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:org.lenskit.predict.ordrec.OrdRecRatingPredictor.java

/**
 * The train function of OrdRec. Get all parameters after learning process.
 *//*www . ja v  a2 s .c o m*/
@SuppressWarnings("ConstantConditions")
private void trainModel(OrdRecModel model, SparseVector ratings, MutableSparseVector scores) {
    RealVector beta = model.getBeta();
    RealVector deltaBeta = new ArrayRealVector(beta.getDimension());
    double dt1;
    // n is the number of iteration;
    for (int j = 0; j < iterationCount; j++) {
        for (VectorEntry rating : ratings) {
            long iid = rating.getKey();
            double score = scores.get(iid);
            int r = quantizer.index(rating.getValue());

            double probEqualR = model.getProbEQ(score, r);
            double probLessR = model.getProbLE(score, r);
            double probLessR_1 = model.getProbLE(score, r - 1);

            double t1 = model.getT1();
            dt1 = learningRate / probEqualR * (probLessR * (1 - probLessR) * dBeta(r, 0, t1)
                    - probLessR_1 * (1 - probLessR_1) * dBeta(r - 1, 0, t1) - regTerm * t1);

            double dbetaK;
            for (int k = 0; k < beta.getDimension(); k++) {
                dbetaK = learningRate / probEqualR
                        * (probLessR * (1 - probLessR) * dBeta(r, k + 1, beta.getEntry(k))
                                - probLessR_1 * (1 - probLessR_1) * dBeta(r - 1, k + 1, beta.getEntry(k))
                                - regTerm * beta.getEntry(k));
                deltaBeta.setEntry(k, dbetaK);
            }
            model.update(dt1, deltaBeta);
        }
    }
}

---

From source file:org.lenskit.transform.quantize.PreferenceDomainQuantizerTest.java

@Test
public void testMakeValues() {
    RealVector vals = PreferenceDomainQuantizer.makeValues(domain);
    RealVector evals = new ArrayRealVector(new double[] { 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0 });
    assertThat(vals.getDimension(), equalTo(evals.getDimension()));
    for (int i = 0; i < vals.getDimension(); i++) {
        assertThat("element " + i, vals.getEntry(i), closeTo(evals.getEntry(i), 1.0e-6));
    }//from  w ww  .  ja v  a2s .c o m
}

---

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

/**
 * Solve a linear matrix equation, or system of linear scalar equations.
 * @param a Coefficient matrix.//from www .  j  a  va2  s.c  om
 * @param b Ordinate or dependent variable? values.
 * @return Solution to the system a x = b. Returned shape is identical to b.
 */
public static Array solve(Array a, Array b) {
    Array r = Array.factory(DataType.DOUBLE, b.getShape());
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix coefficients = new Array2DRowRealMatrix(aa, false);
    DecompositionSolver solver = new LUDecomposition(coefficients).getSolver();
    double[] bb = (double[]) ArrayUtil.copyToNDJavaArray(b);
    RealVector constants = new ArrayRealVector(bb, false);
    RealVector solution = solver.solve(constants);
    for (int i = 0; i < r.getSize(); i++) {
        r.setDouble(i, solution.getEntry(i));
    }

    return r;
}

---

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

/**
 * Calculates the eigen decomposition of a real matrix.
 * The eigen decomposition of matrix A is a set of two matrices: V and D such that 
 * A = V  D  VT. A, V and D are all m  m matrices.
 * @param a Given matrix.//from  w  w w.j av  a  2 s .c om
 * @return Result W/V arrays.
 */
public static Array[] eigen(Array a) {
    int m = a.getShape()[0];
    Array Wa;
    Array Va = Array.factory(DataType.DOUBLE, new int[] { m, m });
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    EigenDecomposition decomposition = new EigenDecomposition(matrix);
    double[] rev = decomposition.getRealEigenvalues();
    double[] iev = decomposition.getImagEigenvalues();
    if (decomposition.hasComplexEigenvalues()) {
        Wa = Array.factory(DataType.OBJECT, new int[] { m });
        for (int i = 0; i < m; i++) {
            Wa.setObject(i, new Complex(rev[i], iev[i]));
            RealVector v = decomposition.getEigenvector(i);
            for (int j = 0; j < v.getDimension(); j++) {
                Va.setDouble(i * m + j, v.getEntry(j));
            }
        }
    } else {
        Wa = Array.factory(DataType.DOUBLE, new int[] { m });
        for (int i = 0; i < m; i++) {
            Wa.setDouble(i, rev[i]);
            RealVector v = decomposition.getEigenvector(i);
            for (int j = 0; j < v.getDimension(); j++) {
                Va.setDouble(i * m + j, v.getEntry(j));
            }
        }
    }

    return new Array[] { Wa, Va };
}

---

From source file:org.opentestsystem.airose.regression.orderedprobit.LinearEquationSolver.java

public static double[][] SolveLinear(double[][] XpX, double[][] XpY) {

    RealMatrix coefficients = new Array2DRowRealMatrix(XpX, false);
    LUDecomposition luDecomposition = new LUDecomposition(coefficients);

