Example usage for org.apache.commons.math3.exception DimensionMismatchException DimensionMismatchException

Introduction

In this page you can find the example usage for org.apache.commons.math3.exception DimensionMismatchException DimensionMismatchException.

Prototype

public DimensionMismatchException(int wrong, int expected) 

Source Link

Document

Construct an exception from the mismatched dimensions.

Usage

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

public void addColumn(String s, double[] col) {
    VecUtils.checkDims(col);//from w ww. ja  v  a2  s .c  o  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);
}

---

From source file:eu.tsp.sal.NPointCrossover.java

/**
 * Helper for {@link #crossover(Chromosome, Chromosome)}. Performs the actual crossover.
 *
 * @param first the first chromosome//  ww w .  ja v a  2  s.  c  o  m
 * @param second the second chromosome
 * @return the pair of new chromosomes that resulted from the crossover
 * @throws DimensionMismatchException if the length of the two chromosomes is different
 * @throws NumberIsTooLargeException if the number of crossoverPoints is too large for the actual chromosomes
 */
private ChromosomePair mate(final AbstractListChromosome<T> first, final AbstractListChromosome<T> second)
        throws DimensionMismatchException, NumberIsTooLargeException {

    final int length = first.getLength();
    if (length != second.getLength()) {
        throw new DimensionMismatchException(second.getLength(), length);
    }
    if (crossoverPoints >= length) {
        throw new NumberIsTooLargeException(crossoverPoints, length, false);
    }

    // array representations of the parents
    final List<T> parent1Rep = first.getRepresentation();
    final List<T> parent2Rep = second.getRepresentation();
    // and of the children
    final List<T> child1Rep = new ArrayList<T>(length);
    final List<T> child2Rep = new ArrayList<T>(length);

    final RandomGenerator random = SensorGeneticAlgorithm.getRandomGenerator();

    List<T> c1 = child1Rep;
    List<T> c2 = child2Rep;

    int remainingPoints = crossoverPoints;
    int lastIndex = 0;
    for (int i = 0; i < crossoverPoints; i++, remainingPoints--) {
        // select the next crossover point at random
        final int crossoverIndex = 1 + lastIndex + random.nextInt(length - lastIndex - remainingPoints);

        // copy the current segment
        for (int j = lastIndex; j < crossoverIndex; j++) {
            c1.add(parent1Rep.get(j));
            c2.add(parent2Rep.get(j));
        }

        // swap the children for the next segment
        List<T> tmp = c1;
        c1 = c2;
        c2 = tmp;

        lastIndex = crossoverIndex;
    }

    // copy the last segment
    for (int j = lastIndex; j < length; j++) {
        c1.add(parent1Rep.get(j));
        c2.add(parent2Rep.get(j));
    }

    return new ChromosomePair(first.newFixedLengthChromosome(child1Rep),
            second.newFixedLengthChromosome(child2Rep));
}

---

From source file:com.clust4j.utils.MatUtils.java

/**
 * Determine whether the col dims of A are equal to the row dims of B
 * @param a/*from  w  ww.j a  v a  2  s.com*/
 * @param b
 */
final static public void checkMultipliability(final double[][] a, final double[][] b) {
    checkDims(a);
    checkDims(b);
    if (a[0].length != b.length)
        throw new DimensionMismatchException(a[0].length, b.length);
}

---

From source file:com.itemanalysis.psychometrics.irt.model.IrmGRM.java

public void setScoreWeights(double[] scoreWeight) throws DimensionMismatchException {
    if (scoreWeight.length != step.length)
        throw new DimensionMismatchException(scoreWeight.length, step.length);
    this.scoreWeight = scoreWeight;
}

---

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

/**
 * Calculates the derivative of the SAIM function with respect to A, B, and h
 * at a given angle/* w w w .ja va  2s.  com*/
 * 
 * @param x - angle in radians
 * @param parameters - array of 3 values:
 *    A - scaling parameter
 *    B - offset parameter, accounting for background
 *    h - height in nm
 * @return - array of 3 values with the partial derivatives for A, B, and h
 */
@Override
public double[] gradient(double x, double... parameters) {
    if (parameters.length != 3)
        throw new DimensionMismatchException(parameters.length, 3);

    angle_ = x;
    double A = parameters[0];
    double h = parameters[2];

    // partial derivative for A is the square of |1+rTE*eiphi(h)|
    Complex rTE = getFresnelTE(angle_);
    double f = 4.0 * Math.PI * sd_.nSample_ * Math.cos(angle_) / sd_.wavelength_;
    double phaseDiff = f * h;
    double c = rTE.getReal();
    double d = rTE.getImaginary();
    double val = 1 + 2 * c * Math.cos(phaseDiff) - 2 * d * Math.sin(phaseDiff) + c * c + d * d;

    // partial derivative for B is 1 or angle
    double bDerivative = 1.0;
    if (sd_.useBAngle_)
        bDerivative = angle_;

