Example usage for org.apache.commons.math3.linear RealMatrix getRowDimension

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear RealMatrix getRowDimension.

Prototype

int getRowDimension();

Source Link

Document

Returns the number of rows in the matrix.

Usage

From source file:edu.cmu.tetrad.util.TetradMatrix.java

public TetradMatrix(RealMatrix matrix) {
    if (matrix == null) {
        throw new IllegalArgumentException("Null matrix.");
    }//from   w  w  w  .  ja  v a  2  s. co m

    this.apacheData = matrix;
    this.m = matrix.getRowDimension();
    this.n = matrix.getColumnDimension();
}

---

From source file:eagle.security.userprofile.model.eigen.UserProfileEigenModeler.java

private void computeCovarianceAndSVD(RealMatrix inputMat, int containsLowVariantCol) {

    int finalMatrixRow = 0;
    int finalMatrixCol = 0;

    LOG.info("containsLowVariantCol size: " + containsLowVariantCol);
    int colDimension = (inputMat.getColumnDimension() - containsLowVariantCol);
    try {/*from w w  w .  j a  v a 2s . co m*/
        finalMatrixWithoutLowVariantCmds = new Array2DRowRealMatrix(inputMat.getRowDimension(), colDimension);
    } catch (NotStrictlyPositiveException e) {
        LOG.error(String.format("Failed to build matrix [rowDimension:%s, columnDimension: %s]",
                inputMat.getRowDimension(), colDimension), e);
        throw e;
    }

    for (int i = 0; i < inputMat.getRowDimension(); i++) {
        for (int j = 0; j < inputMat.getColumnDimension(); j++) {
            if (!statistics[j].isLowVariant()) {
                finalMatrixWithoutLowVariantCmds.setEntry(finalMatrixRow, finalMatrixCol,
                        inputMat.getEntry(i, j));
                finalMatrixCol++;
            }
        }
        finalMatrixCol = 0;
        finalMatrixRow++;
    }

    Covariance cov;
    try {
        cov = new Covariance(finalMatrixWithoutLowVariantCmds.getData());
    } catch (Exception ex) {
        throw new IllegalArgumentException(String.format("Failed to create covariance from matrix [ %s x %s ]",
                finalMatrixWithoutLowVariantCmds.getRowDimension(),
                finalMatrixWithoutLowVariantCmds.getColumnDimension()), ex);
    }
    covarianceMatrix = cov.getCovarianceMatrix();
    SingularValueDecomposition svd = new SingularValueDecomposition(covarianceMatrix);
    diagonalMatrix = svd.getS();
    uMatrix = svd.getU();
    vMatrix = svd.getV();
}

---

From source file:gamlss.utilities.WLSMultipleLinearRegression.java

/**
 * /*from w w  w. j  av a 2  s.c  om*/
 * @param y
 * @param x
 * @param w
 */
private void newSampleDataCopyOriginal(ArrayRealVector y, RealMatrix x, ArrayRealVector w) {
    this.y = y.copy(); //we need this for the fitted values and residuals.
    if (this.isNoIntercept()) {
        this.X = x.copy();

    } else {
        setDesignMatrix(x);//add 1 for the Intercept;
    }
    for (int row = 0; row < x.getRowDimension(); row++) {
        x.setRowVector(row, x.getRowVector(row).mapMultiplyToSelf(w.getEntry(row)));
    }
    //double[][] xw=x.getData();
    //double[] yw= y.ebeMultiply(w).getDataRef();
    //validateSampleData(xw, yw); //we have already checked this in the gamlss algorithm. 
    newYSampleData(y.ebeMultiply(w));
    newXSampleData(x.getData(), w);
}

---

From source file:com.joptimizer.optimizers.LPPresolverTest.java

/**
 * This problem has a deterministic solution.
 *//*from   w  w  w  .j  a  va  2s.  co  m*/
public void testFromFile2() throws Exception {
    log.debug("testFromFile2");

