArgumentException("Matrix row dimensions must agree.");
}
if (!IsFullRank())
{
throw new Exception("Matrix is rank deficient.");
}
// Copy right hand side
int nx = B.ColumnCount;
double[][] X;
if (_solveMatrixWorkspace != null && _solveMatrixWorkspace.RowCount == B.RowCount && _solveMatrixWorkspace.ColumnCount == B.ColumnCount)
{
X = _solveMatrixWorkspace.Array;
MatrixMath.Copy(B, _solveMatrixWorkspace);
}
else
{
X = JaggedArrayMath.GetMatrixCopy(B);
_solveMatrixWorkspace = new JaggedArrayMatrix(X, B.RowCount, B.ColumnCount);
}
// Compute Y = transpose(Q)*B
for (int k = 0; k < n; k++)
{
for (int j = 0; j < nx; j++)
{
double s = 0.0;
for (int i = k; i < m; i++)
{
s += QR[i][k] * X[i][j];
}
s = -s / QR[k][k];
for (int i = k; i < m; i++)
{
X[i][j] += s * QR[i][k];
}
}
}
// Solve R*X = Y;
for (int k = n - 1; k >= 0; k--)
{
for (int j = 0; j < nx; j++)
{
X[k][j] /= Rdiag[k];
}
for (int i = 0; i < k; i++)
{
for (int j = 0; j < nx; j++)
{
X[i][j] -= X[k][j] * QR[i][k];
}
}
}
MatrixMath.Submatrix(_solveMatrixWorkspace, result, 0, 0);
}
/// <summary>
/// Factorize a nonnegative matrix A into two nonnegative matrices B and C so that A is nearly equal to B*C.
/// Tikhonovs the nm f3.
/// </summary>
/// <param name="A">Matrix to factorize.</param>
/// <param name="r">The number of factors.</param>
/// <param name="B0">Original B matrix. Can be null.</param>
/// <param name="C0">Original C matrix. Can be null.</param>
/// <param name="oldalpha">The oldalpha.</param>
/// <param name="oldbeta">The oldbeta.</param>
/// <param name="gammaB">The gamma b.</param>
/// <param name="gammaC">The gamma c.</param>
/// <param name="maxiter">The maxiter.</param>
/// <param name="tol">The tol.</param>
public static void TikhonovNMF3(
IMatrix <double> A,
int r,
IMatrix <double> B0,
IMatrix <double> C0,
IVector <double> oldalpha,
IVector <double> oldbeta,
IMatrix <double> gammaB,
IMatrix <double> gammaC,
int maxiter,
double tol)
{
// The converged version of the algorithm
// Use complementary slackness as stopping criterion
// format long;
// Check the input matrix
if (null == A)
{
throw new ArgumentNullException(nameof(A));
}
if (MatrixMath.Min(A) < 0)
{
throw new ArgumentException("Input matrix must not contain negative elements", nameof(A));
}
int m = A.RowCount;
int n = A.ColumnCount;
// Check input arguments
//if ˜exist(’r’)
if (null == B0)
{
B0 = DoubleMatrix.Random(m, r);
}
if (null == C0)
{
C0 = DoubleMatrix.Random(r, n);
}
if (null == oldalpha)
{
oldalpha = new DoubleVector(n);
}
if (null == oldbeta)
{
oldbeta = new DoubleVector(m);
}
if (null == gammaB)
{
gammaB = new DoubleMatrix(m, 1);
gammaB.SetMatrixElements(0.1); // small values lead to better convergence property
}
if (null == gammaC)
{
gammaC = new DoubleMatrix(n, 1);
gammaC.SetMatrixElements(0.1); // small values lead to better convergence property
}
if (0 == maxiter)
{
maxiter = 1000;
}
if (double.IsNaN(tol) || tol <= 0)
{
tol = 1.0e-9;
}
var B = B0;
B0 = null;
var C = C0;
C0 = null;
var newalpha = oldalpha;
var newbeta = oldbeta;
var AtA = new DoubleMatrix(n, n);
MatrixMath.MultiplyFirstTransposed(A, A, AtA);
double trAtA = MatrixMath.Trace(AtA);
var olderror = new DoubleVector(maxiter + 1);
var BtA = new DoubleMatrix(r, n);
MatrixMath.MultiplyFirstTransposed(B, A, BtA);
var CtBtA = new DoubleMatrix(n, n);
MatrixMath.MultiplyFirstTransposed(C, BtA, CtBtA);
var BtB = new DoubleMatrix(r, r);
MatrixMath.MultiplyFirstTransposed(B, B, BtB);
var BtBC = new DoubleMatrix(r, n);
MatrixMath.Multiply(BtB, C, BtBC);
var CtBtBC = new DoubleMatrix(n, n);
MatrixMath.MultiplyFirstTransposed(C, BtBC, CtBtBC);
var BtDgNewbeta = new DoubleMatrix(r, m);
MatrixMath.MultiplyFirstTransposed(B, DoubleMatrix.Diag(newbeta), BtDgNewbeta);
var BtDgNewbetaB = new DoubleMatrix(r, r); // really rxr ?
