对于使用6阶多项式插值非线性数据,我有一个非常具体的要求。我看过numpy / scipy例程(scipy.interpolate.InterpolatedUnivariateSpline),这些例程最多只能进行5级插值。
即使没有直接函数可以执行此操作,是否有办法在Python中复制Excel的LINEST线性回归算法?LINEST允许进行6度曲线拟合,但是我不希望将Excel用于任何东西,因为此计算是更大的Python脚本的一部分。
任何帮助,将不胜感激!
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
scipy.optimize.curve_fit
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 numpy from scipy.optimize import curve_fit import matplotlib.pyplot as plt def _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 this plt.plot(x, y, label='Test data') plt.plot(x, yfit, label='fitted data') plt.show()