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

Introduction

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

Prototype

public abstract double getEntry(int index) throws OutOfRangeException;

Source Link

Document

Return the entry at the specified index.

Usage

From source file:gamlss.utilities.MatrixFunctions.java

/**
 * Write vector values to CSV file.//from  w w w  .  j  av a 2  s.c  o m
 * @param cmd - path to the file
 * @param v - vector to write
 */
public static void vectorWriteCSV(final String cmd, final RealVector v, boolean append) {
    try {
        // Create file 
        FileWriter fstream = new FileWriter(cmd, append);
        BufferedWriter out = new BufferedWriter(fstream);

        for (int j = 0; j < v.getDimension(); j++) {
            out.write(Double.toString(v.getEntry(j)));
            out.newLine();
        }
        out.close();
    } catch (Exception e) { //Catch exception if any
        System.err.println("Error: " + e.getMessage());
    }
}

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From source file:com.github.thorbenlindhauer.factor.CanonicalGaussianFactor.java

/**
 * mapping must have the size of the new vector; maps a vector to a new size by applying the mapping of the positions
 * and fills the remaining places with 0 values
 *///w w w  .jav a  2s . co m
protected static RealVector padVector(final RealVector vector, int newSize, final int[] mapping) {
    final RealVector newVector = new ArrayRealVector(newSize);

    newVector.walkInOptimizedOrder(new RealVectorChangingVisitor() {

        @Override
        public double visit(int index, double value) {
            if (mapping[index] >= 0) {
                return vector.getEntry(mapping[index]);
            } else {
                return 0;
            }
        }

        @Override
        public void start(int dimension, int start, int end) {
        }

        @Override
        public double end() {
            return 0;
        }
    });
    return newVector;
}

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From source file:edu.tum.cs.vis.model.util.algorithm.ACCUM.java

/**
 * Calculate curvature for vertices of triangle
 * // w  ww  .  j  a v  a 2s  .co m
 * @param curvatures
 *            vertex curvature mapping
 * @param tri
 *            triangle to calculate curvature for
 */
static void calculateCurvatureForTriangle(HashMap<Vertex, Curvature> curvatures, Triangle tri) {
    // Edges
    Vector3f e[] = tri.getEdges();

    // N-T-B coordinate system per face
    Vector3f t = new Vector3f(e[0]);
    t.normalize();
    Vector3f n = new Vector3f();
    n.cross(e[0], e[1]);
    Vector3f b = new Vector3f();
    b.cross(n, t);
    b.normalize();

    // Estimate curvature based on variation of normals
    // along edges
    float m[] = { 0, 0, 0 };
    double w[][] = { { 0, 0, 0 }, { 0, 0, 0 }, { 0, 0, 0 } };
    for (int j = 0; j < 3; j++) {

        float u = e[j].dot(t);
        float v = e[j].dot(b);
        w[0][0] += u * u;
        w[0][1] += u * v;
        // w[1][1] += v*v + u*u;
        // w[1][2] += u*v;
        w[2][2] += v * v;
        Vector3f dn = new Vector3f(tri.getPosition()[(j + 2) % 3].getNormalVector());
        dn.sub(tri.getPosition()[(j + 1) % 3].getNormalVector());
        float dnu = dn.dot(t);
        float dnv = dn.dot(b);
        m[0] += dnu * u;
        m[1] += dnu * v + dnv * u;
        m[2] += dnv * v;
    }
    w[1][1] = w[0][0] + w[2][2];
    w[1][2] = w[0][1];

    RealMatrix coefficients = new Array2DRowRealMatrix(w, false);
    DecompositionSolver solver = new LUDecomposition(coefficients).getSolver();
    if (!solver.isNonSingular()) {
        return;
    }

    RealVector constants = new ArrayRealVector(new double[] { m[0], m[1], m[2] }, false);
    RealVector solution = solver.solve(constants);

    m[0] = (float) solution.getEntry(0);
    m[1] = (float) solution.getEntry(1);
    m[2] = (float) solution.getEntry(2);

    // Push it back out to the vertices
    for (int j = 0; j < 3; j++) {
        Vertex vj = tri.getPosition()[j];

        float c1, c12, c2;
        float ret[] = proj_curv(t, b, m[0], m[1], m[2], curvatures.get(vj).getPrincipleDirectionMax(),
                curvatures.get(vj).getPrincipleDirectionMin());
        c1 = ret[0];
        c12 = ret[1];
        c2 = ret[2];

