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我正在分析一些数据。从理论上讲，应该有两个或多或少重叠的高斯。我发现如果你不拟合两个高斯，但拟合它们的分布函数，拟合数据效果最好。实际上，这种拟合似乎效果很好，但如果我回到数据的密度表示，它看起来有点连线。所附图片为他们自己说话。知道那里出了什么问题吗？适合分布

高斯参数给出什么（绿色真实数据，蓝色是拟合

这是我的代码：`

import numpy as np
import scipy
import scipy.special
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.optimize import leastsq
from scipy.special import erf




def fitfunction(params,Bins):
    amp_ratio, sigma1, sigma2, mu, Delta = params
    return  amp_ratio * 0.5   * (1 + erf((Bins -mu)/np.sqrt(2*sigma1**2)))   + (1-amp_ratio)* 0.5 * (1 + erf((Bins - (mu + Delta))/np.sqrt(2*sigma2**2)))


def errorfunction(params, Reale_werte, Bins):
    amp_ratio, sigma1, sigma2, mu, Delta = params

    if(amp_ratio > 0) and (amp_ratio < 1):
        return (Reale_werte - fitfunction(params, Bins)) 
    else:
        return (Reale_werte - fitfunction(params, Bins))*100

def Gaussians(params, Bins):
    amp_ratio, sigma1, sigma2, mu, Delta = params
    return amp_ratio/np.sqrt(2*np.pi*sigma1*sigma1) * np.exp(-((Bins-mu)**2) / np.sqrt(2*np.pi*sigma1*sigma1)) + (1-amp_ratio)/np.sqrt(2*np.pi*sigma2*sigma2) * np.exp(-((Bins-(mu + Delta))**2) / np.sqrt(2*np.pi*sigma2*sigma2))

def Gaussian1(params, Bins):
    amp_ratio, sigma1, sigma2, mu, Delta = params
    return amp_ratio/np.sqrt(2*np.pi*sigma1*sigma1) * np.exp(-((Bins-mu)**2) / np.sqrt(2*np.pi*sigma1*sigma1))

def Gaussian2(params, Bins):
    amp_ratio, sigma1, sigma2, mu, Delta = params
    return (1-amp_ratio)/np.sqrt(2*np.pi*sigma2*sigma2) * np.exp(-((Bins-(mu + Delta))**2) / np.sqrt(2*np.pi*sigma2*sigma2))


params = 0.25,1,10,1,5
params_init = 0.75, 0.8, 2.5, 1.2, 4

Bins = np.linspace(-4,18,1024)

data = Gaussians(params, Bins)

summe = np.zeros_like(Bins)

for i in range(Bins.shape[0]-1):
    summe[i+1] = summe[i] + data[i]

summe = summe/summe[Bins.shape[0]-1]

params_result = leastsq(errorfunction, params_init, args=(summe, Bins))  
plt.plot(Bins,fitfunction(params_result[0], Bins))
plt.plot(Bins, summe, 'x')
plt.savefig("Distribution.png")
plt.show()



print params_result[0]

plt.plot(Bins,Gaussians(params_result[0], Bins))
plt.plot(Bins, data, 'x')
plt.savefig("Gaussian.png")
plt.show()`
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1 回答 1

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我想知道内核密度估计是否适用于您的情况：

from scipy.stats import kde
import matplotlib.pyplot as plt     
density = kde.gaussian_kde(x)  # your data
xgrid = np.linspace(x.min(), x.max(), 1024)   
plt.plot(xgrid, density(xgrid))
plt.show()
于 2013-12-15T17:34:49.083 回答