Toolbox for Kernel Density Estimation- Download- Souzhen.com
In data analysis and statistical modeling, kernel density estimation is a commonly used nonparametric method for estimating probability density functions. It obtains a continuous density function curve by smoothing the data, so that it can better understand and describe the distribution of the data. In order to facilitate researchers to perform kernel density estimation analysis, the kdetoolbox kernel density estimation toolbox came into being.
kdetoolbox provides a series of functions and tools for kernel density estimation, which can help users quickly and accurately conduct kernel density estimation analysis. The following is a sample code that demonstrates how to use kdetoolbox for kernel density estimation and visualize the results.
import numpy as np
import matplotlib.pyplot as plt
from kdetoolbox import kde
# 生成模拟数据
np.random.seed(0)
data =