具有散景的散点图上的最佳拟合曲线（多项式）

Question

我创建了散景散点图。我想在数据上生成最佳拟合多项式曲线，并将曲线叠加在点云上。

我用 polyfit:

生成了一条二次折线

import numpy as np
from bokeh.plotting import figure, output_file, show
model2 = np.poly1d(np.polyfit(df['Dist'],df['Speed'], 2)
polyline = np.linspace(1,16000,900)
graph = figure(title = "Speed function of flight distance")
graph.scatter(df['Dist'],df['Speed'])
show(graph)

在散点图上显示这条折线的说明是什么？我看到了如何生成，我需要的是折线。

Answer 1

如评论中所述，graph.line() 添加了一个线图。现在，我们只需要一个均匀分布的 x-range，我们在其上绘制拟合函数：

import numpy as np
from bokeh.plotting import figure, output_file, show

#data generation
import pandas as pd
np.random.seed(123)
dist = np.sort(np.random.choice(range(100), 20, replace=False))
speed = 0.3 * dist ** 2 - 2.7 * dist - 1 + np.random.randint(-10, 10, dist.size)
df = pd.DataFrame({'Dist': dist, 'Speed': speed})

model2 = np.poly1d(np.polyfit(df['Dist'], df['Speed'], 2))
x_fit = np.linspace(df['Dist'].min(), df['Dist'].max(), 100)
graph = figure(title = "Speed function of flight distance")
graph.scatter(df['Dist'],df['Speed'])
graph.line(x_fit, model2(x_fit))
show(graph)

示例输出：

具有散景的散点图上的最佳拟合曲线（多项式）

Best fit curve (polynomial) on scatter plot with bokeh

python

bokeh