Example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix.

Prototype

public Array2DRowRealMatrix(final double[][] d, final boolean copyArray)
        throws DimensionMismatchException, NoDataException, NullArgumentException 

Source Link

Document

Create a new RealMatrix using the input array as the underlying data array.

Usage

From source file:mase.app.allocation.AllocationProblem.java

@Override
public EvaluationResult[] evaluateSolution(GroupController gc, long seed) {
    AgentController[] acs = gc.getAgentControllers(numAgents);
    RealMatrix distanceMatrix = new Array2DRowRealMatrix(numAgents, types.length);
    for (int i = 0; i < numAgents; i++) {
        AllocationAgent aa = (AllocationAgent) acs[i];
        for (int j = 0; j < types.length; j++) {
            distanceMatrix.setEntry(i, j, DIST.compute(aa.getLocation(), types[j]));
        }//from w w w. ja va 2  s.  c  om
    }

    DescriptiveStatistics pd = pairDistances(distanceMatrix);

    // fitness
    FitnessResult fr = new FitnessResult(1 - pd.getMean() / FastMath.sqrt(dimensions));

    // individual characterisation -- distance to each type
    List<EvaluationResult> vbrs = new ArrayList<>();
    for (double[] dists : distanceMatrix.getData()) {
        vbrs.add(new VectorBehaviourResult(dists));
    }
    CompoundEvaluationResult ser = new CompoundEvaluationResult(vbrs);

    // aux characterisation -- min, mean, max, sd pair distances
    VectorBehaviourResult aux = new VectorBehaviourResult(pd.getMin(), pd.getMean(), pd.getMax(),
            pd.getStandardDeviation());

    return new EvaluationResult[] { fr, aux, ser };
}

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From source file:net.myrrix.common.math.MatrixUtils.java

/**
 * @param M tall, skinny matrix//from  w ww.  j a  v a2 s.  c  o  m
 * @return MT * M as a dense matrix
 */
public static RealMatrix transposeTimesSelf(FastByIDMap<float[]> M) {
    if (M == null || M.isEmpty()) {
        return null;
    }
    RealMatrix result = null;
    for (FastByIDMap.MapEntry<float[]> entry : M.entrySet()) {
        float[] vector = entry.getValue();
        int dimension = vector.length;
        if (result == null) {
            result = new Array2DRowRealMatrix(dimension, dimension);
        }
        for (int row = 0; row < dimension; row++) {
            float rowValue = vector[row];
            for (int col = 0; col < dimension; col++) {
                result.addToEntry(row, col, rowValue * vector[col]);
            }
        }
    }
    Preconditions.checkNotNull(result);
    return result;
}

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From source file:com.clust4j.data.DataSet.java

public void addColumn(String s, double[] col) {
    VecUtils.checkDims(col);/*from w ww  .j  a v a  2 s .  co m*/

    final int m = col.length;
    if (m != data.getRowDimension())
        throw new DimensionMismatchException(m, data.getRowDimension());

    final int n = data.getColumnDimension();
    s = null == s ? (COL_PREFIX + n) : s;

    String[] newHeaders = new String[n + 1];
    double[][] newData = new double[m][n + 1];
    double[][] oldData = data.getDataRef();

    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n + 1; j++) {
            if (i == 0)
                newHeaders[j] = j != n ? headers[j] : s;
            newData[i][j] = j != n ? oldData[i][j] : col[i];
        }
    }

    this.headers = newHeaders;
    this.data = new Array2DRowRealMatrix(newData, false);
}

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From source file:gamlss.utilities.WLSMultipleLinearRegression.java

