Question

我正在尝试将一些Matlab代码转换为曲线拟合我的数据到python代码，但我很难得到类似的答案。数据是：

x = array([   0.  ,   12.5 ,   24.5 ,   37.75,   54.  ,   70.25,   87.5 ,
    108.5 ,  129.5 ,  150.5 ,  171.5 ,  193.75,  233.75,  273.75])
y = array([-8.79182857, -5.56347794, -5.45683824, -4.30737662, -1.4394612 ,
   -1.58047016, -0.93225927, -0.6719836 , -0.45977157, -0.37622436,
   -0.56115757, -0.3038559 , -0.26594558, -0.26496367])

Matlab代码是：

function [estimates, model] = curvefit(xdata, ydata)
% fits data to the curve y(x)=A-B*e(-lambda*x)

start_point = rand(1,3);

model =@efun;
options = optimset('Display','off','TolFun',1e-16,'TolX',1e-16);
estimates = fminsearch(model, start_point,options);
% expfun accepts curve parameters as inputs, and outputs sse,
% the sum of squares error for A -B* exp(-lambda * xdata) - ydata, 
% and the FittedCurve. 
    function [sse,FittedCurve] = efun(v)
        A=v(1);
        B=v(2);
        lambda=v(3);
        FittedCurve =A - B*exp(-lambda*xdata);
        ErrorVector=FittedCurve-ydata;
        sse = sum(ErrorVector .^2);
    end
end
err = Inf;
numattempts = 100;
for k=1:numattempts
[intermed,model]=curvefit(x, y));
[thiserr,thismodel]=model(intermed);
if thiserr<err
    err = thiserr;
    coeffs = intermed;
    ymodel = thismodel;
end

到目前为止我在Python中有：

import numpy as np
from pandas import Series, DataFrame
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
from scipy.optimize import curve_fit
import pickle

def fitFunc(A, B, k, t):
    return A - B*np.exp(-k*t)
init_vals = np.random.rand(1,3)
fitParams, fitCovariances = curve_fit(fitFunc, y, x], p0=init_vals)

我认为我必须对p0运行100次尝试做一些事情，但是曲线只会收敛大约1/10倍，它会收敛到一条直线，与我在Matlab中得到的值相差不多。我所看到的关于曲线拟合的大多数问题都使用B np.exp（-k t）+ A，但我上面的指数公式是我必须用于此数据的公式。有什么想法吗？谢谢你的时间！

Answer 1

curve_fit(fitFunc, y, x], p0=init_vals)应为curve_fit(fitFunc, x,y, p0=init_vals)，即x在y之前。 fitFunc(A, B, k, t)应为fitFunc(t,A, B, k)。自变量首先出现。请参阅以下代码：

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

x = np.array([   0.  ,   12.5 ,   24.5 ,   37.75,   54.  ,   70.25,   87.5 ,
    108.5 ,  129.5 ,  150.5 ,  171.5 ,  193.75,  233.75,  273.75])
y = np.array([-8.79182857, -5.56347794, -5.45683824, -4.30737662, -1.4394612 ,
   -1.58047016, -0.93225927, -0.6719836 , -0.45977157, -0.37622436,
   -0.56115757, -0.3038559 , -0.26594558, -0.26496367])

def fitFunc(t, A, B, k):
    return A - B*np.exp(-k*t)
init_vals = np.random.rand(1,3)

fitParams, fitCovariances = curve_fit(fitFunc, x, y, p0=init_vals)
print fitParams
plt.plot(x,y)
plt.plot(x,fitFunc(x,*fitParams))
plt.show()

指数曲线拟合与python

1 个答案: