python numpy/scipy 曲线拟合 [英] python numpy/scipy curve fitting
问题描述
我有一些要点，我正在尝试为这些点拟合曲线.我知道存在 scipy.optimize.curve_fit 函数，但我不了解文档，即如何使用该函数.
I have some points and I am trying to fit curve for this points. I know that there exist scipy.optimize.curve_fit function, but I do not understand documentation, i.e how to use this function.
我的观点:np.array([(1, 1), (2, 4), (3, 1), (9, 3)])
谁能解释一下怎么做?
推荐答案
我建议你从简单的多项式拟合开始，scipy.optimize.curve_fit 尝试拟合您必须知道的函数 f点集.
I suggest you to start with simple polynomial fit, scipy.optimize.curve_fit tries to fit a function f that you must know to a set of points.
这是一个使用 numpy.polyfit 和 poly1d 的简单 3 度多项式拟合，第一个执行最小二乘多项式拟合，第二个计算新点:
This is a simple 3 degree polynomial fit using numpy.polyfit and poly1d, the first performs a least squares polynomial fit and the second calculates the new points:
import numpy as np
import matplotlib.pyplot as plt
points = np.array([(1, 1), (2, 4), (3, 1), (9, 3)])
# get x and y vectors
x = points[:,0]
y = points[:,1]
# calculate polynomial
z = np.polyfit(x, y, 3)
f = np.poly1d(z)
# calculate new x's and y's
x_new = np.linspace(x[0], x[-1], 50)
y_new = f(x_new)
plt.plot(x,y,'o', x_new, y_new)
plt.xlim([x[0]-1, x[-1] + 1 ])
plt.show()
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