toy_init_particles = sample_gmm(num_samples,
[1.],
[np.array([-10])],
[np.eye(1)])
true_samples = sample_gmm(num_samples, weights, mus, sigmas)
plotfunc = get_plotfunc(true_samples)
toy_score = gmm_gld(weights, mus, sigmas)
svgd = SVGD(gld=toy_score)
particles= svgd.do_svgd_iterations_optimized(init_particles=toy_init_particles,
num_iterations=num_iterations,
learning_rate=0.1,
plotfunc=plotfunc)
x = np.linspace(-15, 10, 1000)
fig, ax = plt.subplots(1,1)
sns.kdeplot(true_samples.flatten(), ax=ax, label='Sampler')
sns.kdeplot(particles.flatten(), ax=ax, label='SVGD')
ax.legend()
#plt.show()
plt.close()
if __name__ == '__main__':
num_iterations = 5000
num_samples = 100
weights = [1]
mus = [np.array([0, 0])]
sigmas = [np.eye(2)]
init_particles = sample_gmm(num_samples, [1.], [np.array([-7, -7])],
[np.eye(2)])
true_samples = sample_gmm(num_samples, weights, mus, sigmas)
plotfunc = get_plotfunc(true_samples)
gld = gmm_gld(weights, mus, sigmas)
svgd = SVGD(gld=gld)
log_lik_func = log_lik_gmm(weights, mus, sigmas)
report_metrics = metrics(log_lik_func)
particles = svgd.do_svgd_iterations_optimized(
init_particles=init_particles,
num_iterations=num_iterations,
learning_rate=1e-2,
plotfunc=plotfunc,
metrics=report_metrics)
[corr2, 1]
])]
init_particles = sample_gmm(5,
[1.],
[np.array([-10, 0])],
[np.eye(2)])
true_samples = sample_gmm(num_samples, weights, mus, sigmas)
plotfunc = get_plotfunc(true_samples)
gld = gmm_gld(weights, mus, sigmas)
svgd = SVGD(gld=gld)
log_lik_func = log_lik_gmm(weights, mus, sigmas)
report_metrics = metrics(log_lik_func)
particles = svgd.do_svgd_iterations_optimized(
init_particles=init_particles,
num_iterations=num_iterations,
learning_rate=1e-3,
plotfunc=plotfunc,
metrics=report_metrics,
progress_freq=100)