Java LocalizedFormats.INSUFFICIENT_DIMENSION属性代码示例

/**
 * Computes the Spearman's rank correlation coefficient between the two arrays.
 *
 * @param xArray first data array
 * @param yArray second data array
 * @return Returns Spearman's rank correlation coefficient for the two arrays
 * @throws DimensionMismatchException if the arrays lengths do not match
 * @throws MathIllegalArgumentException if the array length is less than 2
 */
public double correlation(final double[] xArray, final double[] yArray) {
    if (xArray.length != yArray.length) {
        throw new DimensionMismatchException(xArray.length, yArray.length);
    } else if (xArray.length < 2) {
        throw new MathIllegalArgumentException(LocalizedFormats.INSUFFICIENT_DIMENSION,
                                               xArray.length, 2);
    } else {
        double[] x = xArray;
        double[] y = yArray;
        if (rankingAlgorithm instanceof NaturalRanking &&
            NaNStrategy.REMOVED == ((NaturalRanking) rankingAlgorithm).getNanStrategy()) {
            final Set<Integer> nanPositions = new HashSet<Integer>();

            nanPositions.addAll(getNaNPositions(xArray));
            nanPositions.addAll(getNaNPositions(yArray));

            x = removeValues(xArray, nanPositions);
            y = removeValues(yArray, nanPositions);
        }
        return new PearsonsCorrelation().correlation(rankingAlgorithm.rank(x), rankingAlgorithm.rank(y));
    }
}
 

/**
 * Returns the sum of the (signed) differences between corresponding elements of the
 * input arrays -- i.e., sum(sample1[i] - sample2[i]).
 *
 * @param sample1  the first array
 * @param sample2  the second array
 * @return sum of paired differences
 * @throws DimensionMismatchException if the arrays do not have the same
 * (positive) length.
 * @throws NoDataException if the sample arrays are empty.
 */
public static double sumDifference(final double[] sample1, final double[] sample2)
throws DimensionMismatchException, NoDataException {
    int n = sample1.length;
    if (n != sample2.length) {
        throw new DimensionMismatchException(n, sample2.length);
    }
    if (n <= 0) {
        throw new NoDataException(LocalizedFormats.INSUFFICIENT_DIMENSION);
    }
    double result = 0;
    for (int i = 0; i < n; i++) {
        result += sample1[i] - sample2[i];
    }
    return result;
}
 

/**
 * Return the current covariance estimate.
 *
 * @return the current covariance
 * @throws NumberIsTooSmallException if the number of observations
 * is < 2
 */
public double getResult() throws NumberIsTooSmallException {
    if (n < 2) {
        throw new NumberIsTooSmallException(LocalizedFormats.INSUFFICIENT_DIMENSION,
                                            n, 2, true);
    }
    if (biasCorrected) {
        return covarianceNumerator / (n - 1d);
    } else {
        return covarianceNumerator / n;
    }
}
 

/**
 * Computes the Pearson's product-moment correlation coefficient between two arrays.
 *
 * <p>Throws MathIllegalArgumentException if the arrays do not have the same length
 * or their common length is less than 2.  Returns {@code NaN} if either of the arrays
 * has zero variance (i.e., if one of the arrays does not contain at least two distinct
 * values).</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @return Returns Pearson's correlation coefficient for the two arrays
 * @throws DimensionMismatchException if the arrays lengths do not match
 * @throws MathIllegalArgumentException if there is insufficient data
 */
public double correlation(final double[] xArray, final double[] yArray) {
    SimpleRegression regression = new SimpleRegression();
    if (xArray.length != yArray.length) {
        throw new DimensionMismatchException(xArray.length, yArray.length);
    } else if (xArray.length < 2) {
        throw new MathIllegalArgumentException(LocalizedFormats.INSUFFICIENT_DIMENSION,
                                               xArray.length, 2);
    } else {
        for(int i=0; i<xArray.length; i++) {
            regression.addData(xArray[i], yArray[i]);
        }
        return regression.getR();
    }
}