    DecompositionSolver solver = luDecomposition.getSolver();

    double[] yRowVector = new double[XpY.length];
    for (int counter1 = 0; counter1 < XpY.length; ++counter1) {
        yRowVector[counter1] = XpY[counter1][0];
    }//  w  w  w  .  j ava 2 s.co  m

    RealVector constants = new ArrayRealVector(yRowVector, false);
    RealVector solution = solver.solve(constants);

    double[][] result = new double[solution.getDimension()][1];
    for (int counter1 = 0; counter1 < solution.getDimension(); ++counter1) {
        result[counter1][0] = solution.getEntry(counter1);
    }
    return result;
}

---

From source file:org.orekit.utils.AngularCoordinates.java

/** Find a vector from two known cross products.
 * <p>// w  w w  .j a  v  a  2  s  . co m
 * We want to find  such that:   v? = c? and   v = c
 * </p>
 * <p>
 * The first equation (  v? = c?) will always be fulfilled exactly,
 * and the second one will be fulfilled if possible.
 * </p>
 * @param v1 vector forming the first known cross product
 * @param c1 know vector for cross product   v?
 * @param v2 vector forming the second known cross product
 * @param c2 know vector for cross product   v
 * @param tolerance relative tolerance factor used to check singularities
 * @return vector  such that:   v? = c? and   v = c
 * @exception MathIllegalArgumentException if vectors are inconsistent and
 * no solution can be found
 */
private static Vector3D inverseCrossProducts(final Vector3D v1, final Vector3D c1, final Vector3D v2,
        final Vector3D c2, final double tolerance) throws MathIllegalArgumentException {

    final double v12 = v1.getNormSq();
    final double v1n = FastMath.sqrt(v12);
    final double v22 = v2.getNormSq();
    final double v2n = FastMath.sqrt(v22);
    final double threshold = tolerance * FastMath.max(v1n, v2n);

    Vector3D omega;

    try {
        // create the over-determined linear system representing the two cross products
        final RealMatrix m = MatrixUtils.createRealMatrix(6, 3);
        m.setEntry(0, 1, v1.getZ());
        m.setEntry(0, 2, -v1.getY());
        m.setEntry(1, 0, -v1.getZ());
        m.setEntry(1, 2, v1.getX());
        m.setEntry(2, 0, v1.getY());
        m.setEntry(2, 1, -v1.getX());
        m.setEntry(3, 1, v2.getZ());
        m.setEntry(3, 2, -v2.getY());
        m.setEntry(4, 0, -v2.getZ());
        m.setEntry(4, 2, v2.getX());
        m.setEntry(5, 0, v2.getY());
        m.setEntry(5, 1, -v2.getX());

        final RealVector rhs = MatrixUtils.createRealVector(
                new double[] { c1.getX(), c1.getY(), c1.getZ(), c2.getX(), c2.getY(), c2.getZ() });

        // find the best solution we can
        final DecompositionSolver solver = new QRDecomposition(m, threshold).getSolver();
        final RealVector v = solver.solve(rhs);
        omega = new Vector3D(v.getEntry(0), v.getEntry(1), v.getEntry(2));

    } catch (SingularMatrixException sme) {

        // handle some special cases for which we can compute a solution
        final double c12 = c1.getNormSq();
        final double c1n = FastMath.sqrt(c12);
        final double c22 = c2.getNormSq();
        final double c2n = FastMath.sqrt(c22);

        if (c1n <= threshold && c2n <= threshold) {
            // simple special case, velocities are cancelled
            return Vector3D.ZERO;
        } else if (v1n <= threshold && c1n >= threshold) {
            // this is inconsistent, if v? is zero, c? must be 0 too
            throw new NumberIsTooLargeException(c1n, 0, true);
        } else if (v2n <= threshold && c2n >= threshold) {
            // this is inconsistent, if v is zero, c must be 0 too
            throw new NumberIsTooLargeException(c2n, 0, true);
        } else if (Vector3D.crossProduct(v1, v2).getNorm() <= threshold && v12 > threshold) {
            // simple special case, v is redundant with v?, we just ignore it
            // use the simplest : orthogonal to both v? and c?
            omega = new Vector3D(1.0 / v12, Vector3D.crossProduct(v1, c1));
        } else {
            throw sme;
        }

    }

    // check results
    final double d1 = Vector3D.distance(Vector3D.crossProduct(omega, v1), c1);
    if (d1 > threshold) {
        throw new NumberIsTooLargeException(d1, 0, true);
    }

    final double d2 = Vector3D.distance(Vector3D.crossProduct(omega, v2), c2);
    if (d2 > threshold) {
        throw new NumberIsTooLargeException(d2, 0, true);
    }

    return omega;

}

---

From source file:org.rhwlab.BHC.LogNode.java

public String vectorAsString(RealVector v) {
    boolean first = true;
    StringBuilder builder = new StringBuilder();
    builder.append("(");
    for (int i = 0; i < v.getDimension(); ++i) {

        if (!first) {
            builder.append(",");
        }/*from   w  ww  .j ava2  s . c o  m*/
        builder.append(String.format("%.2f", v.getEntry(i)));
        first = false;
    }
    builder.append(")");
    return builder.toString();
}

