Question

当我在C＃中使用Math.Exp（）时，我有一些问题吗？这段代码是关于核密度估计的，我对核密度估计没有任何了解。所以我查了一些wiki和一些文章。

我尝试用C＃编写它。问题是＆＃34;距离＆＃34;越来越高，结果变成了0.这让我感到困惑，我找不到任何其他方法来获得正确的结果。

disExp = Math.Pow(Math.E, -(distance / 2 * Math.Pow(h, 2)));

那么，任何人都可以帮我解决问题吗？或者让我对C＃上的核密度估计有所了解。抱歉英语不好。

Answer 1

尝试一下

public static double[,] KernelDensityEstimation(double[] data, double sigma, int nsteps)
    {
        // probability density function (PDF) signal analysis
        // Works like ksdensity in mathlab. 
        // KDE performs kernel density estimation (KDE)on one - dimensional data
        // http://en.wikipedia.org/wiki/Kernel_density_estimation

        // Input:   -data: input data, one-dimensional
        //          -sigma: bandwidth(sometimes called "h")
        //          -nsteps: optional number of abscis points.If nsteps is an
        //          array, the abscis points will be taken directly from it. (default 100)
        // Output:  -x: equispaced abscis points
        //          -y: estimates of p(x)

        // This function is part of the Kernel Methods Toolbox(KMBOX) for MATLAB. 
        // http://sourceforge.net/p/kmbox
        // Converted to C# code by ksandric

        double[,] result = new double[nsteps, 2];
        double[] x = new double[nsteps], y = new double[nsteps];

        double MAX = Double.MinValue, MIN = Double.MaxValue;
        int N = data.Length; // number of data points

        // Find MIN MAX values in data
        for (int i = 0; i < N; i++)
        {
            if (MAX < data[i])
            {
                MAX = data[i];
            }
            if (MIN > data[i])
            {
                MIN = data[i];
            }
        }

        // Like MATLAB linspace(MIN, MAX, nsteps);
        x[0] = MIN;
        for (int i = 1; i < nsteps; i++)
        {
            x[i] = x[i - 1] + ((MAX - MIN) / nsteps);
        }

        // kernel density estimation
        double c = 1.0 / (Math.Sqrt(2 * Math.PI * sigma * sigma));
        for (int i = 0; i < N; i++)
        {
            for (int j = 0; j < nsteps; j++)
            {
                y[j] = y[j] + 1.0 / N * c * Math.Exp(-(data[i] - x[j]) * (data[i] - x[j]) / (2 * sigma * sigma));
            }
        }

        // compilation of the X,Y to result. Good for creating plot(x, y)
        for (int i = 0; i < nsteps; i++)
        {
            result[i, 0] = x[i];
            result[i, 1] = y[i];
        }
        return result;
    }

kernel density estimation C#

plot

C＃Exp无法获得结果

1 个答案: