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:lsafunctions.LSA.java

private static RealMatrix normalizeMatrix(RealMatrix M) {
    double sumColumn = 0;
    //        Matrix row = new Matrix(1, M.getColumnDimension());
    RealMatrix row = MatrixUtils.createRealMatrix(1, M.getColumnDimension());
    for (int j = 0; j < M.getColumnDimension(); j++) {
        sumColumn = 0;//from   www .j a  va  2  s .c  o  m
        for (int k = 0; k < M.getRowDimension(); k++) {
            sumColumn += Math.pow(M.getEntry(k, j), 2);
        }
        sumColumn = Math.sqrt(sumColumn);
        row.setEntry(0, j, sumColumn);
    }
    for (int j = 0; j < M.getColumnDimension(); j++) {
        for (int k = 0; k < M.getRowDimension(); k++) {
            M.setEntry(k, j, M.getEntry(k, j) / row.getEntry(0, j));
        }
    }
    return M;
}

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From source file:edu.oregonstate.eecs.mcplan.util.Csv.java

public static void write(final PrintStream out, final RealMatrix m) {
    final Writer writer = new Writer(out);
    for (int i = 0; i < m.getRowDimension(); ++i) {
        for (int j = 0; j < m.getColumnDimension(); ++j) {
            writer.cell(m.getEntry(i, j));
        }/*from   www.  ja  va2  s .  com*/
        writer.newline();
    }
}

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From source file:cooccurrence.Omer_Levy.java

/**
 * Method that will extract top D singular values from CoVariance Matrix 
 * It will then identify the corresponding columns from U and V and add it to new matrices 
 * @param U/*from  ww w . j a  va2 s . co  m*/
 * @param V
 * @param coVariance
 * @param matrixUd
 * @param matrixVd
 * @param coVarD
 * @param indicesD 
 */
private static void getTopD(RealMatrix U, RealMatrix V, RealMatrix coVariance, RealMatrix matrixUd,
        RealMatrix matrixVd, RealMatrix coVarD, ArrayList<Integer> indicesD) {
    TreeMap<Double, Set<Integer>> tmap = new TreeMap<>();
    for (int i = 0; i < coVariance.getRowDimension(); i++) {
        double val = coVariance.getEntry(i, i);
        if (tmap.containsKey(val)) {
            Set<Integer> temp = tmap.get(val);
            temp.add(i);
        } else {
            Set<Integer> temp = new HashSet<>();
            temp.add(i);
            tmap.put(val, temp);
        }
    }
    Iterator iter = tmap.keySet().iterator();
    while (iter.hasNext()) {
        Double val = (Double) iter.next();
        Set<Integer> indices = tmap.get(val);
        for (int i = 0; i < indices.size(); i++) {
            Iterator iterIndices = indices.iterator();
            while (iterIndices.hasNext()) {
                int index = (Integer) iterIndices.next();
                indicesD.add(index);
                coVarD.addToEntry(index, index, val);
                matrixUd.setColumnVector(index, U.getColumnVector(index));
                matrixVd.setColumnVector(index, V.getColumnVector(index));
            }
        }
    }

}

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From source file:com.yahoo.egads.utilities.SpectralMethods.java

public static TimeSeries.DataSequence mFilter(TimeSeries.DataSequence data, int windowSize,
        FilteringMethod method, double methodParameter) {

    TimeSeries.DataSequence result = new TimeSeries.DataSequence();
    RealMatrix dataMat = MatrixUtils.createRealMatrix(data.size(), 1);

    int i = 0;//from w w w.  jav a2s . c o m
    for (TimeSeries.Entry e : data) {
        dataMat.setEntry(i, 0, e.value);
        i++;
        TimeSeries.Entry eCopy = new TimeSeries.Entry(e);
        result.add(eCopy);
    }

    RealMatrix resultMat = SpectralMethods.mFilter(dataMat, windowSize, method, methodParameter);

    i = 0;
    for (TimeSeries.Entry e : result) {
        e.value = (float) resultMat.getEntry(i, 0);
        i++;
    }

    return result;
}

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From source file:cooccurrence.Omer_Levy.java

/**
 * Generic Method to take squareRoot of individual Values.
 * @param coVariance/*from ww  w. j a  v a  2 s.c  o  m*/
 * @return 
 */
private static RealMatrix squareRoot(RealMatrix coVariance) {
    RealMatrix squareRoot = MatrixUtils.createRealMatrix(coVariance.getRowDimension(),
            coVariance.getColumnDimension());
    for (int i = 0; i < coVariance.getRowDimension(); i++) {
        for (int j = 0; j < coVariance.getColumnDimension(); j++) {
            double val = coVariance.getEntry(i, j);
            val = Math.sqrt(val);
            squareRoot.addToEntry(i, j, val);
        }
    }
    return squareRoot;
}

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From source file:IO.java

public static void display(RealMatrix mat, String name) {

    int row = mat.getRowDimension();
    int col = mat.getColumnDimension();

    System.out.println(name + ":" + row + "x" + col);
    for (int i = 0; i < row; i++) {
        for (int j = 0; j < col; j++) {
            System.out.print(mat.getEntry(i, j) + ",");
        }//from w w  w .  j a  va  2s  . c om
        System.out.println();
    }
}

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From source file:dataGen.DataGen.java