    String problemId = "2";

    double[] c = Utils.loadDoubleArrayFromFile(
            "lp" + File.separator + "presolving" + File.separator + "c" + problemId + ".txt");
    double[][] A = Utils.loadDoubleMatrixFromFile(
            "lp" + File.separator + "presolving" + File.separator + "A" + problemId + ".csv", ",".charAt(0));
    double[] b = Utils.loadDoubleArrayFromFile(
            "lp" + File.separator + "presolving" + File.separator + "b" + problemId + ".txt");
    double[] lb = Utils.loadDoubleArrayFromFile(
            "lp" + File.separator + "presolving" + File.separator + "lb" + problemId + ".txt");
    double[] ub = Utils.loadDoubleArrayFromFile(
            "lp" + File.separator + "presolving" + File.separator + "ub" + problemId + ".txt");
    double s = 0;
    try {
        s = Utils.loadDoubleArrayFromFile(
                "lp" + File.separator + "presolving" + File.separator + "s" + problemId + ".txt")[0];
    } catch (Exception e) {
    }
    double[] expectedSolution = Utils.loadDoubleArrayFromFile(
            "lp" + File.separator + "presolving" + File.separator + "sol" + problemId + ".txt");
    double expectedValue = Utils.loadDoubleArrayFromFile(
            "lp" + File.separator + "presolving" + File.separator + "value" + problemId + ".txt")[0];
    double expectedTolerance = MatrixUtils.createRealMatrix(A)
            .operate(MatrixUtils.createRealVector(expectedSolution)).subtract(MatrixUtils.createRealVector(b))
            .getNorm();

    //must be: A pXn with rank(A)=p < n
    RealMatrix AMatrix = MatrixUtils.createRealMatrix(A);
    SingularValueDecomposition dec = new SingularValueDecomposition(AMatrix);
    int rankA = dec.getRank();
    log.debug("p: " + AMatrix.getRowDimension());
    log.debug("n: " + AMatrix.getColumnDimension());
    log.debug("rank: " + rankA);

    LPPresolver lpPresolver = new LPPresolver();
    lpPresolver.setNOfSlackVariables((short) s);
    lpPresolver.setExpectedSolution(expectedSolution);//this is just for test!
    lpPresolver.presolve(c, A, b, lb, ub);
    int n = lpPresolver.getPresolvedN();

    //deterministic solution
    assertEquals(0, n);
    assertTrue(lpPresolver.getPresolvedC() == null);
    assertTrue(lpPresolver.getPresolvedA() == null);
    assertTrue(lpPresolver.getPresolvedB() == null);
    assertTrue(lpPresolver.getPresolvedLB() == null);
    assertTrue(lpPresolver.getPresolvedUB() == null);
    assertTrue(lpPresolver.getPresolvedYlb() == null);
    assertTrue(lpPresolver.getPresolvedYub() == null);
    assertTrue(lpPresolver.getPresolvedZlb() == null);
    assertTrue(lpPresolver.getPresolvedZub() == null);
    double[] sol = lpPresolver.postsolve(new double[] {});
    assertEquals(expectedSolution.length, sol.length);
    for (int i = 0; i < sol.length; i++) {
        //log.debug("i: " + i);
        assertEquals(expectedSolution[i], sol[i], 1.e-9);
    }
}

---

From source file:com.cloudera.oryx.common.math.LinearSystemSolver.java

public Solver getSolver(RealMatrix M) {
    if (M == null) {
        return null;
    }// w  w  w.j  a v a  2s . c  o m
    RRQRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);
    DecompositionSolver solver = decomposition.getSolver();
    if (solver.isNonSingular()) {
        return new Solver(solver);
    }
    // Otherwise try to report apparent rank
    int apparentRank = decomposition.getRank(0.01); // Better value?
    log.warn(
            "{} x {} matrix is near-singular (threshold {}). Add more data or decrease the "
                    + "value of als.hyperparams.features, to <= about {}",
            M.getRowDimension(), M.getColumnDimension(), SINGULARITY_THRESHOLD, apparentRank);
    throw new SingularMatrixSolverException(apparentRank, "Apparent rank: " + apparentRank);
}

