def gauss_2d(nsamples=1000):
"""
Another simple test plot
1d gaussian sampled from each sampler visualized as a joint 2d gaussian
"""
gaussian = misc.distributions.TestGaussian(ndims=1)
control = Control(gaussian.Xinit, gaussian.E, gaussian.dEdX)
experimental = ContinuousTimeHMC(gaussian.Xinit, gaussian.E, gaussian.dEdX)
with sns.axes_style("white"):
sns.jointplot(control.sample(nsamples)[0], experimental.sample(nsamples)[0], kind="hex", stat_func=None)
def hist_1d(distr, nsamples=1000, nbins=250):
"""
plots a 1d histogram from each sampler
distr is (an unitialized) class from distributions
"""
distribution = distr(ndims=1)
control = Control(distribution.Xinit, distribution.E, distribution.dEdX)
experimental = ContinuousTimeHMC(distribution.Xinit, distribution.E, distribution.dEdX)
plt.hist(control.sample(nsamples)[0], nbins, normed=True, label="Standard Control", alpha=0.5)
plt.hist(experimental.sample(nsamples)[0], nbins, normed=True, label="Continuous-time Control", alpha=0.5)
plt.legend()
