List of usage examples for org.apache.commons.math3.linear FieldMatrix getColumnDimension
int getColumnDimension();
From source file:controller.VisLP.java
private static Point2D[] getFeasibleIntersections(FieldMatrix<BigFraction> cons) { FieldMatrix<BigFraction> N = cons.getSubMatrix(0, cons.getRowDimension() - 1, 0, cons.getColumnDimension() - 2); FieldVector<BigFraction> b = cons.getColumnVector(cons.getColumnDimension() - 1); HashSet<Point2D> points = new HashSet<Point2D>(); unb = new ArrayList<Point2D>(); /* Find all intersections */ for (int i = 0; i < N.getRowDimension(); i++) { for (int j = 0; j < N.getRowDimension(); j++) { if (i == j) continue; FieldMatrix<BigFraction> line1 = N.getRowMatrix(i); FieldMatrix<BigFraction> line2 = N.getRowMatrix(j); BigFraction[] bval = new BigFraction[] { b.getEntry(i), b.getEntry(j) }; FieldVector<BigFraction> bsys = new ArrayFieldVector<BigFraction>(bval); FieldMatrix<BigFraction> sys = LP.addBlock(line1, line2, LP.UNDER); try { FieldVector<BigFraction> point = new FieldLUDecomposition<BigFraction>(sys).getSolver() .getInverse().operate(bsys); double x = point.getEntry(0).doubleValue(); double y = point.getEntry(1).doubleValue(); Point2D p2d = new Point2D.Double(x, y); /* Only add feasible points */ if (feasible(p2d, N, b)) { if (i >= N.getRowDimension() - 1) unb.add(p2d);/*from w w w. j a v a2s. c o m*/ points.add(p2d); } } catch (IllegalArgumentException e) { /* * Two lines that don't intersect forms an invertible * matrix. Skip these points. */ } } } return points.toArray(new Point2D[0]); }
From source file:lirmm.inria.fr.math.TestUtils.java
/** verifies that two matrices are equal */ public static void assertEquals(FieldMatrix<? extends FieldElement<?>> expected, FieldMatrix<? extends FieldElement<?>> observed) { Assert.assertNotNull("Observed should not be null", observed); if (expected.getColumnDimension() != observed.getColumnDimension() || expected.getRowDimension() != observed.getRowDimension()) { StringBuilder messageBuffer = new StringBuilder(); messageBuffer.append("Observed has incorrect dimensions."); messageBuffer/*w w w . ja v a 2s . c o m*/ .append("\nobserved is " + observed.getRowDimension() + " x " + observed.getColumnDimension()); messageBuffer .append("\nexpected " + expected.getRowDimension() + " x " + expected.getColumnDimension()); Assert.fail(messageBuffer.toString()); } for (int i = 0; i < expected.getRowDimension(); ++i) { for (int j = 0; j < expected.getColumnDimension(); ++j) { FieldElement<?> eij = expected.getEntry(i, j); FieldElement<?> oij = observed.getEntry(i, j); Assert.assertEquals(eij, oij); } } }
From source file:model.LP.java
/** * Return a newly created {@code Matrix} with a new block * {@code Matrix} added either horizontally or vertically * next to the original {@code Matrix}./*from w w w. j ava 2s. co m*/ * * @param B * {@code Matrix}to append to the parent {@code Matrix}. * @param modifier * Matrix.HORIZONTAL or Matrix.VERTICAL. * @return * The original {@code Matrix}with a new {@code Matrix} block. */ public static FieldMatrix<BigFraction> addBlock(FieldMatrix<BigFraction> A, FieldMatrix<BigFraction> B, int modifier) { int Am = A.getRowDimension(); int An = A.getColumnDimension(); int Bm = B.getRowDimension(); int Bn = B.getColumnDimension(); String e = String.format( "Illegal operation: Cannot add a matrix block" + " of size %d x %d to a matrix of size %d x %d.", Am, An, Bm, Bn); if ((modifier == RIGHT && Am != Bm || modifier == UNDER && An != Bn)) { throw new IllegalArgumentException(e); } int newm = Am; int newn = An; int ci = 0; int cj = 0; switch (modifier) { case RIGHT: newn += Bn; cj = An; break; case UNDER: /* Fall through */ default: newm += Bm; ci = Am; } BigFraction cdata[][] = new BigFraction[newm][newn]; /* Copy A's data into cdata */ for (int i = 0; i < Am; i++) { for (int j = 0; j < An; j++) { cdata[i][j] = A.getEntry(i, j); } } /* Add the new block of data */ for (int i = 0; i < Bm; i++) { for (int j = 0; j < Bn; j++) { cdata[i + ci][j + cj] = B.getEntry(i, j); } } return new Array2DRowFieldMatrix<BigFraction>(cdata); }