---

From source file:com.cloudera.oryx.common.math.CommonsMathLinearSystemSolver.java

@Override
public Solver getSolver(RealMatrix M) {
    if (M == null) {
        return null;
    }//from www  .  j  av a  2s  .  c o m
    RRQRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);
    DecompositionSolver solver = decomposition.getSolver();
    if (solver.isNonSingular()) {
        return new CommonsMathSolver(solver);
    }
    // Otherwise try to report apparent rank
    int apparentRank = decomposition.getRank(0.01); // Better value?
    log.warn(
            "{} x {} matrix is near-singular (threshold {}). Add more data or decrease the value of model.features, "
                    + "to <= about {}",
            M.getRowDimension(), M.getColumnDimension(), SINGULARITY_THRESHOLD, apparentRank);
    throw new SingularMatrixSolverException(apparentRank, "Apparent rank: " + apparentRank);
}

---

From source file:com.joptimizer.optimizers.LPPresolverTest.java

private void doPresolving(double[] c, double[][] A, double[] b, double[] lb, double[] ub, double s,
        double[] expectedSolution, double expectedValue, double expectedTolerance) throws Exception {

    RealMatrix AMatrix = MatrixUtils.createRealMatrix(A);
    SingularValueDecomposition dec = new SingularValueDecomposition(AMatrix);
    int rankA = dec.getRank();
    log.debug("p: " + AMatrix.getRowDimension());
    log.debug("n: " + AMatrix.getColumnDimension());
    log.debug("rank: " + rankA);

    LPPresolver lpPresolver = new LPPresolver();
    lpPresolver.setNOfSlackVariables((short) s);
    lpPresolver.setExpectedSolution(expectedSolution);//this is just for test!
    //lpPresolver.setExpectedTolerance(expectedTolerance);//this is just for test!
    lpPresolver.presolve(c, A, b, lb, ub);
    int n = lpPresolver.getPresolvedN();
    double[] presolvedC = lpPresolver.getPresolvedC().toArray();
    double[][] presolvedA = lpPresolver.getPresolvedA().toArray();
    double[] presolvedB = lpPresolver.getPresolvedB().toArray();
    double[] presolvedLb = lpPresolver.getPresolvedLB().toArray();
    double[] presolvedUb = lpPresolver.getPresolvedUB().toArray();
    double[] presolvedYlb = lpPresolver.getPresolvedYlb().toArray();
    double[] presolvedYub = lpPresolver.getPresolvedYub().toArray();
    double[] presolvedZlb = lpPresolver.getPresolvedZlb().toArray();
    double[] presolvedZub = lpPresolver.getPresolvedZub().toArray();
    log.debug("n  : " + n);
    log.debug("presolvedC  : " + ArrayUtils.toString(presolvedC));
    log.debug("presolvedA  : " + ArrayUtils.toString(presolvedA));
    log.debug("presolvedB  : " + ArrayUtils.toString(presolvedB));
    log.debug("presolvedLb : " + ArrayUtils.toString(presolvedLb));
    log.debug("presolvedUb : " + ArrayUtils.toString(presolvedUb));
    log.debug("presolvedYlb: " + ArrayUtils.toString(presolvedYlb));
    log.debug("presolvedYub: " + ArrayUtils.toString(presolvedYub));
    log.debug("presolvedZlb: " + ArrayUtils.toString(presolvedZlb));
    log.debug("presolvedZub: " + ArrayUtils.toString(presolvedZub));

    //check objective function
    double delta = expectedTolerance;
    RealVector presolvedX = MatrixUtils.createRealVector(lpPresolver.presolve(expectedSolution));
    log.debug("presolved value: " + MatrixUtils.createRealVector(presolvedC).dotProduct(presolvedX));
    RealVector postsolvedX = MatrixUtils.createRealVector(lpPresolver.postsolve(presolvedX.toArray()));
    double value = MatrixUtils.createRealVector(c).dotProduct(postsolvedX);
    assertEquals(expectedValue, value, delta);