MatrixMath.Multiply(BtDgNewbeta, B, BtDgNewbetaB);
var CDgNewalpha = new DoubleMatrix(r, n);
MatrixMath.Multiply(C, DoubleMatrix.Diag(newalpha), CDgNewalpha);
var CtCDgNewalpha = new DoubleMatrix(n, n);
MatrixMath.MultiplyFirstTransposed(C, CDgNewalpha, CtCDgNewalpha);
olderror[0] =
0.5 * trAtA -
MatrixMath.Trace(CtBtA) +
0.5 * MatrixMath.Trace(CtBtBC) +
0.5 * MatrixMath.Trace(BtDgNewbetaB) +
0.5 * MatrixMath.Trace(CtCDgNewalpha);
double sigma = 1.0e-9;
double delta = sigma;
for (int iteration = 1; iteration <= maxiter; ++iteration)
{
var CCt = new DoubleMatrix(r, r);
MatrixMath.MultiplySecondTransposed(C, C, CCt);
var gradB = new DoubleMatrix(m, r);
var tempMR = new DoubleMatrix(m, r);
//gradB = B*CCt - A*C’ +diag(newbeta)*B;
MatrixMath.Multiply(B, CCt, gradB);
MatrixMath.MultiplySecondTransposed(A, C, tempMR);
MatrixMath.Add(gradB, tempMR, gradB);
MatrixMath.Multiply(DoubleMatrix.Diag(newbeta), B, tempMR);
MatrixMath.Add(gradB, tempMR, gradB);
// Bm = max(B, (gradB < 0) * sigma);
var sigMR = new DoubleMatrix(m, r);
sigMR.SetMatrixElements((i, j) => gradB[i, j] < 0 ? sigma : 0);
var Bm = new DoubleMatrix(m, r);
Bm.SetMatrixElements((i, j) => Math.Max(B[i, j], sigMR[i, j]));
}
}
/// <summary>
/// Execution of the fast nonnegative least squares algorithm. The algorithm finds a vector x with all elements xi>=0 which minimizes |X*x-y|.
/// </summary>
/// <param name="XtX">X transposed multiplied by X, thus a square matrix.</param>
/// <param name="Xty">X transposed multiplied by Y, thus a matrix with one column and same number of rows as X.</param>
/// <param name="isRestrictedToPositiveValues">Function that takes the parameter index as argument and returns true if the parameter at this index is restricted to positive values; otherwise the return value must be false.</param>
/// <param name="tolerance">Used to decide if a solution element is less than or equal to zero. If this is null, a default tolerance of tolerance = MAX(SIZE(XtX)) * NORM(XtX,1) * EPS is used.</param>
/// <param name="x">Output: solution vector (matrix with one column and number of rows according to dimension of X.</param>
/// <param name="w">Output: Lagrange vector. Elements which take place in the fit are set to 0. Elements fixed to zero contain a negative number.</param>
/// <remarks>
/// <para>
/// Literature: Rasmus Bro and Sijmen De Jong, 'A fast non-negativity-constrained least squares algorithm', Journal of Chemometrics, Vol. 11, 393-401 (1997)
/// </para>
/// <para>
/// Algorithm modified by Dirk Lellinger 2015 to allow a mixture of restricted and unrestricted parameters.