/**
* {@inheritDoc}//from   ww w.j  a v  a  2 s  .c om
* <p>This implementation computes and caches the QR decomposition of the X matrix
* once it is successfully loaded.</p>
*/
protected void newXSampleData(double[][] x, ArrayRealVector w) {
    if (x == null) {
        throw new NullArgumentException();
    }
    if (x.length == 0) {
        throw new NoDataException();
    }
    if (this.isNoIntercept()) {
        this.xMatrix = new Array2DRowRealMatrix(x, true);
    } else { // Augment design matrix with initial unitary column
        final int nVars = x[0].length;
        final double[][] xAug = new double[x.length][nVars + 1];
        for (int i = 0; i < x.length; i++) {
            if (x[i].length != nVars) {
                throw new DimensionMismatchException(x[i].length, nVars);
            }
            xAug[i][0] = w.getEntry(i);
            System.arraycopy(x[i], 0, xAug[i], 1, nVars);
        }
        this.xMatrix = new Array2DRowRealMatrix(xAug, false);
    }
    this.qr = new QRDecomposition(getX());
}

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

/**
 * Calculate curvature for vertices of triangle
 * //  w w w.  java2s  .  c  o  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:com.clust4j.algo.preprocess.PreProcessorTests.java

@Test
public void testMedianCenter() {
    final double[][] data = new double[][] { new double[] { 0.005, 0.182751, 0.1284 },
            new double[] { 3.65816, 0.29518, 2.123316 }, new double[] { 4.1234, 0.27395, 1.8900002 } };

    final Array2DRowRealMatrix d = new Array2DRowRealMatrix(data, false);
    MedianCenterer norm = new MedianCenterer().fit(d);
    RealMatrix scaled = norm.transform(d);
    double[][] operated = scaled.getData();

    assertTrue(Precision.equals(VecUtils.median(MatUtils.getColumn(operated, 0)), 0, Precision.EPSILON));
    assertTrue(Precision.equals(VecUtils.median(MatUtils.getColumn(operated, 1)), 0, Precision.EPSILON));
    assertTrue(Precision.equals(VecUtils.median(MatUtils.getColumn(operated, 2)), 0, Precision.EPSILON));

    // test copy/*w  ww .j  a  v a2 s  .  c  om*/
    operated = norm.copy().transform(data);
    assertTrue(Precision.equals(VecUtils.median(MatUtils.getColumn(operated, 0)), 0, Precision.EPSILON));
    assertTrue(Precision.equals(VecUtils.median(MatUtils.getColumn(operated, 1)), 0, Precision.EPSILON));
    assertTrue(Precision.equals(VecUtils.median(MatUtils.getColumn(operated, 2)), 0, Precision.EPSILON));

    // Test inverse transform.
    assertTrue(MatUtils.equalsWithTolerance(data, norm.inverseTransform(scaled).getData(), 1e-8));

    // copy coverage
    norm.copy();

    // test dim mismatch
    boolean a = false;
    try {
        norm.inverseTransform(TestSuite.getRandom(2, 2));
    } catch (DimensionMismatchException dim) {
        a = true;
    } finally {
        assertTrue(a);
    }

    // test not fit
    a = false;
    try {
        new MedianCenterer().transform(d);
    } catch (ModelNotFitException dim) {
        a = true;
    } finally {
        assertTrue(a);
    }
}

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

/**
 * Expands the essence matrix into full design matrix for power
 * calculations using the fixed component only
 *
 * Assumes that row meta data indicates the actual number
 * of repetitions of that row in the full design
 *
 * @return design matrix//  w  w w .j av  a  2  s  .com
 * @throws IllegalArgumentException
 */
public RealMatrix getFullDesignMatrixFixed() {
    if (fixedMatrix == null)
        return null;
    // allocate the full design matrix
    // #rows = total of repetitions for each unique row
    // #columns = number of columns in design matrix
    int fullRows = getTotalSampleSize();
    int fullColumns = fixedMatrix.getColumnDimension();
    Array2DRowRealMatrix fullDesignMatrix = new Array2DRowRealMatrix(fullRows, fullColumns);