From source file:model.LP.java
/** * Initializes a linear program./* w w w . ja va 2 s. c o m*/ * <p> * n being the number of variables and m being the number of constraints, * this {@code constructor} does the following: * <p><blockquote><pre> * B is set to the identity matrix of dimension m. * * The indices of the basic and non-basic variables are set to * 0..n-1 and n-1..n+m-1, respectively. * * The slack variables are called w1..wm. * </pre></blockquote<p> * * @param N * A {@code Matrix} with the coefficients * of the non-basic variables. * @param b * A {@code Matrix} with the upper bounds on * the constraints in the original program. * @param c * A {@code Matrix} with the coefficients of the * decision variables in the original program. * @param x * A {@code HashMap} mapping the indices of the * basic and non-basic variables to their names. */ public LP(FieldMatrix<BigFraction> N, FieldVector<BigFraction> b, FieldVector<BigFraction> c, HashMap<Integer, String> x) { this(null, N, b, c, null, N.copy(), b.copy(), c.mapMultiply(BigFraction.MINUS_ONE).copy(), x, new int[N.getRowDimension()], new int[N.getColumnDimension()]); /* Create an identity matrix of BigFraction's */ int m = N.getRowDimension(); BigFraction[][] Bd = new BigFraction[m][m]; for (int i = 0; i < m; i++) { Arrays.fill(Bd[i], BigFraction.ZERO); Bd[i][i] = BigFraction.ONE; } FieldMatrix<BigFraction> B = new Array2DRowFieldMatrix<BigFraction>(Bd); this.B = B; this.B_ = B.copy(); for (int i = 0; i < Ni.length; i++) Ni[i] = i; for (int i = 0; i < Bi.length; i++) { Bi[i] = i + Ni.length; x.put(Bi[i], "w" + (i + 1)); } }
From source file:model.LP.java
/** * Do one iteration of the simplex method. * * @param entering//from w w w . j a va2 s .c om * Index of variable to enter the basis. * @param leaving * Index of variable to leave the basis. * @return * A linear program after one iteration. */ public LP pivot(int entering, int leaving) { FieldMatrix<BigFraction> bin = new FieldLUDecomposition<BigFraction>(B_).getSolver().getInverse() .multiply(N_); // Step 1: Check for optimpivality // Step 2: Select entering variable. // Naive method. Does not check for optimality. Assumes feasibility. // Entering variable is given. // Step 3: Compute primal step direction. FieldVector<BigFraction> ej = new ArrayFieldVector<BigFraction>(bin.getColumnDimension(), BigFraction.ZERO); ej.setEntry(entering, BigFraction.ONE); FieldVector<BigFraction> psd = bin.operate(ej); // Step 4: Compute primal step length. // Step 5: Select leaving variable. // Leaving variable is given. BigFraction t = b_.getEntry(leaving).divide(psd.getEntry(leaving)); // Step 6: Compute dual step direction. FieldVector<BigFraction> ei = new ArrayFieldVector<BigFraction>(bin.getRowDimension(), BigFraction.ZERO); ei.setEntry(leaving, BigFraction.ONE); FieldVector<BigFraction> dsd = bin.transpose().scalarMultiply(BigFraction.MINUS_ONE).operate(ei); // Step 7: Compute dual step length. BigFraction s = c_.getEntry(entering).divide(dsd.getEntry(entering)); // Step 8: Update current primal and dual solutions. FieldVector<BigFraction> nb_ = b_.subtract(psd.mapMultiply(t)); nb_.setEntry(leaving, t); FieldVector<BigFraction> nc_ = c_.subtract(dsd.mapMultiply(s)); nc_.setEntry(entering, s); // Step 9: Update basis. FieldVector<BigFraction> temp = B_.getColumnVector(leaving); FieldMatrix<BigFraction> nB_ = B_.copy(); nB_.setColumn(leaving, N_.getColumn(entering)); FieldMatrix<BigFraction> nN_ = N_.copy(); nN_.setColumnVector(entering, temp); int[] nBi = Bi.clone(); int[] nNi = Ni.clone(); nBi[leaving] = Ni[entering]; nNi[entering] = Bi[leaving]; return new LP(B, N, b, c, nB_, nN_, nb_, nc_, x, nBi, nNi); }
From source file:model.LP.java
