我试图通过依赖于两个变量的拟合函数拟合一组数据点，我们将它们称为xdata和sdata。问题是我的曲线相当平坦，我希望它或多或少地“遵循点”。
我尝试使用scipy.odr拟合曲线，但是曲线太平了，效果很好：
import numpy as np
from math import pi
from math import sqrt
from math import log
from scipy import optimize
import scipy.optimize
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.odr import *
mudr=np.array([ 57.43708609, 46.26119205, 55.60688742, 33.21615894,
28.27072848, 22.54649007, 21.80662252, 11.21483444, 5.80211921])
#xdata points
dme=array([ 128662.54890776, 105265.32915726, 128652.56835434,
77968.67019573, 66273.56542068, 58464.58559543,
54570.66624991, 27286.90038703, 19480.92689266]) #xdata error
dmss22=np.array([ 4.90050000e+17, 4.90050000e+17, 4.90050000e+17,
4.90050000e+17, 4.90050000e+17, 4.90050000e+17,
4.90050000e+17, 4.90050000e+17, 4.90050000e+17]) #sdata points
dmse=np.array([ 1.09777592e+21, 1.11512117e+21, 1.13381702e+21,
1.15033267e+21, 1.14883089e+21, 1.27076265e+21,
1.22637165e+21, 1.19237598e+21, 1.64539205e+21]) # sdata error
F=np.array([ 115.01944248, 110.24354867, 112.77812389, 104.81830088,
104.35746903, 101.32016814, 100.54513274, 96.94226549,
93.00424779]) #ydata points
dF=np.array([ 72710.75386699, 72590.6256987 , 176539.40403673,
130555.27503081, 124299.52080164, 176426.64340597,
143013.52848306, 122117.93022746, 157547.78395513])#ydata error
def Ffitsso(p,X,B=2.58,Fc=92.2,mu=770,Za=0.9468): #fitfunction
temp1 = (2*B*X[0])/(4*pi*Fc)**2
temp2 = temp1*(afij[0]+afij[1]*np.log((2*B*X[0])/mu**2))
temp3 = temp1**2*(afij[2]+afij[3]*np.log((2*B*X[0])/mu**2)+\
afij[4]*(np.log((2*B*X[0])/mu**2))**2)
temp4 = temp1**3*(afij[5]+afij[6]*np.log((2*B*X[0])/mu**2)+\
afij[7]*(np.log((2*B*X[0])/mu**2))**2+\
afij[8]*(np.log((2*B*X[0])/mu**2))**3)
return Fc/Za*(1+p[0]*X[1])*(1+temp2+temp3+temp4)+p[1]
#fitting using scipy.odr
xtot=np.row_stack( (mudr, dmss22) )
etot=np.row_stack( (Ze, dmss22e) )
fitting = Model(Ffitsso)
mydata = RealData(xtot, F, sx=etot2, sy=dF)
myodr = ODR(mydata, fitting, beta0=[0, 100])
myoutput = myodr.run()
myoutput.pprint()
bet=myoutput.beta
plt.plot(mudr,F,"b^")
plt.plot(mudr,Ffitsso(bet,[mudr,dmss22]))
p [0] * X [0]应该较小，但通过拟合，p [0]的值约为e-18，而dmss22的值约为e -17不够小。
更糟糕的是，它是负数，意味着函数会减少，这本不应该发生，而应该像绘制的数据点那样增加。
编辑：我已修复，不知道它对初始beta值如此敏感，将beta [0] = 1.5 * 10 （-15）设置为有效！**
答案 0 :(得分：1)
这里是使用scipy的差分进化（DE）遗传算法同时具有curve_fit和ODR拟合器的图形拟合器，以为非线性求解器提供初始参数估计。 DE的scipy实现使用Latin Hypercube算法来确保对参数空间的彻底搜索，这需要在其中搜索参数范围-在此示例中，这些范围取自数据的最大值和最小值。请注意，给边界提供初始参数估计要容易得多，而不是给单个特定值赋值。
import numpy, scipy, matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import scipy.odr
from scipy.optimize import differential_evolution
import warnings
xData = numpy.array([1.1, 2.2, 3.3, 4.4, 5.0, 6.6, 7.7, 0.0])
yData = numpy.array([1.1, 20.2, 30.3, 40.4, 50.0, 60.6, 70.7, 0.1])
def func(x, a, b, c, d, offset): # curve fitting function for curve_fit()
return a*numpy.exp(-(x-b)**2/(2*c**2)+d) + offset
def func_wrapper_for_ODR(parameters, x): # parameter order for ODR
return func(x, *parameters)
# function for genetic algorithm to minimize (sum of squared error)
def sumOfSquaredError(parameterTuple):
