def gauss_2d(nsamples=1000):
"""
Another simple test plot
1d gaussian sampled from each sampler visualized as a joint 2d gaussian
"""
gaussian = TestGaussian(ndims=1)
control = HMCBase(distribution=gaussian)
experimental = MarkovJumpHMC(distribution=gaussian, resample=False)
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, control=True, resample=True):
"""
plots a 1d histogram from each sampler
distr is (an unitialized) class from distributions
"""
distribution = distr(ndims=1)
control_smp = HMCBase(distribution=distribution, epsilon=1)
experimental_smp = MarkovJumpHMC(distribution=distribution, resample=resample, epsilon=1)
if control:
plt.hist(control_smp.sample(nsamples)[0], nbins, normed=True, label="Standard HMCBase", alpha=.5)
plt.hist(experimental_smp.sample(nsamples)[0], nbins, normed=True, label="Continuous-time HMCBase",alpha=.5)
plt.legend()
def gauss_2d(nsamples=1000):
"""
Another simple test plot
1d gaussian sampled from each sampler visualized as a joint 2d gaussian
"""
gaussian = TestGaussian(ndims=1)
control = HMCBase(distribution=gaussian)
experimental = MarkovJumpHMC(distribution=gaussian, resample=False)
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, control=True, resample=True):
"""
plots a 1d histogram from each sampler
distr is (an unitialized) class from distributions
"""
distribution = distr(ndims=1)
control_smp = HMCBase(distribution=distribution, epsilon=1)
experimental_smp = MarkovJumpHMC(distribution=distribution,
resample=resample,
epsilon=1)
if control:
plt.hist(control_smp.sample(nsamples)[0],
nbins,
normed=True,
label="Standard HMCBase",
alpha=.5)
plt.hist(experimental_smp.sample(nsamples)[0],
nbins,
normed=True,
label="Continuous-time HMCBase",
alpha=.5)
plt.legend()
