def test_bivariate_conditional_two_sample(noise):
# This test checks joint and conditional simulation of a bivarate normal
# with (correlation 1-noise). The most informative use is plotting but
# there is a numerical test for the conditional distributions.
N_SAMPLES = 100
rng = gu.gen_rng(2)
# Synthetic samples.
linear = Linear(outputs=[0,1], noise=noise, rng=rng)
samples_train = np.asarray(
[[s[0], s[1]] for s in linear.simulate(-1, [0,1], N=N_SAMPLES)])
# Bivariate KDE.
kde = MultivariateKde(
[0,1], None, distargs={O: {ST: [N,N], SA:[{},{}]}}, rng=rng)
# Incorporate observations.
for rowid, x in enumerate(samples_train):
kde.incorporate(rowid, {0: x[0], 1: x[1]})
# Run inference.
kde.transition()
# Generate posterior samples from the joint.
samples_gen = np.asarray(
[[s[0],s[1]] for s in kde.simulate(-1, [0,1], N=N_SAMPLES)])
# Plot comparisons of the joint.
fig, ax = plt.subplots(nrows=1, ncols=2)
plot_data = zip(
ax, ['b', 'r'], ['Train', 'KDE'], [samples_train, samples_gen])
for (a, c, l, s) in plot_data:
a.scatter(s[:,0], s[:,1], color=c, label=l)
a.grid()
a.legend(framealpha=0)
# Generate posterior samples from the conditional.
xs = np.linspace(-3, 3, 100)
cond_samples_a = np.asarray(
[[s[1] for s in linear.simulate(-1, [1], {0: x0}, N=N_SAMPLES)]
for x0 in xs])
cond_samples_b = np.asarray(
[[s[1] for s in kde.simulate(-1, [1], {0: x0}, N=N_SAMPLES)]
for x0 in xs])
# Plot the mean value on the same plots.
for (a, s) in zip(ax, [cond_samples_a, cond_samples_b]):
a.plot(xs, np.mean(s, axis=1), linewidth=3, color='g')
a.set_xlim([-5,4])
a.set_ylim([-5,4])
plt.close('all')
# Perform a two sample test on the means.
mean_a = np.mean(cond_samples_a, axis=1)
mean_b = np.mean(cond_samples_b, axis=1)
_, p = ks_2samp(mean_a, mean_b)
assert .01 < p
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