        Curvature c = curvatures.get(vj);

        double wt;
        if (j == 0)
            wt = tri.getCornerarea().x / vj.getPointarea();
        else if (j == 1)
            wt = tri.getCornerarea().y / vj.getPointarea();
        else
            wt = tri.getCornerarea().z / vj.getPointarea();

        synchronized (c) {
            c.setCurvatureMax((float) (c.getCurvatureMax() + wt * c1));
            c.setCurvatureMinMax((float) (c.getCurvatureMinMax() + wt * c12));
            c.setCurvatureMin((float) (c.getCurvatureMin() + wt * c2));
        }
    }
}

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From source file:lirmm.inria.fr.math.TestUtils.java

/**
 * Asserts that all entries of the specified vectors are equal to within a
 * positive {@code delta}.//w  w w .j  a  v  a 2s .c  o  m
 *
 * @param message the identifying message for the assertion error (can be
 * {@code null})
 * @param expected expected value
 * @param actual actual value
 * @param delta the maximum difference between the entries of the expected
 * and actual vectors for which both entries are still considered equal
 */
public static void assertEquals(final String message, final double[] expected, final RealVector actual,
        final double delta) {
    final String msgAndSep = message.equals("") ? "" : message + ", ";
    Assert.assertEquals(msgAndSep + "dimension", expected.length, actual.getDimension());
    for (int i = 0; i < expected.length; i++) {
        Assert.assertEquals(msgAndSep + "entry #" + i, expected[i], actual.getEntry(i), delta);
    }
}

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From source file:lirmm.inria.fr.math.TestUtils.java

/**
 * Asserts that all entries of the specified vectors are equal to within a
 * positive {@code delta}./*  w  ww  . j  a  v a2 s .  c  o  m*/
 *
 * @param message the identifying message for the assertion error (can be
 * {@code null})
 * @param expected expected value
 * @param actual actual value
 * @param delta the maximum difference between the entries of the expected
 * and actual vectors for which both entries are still considered equal
 */
public static void assertEquals(final String message, final RealVector expected, final RealVector actual,
        final double delta) {
    final String msgAndSep = message.equals("") ? "" : message + ", ";
    Assert.assertEquals(msgAndSep + "dimension", expected.getDimension(), actual.getDimension());
    final int dim = expected.getDimension();
    for (int i = 0; i < dim; i++) {
        Assert.assertEquals(msgAndSep + "entry #" + i, expected.getEntry(i), actual.getEntry(i), delta);
    }
}

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From source file:hivemall.utils.math.MatrixUtils.java

/**
 * Find the first singular vector/value of a matrix A based on the Power method.
 *
 * @see http//from   w  w  w.  j  av  a 2s  . co  m
 *      ://www.cs.yale.edu/homes/el327/datamining2013aFiles/07_singular_value_decomposition.pdf
 * @param A target matrix
 * @param x0 initial vector
 * @param nIter number of iterations for the Power method
 * @param u 1st left singular vector
 * @param v 1st right singular vector
 * @return 1st singular value
 */
public static double power1(@Nonnull final RealMatrix A, @Nonnull final double[] x0, final int nIter,
        @Nonnull final double[] u, @Nonnull final double[] v) {
    Preconditions.checkArgument(A.getColumnDimension() == x0.length,
            "Column size of A and length of x should be same");
    Preconditions.checkArgument(A.getRowDimension() == u.length,
            "Row size of A and length of u should be same");
    Preconditions.checkArgument(x0.length == v.length, "Length of x and u should be same");
    Preconditions.checkArgument(nIter >= 1, "Invalid number of iterations: " + nIter);

    RealMatrix AtA = A.transpose().multiply(A);

    RealVector x = new ArrayRealVector(x0);
    for (int i = 0; i < nIter; i++) {
        x = AtA.operate(x);
    }

    double xNorm = x.getNorm();
    for (int i = 0, n = v.length; i < n; i++) {
        v[i] = x.getEntry(i) / xNorm;
    }

    RealVector Av = new ArrayRealVector(A.operate(v));
    double s = Av.getNorm();

    for (int i = 0, n = u.length; i < n; i++) {
        u[i] = Av.getEntry(i) / s;
    }

    return s;
}

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From source file:SGFilterModified.java

