C# y） ,， 洛博德， 上限， 我猜， 梯度公差， 参数公差， 我们的宽容， 最大迭代次数 ); } 例子

要在x位置10和20处拟合两个高斯：

Func<double, double> fit = GaussianFit.Curvefit(x_data, y_data, 10, 20);

Func-fit=GaussianFit.Curvefit（x_数据，y_数据，10,20）；

查看并将其中的“正弦”替换为“高斯”。@ephraim抱歉，我认为我不再拥有该代码，这是很久以前的事了谢谢你，老问题，很高兴看到它们从未过期：-）做得好，我无法测试它（不再在该应用程序上工作，甚至在Windows上也不能），但不管怎样，我认为你应该为此付出努力；-）。通过查看代码，它看起来就是我要找的。

using MathNet.Numerics;
using MathNet.Numerics.Distributions;
using MathNet.Numerics.LinearAlgebra;
using MathNet.Numerics.LinearAlgebra.Double;
using System;
using System.Linq;

static class GaussianFit
{
    /// <summary>
    /// Non-linear least square Gaussian curve fit to data.
    /// </summary>
    /// <param name="mu1">x position of the first Gaussian.</param>
    /// <param name="mu2">x position of the second Gaussian.</param>
    /// <returns>Array of the Gaussian profile.</returns>
    public Func<double, double> CurveFit(double[] xData, double[] yData, double mu1, double mu2)
    {
        //Define gaussian function
        double gaussian(Vector<double> vectorArg)
        {
            double x = vectorArg.Last();
            double y = 
                vectorArg[0] * Normal.PDF(vectorArg[1], vectorArg[2], x)
                + vectorArg[3] * Normal.PDF(vectorArg[4], vectorArg[5], x);
            return y;
        }

        var lowerBound = new DenseVector(new[] { 0, mu1 * 0.98, 0.05, 0, mu2 * 0.98, 0.05 });
        var upperBound = new DenseVector(new[] { 1e10, mu1 * 1.02, 0.3, 1e10, mu2 * 1.02, 0.3 });
        var initialGuess = new DenseVector(new[] { 1000, mu1, 0.2, 1000, mu2, 0.2 });

        Func<double, double> fit = CurveFuncConstrained(
            xData, yData, gaussian, lowerBound, upperBound, initialGuess
        );

        return fit;
    }

    /// <summary>
    /// Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, x),
    /// returning a function y' for the best fitting curve.
    /// </summary>
    public static Func<double, double> CurveFuncConstrained(
        double[] x,
        double[] y,
        Func<Vector<double>, double> f,
        Vector<double> lowerBound,
        Vector<double> upperBound,
        Vector<double> initialGuess,
        double gradientTolerance = 1e-5,
        double parameterTolerance = 1e-5,
        double functionProgressTolerance = 1e-5,
        int maxIterations = 1000
    )
    {
        var parameters = CurveConstrained(
            x, y, f,
            lowerBound, upperBound, initialGuess,
            gradientTolerance, parameterTolerance, functionProgressTolerance,
            maxIterations
        );
        return z => f(new DenseVector(new[] { parameters[0], parameters[1], parameters[2], parameters[3], parameters[4], parameters[5], z }));
    }

    /// <summary>
    /// Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, x),
    /// returning its best fitting parameter p0, p1 and p2.
    /// </summary>
    public static Vector<double> CurveConstrained(
        double[] x,
        double[] y,
        Func<Vector<double>, double> f,
        Vector<double> lowerBound,
        Vector<double> upperBound,
        Vector<double> initialGuess,
        double gradientTolerance = 1e-5,
        double parameterTolerance = 1e-5,
        double functionProgressTolerance = 1e-5,
        int maxIterations = 1000
    )
    {
        return FindMinimum.OfFunctionConstrained(
            (p) => Distance.Euclidean(
                Generate.Map(
                    x, 
                    t => f(new DenseVector(new[] { p[0], p[1], p[2], p[3], p[4], p[5], t }))
                    ), 
                y),
            lowerBound,
            upperBound,
            initialGuess,
            gradientTolerance,
            parameterTolerance,
            functionProgressTolerance,
            maxIterations
        );
    }

Func<double, double> fit = GaussianFit.Curvefit(x_data, y_data, 10, 20);