def test_univariate_two_sample(i):
# This test ensures posterior sampling of uni/bimodal dists on R. When the
# plot is shown, a density curve overlays the samples which is useful for
# seeing that logpdf/simulate agree.
N_SAMPLES = 100
rng = gu.gen_rng(2)
# Synthetic samples.
samples_train = SAMPLES[i](N_SAMPLES, rng)
samples_test = SAMPLES[i](N_SAMPLES, rng)
# Univariate KDE.
kde = MultivariateKde([3], None, distargs={O: {ST: [N], SA:[{}]}}, rng=rng)
# Incorporate observations.
for rowid, x in enumerate(samples_train):
kde.incorporate(rowid, {3: x})
# Run inference.
kde.transition()
# Generate posterior samples.
samples_gen = [s[3] for s in kde.simulate(-1, [3], N=N_SAMPLES)]
# Plot comparison of all train, test, and generated samples.
fig, ax = plt.subplots()
ax.scatter(samples_train, [0]*len(samples_train), color='b', label='Train')
ax.scatter(samples_gen, [1]*len(samples_gen), color='r', label='KDE')
ax.scatter(samples_test, [2]*len(samples_test), color='g', label='Test')
# Overlay the density function.
xs = np.linspace(ax.get_xlim()[0], ax.get_xlim()[1], 200)
pdfs = [kde.logpdf(-1, {3: x}) for x in xs]
# Convert the pdfs from the range to 1 to 1.5 by rescaling.
pdfs_plot = np.exp(pdfs)+1
pdfs_plot = (pdfs_plot/max(pdfs_plot)) * 1.5
ax.plot(xs, pdfs_plot, color='k')
# Clear up some labels.
ax.set_title('Univariate KDE Posterior versus Generator')
ax.set_xlabel('x')
ax.set_yticklabels([])
# Show the plot.
ax.grid()
plt.close()
# KS test
_, p = ks_2samp(samples_test, samples_gen)
assert .05 < p
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