/** * Find a leaving variable index that is the most * bounding on the given entering variable index. * * @param entering/* w ww. ja va 2 s. co m*/ * an entering variable index. * @param dual * If true, find a leaving variable index for the dual dictionary. * Otherwise, find one for the primal dictionary. * @return * A leaving variable index. */ private int leaving(int entering, boolean dual) { FieldVector<BigFraction> check; FieldVector<BigFraction> sd; FieldMatrix<BigFraction> bin = new FieldLUDecomposition<BigFraction>(B_).getSolver().getInverse() .multiply(N_); if (dual) { check = c_; FieldVector<BigFraction> unit = new ArrayFieldVector<BigFraction>(bin.getRowDimension(), BigFraction.ZERO); unit.setEntry(entering, BigFraction.ONE); sd = bin.transpose().scalarMultiply(BigFraction.MINUS_ONE).operate(unit); } else { check = b_; FieldVector<BigFraction> unit = new ArrayFieldVector<BigFraction>(bin.getColumnDimension(), BigFraction.ZERO); unit.setEntry(entering, BigFraction.ONE); sd = bin.operate(unit); } boolean unbounded = true; int index = -1; /* Check for unboundedness and find first non-zero element in check */ for (int i = 0; i < sd.getDimension(); i++) { if (!check.getEntry(i).equals(BigFraction.ZERO) && index == -1) { index = i; } if (sd.getEntry(i).compareTo(BigFraction.ZERO) > 0) { unbounded = false; } } String e = "Program is unbounded."; if (unbounded) throw new RuntimeException(e); /* Set temporarily max value as ratio of the first divisible pair. */ BigFraction max = sd.getEntry(index).divide(check.getEntry(index)); for (int i = 0; i < sd.getDimension(); i++) { BigFraction num = sd.getEntry(i); BigFraction denom = check.getEntry(i); if (!denom.equals(BigFraction.ZERO)) { BigFraction val = num.divide(denom); if (val.compareTo(max) > 0) { max = val; index = i; } } else { if (num.compareTo(BigFraction.ZERO) > 0) return i; } } return index; }
From source file:org.briljantframework.array.Matrices.java
/** * Convert the field matrix to an array. * /*from w w w. j ava 2 s . co m*/ * @param matrix the matrix * @return a new array */ public static ComplexArray toArray(FieldMatrix<Complex> matrix) { ComplexArray array = Arrays.complexArray(matrix.getRowDimension(), matrix.getColumnDimension()); matrix.walkInOptimizedOrder(new DefaultFieldMatrixPreservingVisitor<Complex>(Complex.ZERO) { @Override public void visit(int row, int column, Complex value) { array.set(row, column, value); } }); return array; }
From source file:org.rhwlab.BHCnotused.GaussianGIWPrior.java
public void init() { int n = data.size(); int d = m.getDimension(); Dfp rP = r.add(n);// w w w. java2s.c o m // System.out.printf("rP=%s\n",rP.toString()); double nuP = nu + n; // System.out.printf("nuP=%e\n", nuP); FieldMatrix C = new Array2DRowFieldMatrix(field, d, d); for (int row = 0; row < C.getRowDimension(); ++row) { for (int col = 0; col < C.getColumnDimension(); ++col) { C.setEntry(row, col, field.getZero()); } } FieldVector X = new ArrayFieldVector(field, d); // a vector of zeros for (FieldVector v : data) { X = X.add(v); FieldMatrix v2 = v.outerProduct(v); C = C.add(v2); } FieldVector mP = (m.mapMultiply(r).add(X)).mapDivide(r.add(n)); FieldMatrix Sp = C.add(S); FieldMatrix rmmP = mP.outerProduct(mP).scalarMultiply(rP); Sp = Sp.add(rmm).subtract(rmmP); FieldLUDecomposition ed = new FieldLUDecomposition(Sp); Dfp det = (Dfp) ed.getDeterminant(); Dfp detSp = det.pow(field.newDfp(nuP / 2.0)); Dfp gamma = field.getOne(); Dfp gammaP = field.getOne(); for (int i = 1; i <= d; ++i) { gamma = gamma.multiply(Gamma.gamma((nu + 1 - i) / 2.0)); gammaP = gammaP.multiply(Gamma.gamma((nuP + 1 - i) / 2.0)); } Dfp t1 = field.getPi().pow(-n * d / 2.0); Dfp t2 = r.divide(rP).pow(d / 2.0); Dfp t3 = detS.divide(detSp); Dfp t4 = gammaP.divide(gamma); Dfp t34 = t3.multiply(t4); /* System.out.printf("detSp=%s\n", detSp.toString()); System.out.printf("det=%s\n", det.toString()); System.out.printf("gamma=%s\n", gamma.toString()); System.out.printf("gammaP=%s\n", gammaP.toString()); System.out.printf("t1=%s\n", t1.toString()); System.out.printf("t2=%s\n", t2.toString()); System.out.printf("t3=%s\n", t3.toString()); System.out.printf("t4=%s\n", t4.toString()); */ likelihood = t2.multiply(t34).multiply(t1); double realLike = likelihood.getReal(); // System.out.printf("Likelihood=%e\n", realLike); int uhfd = 0; }