/**
 * Computes Savitzky-Golay coefficients for given parameters
 * /* w  w w .  ja  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");
    //create a matrix 
    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;
    // make a vector 
    RealVector bVec = new ArrayRealVector(b);
    // get LUDecompostion solver 
    DecompositionSolver solver = new LUDecomposition(matrix).getSolver();
    // Finally solve matrices
    bVec = solver.solve(bVec);
    double[] coeffs = new double[nl + nr + 1];
    for (int n = -nl; n <= nr; n++) {
        sum = bVec.getEntry(0);
        for (int m = 1; m <= degree; m++)
            sum += bVec.getEntry(m) * pow(n, m);
        coeffs[n + nl] = sum;
    }
    return coeffs;
}

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From source file:ImageProc.java

public static float[][] colormapJet(int nClass) {
    int n;//from  ww w.  j a v a  2 s. c  o m
    n = (int) Math.ceil((float) nClass / 4);
    ArrayRealVector u, r, g, b;
    RealVector R, G, B;

    u = new ArrayRealVector(3 * n - 1);
    for (int i = 0; i < n; i++) {
        u.setEntry(i, (i + 1.0) / n);
        u.setEntry(u.getDimension() - i - 1, (i + 1.0) / n);
    }
    u.setSubVector(n, new ArrayRealVector(n, 1));

    g = new ArrayRealVector(u.getDimension());

    float m;
    m = (float) Math.ceil((float) n / 2);
    if (nClass % 4 == 1)
        m = m - 1;

    for (int i = 0; i < g.getDimension(); i++) {
        g.setEntry(i, (i + 1 + m));
    }

    r = g.add(new ArrayRealVector(g.getDimension(), n));
    b = g.subtract(new ArrayRealVector(g.getDimension(), n));

    R = new ArrayRealVector();
    G = new ArrayRealVector();
    B = new ArrayRealVector();

    for (int i = 0; i < r.getDimension(); i++) {
        if (r.getEntry(i) <= nClass)
            R = R.append(r.getEntry(i));
    }

    for (int i = 0; i < g.getDimension(); i++) {
        if (g.getEntry(i) <= nClass)
            G = G.append(g.getEntry(i));
    }

    for (int i = 0; i < b.getDimension(); i++) {
        if (b.getEntry(i) >= 1)
            B = B.append(b.getEntry(i));
    }

    float[][] J = new float[nClass][3];
    int index;
    for (int i = 0; i < R.getDimension(); i++) {
        index = (int) R.getEntry(i);
        J[index - 1][0] = (float) u.getEntry(i);
    }

    for (int i = 0; i < G.getDimension(); i++) {
        index = (int) G.getEntry(i);
        J[index - 1][1] = (float) u.getEntry(i);
    }

    for (int i = u.getDimension() - B.getDimension(), j = 0; i < u.getDimension(); i++, j++) {
        index = (int) B.getEntry(j);
        J[index - 1][2] = (float) u.getEntry(i);
    }

    /*
    System.out.println("\nRGB Matrix:");
    for(int i=0;i<nClass;i++){
    for(int j=0;j<3;j++){
        System.out.print(J[i][j]+"\t");
    }
    System.out.println();
    }
    */
    return J;
}

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

public void update(RealVector v) {
    for (int i = 0; i < v.getDimension(); i++) {
        double vi = v.getEntry(i);
        for (int j = i; j < v.getDimension(); j++) {
            Index idx = new Index(i, j);
            CoMoment cm = cache.get(idx);
            if (cm == null) {
                cm = new CoMoment();
                cache.put(idx, cm);/*from  www.j av a 2 s.co  m*/
            }
            cm.update(vi, v.getEntry(j));
        }
    }
}

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From source file:edu.ucsf.valelab.saim.calculations.SaimParameterValidator.java

/**
 * Very simple validator that uses lower and upper bounds to restrict
 * parameter values/*from   ww w . j a v a 2s .  c o m*/
 * @param params
 * @return 
 */
@Override
public RealVector validate(RealVector params) {
    if (params.getDimension() != 3)
        throw new DimensionMismatchException(params.getDimension(), 3);
    RealVector n = params.copy();
    for (int i = 0; i < 3; i++) {
        if (n.getEntry(i) < lowerBounds_[i])
            n.setEntry(i, lowerBounds_[i]);
        if (n.getEntry(i) > upperBounds_[i])
            n.setEntry(i, upperBounds_[i]);
    }
    return n;
}

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