def generate_figure_samples(samples_per_frame, n_frames, burnin = int(1e4)):
""" Generates the figure
:param samples_per_frame: number of sample steps between each frame
:param n_frames: number of frames to draw
:returns: None
:rtype: None
"""
n_samples = samples_per_frame * n_frames
ndims = 36
nbasis = 72
rand_val = rand(ndims,nbasis/2,density=0.25)
W = np.concatenate([rand_val.toarray(), -rand_val.toarray()],axis=1)
logalpha = np.random.randn(nbasis, 1)
poe = ProductOfT(nbatch=1, W=W, logalpha=logalpha)
## NUTS uses a different number of grad evals for each update step!!
## makes it very hard to compare against others w/ same number of update steps
# # NUTS
# print "NUTS"
# nuts_init = poe.Xinit[:, 0]
# nuts_samples = nuts6(poe.reset(), n_samples, nuts_burnin, nuts_init)[0]
# nuts_frames = [nuts_samples[f_idx * samples_per_frame, :] for f_idx in xrange(0, n_frames)]
# x_init = nuts_samples[0, :].reshape(ndims, 1)
## burnin
print "MJHMC burnin"
x_init = poe.Xinit #[:, [0]]
mjhmc = MarkovJumpHMC(distribution=poe.reset(), **mjhmc_params)
mjhmc.state = HMCState(x_init.copy(), mjhmc)
mjhmc_samples = mjhmc.sample(burnin)
print mjhmc_samples.shape
x_init = mjhmc_samples[:, [0]]
# control HMC
print "Control"
hmc = ControlHMC(distribution=poe.reset(), **control_params)
hmc.state = HMCState(x_init.copy(), hmc)
hmc_samples = hmc.sample(n_samples)
hmc_frames = [hmc_samples[:, f_idx * samples_per_frame].copy() for f_idx in xrange(0, n_frames)]
# MJHMC
print "MJHMC"
mjhmc = MarkovJumpHMC(distribution=poe.reset(), resample=False, **mjhmc_params)
mjhmc.state = HMCState(x_init.copy(), mjhmc)
mjhmc_samples = mjhmc.sample(n_samples)
mjhmc_frames = [mjhmc_samples[:, f_idx * samples_per_frame].copy() for f_idx in xrange(0, n_frames)]
print mjhmc.r_count, hmc.r_count
print mjhmc.l_count, hmc.l_count
print mjhmc.f_count, hmc.f_count
print mjhmc.fl_count, hmc.fl_count
frames = [mjhmc_frames, hmc_frames]
names = ['MJHMC', 'ControlHMC']
frame_grads = [f_idx * samples_per_frame for f_idx in xrange(0, n_frames)]
return frames, names, frame_grads
def generate_figure_samples(samples_per_frame, n_frames, burnin=int(1e4)):
""" Generates the figure
:param samples_per_frame: number of sample steps between each frame
:param n_frames: number of frames to draw
:returns: None
:rtype: None
"""
n_samples = samples_per_frame * n_frames
ndims = 36
nbasis = 72
rand_val = rand(ndims, nbasis / 2, density=0.25)
W = np.concatenate([rand_val.toarray(), -rand_val.toarray()], axis=1)
logalpha = np.random.randn(nbasis, 1)
poe = ProductOfT(nbatch=1, W=W, logalpha=logalpha)
## NUTS uses a different number of grad evals for each update step!!
## makes it very hard to compare against others w/ same number of update steps
# # NUTS
# print "NUTS"
# nuts_init = poe.Xinit[:, 0]
# nuts_samples = nuts6(poe.reset(), n_samples, nuts_burnin, nuts_init)[0]
# nuts_frames = [nuts_samples[f_idx * samples_per_frame, :] for f_idx in xrange(0, n_frames)]
# x_init = nuts_samples[0, :].reshape(ndims, 1)
## burnin
print "MJHMC burnin"
x_init = poe.Xinit # [:, [0]]
mjhmc = MarkovJumpHMC(distribution=poe.reset(), **mjhmc_params)
mjhmc.state = HMCState(x_init.copy(), mjhmc)
mjhmc_samples = mjhmc.sample(burnin)
print mjhmc_samples.shape
x_init = mjhmc_samples[:, [0]]
# control HMC
print "Control"
hmc = ControlHMC(distribution=poe.reset(), **control_params)
hmc.state = HMCState(x_init.copy(), hmc)
hmc_samples = hmc.sample(n_samples)
hmc_frames = [hmc_samples[:, f_idx * samples_per_frame].copy() for f_idx in xrange(0, n_frames)]
# MJHMC
print "MJHMC"
mjhmc = MarkovJumpHMC(distribution=poe.reset(), resample=False, **mjhmc_params)
mjhmc.state = HMCState(x_init.copy(), mjhmc)
mjhmc_samples = mjhmc.sample(n_samples)
mjhmc_frames = [mjhmc_samples[:, f_idx * samples_per_frame].copy() for f_idx in xrange(0, n_frames)]
print mjhmc.r_count, hmc.r_count
print mjhmc.l_count, hmc.l_count
print mjhmc.f_count, hmc.f_count
print mjhmc.fl_count, hmc.fl_count
frames = [mjhmc_frames, hmc_frames]
names = ["MJHMC", "ControlHMC"]
frame_grads = [f_idx * samples_per_frame for f_idx in xrange(0, n_frames)]
return frames, names, frame_grads
def hist_2d(distribution, nsamples, **kwargs):
"""
Plots a 2d hexbinned histogram of distribution
"""
distr = distribution(ndims=2)
sampler = MarkovJumpHMC(distr.Xinit, distr.E, distr.dEdX, **kwargs)
samples = sampler.sample(nsamples)
with sns.axes_style("white"):
sns.jointplot(samples[0], samples[1], kind="kde", stat_func=None)
def hist_2d(distr, nsamples, **kwargs):
"""
Plots a 2d hexbinned histogram of distribution
Args:
distr: Distribution object
nsamples: number of samples to use to generate plot
"""
sampler = MarkovJumpHMC(distribution=distr, **kwargs)
samples = sampler.sample(nsamples)
with sns.axes_style("white"):
g = sns.jointplot(samples[0], samples[1], kind='kde', stat_func=None)
return g
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 hist_2d(distr, nsamples, **kwargs):
"""
Plots a 2d hexbinned histogram of distribution
Args:
distr: Distribution object
nsamples: number of samples to use to generate plot
"""
sampler = MarkovJumpHMC(distribution=distr, **kwargs)
samples = sampler.sample(nsamples)
with sns.axes_style("white"):
g = sns.jointplot(samples[0], samples[1], kind='kde', stat_func=None)
return g
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()
