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

Introduction

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

Prototype

public NoDataException(Localizable specific) 

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Document

Construct the exception with a specific context.

Usage

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

/**
 * Create a new {@code BigSparseRealMatrix} using the input array as the
 * underlying data array./*from   w  ww .j a  v a2 s  .c  o m*/
 *
 * @param data Data for the new matrix.
 * @throws DimensionMismatchException if {@code d} is not rectangular.
 * @throws NoDataException if {@code d} i or column dimension is zero.
 */
public BigSparseRealMatrix(final double[][] data) throws DimensionMismatchException, NoDataException {
    MathUtils.checkNotNull(data);
    this.entries = new OpenLongToDoubleHashMap(0.0);
    rows = data.length;
    if (rows == 0) {
        throw new NoDataException(LocalizedFormats.AT_LEAST_ONE_ROW);
    }
    columns = data[0].length;
    if (columns == 0) {
        throw new NoDataException(LocalizedFormats.AT_LEAST_ONE_COLUMN);
    }
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < columns; j++) {
            setEntry(i, j, data[i][j]);
        }
    }
}

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From source file:logic.ApachePolyNewtonForm.java

/**
 * Verifies that the input arrays are valid.
 * <p>// w  w w  .  j a v  a  2  s.co  m
 * The centers must be distinct for interpolation purposes, but not
 * for general use. Thus it is not verified here.</p>
 *
 * @param a the coefficients in Newton form formula
 * @param c the centers
 * @throws NullArgumentException if any argument is {@code null}.
 * @throws NoDataException if any array has zero length.
 * @throws DimensionMismatchException if the size difference between
 * {@code a} and {@code c} is not equal to 1.
 * @see org.apache.commons.math3.analysis.interpolation.DividedDifferenceInterpolator#computeDividedDifference(double[],
 * double[])
 */
protected static void verifyInputArray(double a[], double c[])
        throws NullArgumentException, NoDataException, DimensionMismatchException {
    MathUtils.checkNotNull(a);
    MathUtils.checkNotNull(c);
    if (a.length == 0 || c.length == 0) {
        throw new NoDataException(LocalizedFormats.EMPTY_POLYNOMIALS_COEFFICIENTS_ARRAY);
    }
    if (a.length != c.length + 1) {
        throw new DimensionMismatchException(LocalizedFormats.ARRAY_SIZES_SHOULD_HAVE_DIFFERENCE_1, a.length,
                c.length);
    }
}

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From source file:experiment.SimpleRegression_bug.java

/**
 * Performs a regression on data present in buffers and outputs a RegressionResults object.
 *
 * <p>If there are fewer than 3 observations in the model and {@code hasIntercept} is true
 * a {@code NoDataException} is thrown.  If there is no intercept term, the model must
 * contain at least 2 observations.</p>
 *
 * @return RegressionResults acts as a container of regression output
 * @throws ModelSpecificationException if the model is not correctly specified
 * @throws NoDataException if there is not sufficient data in the model to
 * estimate the regression parameters//from www  .j a v a 2 s  .c o  m
 */
public RegressionResults regress() throws ModelSpecificationException, NoDataException {
    if (hasIntercept) {
        if (n < 3) {
            throw new NoDataException(LocalizedFormats.NOT_ENOUGH_DATA_REGRESSION);
        }
        if (FastMath.abs(sumXX) > Precision.SAFE_MIN) {
            final double[] params = new double[] { getIntercept(), getSlope() };
            final double mse = getMeanSquareError();
            final double _syy = sumYY + sumY * sumY / n;
            final double[] vcv = new double[] { mse * (xbar * xbar / sumXX + 1.0 / n), -xbar * mse / sumXX,
                    mse / sumXX };
            return new RegressionResults(params, new double[][] { vcv }, true, n, 2, sumY, _syy,
                    getSumSquaredErrors(), true, false);
        } else {
            final double[] params = new double[] { sumY / n, Double.NaN };
            //final double mse = getMeanSquareError();
            final double[] vcv = new double[] { ybar / (n - 1.0), Double.NaN, Double.NaN };
            return new RegressionResults(params, new double[][] { vcv }, true, n, 1, sumY, sumYY,
                    getSumSquaredErrors(), true, false);
        }
    } else {
        if (n < 2) {
            throw new NoDataException(LocalizedFormats.NOT_ENOUGH_DATA_REGRESSION);
        }
        if (!Double.isNaN(sumXX)) {
            final double[] vcv = new double[] { getMeanSquareError() / sumXX };
            final double[] params = new double[] { sumXY / sumXX };
            return new RegressionResults(params, new double[][] { vcv }, true, n, 1, sumY, sumYY,
                    getSumSquaredErrors(), false, false);
        } else {
            final double[] vcv = new double[] { Double.NaN };
            final double[] params = new double[] { Double.NaN };
            return new RegressionResults(params, new double[][] { vcv }, true, n, 1, Double.NaN, Double.NaN,
                    Double.NaN, false, false);
        }
    }
}

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