Python curve_fit范围和初始条件的选择会影响结果 [英] Python curve_fit choice of bounds and initial condition affect the result

问题描述

我有一个数据集，该数据集由两个我要使用optimalization.curve_fit确定的自由参数来描述.该模型定义如下

I have a data set that is described by two free parameters which I want to determine using optimalization.curve_fit. The model is defined as follows

def func(x, a, b,):
    return a*x*np.sqrt(1-b*x)

而配件部分为

popt, pcov = opt.curve_fit(f = func, xdata = x_data, ydata= y_data, p0 
= init_guess, bounds = ([a_min, b_min], [a_max, b_max]))

a和b的解的结果在很大程度上取决于我对init_guess的选择，即最初的猜测以及范围的选择. 有办法解决这个问题吗?

The outcome of the solutions for a and b depends quite strong on my choice of init_guess, i.e. the initial guess and also on the choice of the bounds. Is there a way the solve this?

推荐答案

Python scipy模块的作者已将差异进化遗传算法作为模块scipy.optimize.differential_evolution包含在scipy的优化代码中.此模块可用于随机查找初始参数值以进行非线性回归.

The authors of the Python scipy module have included the Differential Evolution genetic algorithm in scipy's optimization code as the module scipy.optimize.differential_evolution. This module can be used to stochastically find initial parameter values for non-linear regression.

以下是 RamanSpectroscopyFit 的示例代码，该代码使用scipy的遗传算法进行初始参数估计以拟合拉曼光谱数据:

Here is example code from RamanSpectroscopyFit, which uses scipy's genetic algorithm for initial parameter estimation for fitting Raman spectroscopy data:

import numpy as np
import pickle # for loading pickled test data
import matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import warnings

from scipy.optimize import differential_evolution


# Double Lorentzian peak function
# bounds on parameters are set in generate_Initial_Parameters() below
def double_Lorentz(x, a, b, A, w, x_0, A1, w1, x_01):
    return a*x+b+(2*A/np.pi)*(w/(4*(x-x_0)**2 + w**2))+(2*A1/np.pi)*(w1/(4*(x-x_01)**2 + w1**2))


# function for genetic algorithm to minimize (sum of squared error)
# bounds on parameters are set in generate_Initial_Parameters() below
def sumOfSquaredError(parameterTuple):
    warnings.filterwarnings("ignore") # do not print warnings by genetic algorithm
    return np.sum((yData - double_Lorentz(xData, *parameterTuple)) ** 2)


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([-1.0, 1.0]) # parameter bounds for a
    parameterBounds.append([maxY/-2.0, maxY/2.0]) # parameter bounds for b
    parameterBounds.append([0.0, maxY*100.0]) # parameter bounds for A
    parameterBounds.append([0.0, maxY/2.0]) # parameter bounds for w
    parameterBounds.append([minX, maxX]) # parameter bounds for x_0
    parameterBounds.append([0.0, maxY*100.0]) # parameter bounds for A1
    parameterBounds.append([0.0, maxY/2.0]) # parameter bounds for w1
    parameterBounds.append([minX, maxX]) # parameter bounds for x_01

    # "seed" the numpy random number generator for repeatable results
    result = differential_evolution(sumOfSquaredError, parameterBounds, seed=3)
    return result.x



# load the pickled test data from original Raman spectroscopy
data = pickle.load(open('data.pkl', 'rb'))
xData = data[0]
yData = data[1]

# generate initial parameter values
initialParameters = generate_Initial_Parameters()

# curve fit the test data
fittedParameters, niepewnosci = curve_fit(double_Lorentz, xData, yData, initialParameters)

# create values for display of fitted peak function
a, b, A, w, x_0, A1, w1, x_01 = fittedParameters
y_fit = double_Lorentz(xData, a, b, A, w, x_0, A1, w1, x_01)

plt.plot(xData, yData) # plot the raw data
plt.plot(xData, y_fit) # plot the equation using the fitted parameters
plt.show()

print(fittedParameters)

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