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Python: Generate random values from empirical distribution

In Java, I usually rely on the org.apache.commons.math3.random.EmpiricalDistribution class to do the following:

Is there any Python library that provides the same functionality? It seems like scipy.stats.gaussian_kde.resample does something similar, but I'm not sure if it implements the same procedure as the Java type I'm familiar with.

1 Answer
1

import numpy as np
import scipy
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, normed=True)

# Histogram of datapoints sampled from KDE
plt.hist(new_sample_data, label='sample from KDE', bins=bins,
histtype='step', linewidth=1.5, normed=True)

# Visualize the kde itself
y_kde = sample_pdf(bins)
plt.plot(bins, y_kde, label='KDE')
plt.legend();


new_sample_data should be drawn from roughly the same distribution as the original data (to the degree that the KDE is a good approximation to the original distribution).

new_sample_data

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