在Java中,我通常依赖org.apache.commons.math3.random.EmpiricalDistribution类来执行以下操作：
有没有提供相同功能的Python库？似乎scipy.stats.gaussian_kde.resample做了类似的事情,但我不确定它是否实现了与我熟悉的Java类型相同的过程.
最佳答案
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
# This represents the original "empirical" sample -- I fake it by
# sampling from a normal distribution
orig_sample_data = np.random.normal(size=10000)
# Generate a KDE from the empirical sample
sample_pdf = scipy.stats.gaussian_kde(orig_sample_data)
# Sample new datapoints from the KDE
new_sample_data = sample_pdf.resample(10000).T[:,0]
# Histogram of initial empirical sample
cnts,bins,p = plt.hist(orig_sample_data,label='original sample',bins=100,histtype='step',linewidth=1.5,density=True)
# Histogram of datapoints sampled from KDE
plt.hist(new_sample_data,label='sample from KDE',bins=bins,density=True)
# Visualize the kde itself
y_kde = sample_pdf(bins)
plt.plot(bins,y_kde,label='KDE')
plt.legend()
plt.show(block=False)
new_sample_data应该从与原始数据大致相同的分布中绘制(达到KDE与原始分布的良好近似程度).