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:lsafunctions.LSA.java

public static RealMatrix calculateTfIdf(RealMatrix M) {
    int tf;/*from w  ww  .  ja  va  2 s . c  om*/
    double idf;
    int df;
    double ndf;
    for (int j = 0; j < M.getRowDimension(); j++) {

        df = calcDf(j, M);
        // System.out.println("J:"+j+"  df:"+df);

        for (int k = 0; k < M.getColumnDimension(); k++) {
            tf = (int) M.getEntry(j, k);
            ndf = M.getColumnDimension() / df;
            idf = Math.log(ndf) / Math.log(2);
            M.setEntry(j, k, idf * tf);
        }
    }
    //M.print(NumberFormat.INTEGER_FIELD, M.getColumnDimension());
    M = normalizeMatrix(M);
    return M;
}

From source file:edu.byu.nlp.stats.DirichletMLEOptimizableTest.java

/**
 * Computes a Newton-Raphson update in-place to alpha.
 */// w  w  w.  j a v a 2s  .c  om
private static RealVector newtonRaphsonUpdate(final double[][] data, double[] alpha) {
    // We'll compute the gold-standard value the "long" way (taking the inverse of the Hessian)
    RealMatrix hessian = new Array2DRowRealMatrix(alpha.length, alpha.length);
    for (int r = 0; r < hessian.getRowDimension(); r++) {
        for (int c = 0; c < hessian.getColumnDimension(); c++) {
            hessian.addToEntry(r, c, data.length * Gamma.trigamma(DoubleArrays.sum(alpha)));
            if (r == c) {
                hessian.addToEntry(r, c, -data.length * Gamma.trigamma(alpha[r]));
            }
        }
    }
    RealVector derivative = new ArrayRealVector(alpha.length);
    for (int k = 0; k < alpha.length; k++) {
        derivative.setEntry(k,
                data.length * (Gamma.digamma(DoubleArrays.sum(alpha)) - Gamma.digamma(alpha[k])));
        for (double[] theta : data) {
            derivative.addToEntry(k, theta[k]);
        }
    }

    RealMatrix hessianInverse = new LUDecomposition(hessian).getSolver().getInverse();
    RealVector negDiff = hessianInverse.preMultiply(derivative);
    negDiff.mapMultiplyToSelf(-1.0);

    RealVector expected = new ArrayRealVector(alpha, true);
    return expected.add(negDiff);
}

From source file:edu.byu.nlp.stats.GammaDistribution.java

/**
 * Samples a new Gamma distributed random variate for each parameter setting specified as elements
 * of the shapes matrix.//from  ww w.  ja  v  a  2 s  .c  o m
 */
public static double[][] sample(RealMatrix shapes, RandomGenerator rnd) {
    double[][] gammas = new double[shapes.getRowDimension()][shapes.getColumnDimension()];
    for (int i = 0; i < shapes.getRowDimension(); i++) {
        for (int j = 0; j < shapes.getColumnDimension(); j++) {
            gammas[i][j] = sample(shapes.getEntry(i, j), rnd);
        }
    }
    return gammas;
}

From source file:lsafunctions.LSA.java

public static double cosinSim(int v1, int v2, RealMatrix Vt) {
    double sim = 0.0;
    double sumNum = 0.0;
    double fdenom = 0.0;
    double sdenom = 0.0;

    for (int j = 0; j < Vt.getRowDimension(); j++) {
        sumNum += Vt.getEntry(j, v1) * Vt.getEntry(j, v2);
        fdenom += Math.pow(Vt.getEntry(j, v1), 2);
        sdenom += Math.pow(Vt.getEntry(j, v2), 2);
    }/*from w w  w  . jav  a2 s .  c  om*/
    sim = sumNum / (Math.sqrt(fdenom) * Math.sqrt(sdenom));

    return sim;
}

From source file:edu.washington.gs.skyline.model.quantification.DesignMatrix.java

private static RealMatrix matrixFromColumnVectors(double[][] columnVectors) {
    RealMatrix realMatrix = MatrixUtils.createRealMatrix(columnVectors[0].length, columnVectors.length);
    for (int iRow = 0; iRow < realMatrix.getRowDimension(); iRow++) {
        for (int iCol = 0; iCol < realMatrix.getColumnDimension(); iCol++) {
            realMatrix.setEntry(iRow, iCol, columnVectors[iCol][iRow]);
        }//from  ww w . j  ava 2 s.c  o m
    }
    return realMatrix;
}