warnings.filterwarnings("ignore") # do not print warnings by genetic algorithm
val = func(xData, *parameterTuple)
return numpy.sum((yData - val) ** 2.0)
def generate_Initial_Parameters():
# min and max used for bounds
maxX = max(xData)
minX = min(xData)
maxY = max(yData)
minY = min(yData)
parameterBounds = []
parameterBounds.append([minY, maxY]) # search bounds for a
parameterBounds.append([minX, maxX]) # search bounds for b
parameterBounds.append([minX, maxX]) # search bounds for c
parameterBounds.append([minY, maxY]) # search bounds for d
parameterBounds.append([0.0, maxY]) # search bounds for Offset
# "seed" the numpy random number generator for repeatable results
result = differential_evolution(sumOfSquaredError, parameterBounds, seed=3)
return result.x
geneticParameters = generate_Initial_Parameters()
##########################
# curve_fit section
##########################
fittedParameters_curvefit, pcov = curve_fit(func, xData, yData, geneticParameters)
print('Fitted parameters curve_fit:', fittedParameters_curvefit)
print()
modelPredictions_curvefit = func(xData, *fittedParameters_curvefit)
absError_curvefit = modelPredictions_curvefit - yData
SE_curvefit = numpy.square(absError_curvefit) # squared errors
MSE_curvefit = numpy.mean(SE_curvefit) # mean squared errors
RMSE_curvefit = numpy.sqrt(MSE_curvefit) # Root Mean Squared Error, RMSE
Rsquared_curvefit = 1.0 - (numpy.var(absError_curvefit) / numpy.var(yData))
print()
print('RMSE curve_fit:', RMSE_curvefit)
print('R-squared curve_fit:', Rsquared_curvefit)
print()
##########################
# ODR section
##########################
data = scipy.odr.odrpack.Data(xData,yData)
model = scipy.odr.odrpack.Model(func_wrapper_for_ODR)
odr = scipy.odr.odrpack.ODR(data, model, beta0=geneticParameters)
# Run the regression.
odr_out = odr.run()
print('Fitted parameters ODR:', odr_out.beta)
print()
modelPredictions_odr = func(xData, *odr_out.beta)
absError_odr = modelPredictions_odr - yData
SE_odr = numpy.square(absError_odr) # squared errors
MSE_odr = numpy.mean(SE_odr) # mean squared errors
RMSE_odr = numpy.sqrt(MSE_odr) # Root Mean Squared Error, RMSE
Rsquared_odr = 1.0 - (numpy.var(absError_odr) / numpy.var(yData))
print()
print('RMSE ODR:', RMSE_odr)
print('R-squared ODR:', Rsquared_odr)
print()
##########################################################
# graphics output section
def ModelsAndScatterPlot(graphWidth, graphHeight):
f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)
axes = f.add_subplot(111)
# first the raw data as a scatter plot
axes.plot(xData, yData, 'D')
# create data for the fitted equation plots
xModel = numpy.linspace(min(xData), max(xData))
yModel_curvefit = func(xModel, *fittedParameters_curvefit)
yModel_odr = func(xModel, *odr_out.beta)
# now the models as line plots
axes.plot(xModel, yModel_curvefit)
axes.plot(xModel, yModel_odr)
axes.set_xlabel('X Data') # X axis data label
axes.set_ylabel('Y Data') # Y axis data label
plt.show()
plt.close('all') # clean up after using pyplot
graphWidth = 800
graphHeight = 600
ModelsAndScatterPlot(graphWidth, graphHeight)