c# LOESS/LOWESS regression

Question

Do you know of a .net library to perform a LOESS/LOWESS regression? (preferably free/open source)

Answer 1

Port from java to c#

public class LoessInterpolator
{
    public static double DEFAULT_BANDWIDTH = 0.3;
    public static int DEFAULT_ROBUSTNESS_ITERS = 2;

    /**
     * The bandwidth parameter: when computing the loess fit at
     * a particular point, this fraction of source points closest
     * to the current point is taken into account for computing
     * a least-squares regression.
     * 
     * A sensible value is usually 0.25 to 0.5.
     */
    private double bandwidth;

    /**
     * The number of robustness iterations parameter: this many
     * robustness iterations are done.
     * 
     * A sensible value is usually 0 (just the initial fit without any
     * robustness iterations) to 4.
     */
    private int robustnessIters;

    public LoessInterpolator()
    {
        this.bandwidth = DEFAULT_BANDWIDTH;
        this.robustnessIters = DEFAULT_ROBUSTNESS_ITERS;
    }

    public LoessInterpolator(double bandwidth, int robustnessIters)
    {
        if (bandwidth < 0 || bandwidth > 1)
        {
            throw new ApplicationException(string.Format("bandwidth must be in the interval [0,1], but got {0}", bandwidth));
        }
        this.bandwidth = bandwidth;
        if (robustnessIters < 0)
        {
            throw new ApplicationException(string.Format("the number of robustness iterations must be non-negative, but got {0}", robustnessIters));
        }
        this.robustnessIters = robustnessIters;
    }

    /**
     * Compute a loess fit on the data at the original abscissae.
     *
     * @param xval the arguments for the interpolation points
     * @param yval the values for the interpolation points
     * @return values of the loess fit at corresponding original abscissae
     * @throws MathException if some of the following conditions are false:
     * <ul>
     * <li> Arguments and values are of the same size that is greater than zero</li>
     * <li> The arguments are in a strictly increasing order</li>
     * <li> All arguments and values are finite real numbers</li>
     * </ul>
     */
    public double[] smooth(double[] xval, double[] yval)
    {
        if (xval.Length != yval.Length)
        {
            throw new ApplicationException(string.Format("Loess expects the abscissa and ordinate arrays to be of the same size, but got {0} abscisssae and {1} ordinatae", xval.Length, yval.Length));
        }
        int n = xval.Length;
        if (n == 0)
        {
            throw new ApplicationException("Loess expects at least 1 point");
        }

        checkAllFiniteReal(xval, true);
        checkAllFiniteReal(yval, false);
        checkStrictlyIncreasing(xval);

        if (n == 1)
        {
            return new double[] { yval[0] };
        }

        if (n == 2)
        {
            return new double[] { yval[0], yval[1] };
        }

        int bandwidthInPoints = (int)(bandwidth * n);

        if (bandwidthInPoints < 2)
        {
            throw new ApplicationException(string.Format("the bandwidth must be large enough to accomodate at least 2 points. There are {0} " +
                " data points, and bandwidth must be at least {1} but it is only {2}",
                n, 2.0 / n, bandwidth
            ));
        }

        double[] res = new double[n];

        double[] residuals = new double[n];
        double[] sortedResiduals = new double[n];

        double[] robustnessWeights = new double[n];

        // Do an initial fit and 'robustnessIters' robustness iterations.
        // This is equivalent to doing 'robustnessIters+1' robustness iterations
        // starting with all robustness weights set to 1.
        for (int i = 0; i < robustnessWeights.Length; i++) robustnessWeights[i] = 1;
        for (int iter = 0; iter <= robustnessIters; ++iter)
        {
            int[] bandwidthInterval = { 0, bandwidthInPoints - 1 };
            // At each x, compute a local weighted linear regression
            for (int i = 0; i < n; ++i)
            {
                double x = xval[i];

                // Find out the interval of source points on which
                // a regression is to be made.
                if (i > 0)
                {
                    updateBandwidthInterval(xval, i, bandwidthInterval);
                }

                int ileft = bandwidthInterval[0];
                int iright = bandwidthInterval[1];

                // Compute the point of the bandwidth interval that is
                // farthest from x
                int edge;
                if (xval[i] - xval[ileft] > xval[iright] - xval[i])
                {
                    edge = ileft;
                }
                else
                {
                    edge = iright;
                }

