您可以使用
scipy.optimize.curve_fit（在合理的情况下）使您想要的任何功能适合数据。该功能的签名是
curve_fit(f, xdata, ydata, p0=None, sigma=None, **kw)
并使用非线性最小二乘拟合将函数拟合到f数据ydata(xdata)。在您的情况下，我会尝试类似的方法：
import numpyfrom scipy.optimize import curve_fitimport matplotlib.pyplot as pltdef _polynomial(x, *p): """Polynomial fitting function of arbitrary degree.""" poly = 0. for i, n in enumerate(p): poly += n * x**i return poly# Define some test data:x = numpy.linspace(0., numpy.pi)y = numpy.cos(x) + 0.05 * numpy.random.normal(size=len(x))# p0 is the initial guess for the fitting coefficients, set the length# of this to be the order of the polynomial you want to fit. Here I# have set all the initial guesses to 1., you may have a better idea of# what values to expect based on your data.p0 = numpy.ones(6,)coeff, var_matrix = curve_fit(_polynomial, x, y, p0=p0)yfit = [_polynomial(xx, *tuple(coeff)) for xx in x] # I'm sure there is a better # way of doing thisplt.plot(x, y, label='Test data')plt.plot(x, yfit, label='fitted data')plt.show()
