如何拟合数据然后从拟合函数中采样以绘制曲线 [英] how to fit data and then sample from the fitted function to draw curve

问题描述

给定两个数组 x 和 y，我尝试使用 np.polyfit 函数来拟合数据，方法如下:

Given two arrays x and y,I was trying to use np.polyfit function to fit the data,using the following way:

z = np.polyfit(x, y, 20)
f = np.poly1d(z)

但由于我想绘制折线图而不是平滑曲线，因此我使用此函数 f 对绘制线的数组进行采样.

but since i want to plot a line chart instead of a smooth curve, so then i use this function f to sample an array for plotting line.

x_new = np.linspace(x[0], x[-1], fitting_size)
y_new = np.zeros(fitting_size)
for t in range(fitting_size):
   y_new[t] = f(x_new[t])

plt.plot(x_new, y_new, marker='v', ms=1)

问题是上面的段码仍然给了我一个平滑的曲线.我该如何解决?谢谢.

The problem is that the above segment code stills gives me a smooth curve. How can i fix it? Thanks.

推荐答案

不幸的是，这个问题背后的意图有点不清楚.但是，如果要执行线性拟合，则需要向 polyfit 提供度数 deg=1.那么就没有理由从拟合中取样；可以简单地使用相同的输入数组并对其应用拟合函数.

Unfortunately the intention behind the question is a bit unclear. However, if you want to perform a linear fit, you need to provide the degree deg=1 to polyfit. There is then no reason to sample from the fit; one can simply use the same input array and apply the fitting function to it.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-1,5,20)
y = 3*x**2+np.random.rand(len(x))*10

z = np.polyfit(x, y, 1)
f = np.poly1d(z)

z2 = np.polyfit(x, y, 2)
f2 = np.poly1d(z2)


plt.plot(x,y, marker=".", ls="", c="k", label="data")
plt.plot(x, f(x), label="linear fit")
plt.plot(x, f2(x), label="quadratic fit")
plt.legend()
plt.show()

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