Example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix.

Prototype

public Array2DRowRealMatrix(final double[][] d, final boolean copyArray)
        throws DimensionMismatchException, NoDataException, NullArgumentException

Source Link

Document

Create a new RealMatrix using the input array as the underlying data array.

Usage

From source file:com.insightml.models.regression.OLS.java

public static Array2DRowRealMatrix addIntercept2(final double[][] x) {
    return new Array2DRowRealMatrix(addIntercept(x), false);
}

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

private static RealMatrix randomSymmetricMatrix(int dimension) {
    RandomGenerator random = RandomManager.getRandom();
    RealMatrix symmetric = new Array2DRowRealMatrix(dimension, dimension);
    for (int j = 0; j < dimension; j++) {
        // Diagonal
        symmetric.setEntry(j, j, random.nextDouble());
        for (int k = j + 1; k < dimension; k++) {
            // Off-diagonal
            double d = random.nextDouble();
            symmetric.setEntry(j, k, d);
            symmetric.setEntry(k, j, d);
        }/* w ww  . j a va  2 s  .  c om*/
    }
    return symmetric;
}

From source file:edu.byu.nlp.classify.eval.ConfusionMatrix.java

public ConfusionMatrix(int numLabels, int K, Indexer<String> labels) {
    this.labels = labels;
    this.matrix = new Array2DRowRealMatrix(numLabels, K);
}

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

@Test
public void testSolveFToD() {
    double[][] data = { new double[] { 1.0, 2.0 }, new double[] { 3.0, 5.0 } };
    RealMatrix M = new Array2DRowRealMatrix(data, false);
    LinearSystemSolver cmLinearSystemSolver = new CommonsMathLinearSystemSolver();
    Solver solver = cmLinearSystemSolver.getSolver(M);
    assertArrayEquals(new double[] { -12.0, 7.0 }, solver.solveFToD(new float[] { 2.0f, -1.0f }));
}

From source file:com.cloudera.oryx.kmeans.computation.covariance.CovarianceDataBuilder.java

CovarianceDataBuilder(int n) {
    this.means = new ArrayRealVector(n);
    this.rawCoMoment = new Array2DRowRealMatrix(n, n);
}

From source file:com.itemanalysis.psychometrics.statistics.MatrixToVector.java

public MatrixToVector(RealMatrix matrix) {
    this.matrix = matrix;
    numEelements = (int) ((matrix.getColumnDimension() * (matrix.getColumnDimension() + 1)) / 2.0);
    realVector = new Array2DRowRealMatrix(numEelements, 1);
}

From source file:edu.dfci.cccb.mev.math.commons.mock.Array2DRealMatrixBuilder.java

@Override
public RealMatrix build(int height, int width) throws MatrixBuilderException {
    return new Array2DRowRealMatrix(height, width);
}

From source file:mr.go.sgfilter.SGFilter.java

/**
 * Computes Savitzky-Golay coefficients for given parameters
 * //from ww  w  .  j  a  v  a 2  s .  c o  m
 * @param nl
 *            numer of past data points filter will use
 * @param nr
 *            number of future data points filter will use
 * @param degree
 *            order of smoothin polynomial
 * @return Savitzky-Golay coefficients
 * @throws IllegalArgumentException
 *             if {@code nl < 0} or {@code nr < 0} or {@code nl + nr <
 *             degree}
 */
public static double[] computeSGCoefficients(int nl, int nr, int degree) {
    if (nl < 0 || nr < 0 || nl + nr < degree)
        throw new IllegalArgumentException("Bad arguments");
    Array2DRowRealMatrix matrix = new Array2DRowRealMatrix(degree + 1, degree + 1);
    double[][] a = matrix.getDataRef();
    double sum;
    for (int i = 0; i <= degree; i++) {
        for (int j = 0; j <= degree; j++) {
            sum = (i == 0 && j == 0) ? 1 : 0;
            for (int k = 1; k <= nr; k++)
                sum += pow(k, i + j);
            for (int k = 1; k <= nl; k++)
                sum += pow(-k, i + j);
            a[i][j] = sum;
        }
    }
    double[] b = new double[degree + 1];
    b[0] = 1;
    ArrayRealVector b1 = new ArrayRealVector(b);
    b1 = (ArrayRealVector) (new LUDecomposition(matrix)).getSolver().solve(b1);
    b = b1.toArray();
    double[] coeffs = new double[nl + nr + 1];
    for (int n = -nl; n <= nr; n++) {
        sum = b[0];
        for (int m = 1; m <= degree; m++)
            sum += b[m] * pow(n, m);
        coeffs[n + nl] = sum;
    }
    return coeffs;
}

From source file:com.itemanalysis.psychometrics.factoranalysis.QuartiminCriteria.java

public void computeValues(RealMatrix L) {
    int k = L.getColumnDimension();
    int p = L.getRowDimension();
    RealMatrix I = new IdentityMatrix(p);
    RealMatrix L2 = MatrixUtils.multiplyElements(L, L);

    RealMatrix N = new Array2DRowRealMatrix(k, k);
    N.walkInRowOrder(new DefaultRealMatrixChangingVisitor() {
        @Override/*from  w ww  . j a va2  s .  c  om*/
        public double visit(int row, int column, double value) {
            if (row == column)
                return 0.0;
            return 1.0;
        }
    });

    RealMatrix X = L2.multiply(N);
    double sum = MatrixUtils.sumMatrix(MatrixUtils.multiplyElements(L2, X));
    functionValue = sum / 4.0;
    gradient = MatrixUtils.multiplyElements(L, X);

}

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

@Test
public void testSolveDToF() {
    double[][] data = { new double[] { 1.0, 2.0 }, new double[] { 3.0, 5.0 } };
    RealMatrix M = new Array2DRowRealMatrix(data, false);
    LinearSystemSolver cmLinearSystemSolver = new CommonsMathLinearSystemSolver();
    Solver solver = cmLinearSystemSolver.getSolver(M);
    assertArrayEquals(new float[] { -12.0f, 7.0f }, solver.solveDToF(new double[] { 2.0, -1.0 }));
}