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Fixed MCMC sampler.

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This commit is contained in:
Ilias Bilionis 2015-08-04 01:18:32 -04:00
parent f60abf7262
commit d3730bb518
4 changed files with 61 additions and 18 deletions
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1
GPy/inference/mcmc/__init__.py
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@ -1 +1,2 @@
from hmc import HMC
from samplers import *

32
GPy/inference/mcmc/samplers.py
View file

@ -4,23 +4,18 @@
import numpy as np
from scipy import linalg, optimize
import Tango
import sys
import re
import numdifftools as ndt
import pdb
import cPickle
class Metropolis_Hastings:
def __init__(self,model,cov=None):
"""Metropolis Hastings, with tunings according to Gelman et al. """
self.model = model
current = self.model._get_params_transformed()
current = self.model.optimizer_array
self.D = current.size
self.chains = []
if cov is None:
self.cov = model.Laplace_covariance()
self.cov = np.eye(self.D)
else:
self.cov = cov
self.scale = 2.4/np.sqrt(self.D)
@ -31,20 +26,20 @@ class Metropolis_Hastings:
if start is None:
self.model.randomize()
else:
self.model._set_params_transformed(start)
self.model.optimizer_array = start
def sample(self, Ntotal, Nburn, Nthin, tune=True, tune_throughout=False, tune_interval=400):
current = self.model._get_params_transformed()
fcurrent = self.model.log_likelihood() + self.model.log_prior()
def sample(self, Ntotal=10000, Nburn=1000, Nthin=10, tune=True, tune_throughout=False, tune_interval=400):
current = self.model.optimizer_array
fcurrent = self.model.log_likelihood() + self.model.log_prior() + \
self.model._log_det_jacobian()
accepted = np.zeros(Ntotal,dtype=np.bool)
for it in range(Ntotal):
print "sample %d of %d\r"%(it,Ntotal),
sys.stdout.flush()
prop = np.random.multivariate_normal(current, self.cov*self.scale*self.scale)
self.model._set_params_transformed(prop)
fprop = self.model.log_likelihood() + self.model.log_prior()
self.model.optimizer_array = prop
fprop = self.model.log_likelihood() + self.model.log_prior() + \
self.model._log_det_jacobian()
if fprop>fcurrent:#sample accepted, going 'uphill'
accepted[it] = True
@ -72,10 +67,11 @@ class Metropolis_Hastings:
def predict(self,function,args):
"""Make a prediction for the function, to which we will pass the additional arguments"""
param = self.model._get_params()
param = self.model.param_array
fs = []
for p in self.chain:
self.model._set_params(p)
self.model.param_array = p
fs.append(function(*args))
self.model._set_params(param)# reset model to starting state
# reset model to starting state
self.model.param_array = param
return fs