コード例 #1
0
        public static void Test01e_2()
        {
            var X = new DoubleMatrix(new double[, ] {
                { 73, 746, 743, 106 }, { 584, 531, 420, 579 }, { 255, 562, 234, 693 }, { 360, 474, 381, 484 }, { 301, 78, 68, 313 }
            });
            var y = new DoubleMatrix(new double[, ] {
                { 803 }, { 292 }, { 230 }, { 469 }, { 655 }
            });

            var XtX = X.GetTranspose() * X;
            var Xty = X.GetTranspose() * y;

            var solver   = new DoubleLUDecomp(XtX);
            var expected = solver.Solve(Xty);

            FastNonnegativeLeastSquares.Execution(XtX, Xty, (i) => false, null, out var x, out var w);

            Assert.AreEqual(expected[0, 0], x[0, 0], 1e-4);
            Assert.AreEqual(expected[1, 0], x[1, 0], 1e-4);
            Assert.AreEqual(expected[2, 0], x[2, 0], 1e-4);
            Assert.AreEqual(expected[3, 0], x[3, 0], 1e-4);

            Assert.AreEqual(0, w[0, 0], 1e-8);
            Assert.AreEqual(0, w[1, 0], 1e-8);
            Assert.AreEqual(0, w[2, 0], 1e-8);
            Assert.AreEqual(0, w[3, 0], 1e-8);
        }
コード例 #2
0
        /// <summary>
        /// Execution of the fast nonnegative least squares algorithm. The algorithm finds a vector x with all elements xi&gt;=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);
            }
        }