---

From source file:org.rhwlab.BHC.NodeBase.java

public Element formElementXML() {
    List<MicroCluster> micros = new ArrayList<>();
    this.getDataAsMicroCluster(micros);
    int voxels = 0;
    long intensity = 0;

    for (MicroCluster micro : micros) {
        voxels = voxels + micro.getPointCount();
        intensity = intensity + micro.getTotalIntensity();
    }/* ww  w.  ja  v  a 2 s. c  om*/
    RealVector mu = MicroCluster.mean(micros);
    RealMatrix W = MicroCluster.precision(micros, mu);
    if (W != null) {
        Element clusterEle = new Element("GaussianMixtureModel");
        //clusterEle.setAttribute("id", String.format("%d", id));
        //           clusterEle.setAttribute("parent", "-1");
        clusterEle.setAttribute("count", String.format("%d", micros.size()));
        clusterEle.setAttribute("voxels", String.format("%d", voxels));
        clusterEle.setAttribute("intensity", String.format("%d", intensity));

        clusterEle.setAttribute("intensityRSD", Double.toString(getIntensityRSD()));
        clusterEle.setAttribute("sourceNode", String.format("%d", label));

        clusterEle.setAttribute("posterior", Double.toString(lnR));

        clusterEle.setAttribute("x", Double.toString(mu.getEntry(0)));
        clusterEle.setAttribute("y", Double.toString(mu.getEntry(1)));
        clusterEle.setAttribute("z", Double.toString(mu.getEntry(2)));

        StringBuilder builder = new StringBuilder();
        for (int row = 0; row < W.getRowDimension(); ++row) {
            for (int col = 0; col < W.getColumnDimension(); ++col) {
                if (row > 0 || col > 0) {
                    builder.append(" ");
                }
                builder.append(W.getEntry(row, col));
            }
        }
        clusterEle.setAttribute("precision", builder.toString());
        return clusterEle;
    } else {
        return null;
    }
}

---

From source file:org.rhwlab.BHCnotused.Algorithm.java

public void init(int seg) {
    clusters = new ArrayList<>();
    for (int n = 0; n < source.getClusterCount(); ++n) {
        RealVector v = source.getCenter(n);
        Dfp[] z = new Dfp[v.getDimension()];
        for (int i = 0; i < z.length; ++i) {
            z[i] = field.newDfp(v.getEntry(i));
        }//from   w  ww  . j av a 2s  .  co  m
        LabeledFieldVector fv = new LabeledFieldVector(z, n);
        Cluster cluster = new Cluster(new GaussianGIWPrior(fv));
        clusters.add(cluster);
    }
    pairs = new HashMap<>();
    for (int i = 0; i < clusters.size() - 1; ++i) {
        Cluster clusterI = clusters.get(i);
        HashMap<Cluster, Cluster> map = new HashMap<>();
        for (int j = i + 1; j < clusters.size(); ++j) {
            Cluster clusterJ = clusters.get(j);
            Cluster T = new Cluster(clusterI, clusterJ);
            map.put(clusterJ, T);
        }
        pairs.put(clusterI, map);
    }
}

---

From source file:org.rhwlab.BHCnotused.GaussianGIWPrior.java

static public void main(String[] args) throws Exception {
    System.out.println("GaussianGIWPrior");
    field = new DfpField(20); // 20 decimal digits
    Cluster.setDfpField(field);/*from   w w  w  .  j a va 2  s .  c o m*/
    ThreadedAlgorithm.setDfpField(field);
    GaussianGIWPrior.setDfpField(field);

    //        SegmentedTiffDataSource source = new SegmentedTiffDataSource("/nfs/waterston/pete/Segmentation/Cherryimg75.tif",
    //                "/nfs/waterston/pete/Segmentation/Cherryimg75_SimpleSegmentation.tiff",1);  // background is seg=1
    //       source = new MicroClusterDataSource("/nfs/waterston/pete/Segmentation/Cherryimg_SimpleSegmentationMulti0350.xml");
    source = new MicroClusterDataSource(
            "/nfs/waterston/pete/Segmentation/Cherryimg_SimpleSegmentation0075.save");
    RealVector mean = source.getDataMean();
    Dfp[] mu = new Dfp[mean.getDimension()];
    for (int i = 0; i < mu.length; ++i) {
        mu[i] = field.newDfp(mean.getEntry(i));
    }
    GaussianGIWPrior.setParameters(4.0, 0.001, mu, 200.0);
    Cluster.setAlpha(30);
    ThreadedAlgorithm alg = new ThreadedAlgorithm();
    alg.setSource(source);
    alg.init(2);
    alg.run();
    alg.saveResultAsXML("/nfs/waterston/pete/Segmentation/Cherryimg_SimpleSegmentation0075_BHC.xml");
    int hfuis = 0;
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• »