/**
 * Do the yet created data stored in the matrix fulfill the Anti-Correlation condition?
 * Returns true if more than the half of all correlation coefficients are below the supremum coefficient.
 * @param corrMatrix/*  w w  w .j  a v  a2  s.c o  m*/
 *       The Data-Matrix
 * @param corrCoeff
 *       the Supremum correlation coefficient
 * @param dimensions
 *       how many dimensions does the test file have?
 * @return
 *       true if more than the half of all correlation coefficients are below the supremum coefficient.
 */
public static boolean isCorrMatrixValid(RealMatrix corrMatrix, double corrCoeff, int dimensions) {
    int count = 0;
    int maxVariance = sum(dimensions - 1) / 2;
    for (int i = 1; i < corrMatrix.getColumnDimension(); i++) {
        for (int j = 0; j < i; j++) {
            if (corrMatrix.getEntry(i, j) > corrCoeff) {
                if (count < maxVariance)
                    count++;
                else
                    return false;
            }
        }
    }
    return true;
}

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From source file:edu.mit.genecircuits.GcUtils.java

/** Convert dense Apache Commons to Colt matrix */
static public DoubleMatrix2D apache2colt(RealMatrix apache) {

    // Convert apache commons matrix to colt
    DoubleMatrix2D colt = new DenseDoubleMatrix2D(apache.getRowDimension(), apache.getColumnDimension());
    for (int i = 0; i < apache.getRowDimension(); i++)
        for (int j = 0; j < apache.getColumnDimension(); j++)
            colt.set(i, j, apache.getEntry(i, j));

    return colt;/* www.java  2s  . c  o  m*/
}

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From source file:edu.cudenver.bios.matrix.OrthogonalPolynomials.java

/**
 * Computes orthogonal polynomial contrasts for the specified data values.  Currently only
 * supports fitting (not prediction contrasts).  
 * //from ww w  . j av  a 2 s. co  m
 *    @param x the points at which the polynomials will be evaluated
 * @param maxDegree contrasts will be computed for degrees 1 to maxDegree
 * @return matrix containing 0th,1st, 2nd,...maxDegree-th degree contrasts in each column
 * @throws IllegalArgumentException
 */
public static RealMatrix orthogonalPolynomialCoefficients(double[] x, int maxDegree)
        throws IllegalArgumentException {
    if (x == null)
        throw new IllegalArgumentException("no data specified");
    if (maxDegree < 1)
        throw new IllegalArgumentException("max polynomial degree must be greater than 1");
    // count number of unique values
    HashSet<Double> s = new HashSet<Double>();
    for (double i : x)
        s.add(i);
    int uniqueCount = s.size();
    if (maxDegree >= uniqueCount)
        throw new IllegalArgumentException(
                "max polynomial degree must be less than the number of unique points");

    // center the data
    double xBar = StatUtils.mean(x);
    double[] xCentered = new double[x.length];
    for (int i = 0; i < x.length; i++)
        xCentered[i] = x[i] - xBar;
    // compute an "outer product" of the centered x vector and a vector 
    // containing the sequence 0 to maxDegree-1, but raise the x values
    // to the power in the sequence array
    double[][] xOuter = new double[x.length][maxDegree + 1];
    int row = 0;
    for (double xValue : xCentered) {
        for (int col = 0; col <= maxDegree; col++) {
            xOuter[row][col] = Math.pow(xValue, col);
        }
        row++;
    }
    // do some mysterious QR decomposition stuff.  See Emerson (1968)
    RealMatrix outerVector = new Array2DRowRealMatrix(xOuter);
    QRDecomposition qrDecomp = new QRDecomposition(outerVector);

    RealMatrix z = MatrixUtils.getDiagonalMatrix(qrDecomp.getR());
    RealMatrix raw = qrDecomp.getQ().multiply(z);

    // column sum of squared elements in raw
    double[] normalizingConstants = new double[raw.getColumnDimension()];
    for (int col = 0; col < raw.getColumnDimension(); col++) {
        normalizingConstants[col] = 0;
        for (row = 0; row < raw.getRowDimension(); row++) {
            double value = raw.getEntry(row, col);
            normalizingConstants[col] += value * value;
        }
    }

    // now normalize the raw values
    for (int col = 0; col < raw.getColumnDimension(); col++) {
        double normalConstantSqrt = Math.sqrt(normalizingConstants[col]);
        for (row = 0; row < raw.getRowDimension(); row++) {
            raw.setEntry(row, col, raw.getEntry(row, col) / normalConstantSqrt);
        }
    }

    return raw;
}

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

/**
 * Elementwise multiplication of two matrices.
 *
 * @param A a matrix that is multiplied by the elements of B
 * @param B another matrix//from  w w  w.  j a va 2  s.com
 * @throws DimensionMismatchException
 */
public static void multiplyElementsBy(RealMatrix A, RealMatrix B) throws DimensionMismatchException {
    int nrow = A.getRowDimension();
    int ncol = A.getColumnDimension();
    if (nrow != B.getRowDimension()) {
        throw new DimensionMismatchException(nrow, B.getRowDimension());
    }
    if (ncol != B.getColumnDimension()) {
        throw new DimensionMismatchException(ncol, B.getColumnDimension());
    }

    RealMatrix M = new Array2DRowRealMatrix(nrow, ncol);
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            A.multiplyEntry(i, j, B.getEntry(i, j));
        }
    }
}

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