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