    //check postsolved constraints
    for (int i = 0; i < lb.length; i++) {
        double di = Double.isNaN(lb[i]) ? -Double.MAX_VALUE : lb[i];
        assertTrue(di <= postsolvedX.getEntry(i) + delta);
    }/*from   ww w .ja v  a  2 s . com*/
    for (int i = 0; i < ub.length; i++) {
        double di = Double.isNaN(ub[i]) ? Double.MAX_VALUE : ub[i];
        assertTrue(di + delta >= postsolvedX.getEntry(i));
    }
    RealVector Axmb = AMatrix.operate(postsolvedX).subtract(MatrixUtils.createRealVector(b));
    assertEquals(0., Axmb.getNorm(), expectedTolerance);

    //check presolved constraints
    assertEquals(presolvedLb.length, presolvedX.getDimension());
    assertEquals(presolvedUb.length, presolvedX.getDimension());
    AMatrix = MatrixUtils.createRealMatrix(presolvedA);
    RealVector bvector = MatrixUtils.createRealVector(presolvedB);
    for (int i = 0; i < presolvedLb.length; i++) {
        double di = Double.isNaN(presolvedLb[i]) ? -Double.MAX_VALUE : presolvedLb[i];
        assertTrue(di <= presolvedX.getEntry(i) + delta);
    }
    for (int i = 0; i < presolvedUb.length; i++) {
        double di = Double.isNaN(presolvedUb[i]) ? Double.MAX_VALUE : presolvedUb[i];
        assertTrue(di + delta >= presolvedX.getEntry(i));
    }
    Axmb = AMatrix.operate(presolvedX).subtract(bvector);
    assertEquals(0., Axmb.getNorm(), expectedTolerance);

    //check rank(A): must be A pXn with rank(A)=p < n
    AMatrix = MatrixUtils.createRealMatrix(presolvedA);
    dec = new SingularValueDecomposition(AMatrix);
    rankA = dec.getRank();
    log.debug("p: " + AMatrix.getRowDimension());
    log.debug("n: " + AMatrix.getColumnDimension());
    log.debug("rank: " + rankA);
    assertEquals(AMatrix.getRowDimension(), rankA);
    assertTrue(rankA < AMatrix.getColumnDimension());
}

---

From source file:edu.cmu.tetrad.util.TetradMatrix1.java

public TetradMatrix1(RealMatrix matrix) {
    if (matrix == null) {
        throw new IllegalArgumentException("Null matrix.");
    }//from  www .  ja va 2  s.  c  o m

    this.apacheData = matrix;
    this.m = matrix.getRowDimension();
    this.n = matrix.getColumnDimension();
}

---

From source file:msi.gama.util.matrix.GamaFloatMatrix.java

public GamaFloatMatrix(final RealMatrix rm) {
    super(rm.getColumnDimension(), rm.getRowDimension(), Types.FLOAT);
    matrix = new double[rm.getColumnDimension() * rm.getRowDimension()];
    updateMatrix(rm);/*from  w w w . j  av  a2 s  .c o  m*/
}

---

From source file:edu.cmu.tetrad.util.TetradMatrix.java

public TetradMatrix(RealMatrix matrix, int rows, int columns) {
    if (matrix == null) {
        throw new IllegalArgumentException("Null matrix.");
    }/*from  ww w. jav a2 s.  c o m*/

    this.apacheData = matrix;
    this.m = rows;
    this.n = columns;

    int _rows = matrix.getRowDimension();
    int _cols = matrix.getColumnDimension();
    if (_rows != 0 && _rows != rows)
        throw new IllegalArgumentException();
    if (_cols != 0 && _cols != columns)
        throw new IllegalArgumentException();
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• 22
• 23
• 24
• 25
• 26
• 27
• 28
• 29
• 30
• 31
• 32
• »