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

Introduction

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

Prototype

public abstract int getDimension();

Source Link

Document

Returns the size of the vector.

Usage

From source file:org.rhwlab.dispim.datasource.MicroCluster.java

License:asdf

public static RealMatrix precision(List<MicroCluster> data, RealVector mu) {
    RealMatrix ret = new Array2DRowRealMatrix(mu.getDimension(), mu.getDimension());
    RealVector v = new ArrayRealVector(mu.getDimension());
    long n = 0;//ww  w  . j  a  v a2  s  .  c o  m
    for (MicroCluster micro : data) {
        for (int p = 0; p < micro.points.length; ++p) {
            for (int d = 0; d < mu.getDimension(); ++d) {
                v.setEntry(d, micro.points[p][d]);
            }
            RealVector del = v.subtract(mu);
            ret = ret.add(del.outerProduct(del));
            ++n;
        }

    }
    ret = ret.scalarMultiply(1.0 / n);
    LUDecomposition lud = new LUDecomposition(ret);
    RealMatrix prec = null;
    if (lud.getSolver().isNonSingular()) {
        prec = lud.getSolver().getInverse();
    }
    return prec;
}

---

From source file:playground.sergioo.facilitiesGenerator2012.WorkFacilitiesGeneration.java

private static Set<PointPerson> getPCATransformation(Collection<PointPerson> points) {
    RealMatrix pointsM = new Array2DRowRealMatrix(points.iterator().next().getDimension(), points.size());
    int k = 0;//from   w w w . jav a  2s . c  om
    for (PointND<Double> point : points) {
        for (int f = 0; f < point.getDimension(); f++)
            pointsM.setEntry(f, k, point.getElement(f));
        k++;
    }
    RealMatrix means = new Array2DRowRealMatrix(pointsM.getRowDimension(), 1);
    for (int r = 0; r < means.getRowDimension(); r++) {
        double mean = 0;
        for (int c = 0; c < pointsM.getColumnDimension(); c++)
            mean += pointsM.getEntry(r, c) / pointsM.getColumnDimension();
        means.setEntry(r, 0, mean);
    }
    RealMatrix deviations = new Array2DRowRealMatrix(pointsM.getRowDimension(), pointsM.getColumnDimension());
    for (int r = 0; r < deviations.getRowDimension(); r++)
        for (int c = 0; c < deviations.getColumnDimension(); c++)
            deviations.setEntry(r, c, pointsM.getEntry(r, c) - means.getEntry(r, 0));
    RealMatrix covariance = deviations.multiply(deviations.transpose())
            .scalarMultiply(1 / (double) pointsM.getColumnDimension());
    EigenDecomposition eigenDecomposition = new EigenDecomposition(covariance, 0);
    RealMatrix eigenVectorsT = eigenDecomposition.getVT();
    RealVector eigenValues = new ArrayRealVector(eigenDecomposition.getD().getRowDimension());
    for (int r = 0; r < eigenDecomposition.getD().getRowDimension(); r++)
        eigenValues.setEntry(r, eigenDecomposition.getD().getEntry(r, r));
    for (int i = 0; i < eigenValues.getDimension(); i++) {
        for (int j = i + 1; j < eigenValues.getDimension(); j++)
            if (eigenValues.getEntry(i) < eigenValues.getEntry(j)) {
                double tempValue = eigenValues.getEntry(i);
                eigenValues.setEntry(i, eigenValues.getEntry(j));
                eigenValues.setEntry(j, tempValue);
                RealVector tempVector = eigenVectorsT.getRowVector(i);
                eigenVectorsT.setRowVector(i, eigenVectorsT.getRowVector(j));
                eigenVectorsT.setRowVector(j, tempVector);
            }
        eigenVectorsT.setRowVector(i,
                eigenVectorsT.getRowVector(i).mapMultiply(Math.sqrt(1 / eigenValues.getEntry(i))));
    }
    RealVector standardDeviations = new ArrayRealVector(pointsM.getRowDimension());
    for (int r = 0; r < covariance.getRowDimension(); r++)
        standardDeviations.setEntry(r, Math.sqrt(covariance.getEntry(r, r)));
    double zValue = standardDeviations.dotProduct(new ArrayRealVector(pointsM.getRowDimension(), 1));
    RealMatrix zScore = deviations.scalarMultiply(1 / zValue);
    pointsM = eigenVectorsT.multiply(zScore);
    Set<PointPerson> pointsC = new HashSet<PointPerson>();
    k = 0;
    for (PointPerson point : points) {
        PointPerson pointC = new PointPerson(point.getId(), point.getOccupation(),
                new Double[] { pointsM.getEntry(0, k), pointsM.getEntry(1, k) }, point.getPlaceType());
        pointC.setWeight(point.getWeight());
        pointsC.add(pointC);
        k++;
    }
    return pointsC;
}

