return instance
def get_KernelAdaptiveLangevin_instance(D, target_log_pdf):
step_size = 1.
m = 500
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=1., m=m, D=D)
logger.info("kernel exp family uses %s" % surrogate.get_parameters())
instance = KernelAdaptiveLangevin(D, target_log_pdf, surrogate, step_size)
return instance
if __name__ == '__main__':
Log.set_loglevel(20)
D = 2
bananicity = 0.03
V = 100
true_mean = np.zeros(D)
true_var = np.ones(D)
true_var[0] = 100
true_var[1] = 20
true_cov = np.diag(true_var)
num_iter_per_particle = 200
population_sizes = [5, 10, 25, 50, 100]
num_repetitions = 30
proposal_mu = np.array([ 0.19404095, -0.14017837, 0.35465807, -0.22049461, -4.53669311])
proposal_var = np.array([ 0.0898059 , 0.07987 , 0.60627872, 2.79810551, 4.48372874])
proposal_L_C = np.diag(np.sqrt(proposal_var))
proposal_L_C *= 2
# these results were averaged over many repetitions.
# mean: array([ 0.18482533, -0.13985885, 0.29183487, -0.52620493, -4.62597391])
# var: array([ 0.10732838, 0.10290396, 0.51048946, 2.92114185, 4.70979457])
# np.mean(var): 1.670
# np.linalg.norm(mean): 4.671
instance = AdaptiveIndependentMetropolis(D, target_log_pdf, gamma2, proposal_mu, proposal_L_C)
return instance
if __name__ == '__main__':
Log.set_loglevel(10)
num_iter_per_particle = 100
population_size = 50
num_iter = population_size * num_iter_per_particle
mdl = SVoneSP500Model()
target_log_pdf = mdl.get_logpdf_closure()
# number of particles used for integrating out the latent variables
mdl.mdl_param.NX = mdl.mdl_param.NX * 1
D = mdl.dim
start = np.zeros(D)
sampler = get_AdaptiveIndependentMetropolis_instance(D, target_log_pdf)
return instance
def get_AdaptiveLangevin(D, target_log_pdf, grad, prec=False, step_size=1):
acc_star = 0.574
gamma2 = 0.1
instance = AdaptiveLangevin(D, target_log_pdf, grad, step_size,
one_over_4th_root_t_schedule, acc_star)
instance.do_preconditioning = prec
instance.manual_gradient_step_size = 1
return instance
if __name__ == '__main__':
Log.set_loglevel(20)
max_moment = 8
D = 10
pop_size = 10
bananicity = 0.03
V = 100
Z = sample_banana(3000, D, bananicity, V)
moments = np.array([(Z**i).mean(0) for i in range(1, max_moment)])
pk = PolynomialKernel(3)
print(moments)
true_correct = np.linalg.inv(np.cov(Z.T))
target_log_pdf = lambda x: log_banana_pdf(
x, bananicity, V, compute_grad=False)
target_grad = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=True)
samplers = [
