如何在Python中拟合双高斯分布？

Question

我试图使用Python 获得数据(链接)的双高斯分布.原始数据的格式如下:

对于给定的数据,我想获得图中所示峰值的两个高斯分布.我尝试使用以下代码(源代码):

from sklearn import mixture
import matplotlib.pyplot
import matplotlib.mlab
import numpy as np
from pylab import *
data = np.genfromtxt('gaussian_fit.dat', skiprows = 1)
x = data[:, 0]
y = data[:, 1]
clf = mixture.GMM(n_components=2, covariance_type='full')
clf.fit((y, x))
m1, m2 = clf.means_
w1, w2 = clf.weights_
c1, c2 = clf.covars_
fig = plt.figure(figsize = (5, 5))
plt.subplot(111)
plotgauss1 = lambda x: plot(x,w1*matplotlib.mlab.normpdf(x,m1,np.sqrt(c1))[0], linewidth=3)
plotgauss2 = lambda x: plot(x,w2*matplotlib.mlab.normpdf(x,m2,np.sqrt(c2))[0], linewidth=3)
fig.savefig('gaussian_fit.pdf')

但我无法获得所需的输出.那么,如何在Python中获得双高斯分布？

更新

我能够使用以下代码拟合单个高斯分布:

import pylab as plb
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy import asarray as ar,exp
import numpy as np

data = np.genfromtxt('gaussian_fit.dat', skiprows = 1)
x = data[:, 0]
y = data[:, 1]
n = len(x)
mean = sum(x*y)/n
sigma = sum(y*(x-mean)**2)/n


def gaus(x,a,x0,sigma):
    return a*exp(-(x-x0)**2/(2*sigma**2))


popt,pcov = curve_fit(gaus, x, y ,p0 = [1, mean, sigma])


fig = plt.figure(figsize = (5, 5))
plt.subplot(111)
plt.plot(x, y, label='Raw')
plt.plot(x, gaus(x, *popt), 'o', markersize = 4, label='Gaussian fit')
plt.xlabel('X')
plt.ylabel('Y')
plt.legend()
fig.savefig('gaussian_fit.pdf')

Answer 1

你不能为此使用scikit-learn,因为你没有处理一组你想要估计其分布的样本.您当然可以将曲线转换为PDF,对其进行采样,然后尝试使用高斯混合模型进行拟合,但这似乎对我来说有点过分.

这是使用简单最小二乘曲线拟合的解决方案.为了使它工作,我不得不删除背景,即忽略所有数据点y < 5,并提供一个良好的起始矢量leastsq,可以从数据图中估计.

找到起始向量

通过最小二乘法找到的参数向量是向量

params = [c1, mu1, sigma1, c2, mu2, sigma2]

这里,c1并且c2是两个高斯的缩放因子,即它们的高度,mu1并且mu2是平均值,即峰的水平位置sigma1和sigma2确定高斯宽度的标准偏差.为了找到一个起始载体我只是看着数据的曲线和估计的两个峰的(=高度c1,c2(=分别)和它们的水平位置mu1,mu1分别).sigma1并且sigma2只是设置为1.0.

码

from sklearn import mixture
import matplotlib.pyplot
import matplotlib.mlab
import numpy as np
from pylab import *
from scipy.optimize import leastsq

data = np.genfromtxt('gaussian_fit.dat', skiprows = 1)
x = data[:, 0]
y = data[:, 1]

def double_gaussian( x, params ):
    (c1, mu1, sigma1, c2, mu2, sigma2) = params
    res =   c1 * np.exp( - (x - mu1)**2.0 / (2.0 * sigma1**2.0) ) \
          + c2 * np.exp( - (x - mu2)**2.0 / (2.0 * sigma2**2.0) )
    return res

def double_gaussian_fit( params ):
    fit = double_gaussian( x, params )
    return (fit - y_proc)

# Remove background.
y_proc = np.copy(y)
y_proc[y_proc < 5] = 0.0

# Least squares fit. Starting values found by inspection.
fit = leastsq( double_gaussian_fit, [13.0,-13.0,1.0,60.0,3.0,1.0] )
plot( x, y, c='b' )
plot( x, double_gaussian( x, fit[0] ), c='r' )