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

Introduction

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

Prototype

double getEntry(int row, int column) throws OutOfRangeException;

Source Link

Document

Get the entry in the specified row and column.

Usage

From source file:org.meteoinfo.math.linalg.LinalgUtil.java

/**
 * Calculates the Cholesky decomposition of a matrix.
 * The Cholesky decomposition of a real symmetric positive-definite matrix A consists of a 
 * lower triangular matrix L with same size such that: A = LLT. In a sense, this is 
 * the square root of A./*w w  w  .ja  v a  2s . c  om*/
 * @param a The given matrix.
 * @return Result array.
 */
public static Array cholesky(Array a) {
    Array r = Array.factory(DataType.DOUBLE, a.getShape());
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    CholeskyDecomposition decomposition = new CholeskyDecomposition(matrix);
    RealMatrix L = decomposition.getL();
    int n = L.getColumnDimension();
    int m = L.getRowDimension();
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            r.setDouble(i * n + j, L.getEntry(i, j));
        }
    }

    return r;
}

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From source file:org.meteoinfo.math.linalg.LinalgUtil.java

/**
 * Calculates the LUP-decomposition of a square matrix.
 * The LUP-decomposition of a matrix A consists of three matrices L, U and P that satisfy: 
 * PA = LU. L is lower triangular (with unit diagonal terms), U is upper triangular and P is 
 * a permutation matrix. All matrices are mm.
 * @param a Given matrix./*from  w w w  .  j  a  v a 2 s  .  c o m*/
 * @return Result P/L/U arrays.
 */
public static Array[] lu(Array a) {
    Array Pa = Array.factory(DataType.DOUBLE, a.getShape());
    Array La = Array.factory(DataType.DOUBLE, a.getShape());
    Array Ua = Array.factory(DataType.DOUBLE, a.getShape());
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    LUDecomposition decomposition = new LUDecomposition(matrix);
    RealMatrix P = decomposition.getP();
    RealMatrix L = decomposition.getL();
    RealMatrix U = decomposition.getU();
    int n = L.getColumnDimension();
    int m = L.getRowDimension();
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            Pa.setDouble(i * n + j, P.getEntry(i, j));
            La.setDouble(i * n + j, L.getEntry(i, j));
            Ua.setDouble(i * n + j, U.getEntry(i, j));
        }
    }

    return new Array[] { Pa, La, Ua };
}

---

From source file:org.meteoinfo.math.linalg.LinalgUtil.java

/**
 * Calculates the QR-decomposition of a matrix.
 * The QR-decomposition of a matrix A consists of two matrices Q and R that satisfy: A = QR, Q 
 * is orthogonal (QTQ = I), and R is upper triangular. If A is mn, Q is mm and R mn.
 * @param a Given matrix./*from  www  .j a  v  a  2  s.  co  m*/
 * @return Result Q/R arrays.
 */
public static Array[] qr(Array a) {
    int m = a.getShape()[0];
    int n = a.getShape()[1];
    Array Qa = Array.factory(DataType.DOUBLE, new int[] { m, m });
    Array Ra = Array.factory(DataType.DOUBLE, a.getShape());
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    QRDecomposition decomposition = new QRDecomposition(matrix);
    RealMatrix Q = decomposition.getQ();
    RealMatrix R = decomposition.getR();
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < m; j++) {
            Qa.setDouble(i * m + j, Q.getEntry(i, j));
        }
    }
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            Ra.setDouble(i * n + j, R.getEntry(i, j));
        }
    }

    return new Array[] { Qa, Ra };
}

---

From source file:org.meteoinfo.math.linalg.LinalgUtil.java

/**
 * Calculates the compact Singular Value Decomposition of a matrix.
 * The Singular Value Decomposition of matrix A is a set of three matrices: U,  and V 
 * such that A = U    VT. Let A be a m  n matrix, then U is a m  p orthogonal 
 * matrix,  is a p  p diagonal matrix with positive or null elements, V is a p  n 
 * orthogonal matrix (hence VT is also orthogonal) where p=min(m,n).
 * @param a Given matrix.//from   w  w w  . j a v  a  2  s.c om
 * @return Result U/S/V arrays.
 */
public static Array[] svd(Array a) {
    int m = a.getShape()[0];
    int n = a.getShape()[1];
    int k = Math.min(m, n);
    Array Ua = Array.factory(DataType.DOUBLE, new int[] { m, k });
    Array Va = Array.factory(DataType.DOUBLE, new int[] { k, n });
    Array Sa = Array.factory(DataType.DOUBLE, new int[] { k });
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    SingularValueDecomposition decomposition = new SingularValueDecomposition(matrix);
    RealMatrix U = decomposition.getU();
    RealMatrix V = decomposition.getVT();
    double[] sv = decomposition.getSingularValues();
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < k; j++) {
            Ua.setDouble(i * k + j, U.getEntry(i, j));
        }
    }
    for (int i = 0; i < k; i++) {
        for (int j = 0; j < n; j++) {
            Va.setDouble(i * n + j, V.getEntry(i, j));
        }
    }
    for (int i = 0; i < k; i++) {
        Sa.setDouble(i, sv[i]);
    }

    return new Array[] { Ua, Sa, Va };
}

---

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

/**
 * Computes covariances for pairs of arrays or columns of a matrix.
 *
 * @param x X data/*www  .  j ava 2  s.  c o 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.ja  va 2 s  .  c o  m*/
 * @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  www .  j  ava  2  s .c om
 * @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//from  w  w  w.  j  a v a 2s  . 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;
    }
}

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From source file:org.micromanager.plugins.magellan.coordinates.AffineCalibrator.java

private AffineTransform computeAffine(Point2D.Double pix1, Point2D.Double pix2, Point2D.Double pix3,
        Point2D.Double stage1, Point2D.Double stage2, Point2D.Double stage3) {
    //solve system x' = xA for A, x is stage points and x' is pixel points
    //6x6 matrix/*from   ww w .jav  a2  s.c o m*/
    RealMatrix pixPoints = new Array2DRowRealMatrix(new double[][] { { pix1.x, pix1.y, 1, 0, 0, 0 },
            { 0, 0, 0, pix1.x, pix1.y, 1 }, { pix2.x, pix2.y, 1, 0, 0, 0 }, { 0, 0, 0, pix2.x, pix2.y, 1 },
            { pix3.x, pix3.y, 1, 0, 0, 0 }, { 0, 0, 0, pix3.x, pix3.y, 1 } });
    //6x1 matrix
    RealMatrix stagePoints = new Array2DRowRealMatrix(
            new double[] { stage1.x, stage1.y, stage2.x, stage2.y, stage3.x, stage3.y });
    //invert stagePoints matrix
    RealMatrix stagePointsInv = new LUDecomposition(pixPoints).getSolver().getInverse();
    RealMatrix A = stagePointsInv.multiply(stagePoints);
    AffineTransform transform = new AffineTransform(
            new double[] { A.getEntry(0, 0), A.getEntry(3, 0), A.getEntry(1, 0), A.getEntry(4, 0) });
    return transform;
}

---

From source file:org.moeaframework.TestUtils.java

/**
 * Asserts that the two matrices are equal.
 * /*from   w ww.  j a  v  a2  s .  c  om*/
 * @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));
        }
    }
}

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