奇怪的行为在scipy.optimize.leastsq

我已经注意到一些不寻常的行为时，试图适应一些嘈杂的数据：当我改变阵列的长度，我得到疯狂不同的拟合。奇怪的行为在scipy.optimize.leastsq

import numpy as np 
import matplotlib.pyplot as plt 
# set up true function and "measured" data 
x = np.linspace(0, 6e-2, 500); 
A, k, theta = 10, 1.0/3e-2, np.pi/6; 
y_true = A * np.sin(2 * np.pi * k * x + theta); 
y_meas = y_true + 2*np.random.randn(x.size); 
plt.plot(x, y_meas); 
plt.plot(x, y_true); 
plt.show()

哪个给出了这样的形象：

我已经创造了一些辅助功能，然后我做了最小二乘法拟合：

# residual function, e_i 
def residuals(p, y, x): 
    A, k, theta = p; 
    err = y - A * np.sin(2 * np.pi * k * x + theta); 
    return err; 

def peval(x, p): 
    return p[0] * np.sin(2 * np.pi * p[1] * x + p[2]); 

# starting values of A, k and theta 
p0 = [12, 1/2.3e-2, np.pi/3]; 
print(np.array(p0)); 

# do least squares fitting 
from scipy.optimize import leastsq 

plsq = leastsq(residuals, p0, args=(y_meas, x)); 
print(plsq[0]); print(np.array([A, k, theta]));

绘制这给：

plt.plot(x, peval(x, plsq[0])) 
plt.plot(x, y_meas,'ro') 
plt.plot(x, y_true); 
plt.title('Least-squares fit to noisy data'); 
plt.legend(['Fit', 'Noisy', 'True']);

当我改变我的设置为：

x = np.linspace(0, 18e-2, 500); 
A, k, theta = 10, 1.0/3e-2, np.pi/6; 
y_true = A * np.sin(2 * np.pi * k * x + theta); 
y_meas = y_true + 2*np.random.randn(x.size);

（即我三倍的时间，我衡量）的长度，然后运行该代码的其余部分，我得到的配合变为：