def get_benchmark_samples_pmc():
# load benchmark samples, make sure its a particular file version
benchmark_samples_fname = "pmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "d53e505730c41fbe413188530916d9a402e21a87"
assert_file_has_sha1sum(benchmark_samples_fname, benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0, len(benchmark_samples), step=50)]
return benchmark_samples
def get_benchmark_samples_mcmc():
# load benchmark samples, make sure its a particular file version
benchmark_samples_fname = "mcmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "dd71899bf8ead3972de45543b09af95dc858a208"
assert_file_has_sha1sum(benchmark_samples_fname, benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0, len(benchmark_samples), step=100)]
return benchmark_samples
def get_benchmark_samples_pmc():
# load benchmark samples, make sure its a particular file version
benchmark_samples_fname = "pmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "d53e505730c41fbe413188530916d9a402e21a87"
assert_file_has_sha1sum(benchmark_samples_fname, benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0,
len(benchmark_samples),
step=50)]
return benchmark_samples
def get_benchmark_samples_mcmc():
# load benchmark samples, make sure its a particular file version
benchmark_samples_fname = "mcmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "dd71899bf8ead3972de45543b09af95dc858a208"
assert_file_has_sha1sum(benchmark_samples_fname, benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0,
len(benchmark_samples),
step=100)]
return benchmark_samples
def get_StaticMetropolis_instance(D, target_log_pdf):
step_size = 0.002
acc_star = None
schedule = None
instance = StaticMetropolis(D, target_log_pdf, step_size, schedule, acc_star)
# give proposal variance a meaningful shape from previous samples
benchmark_samples_fname = "pmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "d53e505730c41fbe413188530916d9a402e21a87"
assert_file_has_sha1sum(benchmark_samples_fname, benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0, len(benchmark_samples), step=50)]
instance.L_C = np.linalg.cholesky(np.cov(benchmark_samples.T))
return instance
def get_StaticMetropolis_instance(D, target_log_pdf):
step_size = 0.002
acc_star = None
schedule = None
instance = StaticMetropolis(D, target_log_pdf, step_size, schedule,
acc_star)
# give proposal variance a meaningful shape from previous samples
benchmark_samples_fname = "pmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "d53e505730c41fbe413188530916d9a402e21a87"
assert_file_has_sha1sum(benchmark_samples_fname,
benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0,
len(benchmark_samples),
step=50)]
instance.L_C = np.linalg.cholesky(np.cov(benchmark_samples.T))
return instance
from matplotlib.lines import Line2D
import os
from kameleon_rks.examples.plotting import visualise_pairwise_marginals
from kameleon_rks.experiments.kernel_gradient_is.pmc_sv import result_fname
from kameleon_rks.experiments.tools import assert_file_has_sha1sum
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
if False:
# plot benchmark samples, make sure its a particular file version
benchmark_samples_fname = "pmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "d53e505730c41fbe413188530916d9a402e21a87"
assert_file_has_sha1sum(benchmark_samples_fname, benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0,
len(benchmark_samples),
step=50)]
mean = np.mean(benchmark_samples, axis=0)
var = np.var(benchmark_samples, axis=0)
print("%d benchmark samples" % len(benchmark_samples))
print "mean:", repr(mean)
print "var:", repr(var)
print "np.mean(var): %.3f" % np.mean(var)
print "np.linalg.norm(mean): %.3f" % np.linalg.norm(mean)
visualise_pairwise_marginals(benchmark_samples)
plt.show()
from matplotlib.lines import Line2D
import os
from kameleon_rks.examples.plotting import visualise_pairwise_marginals
from kameleon_rks.experiments.kernel_gradient_is.pmc_sv import result_fname
from kameleon_rks.experiments.tools import assert_file_has_sha1sum
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
if False:
# plot benchmark samples, make sure its a particular file version
benchmark_samples_fname = "pmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "d53e505730c41fbe413188530916d9a402e21a87"
assert_file_has_sha1sum(benchmark_samples_fname, benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0, len(benchmark_samples), step=50)]
mean = np.mean(benchmark_samples, axis=0)
var = np.var(benchmark_samples, axis=0)
print("%d benchmark samples" % len(benchmark_samples))
print "mean:", repr(mean)
print "var:", repr(var)
print "np.mean(var): %.3f" % np.mean(var)
print "np.linalg.norm(mean): %.3f" % np.linalg.norm(mean)
visualise_pairwise_marginals(benchmark_samples)
plt.show()
# from kameleon_rks.experiments.latex_plot_init import plt