                // Compute a least-squares linear fit weighted by
                // the product of robustness weights and the tricube
                // weight function.
                // See http://en.wikipedia.org/wiki/Linear_regression
                // (section "Univariate linear case")
                // and http://en.wikipedia.org/wiki/Weighted_least_squares
                // (section "Weighted least squares")
                double sumWeights = 0;
                double sumX = 0, sumXSquared = 0, sumY = 0, sumXY = 0;
                double denom = Math.Abs(1.0 / (xval[edge] - x));
                for (int k = ileft; k <= iright; ++k)
                {
                    double xk = xval[k];
                    double yk = yval[k];
                    double dist;
                    if (k < i)
                    {
                        dist = (x - xk);
                    }
                    else
                    {
                        dist = (xk - x);
                    }
                    double w = tricube(dist * denom) * robustnessWeights[k];
                    double xkw = xk * w;
                    sumWeights += w;
                    sumX += xkw;
                    sumXSquared += xk * xkw;
                    sumY += yk * w;
                    sumXY += yk * xkw;
                }

                double meanX = sumX / sumWeights;
                double meanY = sumY / sumWeights;
                double meanXY = sumXY / sumWeights;
                double meanXSquared = sumXSquared / sumWeights;

                double beta;
                if (meanXSquared == meanX * meanX)
                {
                    beta = 0;
                }
                else
                {
                    beta = (meanXY - meanX * meanY) / (meanXSquared - meanX * meanX);
                }

                double alpha = meanY - beta * meanX;

                res[i] = beta * x + alpha;
                residuals[i] = Math.Abs(yval[i] - res[i]);
            }

            // No need to recompute the robustness weights at the last
            // iteration, they won't be needed anymore
            if (iter == robustnessIters)
            {
                break;
            }

            // Recompute the robustness weights.

            // Find the median residual.
            // An arraycopy and a sort are completely tractable here, 
            // because the preceding loop is a lot more expensive
            System.Array.Copy(residuals, sortedResiduals, n);
            //System.arraycopy(residuals, 0, sortedResiduals, 0, n);
            Array.Sort<double>(sortedResiduals);
            double medianResidual = sortedResiduals[n / 2];

            if (medianResidual == 0)
            {
                break;
            }

            for (int i = 0; i < n; ++i)
            {
                double arg = residuals[i] / (6 * medianResidual);
                robustnessWeights[i] = (arg >= 1) ? 0 : Math.Pow(1 - arg * arg, 2);
            }
        }

        return res;
    }

    /**
     * Given an index interval into xval that embraces a certain number of
     * points closest to xval[i-1], update the interval so that it embraces
     * the same number of points closest to xval[i]
     *
     * @param xval arguments array
     * @param i the index around which the new interval should be computed
     * @param bandwidthInterval a two-element array {left, right} such that: <p/>
     * <tt>(left==0 or xval[i] - xval[left-1] > xval[right] - xval[i])</tt>
     * <p/> and also <p/>
     * <tt>(right==xval.length-1 or xval[right+1] - xval[i] > xval[i] - xval[left])</tt>.
     * The array will be updated.
     */
    private static void updateBandwidthInterval(double[] xval, int i, int[] bandwidthInterval)
    {
        int left = bandwidthInterval[0];
        int right = bandwidthInterval[1];

        // The right edge should be adjusted if the next point to the right
        // is closer to xval[i] than the leftmost point of the current interval
        int nextRight = nextNonzero(weights, right);
        if (nextRight < xval.Length && xval[nextRight] - xval[i] < xval[i] - xval[left])
        {
            int nextLeft = nextNonzero(weights, bandwidthInterval[0]);
            bandwidthInterval[0] = nextLeft;
            bandwidthInterval[1] = nextRight;
        }
    }

    /**
     * Compute the 
     * <a href="http://en.wikipedia.org/wiki/Local_regression#Weight_function">tricube</a>
     * weight function
     *
     * @param x the argument
     * @return (1-|x|^3)^3
     */
    private static double tricube(double x)
    {
        double tmp = Math.abs(x);
               tmp = 1 - tmp * tmp * tmp;
        return tmp * tmp * tmp;
    }

    /**
     * Check that all elements of an array are finite real numbers.
     *
     * @param values the values array
     * @param isAbscissae if true, elements are abscissae otherwise they are ordinatae
     * @throws MathException if one of the values is not
     *         a finite real number
     */
    private static void checkAllFiniteReal(double[] values, bool isAbscissae)
    {
        for (int i = 0; i < values.Length; i++)
        {
            double x = values[i];
            if (Double.IsInfinity(x) || Double.IsNaN(x))
            {
                string pattern = isAbscissae ?
                        "all abscissae must be finite real numbers, but {0}-th is {1}" :
                        "all ordinatae must be finite real numbers, but {0}-th is {1}";
                throw new ApplicationException(string.Format(pattern, i, x));
            }
        }
    }

    /**
     * Check that elements of the abscissae array are in a strictly
     * increasing order.
     *
     * @param xval the abscissae array
     * @throws MathException if the abscissae array
     * is not in a strictly increasing order
     */
    private static void checkStrictlyIncreasing(double[] xval)
    {
        for (int i = 0; i < xval.Length; ++i)
        {
            if (i >= 1 && xval[i - 1] >= xval[i])
            {
                throw new ApplicationException(string.Format(
                        "the abscissae array must be sorted in a strictly " +
                        "increasing order, but the {0}-th element is {1} " +
                        "whereas {2}-th is {3}",
                        i - 1, xval[i - 1], i, xval[i]));
            }
        }
    }
}