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

Introduction

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

Prototype

int getColumnDimension();

Source Link

Document

Returns the number of columns in the matrix.

Usage

From source file:org.meteoinfo.math.stats.StatsUtil.java

/**
 * Computes covariances for pairs of arrays or columns of a matrix.
 *
 * @param x X data//ww w. ja  v a2  s.co m
 * @param y Y data
 * @param bias If true, returned value will be bias-corrected
 * @return The covariance matrix
 */
public static Array cov(Array x, Array y, boolean bias) {
    int m = x.getShape()[0];
    int n = 1;
    if (x.getRank() == 2)
        n = x.getShape()[1];
    double[][] aa = new double[m][n * 2];
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n * 2; j++) {
            if (j < n) {
                aa[i][j] = x.getDouble(i * n + j);
            } else {
                aa[i][j] = y.getDouble(i * n + j - n);
            }
        }
    }
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    Covariance cov = new Covariance(matrix, bias);
    RealMatrix mcov = cov.getCovarianceMatrix();
    m = mcov.getColumnDimension();
    n = mcov.getRowDimension();
    Array r = Array.factory(DataType.DOUBLE, new int[] { m, n });
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            r.setDouble(i * n + j, mcov.getEntry(i, j));
        }
    }

    return r;
}

From source file:org.meteoinfo.math.stats.StatsUtil.java

/**
 * Computes covariances for columns of a matrix.
 * @param a Matrix data//from  w w w  .  j a  v a2 s .  c  om
 * @param bias If true, returned value will be bias-corrected
 * @return Covariant matrix or value
 */
public static Object cov(Array a, boolean bias) {
    if (a.getRank() == 1) {
        double[] ad = (double[]) ArrayUtil.copyToNDJavaArray(a);
        Covariance cov = new Covariance();
        return cov.covariance(ad, ad);
    } else {
        double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
        RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
        Covariance cov = new Covariance(matrix, bias);
        RealMatrix mcov = cov.getCovarianceMatrix();
        int m = mcov.getColumnDimension();
        int n = mcov.getRowDimension();
        Array r = Array.factory(DataType.DOUBLE, new int[] { m, n });
        for (int i = 0; i < m; i++) {
            for (int j = 0; j < n; j++) {
                r.setDouble(i * n + j, mcov.getEntry(i, j));
            }
        }

        return r;
    }
}

From source file:org.meteoinfo.math.stats.StatsUtil.java

/**
 * Computes Spearman's rank correlation for pairs of arrays or columns of a matrix.
 *
 * @param x X data/*from  ww  w  .  jav a2 s  .  co m*/
 * @param y Y data
 * @return Spearman's rank correlation
 */
public static Array spearmanr(Array x, Array y) {
    int m = x.getShape()[0];
    int n = 1;
    if (x.getRank() == 2)
        n = x.getShape()[1];
    double[][] aa = new double[m][n * 2];
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n * 2; j++) {
            if (j < n) {
                aa[i][j] = x.getDouble(i * n + j);
            } else {
                aa[i][j] = y.getDouble(i * n + j - n);
            }
        }
    }
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    SpearmansCorrelation cov = new SpearmansCorrelation(matrix);
    RealMatrix mcov = cov.getCorrelationMatrix();
    m = mcov.getColumnDimension();
    n = mcov.getRowDimension();
    Array r = Array.factory(DataType.DOUBLE, new int[] { m, n });
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            r.setDouble(i * n + j, mcov.getEntry(i, j));
        }
    }

    return r;
}

From source file:org.meteoinfo.math.stats.StatsUtil.java

/**
 * Computes Spearman's rank correlation for columns of a matrix.
 * @param a Matrix data//  w  w  w.  j  av a 2 s.  c  o m
 * @return Spearman's rank correlation
 */
public static Object spearmanr(Array a) {
    if (a.getRank() == 1) {
        double[] ad = (double[]) ArrayUtil.copyToNDJavaArray(a);
        Covariance cov = new Covariance();
        return cov.covariance(ad, ad);
    } else {
        double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
        RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
        SpearmansCorrelation cov = new SpearmansCorrelation(matrix);
        RealMatrix mcov = cov.getCorrelationMatrix();
        int m = mcov.getColumnDimension();
        int n = mcov.getRowDimension();
        Array r = Array.factory(DataType.DOUBLE, new int[] { m, n });
        for (int i = 0; i < m; i++) {
            for (int j = 0; j < n; j++) {
                r.setDouble(i * n + j, mcov.getEntry(i, j));
            }
        }

        return r;
    }
}

From source file:org.moeaframework.TestUtils.java

/**
 * Asserts that the two matrices are equal.
 * /*from  w  w w  .  j av  a2 s  .c  o m*/
 * @param rm1 the first matrix
 * @param rm2 the second matrix
 * @param error the equality comparison used to assert pairwise values 
 *        are equal
 */
public static void assertEquals(RealMatrix rm1, RealMatrix rm2, FloatingPointError error) {
    Assert.assertEquals(rm1.getRowDimension(), rm2.getRowDimension());
    Assert.assertEquals(rm1.getColumnDimension(), rm2.getColumnDimension());

    for (int i = 0; i < rm1.getRowDimension(); i++) {
        for (int j = 0; j < rm2.getColumnDimension(); j++) {
            error.assertEquals(rm1.getEntry(i, j), rm2.getEntry(i, j));
        }
    }
}