/// </para>
/// </remarks>
public static void Execution(IROMatrix <double> XtX, IROMatrix <double> Xty, Func <int, bool> isRestrictedToPositiveValues, double?tolerance, out IMatrix <double> x, out IMatrix <double> w)
{
if (null == XtX)
{
throw new ArgumentNullException(nameof(XtX));
}
if (null == Xty)
{
throw new ArgumentNullException(nameof(Xty));
}
if (null == isRestrictedToPositiveValues)
{
throw new ArgumentNullException(nameof(isRestrictedToPositiveValues));
}
if (XtX.RowCount != XtX.ColumnCount)
{
throw new ArgumentException("Matrix should be a square matrix", nameof(XtX));
}
if (Xty.ColumnCount != 1)
{
throw new ArgumentException(nameof(Xty) + " should be a column vector (number of columns should be equal to 1)", nameof(Xty));
}
if (Xty.RowCount != XtX.ColumnCount)
{
throw new ArgumentException("Number of rows in " + nameof(Xty) + " should match number of columns in " + nameof(XtX), nameof(Xty));
}
var matrixGenerator = new Func <int, int, DoubleMatrix>((rows, cols) => new DoubleMatrix(rows, cols));
// if nargin < 3
// tol = 10 * eps * norm(XtX, 1) * length(XtX);
// end
double tol = tolerance.HasValue ? tolerance.Value : 10 * DoubleConstants.DBL_EPSILON * MatrixMath.Norm(XtX, MatrixNorm.M1Norm) * Math.Max(XtX.RowCount, XtX.ColumnCount);
// [m, n] = size(XtX);
int n = XtX.ColumnCount;
// P = zeros(1, n);
// Z = 1:n;
var P = new bool[n]; // POSITIVE SET: all indices which are currently not fixed are marked with TRUE (Negative set is simply this, but inverted)
bool initializationOfSolutionRequired = false;
for (int i = 0; i < n; ++i)
{
bool isNotRestricted = !isRestrictedToPositiveValues(i);
P[i] = isNotRestricted;
initializationOfSolutionRequired |= isNotRestricted;
}
// x = P';
x = matrixGenerator(n, 1);
// w = Xty-XtX*x;
w = matrixGenerator(n, 1);
MatrixMath.Copy(Xty, w);
var helper_n_1 = matrixGenerator(n, 1);
MatrixMath.Multiply(XtX, x, helper_n_1);
MatrixMath.Subtract(w, helper_n_1, w);
// set up iteration criterion
int iter = 0;
int itmax = 30 * n;
// outer loop to put variables into set to hold positive coefficients
// while any(Z) & any(w(ZZ) > tol)
while (initializationOfSolutionRequired || (P.Any(ele => false == ele) && w.Any((r, c, ele) => false == P[r] && ele > tol)))
{
if (initializationOfSolutionRequired)
{
initializationOfSolutionRequired = false;
}
else
{
// [wt, t] = max(w(ZZ));
// t = ZZ(t);
int t = -1; // INDEX
double wt = double.NegativeInfinity;
for (int i = 0; i < n; ++i)
{
if (!P[i])
{
if (w[i, 0] > wt)
{
wt = w[i, 0];
t = i;
}
}
}
// P(1, t) = t;
// Z(t) = 0;
P[t] = true;
}
// z(PP')=(Xty(PP)'/XtX(PP,PP)');
var subXty = Xty.SubMatrix(P, 0, matrixGenerator); // Xty(PP)'
var subXtX = XtX.SubMatrix(P, P, matrixGenerator);
var solver = new DoubleLUDecomp(subXtX);
var subSolution = solver.Solve(subXty);
var z = matrixGenerator(n, 1);
for (int i = 0, ii = 0; i < n; ++i)
{
z[i, 0] = P[i] ? subSolution[ii++, 0] : 0;
}
// C. Inner loop (to remove elements from the positive set which no longer belong to)
while (z.Any((r, c, ele) => true == P[r] && ele <= tol && isRestrictedToPositiveValues(r)) && iter < itmax)
{
++iter;
// QQ = find((z <= tol) & P');
//alpha = min(x(QQ)./ (x(QQ) - z(QQ)));
double alpha = double.PositiveInfinity;
for (int i = 0; i < n; ++i)
{
if ((z[i, 0] <= tol && true == P[i] && isRestrictedToPositiveValues(i)))
{
alpha = Math.Min(alpha, x[i, 0] / (x[i, 0] - z[i, 0]));
}
}
// x = x + alpha * (z - x);
for (int i = 0; i < n; ++i)
{
x[i, 0] += alpha * (z[i, 0] - x[i, 0]);
}
// ij = find(abs(x) < tol & P' ~= 0);
// Z(ij) = ij';
// P(ij) = zeros(1, length(ij));
for (int i = 0; i < n; ++i)
{
if (Math.Abs(x[i, 0]) < tol && P[i] == true && isRestrictedToPositiveValues(i))
{
P[i] = false;
}
}
//PP = find(P);
//ZZ = find(Z);
//nzz = size(ZZ);
//z(PP) = (Xty(PP)'/XtX(PP,PP)');
subXty = Xty.SubMatrix(P, 0, matrixGenerator);
subXtX = XtX.SubMatrix(P, P, matrixGenerator);
solver = new DoubleLUDecomp(subXtX);
subSolution = solver.Solve(subXty);
for (int i = 0, ii = 0; i < n; ++i)
{
z[i, 0] = P[i] ? subSolution[ii++, 0] : 0;
}
} // end inner loop
MatrixMath.Copy(z, x);
MatrixMath.Copy(Xty, w);
MatrixMath.Multiply(XtX, x, helper_n_1);
MatrixMath.Subtract(w, helper_n_1, w);
}
}
/// <summary>
/// Represents the content of the matrix as a string.
