python numpy/scipy曲线拟合 [英] python numpy/scipy curve fitting

问题描述

我有一些要点，我正在努力为这些要点拟合曲线.我知道存在scipy.optimize.curve_fit函数，但是我不了解文档，即如何使用此函数.

我的观点:np.array([(1, 1), (2, 4), (3, 1), (9, 3)])

有人可以解释如何做吗?

解决方案

我建议您从简单的多项式拟合开始，scipy.optimize.curve_fit尝试将一组点必须知道的函数f拟合. >

这是使用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:

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()

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.

My points: np.array([(1, 1), (2, 4), (3, 1), (9, 3)])

Can anybody explain how to do that?

解决方案

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.

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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