1D bimodal exampleΒΆ

This example shows how to use lightkde.kde_1d and how it compares to scipy.stats.gaussian_kde for a bimodal univariate case.

Import packages:

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde, norm

from lightkde import kde_1d

Generate synthetic data from two univariate normal distributions:

sample = np.hstack((norm.rvs(size=2_000), 0.3 * norm.rvs(size=1_000) + 5))

Estimate kernel density using lightkde:

density_vec, x_vec = kde_1d(sample_vec=sample)

Estimate kernel density using scipy:

gkde = gaussian_kde(dataset=sample)
scipy_density_vec = gkde.evaluate(x_vec)

Plot the data against the kernel density estimates:

plt.plot(x_vec, density_vec, "--r", label="lightkde")
plt.plot(x_vec, scipy_density_vec, label="scipy.stats.gaussian_kde")
plt.hist(sample, bins=100, density=True, alpha=0.5, label="data")
plot bimodal kde 1d

The scipy method oversmooths the kernel density and it is far from the histogram of the data that it is expected to follow.

Total running time of the script: ( 0 minutes 1.551 seconds)

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