    // copy in the columns
    // case 1: if predictor in a given column is fixed, the values are copied from
    // the design matrix
    // case 2: if the predictor is random, the values are set to a random value from
    // a normal curve with the mean/variance specified in the column meta data
    int fullDesignColumn = 0;
    for (int col = 0; col < fixedMatrix.getColumnDimension(); col++, fullDesignColumn++) {
        fillFixedColumn(col, fullDesignColumn, fullDesignMatrix);
    }
    return fullDesignMatrix;
}

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From source file:com.itemanalysis.psychometrics.mixture.MvNormalMixtureModel.java

public double mStep() {
    RealMatrix[] M = new Array2DRowRealMatrix[groups];
    RealMatrix[] S = new Array2DRowRealMatrix[groups];
    double[] T1 = new double[groups];
    RealMatrix[] T2 = new Array2DRowRealMatrix[groups];
    RealMatrix[] T3 = new Array2DRowRealMatrix[groups];
    double pp = 0.0;
    RealMatrix rowData = null;//  w w w. j  av a 2s  .c  o  m
    RealMatrix temp = null;

    try {
        //estimate new means, covariances, and mixing proportions
        for (int g = 0; g < groups; g++) {
            T2[g] = new Array2DRowRealMatrix(dimensions, 1);
            T3[g] = new Array2DRowRealMatrix(dimensions, dimensions);
            for (int i = 0; i < sampleSize; i++) {
                pp = posteriorProbability(g, i);
                rowData = data.getRowMatrix(i).transpose();
                T1[g] += pp;
                T2[g] = T2[g].add(rowData.scalarMultiply(pp));
                temp = rowData.scalarMultiply(pp).multiply(rowData.transpose());
                T3[g] = T3[g].add(temp);
            }

        }

        for (int g = 0; g < groups; g++) {
            M[g] = T2[g].scalarMultiply(1.0 / T1[g]);
            temp = T2[g].scalarMultiply(1.0 / T1[g]).multiply(T2[g].transpose());
            S[g] = T3[g].subtract(temp).scalarMultiply(1.0 / T1[g]);
        }
    } catch (SingularMatrixException ex) {
        statusMessage = "Singular Matrix";
    }

    //apply constraints
    if (sameVarianceWithin)
        setCommonVarianceWithinClass(S);
    if (sameCovarianceWithin && !localIndependence)
        setCommonCovarianceWithinClass(S);
    if (localIndependence)
        setLocalIndependence(S);
    if (sameCovarianceBetween)
        setCommonCovariance(S);

    piKp1 = computeMixingProportions();

    //set values of new estimates for eStep (i.e. posterior probability) and computation of loglikelihood
    MvNormalComponentDistribution dist = null;
    for (int g = 0; g < groups; g++) {
        dist = (MvNormalComponentDistribution) compDistribution[g];
        dist.setMixingProportion(piKp1[g]);
        dist.setMean(M[g]);
        dist.setCovariance(S[g]);
    }

    double ll = loglikelihood();
    return ll;
}

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From source file:com.joptimizer.functions.SOCPLogarithmicBarrier.java

private RealMatrix buildJ(SOCPConstraintParameters param, RealVector X) {
    RealMatrix J = new Array2DRowRealMatrix(dim, param.getA().getRowDimension() + 1);
    J.setSubMatrix(param.getA().transpose().getData(), 0, 0);
    J.setColumnVector(param.getA().getRowDimension(), param.getC());
    return J;/*from w ww . j  ava  2 s  . co m*/
}

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From source file:com.clust4j.algo.MeanShiftTests.java

@Test
public void testMeanShiftAutoBwEstimate1() {
    final double[][] x = TestSuite.bigMatrix;
    double bw = MeanShift.autoEstimateBW(new Array2DRowRealMatrix(x, false), 0.3, Distance.EUCLIDEAN,
            new Random(), false);
    new MeanShift(new Array2DRowRealMatrix(x), bw).fit();
}

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