    // partial derivate for h is 
    //     - 2*A*c*f*sin(fh) - 2*A*d*f*cos(fh)
    // where f = phaseDiffFactor
    // c = rTE.Real(), and d = rTE.Imaginary()

    double pdh = -2 * A * f * (c * Math.sin(phaseDiff) + d * Math.cos(phaseDiff));

    double result[] = { val, bDerivative, pdh };
    return result;
}

---

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

/**
 * Constructor with custom MeanShiftPlanner
 * @param data/* w  w w  .  j  a v a  2 s .  co  m*/
 * @param planner
 */
protected MeanShift(RealMatrix data, MeanShiftParameters planner) {
    super(data, planner);

    // Check bandwidth...
    if (planner.getBandwidth() <= 0.0)
        error(new IllegalArgumentException("bandwidth " + "must be greater than 0.0"));

    // Check seeds dimension
    if (null != planner.getSeeds()) {
        if (planner.getSeeds().length == 0)
            error(new IllegalArgumentException("seeds " + "length must be greater than 0"));

        // Throws NonUniformMatrixException if non uniform...
        MatUtils.checkDimsForUniformity(planner.getSeeds());

        if (planner.getSeeds()[0].length != (n = this.data.getColumnDimension()))
            error(new DimensionMismatchException(planner.getSeeds()[0].length, n));

        if (planner.getSeeds().length > this.data.getRowDimension())
            error(new IllegalArgumentException("seeds " + "length cannot exceed number of datapoints"));

        info("initializing kernels from given seeds");

        // Handle the copying in the planner
        seeds = planner.getSeeds();
    } else { // Default = all*/
        info("no seeds provided; defaulting to all datapoints");
        seeds = this.data.getData(); // use THIS as it's already scaled...
        n = this.data.getColumnDimension();
    }

    /*
     * Check metric for validity
     */
    if (!isValidMetric(this.dist_metric)) {
        warn(this.dist_metric.getName() + " is not valid for " + getName() + ". "
                + "Falling back to default Euclidean dist");
        setSeparabilityMetric(DEF_DIST);
    }

    this.maxIter = planner.getMaxIter();
    this.tolerance = planner.getConvergenceTolerance();

    this.autoEstimate = planner.getAutoEstimate();
    final LogTimer aeTimer = new LogTimer();

    /*
     * Assign bandwidth
     */
    this.bandwidth =
            /* if all singular, just pick a number... */
            this.singular_value ? 0.5 :
            /* Otherwise if we're auto-estimating, estimate it */
                    autoEstimate ? autoEstimateBW(this, planner.getAutoEstimationQuantile())
                            : planner.getBandwidth();

    /*
     * Give auto-estimation timer update   
     */
    if (autoEstimate && !this.singular_value)
        info("bandwidth auto-estimated in " + (parallel ? "parallel in " : "") + aeTimer.toString());

    logModelSummary();
}

---

From source file:com.wwidesigner.optimization.BaseObjectiveFunction.java

/**
 * Calculate errors at each fingering target.
 * /*w w  w  .  j  a v a 2  s .  com*/
 * @param point
 *            - geometry values to test. point.length == nrDimensions.
 * @return array of error values, one for each fingering target.
 * @throws OperationCancelledException
 * @throws DimensionMismatchException.
 */
public double[] getErrorVector(double[] point) {
    if (cancel) {
        cancel = false;
        throw new OperationCancelledException("Operation cancelled.");
    }
    if (point.length != nrDimensions) {
        throw new DimensionMismatchException(point.length, nrDimensions);
    }
    setGeometryPoint(point);
    double[] errorVector = evaluator.calculateErrorVector(fingeringTargets);
    return errorVector;
}

---

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

public void addColumns(String[] s, double[][] cols) {
    MatUtils.checkDims(cols);/* w w  w.  j a v  a2s .com*/

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

    int i, j;
    final int n = data.getColumnDimension(), newN = n + cols[0].length;

    // build headers
    if (null == s) {
        s = new String[cols[0].length];
        for (i = 0, j = n; i < cols[0].length; i++, j++)
            s[i] = COL_PREFIX + j;
    } else {
        // Ensure no nulls
        for (i = 0, j = n; i < cols[0].length; i++, j++)
            s[i] = null == s[i] ? (COL_PREFIX + j) : s[i];
    }

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

    for (i = 0; i < m; i++) {
        for (j = 0; j < newN; j++) {
            if (i == 0) {
                newHeaders[j] = j < n ? headers[j] : s[j - n];
            }

            newData[i][j] = j < n ? oldData[i][j] : cols[i][j - n];
        }
    }

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

---

From source file:MultivariateNormalDistribution.java

/** {@inheritDoc} */
public double density(final double[] vals) throws DimensionMismatchException {
    final int dim = getDimension();
    if (vals.length != dim) {
        throw new DimensionMismatchException(vals.length, dim);
    }/*from   w  w w. j a  v a  2 s  .  co  m*/

    return FastMath.pow(2 * FastMath.PI, -dim / 2) * FastMath.pow(covarianceMatrixDeterminant, -0.5)
            * getExponentTerm(vals);
}

---

From source file:gamlss.utilities.WLSMultipleLinearRegression.java

/**
* {@inheritDoc}/*from w ww.j  a v a2 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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