From source file:cooccurrence.Omer_Levy.java

/**
 * Generic Method to take squareRoot of individual Values.
 * @param coVariance/*from   w w w  .ja va  2  s . co  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;
}

From source file:game.utils.Utils.java

public static String matrixToString(RealMatrix matrix) {
    StringBuilder ret = new StringBuilder();

    for (int i = 0; i < matrix.getRowDimension(); i++) {
        for (int j = 0; j < matrix.getColumnDimension(); j++) {
            ret.append(String.format("%.5f", matrix.getEntry(i, j)));
            if (j == matrix.getColumnDimension() - 1)
                ret.append('\n');
            else/*from   w w w. j  av  a  2s.c  o  m*/
                ret.append(", ");
        }
    }

    return ret.toString();
}

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  v  a  2  s  .  co  m
        System.out.println();
    }
}

From source file:net.sf.dsp4j.octave_3_2_4.m.polynomial.Roots.java

public static Complex[] roots(RealVector v) {

    if (v.isInfinite() || v.isNaN()) {
        throw new RuntimeException("roots: inputs must not contain Inf or NaN");
    }//  ww  w .j  a v  a  2s  .c  om

    int n = v.getDimension();

    // ## If v = [ 0 ... 0 v(k+1) ... v(k+l) 0 ... 0 ], we can remove the
    // ## leading k zeros and n - k - l roots of the polynomial are zero.

    int[] f = new int[v.getDimension()];
    if (v.getDimension() > 0) {
        int fI = 0;
        double max = v.getMaxValue();
        double min = FastMath.abs(v.getMinValue());
        if (min > max) {
            max = min;
        }
        RealVector v1 = v.mapDivide(max);
        f = OctaveBuildIn.find(v1);
    }

    Complex[] r = new Complex[0];
    if (f.length > 0 && n > 1) {
        v = v.getSubVector(f[0], f[f.length - 1] - f[0] + 1);
        if (v.getDimension() > 1) {
            double[] ones = new double[v.getDimension() - 2];
            Arrays.fill(ones, 1);
            RealMatrix A = OctaveBuildIn.diag(ones, -1);
            for (int i = 0; i < A.getRowDimension(); i++) {
                A.setEntry(i, 0, -v.getEntry(i + 1) / v.getEntry(0));
            }
            try {
                r = Eig.eig(A);
            } catch (Exception e) {
                throw new RuntimeException(e);
            }
            if (f[f.length - 1] < n) {
                int diffLength = n - 1 - f[f.length - 1];
                if (diffLength > 0) {
                    int rl = r.length;
                    r = Arrays.copyOf(r, r.length + diffLength);
                    Arrays.fill(r, rl, r.length, Complex.ZERO);
                }
            }
        } else {
            r = new Complex[n - f[f.length - 1]];
            Arrays.fill(r, Complex.ZERO);
        }
    } else {
        r = new Complex[0];
    }
    return r;

}

From source file:ch.zhaw.iamp.rct.weights.Weights.java

private static RealMatrix addNoise(final RealMatrix A) {
    RealMatrix buffer = A.copy();//from   ww  w . j  av  a  2 s  .c o m
    RealMatrix noise = MatrixUtils.createRealMatrix(A.getRowDimension(), A.getColumnDimension());

    for (int i = 0; i < A.getRowDimension(); ++i) {
        for (int j = 0; j < A.getColumnDimension(); ++j) {
            double noiseSign = Math.random() > 0.5 ? 1 : -1;
            double noiseAmplitude = 1;
            noise.setEntry(i, j, noiseSign * Math.random() * noiseAmplitude);
        }
    }

    buffer = buffer.add(noise);
    return buffer;
}