---

From source file:rcdemo.math.ClosedHermiteSpline.java

public static RealMatrix generate(RealVector p) {
    int n = p.getDimension();
    RealMatrix A = MatrixUtils.createRealMatrix(n, n);
    RealMatrix B = MatrixUtils.createRealMatrix(n, n);
    for (int i = 0; i < n; i++) {
        int ip = (i + 1) % n;
        int im = (i - 1 + n) % n;
        A.setEntry(i, im, 2);//from w w  w  .  j  a v  a 2s  .  c  o m
        A.setEntry(i, i, 8);
        A.setEntry(i, ip, 2);
        B.setEntry(i, im, -6);
        B.setEntry(i, ip, 6);
    }
    RealVector Bp = B.operate(p);
    DecompositionSolver solver = new CholeskyDecomposition(A).getSolver();
    RealVector m = solver.solve(Bp);
    RealMatrix a = MatrixUtils.createRealMatrix(4, n);
    for (int i = 0; i < n; i++) {
        int ip = (i + 1) % n;
        a.setEntry(0, i, p.getEntry(i));
        a.setEntry(1, i, m.getEntry(i));
        a.setEntry(2, i, p.getEntry(ip));
        a.setEntry(3, i, m.getEntry(ip));
    }
    return a;
}

---

From source file:rcdemo.physics.CombinedForceModel.java

@Override
public RealVector getForce(RealVector x, RealVector v) {
    ArrayRealVector F = new ArrayRealVector(x.getDimension());
    for (int i = 0; i < models.size(); i++) {
        ForceModel model = models.get(i);
        F.combineToSelf(1.0, factors.get(i), model.getForce(x, v));
    }//from   w  w w  . j a  v  a  2  s .c  o  m
    return F;
}

---

From source file:rcdemo.physics.ZeroForceModel.java

@Override
public RealVector getForce(RealVector x, RealVector v) {
    return new ArrayRealVector(x.getDimension());
}

---

From source file:syncleus.gremlann.model.som.SOM.java

private void updateBounds(final RealVector position) {
    //make sure we have the proper dimentionality
    if (position.getDimension() != getDimension())
        throw new IllegalArgumentException("Dimentionality mismatch");

    for (int dimensionIndex = 0; dimensionIndex < position.getDimension(); dimensionIndex++) {
        final double v = position.getEntry(dimensionIndex);

        if (this.getUpperBounds().getEntry(dimensionIndex) < v)
            this.getUpperBounds().setEntry(dimensionIndex, v);
        if (this.getLowerBounds().getEntry(dimensionIndex) > v)
            this.getLowerBounds().setEntry(dimensionIndex, v);
    }/*from w w  w.  j  ava  2s  .  c  o  m*/
}

---

From source file:syncleus.gremlann.model.som.SOM.java

/**
 * Creates a new point in the output lattice at the given position. This will
 * automatically have all inputs connected to it.
 *
 * @param position The position of the new output in the lattice.
 * @since 2.0/*from w  ww.  j  a v a  2 s . c  o m*/
 */
public Vertex addOutput(final RealVector position) {
    ensureCorrectOutputDimensions(position.getDimension());

    // increase the upper bounds if needed
    this.updateBounds(position);

    // create and add the new output neuron

    Vertex output = newNeuronVertex("output", meta.graph());
    outputs.add(output);

    setPosition(output, position);

    // connect all inputs to the new neuron
    for (final Vertex input : this.getInputs()) {
        weight.set(input, output, getRandomInitialSynapseWeight());
    }

    return output;
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• »