From source file:org.moeaframework.util.RotationMatrixBuilderTest.java

/**
 * Tests if the matrix is the identity matrix.
 * /*  ww  w .  j  a  v  a2 s.  c  o  m*/
 * @param rm the matrix to test
 */
public void testIdentityMatrix(RealMatrix rm) {
    for (int i = 0; i < rm.getRowDimension(); i++) {
        for (int j = 0; j < rm.getColumnDimension(); j++) {
            if (i == j) {
                Assert.assertEquals(1.0, rm.getEntry(i, j), Settings.EPS);
            } else {
                Assert.assertEquals(0.0, rm.getEntry(i, j), Settings.EPS);
            }
        }
    }
}

From source file:org.nd4j.linalg.Nd4jTestsComparisonFortran.java

@Test
public void testGemvApacheCommons() {

    int[] rowsArr = new int[] { 4, 4, 4, 8, 8, 8 };
    int[] colsArr = new int[] { 2, 1, 10, 2, 1, 10 };

    for (int x = 0; x < rowsArr.length; x++) {
        int rows = rowsArr[x];
        int cols = colsArr[x];

        List<Pair<INDArray, String>> matrices = NDArrayCreationUtil.getAllTestMatricesWithShape(rows, cols,
                12345);// w ww.  j  a v a  2 s  .  c o  m
        List<Pair<INDArray, String>> vectors = NDArrayCreationUtil.getAllTestMatricesWithShape(cols, 1, 12345);

        for (int i = 0; i < matrices.size(); i++) {
            for (int j = 0; j < vectors.size(); j++) {

                Pair<INDArray, String> p1 = matrices.get(i);
                Pair<INDArray, String> p2 = vectors.get(j);
                String errorMsg = getTestWithOpsErrorMsg(i, j, "mmul", p1, p2);

                INDArray m = p1.getFirst();
                INDArray v = p2.getFirst();

                RealMatrix rm = new BlockRealMatrix(m.rows(), m.columns());
                for (int r = 0; r < m.rows(); r++) {
                    for (int c = 0; c < m.columns(); c++) {
                        double d = m.getDouble(r, c);
                        rm.setEntry(r, c, d);
                    }
                }

                RealMatrix rv = new BlockRealMatrix(cols, 1);
                for (int r = 0; r < v.rows(); r++) {
                    double d = v.getDouble(r, 0);
                    rv.setEntry(r, 0, d);
                }

                INDArray gemv = m.mmul(v);
                RealMatrix gemv2 = rm.multiply(rv);

                assertArrayEquals(new int[] { rows, 1 }, gemv.shape());
                assertArrayEquals(new int[] { rows, 1 },
                        new int[] { gemv2.getRowDimension(), gemv2.getColumnDimension() });

                //Check entries:
                for (int r = 0; r < rows; r++) {
                    double exp = gemv2.getEntry(r, 0);
                    double act = gemv.getDouble(r, 0);
                    assertEquals(errorMsg, exp, act, 1e-5);
                }
            }
        }
    }
}

From source file:org.opentestsystem.airose.linear.Matrices.java

public static void copyTo(Matrix matrix, Matrix output) {
    RealMatrix source = matrix._matrix;
    RealMatrix destination = output._matrix;
    for (int row = 0; row < source.getRowDimension(); ++row) {
        for (int column = 0; column < source.getColumnDimension(); ++column) {
            destination.setEntry(row, column, source.getEntry(row, column));
        }// w w w  .java 2  s  .c  o m
    }
}

From source file:org.pmad.gmm.HessenbergTransformer.java

/**
 * Build the transformation to Hessenberg form of a general matrix.
 *
 * @param matrix matrix to transform//from   w w  w  . j a  v a 2  s . c  om
 * @throws NonSquareMatrixException if the matrix is not square
 */
public HessenbergTransformer(final RealMatrix matrix) {
    if (!matrix.isSquare()) {
        throw new NonSquareMatrixException(matrix.getRowDimension(), matrix.getColumnDimension());
    }

    final int m = matrix.getRowDimension();
    householderVectors = matrix.getData();
    ort = new double[m];
    cachedP = null;
    cachedPt = null;
    cachedH = null;

    // transform matrix
    transform();
}

From source file:org.pmad.gmm.MyEDC.java

/**
 * Calculates the eigen decomposition of the given real matrix.
 * <p>/*from ww  w . j  a v  a  2 s  . co m*/
 * Supports decomposition of a general matrix since 3.1.
 *
 * @param matrix Matrix to decompose.
 * @throws MaxCountExceededException if the algorithm fails to converge.
 * @throws MathArithmeticException if the decomposition of a general matrix
 * results in a matrix with zero norm
 * @since 3.1
 */
public MyEDC(final RealMatrix matrix) throws MathArithmeticException {
    final double symTol = 10 * matrix.getRowDimension() * matrix.getColumnDimension() * Precision.EPSILON;
    isSymmetric = MatrixUtils.isSymmetric(matrix, symTol);
    if (isSymmetric) {
        transformToTridiagonal(matrix);
        findEigenVectors(transformer.getQ().getData());
    } else {
        final SchurTransformer t = transformToSchur(matrix);
        findEigenVectorsFromSchur(t);
    }
}