/// </summary>
/// <returns></returns>
public override string ToString()
{
return(MatrixMath.MatrixToString(null, this));
}
/* Modified algorithm which is better for M>>N */
int gsl_linalg_SV_decomp_mod(IROMatrix A,
IROMatrix X,
IMatrix V, IVector S, IVector work)
{
int i, j;
int M = A.Rows;
int N = A.Columns;
if (M < N)
{
throw new ArgumentException("svd of MxN matrix, M<N, is not implemented");
}
else if (V.Rows != N)
{
throw new ArgumentException("square matrix V must match second dimension of matrix A");
}
else if (V.Rows != V.Columns)
{
throw new ArgumentException("matrix V must be square");
}
else if (X.Rows != N)
{
throw new ArgumentException("square matrix X must match second dimension of matrix A");
}
else if (X.Rows != X.Columns)
{
throw new ArgumentException("matrix X must be square");
}
else if (S.Length != N)
{
throw new ArgumentException("length of vector S must match second dimension of matrix A");
}
else if (work.Length != N)
{
throw new ArgumentException("length of workspace must match second dimension of matrix A");
}
if (N == 1)
{
IROVector column = MatrixMath.ColumnToROVector(A, 0); // gsl_vector_view = gsl_matrix_column(A, 0);
double norm = VectorMath.GetNorm(column); //gsl_blas_dnrm2(&column.vector);
S[0] = norm; // gsl_vector_set(S, 0, norm);
V[0, 0] = 1; //gsl_matrix_set(V, 0, 0, 1.0);
if (norm != 0.0)
{
gsl_blas_dscal(1.0 / norm, &column.vector);
}
return(GSL_SUCCESS);
}
/* Convert A into an upper triangular matrix R */
for (i = 0; i < N; i++)
{
IVector c = MatrixMath.ColumnToVector(A, i); // gsl_vector_view c = gsl_matrix_column(A, i);
gsl_vector_view v = gsl_vector_subvector(&c.vector, i, M - i);
double tau_i = gsl_linalg_householder_transform(&v.vector);
/* Apply the transformation to the remaining columns */
if (i + 1 < N)
{
gsl_matrix_view m =
gsl_matrix_submatrix(A, i, i + 1, M - i, N - (i + 1));
gsl_linalg_householder_hm(tau_i, &v.vector, &m.matrix);
}
gsl_vector_set(S, i, tau_i);
}
/* Copy the upper triangular part of A into X */
for (i = 0; i < N; i++)
{
for (j = 0; j < i; j++)
{
gsl_matrix_set(X, i, j, 0.0);
}
{
double Aii = gsl_matrix_get(A, i, i);
gsl_matrix_set(X, i, i, Aii);
}
for (j = i + 1; j < N; j++)
{
double Aij = gsl_matrix_get(A, i, j);
gsl_matrix_set(X, i, j, Aij);
}
}
/* Convert A into an orthogonal matrix L */
for (j = N; j > 0 && j--;)
{
/* Householder column transformation to accumulate L */
double tj = gsl_vector_get(S, j);
gsl_matrix_view m = gsl_matrix_submatrix(A, j, j, M - j, N - j);
gsl_linalg_householder_hm1(tj, &m.matrix);
}
/* unpack R into X V S */
gsl_linalg_SV_decomp(X, V, S, work);
/* Multiply L by X, to obtain U = L X, stored in U */
{
gsl_vector_view sum = gsl_vector_subvector(work, 0, N);
for (i = 0; i < M; i++)
{
gsl_vector_view L_i = gsl_matrix_row(A, i);
gsl_vector_set_zero(&sum.vector);
for (j = 0; j < N; j++)
{
double Lij = gsl_vector_get(&L_i.vector, j);
gsl_vector_view X_j = gsl_matrix_row(X, j);
gsl_blas_daxpy(Lij, &X_j.vector, &sum.vector);
}
gsl_vector_memcpy(&L_i.vector, &sum.vector);
}
}
return(GSL_SUCCESS);
}
