def get_OracleKernelAdaptiveLangevin_instance(D, target_log_pdf):
step_size = 1.
m = 500
N = 5000
Z = sample_banana(N, D, bananicity, V)
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=.001, m=m, D=D)
surrogate.fit(Z)
if False:
param_bounds = {'sigma': [-2, 3]}
bo = BayesOptSearch(surrogate, Z, param_bounds)
best_params = bo.optimize()
surrogate.set_parameters_from_dict(best_params)
if False:
sigma = 1. / gamma_median_heuristic(Z)
surrogate.set_parameters_from_dict({'sigma': sigma})
logger.info("kernel exp family uses %s" % surrogate.get_parameters())
if False:
import matplotlib.pyplot as plt
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(surrogate, Z, Xs, Ys)
plt.show()
instance = OracleKernelAdaptiveLangevin(D, target_log_pdf, surrogate, step_size)
return instance
def get_OracleKernelAdaptiveLangevin_instance(D, target_log_pdf):
step_size = 1.
m = 500
N = 5000
Z = sample_banana(N, D, bananicity, V)
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=.001, m=m, D=D)
surrogate.fit(Z)
if False:
param_bounds = {'sigma': [-2, 3]}
bo = BayesOptSearch(surrogate, Z, param_bounds)
best_params = bo.optimize()
surrogate.set_parameters_from_dict(best_params)
if False:
sigma = 1. / gamma_median_heuristic(Z)
surrogate.set_parameters_from_dict({'sigma': sigma})
logger.info("kernel exp family uses %s" % surrogate.get_parameters())
if False:
import matplotlib.pyplot as plt
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(surrogate, Z, Xs, Ys)
plt.show()
instance = OracleKernelAdaptiveLangevin(D, target_log_pdf, surrogate,
step_size)
return instance
def get_OracleKameleon_instance(D, target_log_pdf):
step_size = 30.
schedule = one_over_sqrt_t_schedule
acc_star = 0.234
gamma2 = 0.1
n = 500
Z = sample_banana(N=n, D=D, bananicity=0.03, V=100)
kernel_sigma = 1. / gamma_median_heuristic(Z)
instance = OracleKameleon(D, target_log_pdf, n, kernel_sigma, step_size, gamma2, schedule, acc_star)
instance.set_batch(Z)
return instance
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0, len(benchmark_samples), step=100)]
return benchmark_samples
if __name__ == "__main__":
benchmark_samples = get_benchmark_samples_mcmc()
true_mean = np.mean(benchmark_samples, axis=0)
true_var = np.var(benchmark_samples, axis=0)
print("%d benchmark samples" % len(benchmark_samples))
print "mean:", repr(true_mean)
print "var:", repr(true_var)
print "np.mean(var): %.3f" % np.mean(true_var)
print "np.linalg.norm(mean): %.3f" % np.linalg.norm(true_mean)
print "median heuristic sigma: %.3f" % (1. / gamma_median_heuristic(benchmark_samples))
visualise_pairwise_marginals(benchmark_samples)
plt.show()
m = 1000
lmbda = 1.
res = 10
log2_sigmas = np.linspace(-4, 10, res)
log2_sigmas = np.log2([0.5, 0.7, 0.9, 1.1, 1.3, 1.5])
print "log2_sigmas:", log2_sigmas
print "sigmas:", 2 ** log2_sigmas
Js_mean = np.zeros(res)
Js_var = np.zeros(res)
step=100)]
return benchmark_samples
if __name__ == "__main__":
benchmark_samples = get_benchmark_samples_mcmc()
true_mean = np.mean(benchmark_samples, axis=0)
true_var = np.var(benchmark_samples, axis=0)
print("%d benchmark samples" % len(benchmark_samples))
print "mean:", repr(true_mean)
print "var:", repr(true_var)
print "np.mean(var): %.3f" % np.mean(true_var)
print "np.linalg.norm(mean): %.3f" % np.linalg.norm(true_mean)
print "median heuristic sigma: %.3f" % (
1. / gamma_median_heuristic(benchmark_samples))
visualise_pairwise_marginals(benchmark_samples)
plt.show()
m = 1000
lmbda = 1.
res = 10
log2_sigmas = np.linspace(-4, 10, res)
log2_sigmas = np.log2([0.5, 0.7, 0.9, 1.1, 1.3, 1.5])
print "log2_sigmas:", log2_sigmas
print "sigmas:", 2**log2_sigmas
Js_mean = np.zeros(res)
Js_var = np.zeros(res)