From 637be86369ac087c018f4186ab7cb49b5c34e8be Mon Sep 17 00:00:00 2001
From: Max Zwiessele <ibinbei@gmail.com>
Date: Fri, 18 Sep 2015 16:21:31 +0100
Subject: [PATCH] [yak shaving]

---
 GPy/core/parameterization/parameterized.py |  4 ++--
 GPy/inference/mcmc/samplers.py             |  2 +-
 GPy/util/initialization.py                 | 14 +++++++++++---
 3 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/GPy/core/parameterization/parameterized.py b/GPy/core/parameterization/parameterized.py
index 5aa95695..7f67c497 100644
--- a/GPy/core/parameterization/parameterized.py
+++ b/GPy/core/parameterization/parameterized.py
@@ -314,8 +314,8 @@ class Parameterized(Parameterizable):
                 if name in pnames:
                     param = self.parameters[pnames.index(name)]
                     param[:] = val; return
-            except AttributeError:
-                pass
+            except AttributeError as a:
+                raise
         return object.__setattr__(self, name, val);
 
     #===========================================================================
diff --git a/GPy/inference/mcmc/samplers.py b/GPy/inference/mcmc/samplers.py
index 12939cfe..5ec684cc 100644
--- a/GPy/inference/mcmc/samplers.py
+++ b/GPy/inference/mcmc/samplers.py
@@ -14,7 +14,7 @@ except ImportError:
     import pickle
 
 
-class Metropolis_Hastings:
+class Metropolis_Hastings(object):
     def __init__(self,model,cov=None):
         """Metropolis Hastings, with tunings according to Gelman et al. """
         self.model = model
diff --git a/GPy/util/initialization.py b/GPy/util/initialization.py
index 1cf9e846..1e19a321 100644
--- a/GPy/util/initialization.py
+++ b/GPy/util/initialization.py
@@ -5,15 +5,23 @@ Created on 24 Feb 2014
 '''
 
 import numpy as np
-from GPy.util.pca import PCA
+from ..util.pca import PCA
 
 def initialize_latent(init, input_dim, Y):
-    Xr = np.asfortranarray(np.random.randn(Y.shape[0], input_dim))
-    if init == 'PCA':
+    Xr = np.asfortranarray(np.random.normal(0, 1, (Y.shape[0], input_dim)))
+    if 'PCA' in init:
         p = PCA(Y)
         PC = p.project(Y, min(input_dim, Y.shape[1]))
         Xr[:PC.shape[0], :PC.shape[1]] = PC
         var = .1*p.fracs[:input_dim]
+    elif init in 'empirical_samples':
+        from ..util.linalg import tdot
+        from ..util import diag
+        YYT = tdot(Y)
+        diag.add(YYT, 1e-6)
+        EMP = np.asfortranarray(np.random.multivariate_normal(np.zeros(Y.shape[0]), YYT, min(input_dim, Y.shape[1])).T)
+        Xr[:EMP.shape[0], :EMP.shape[1]] = EMP
+        var = np.random.uniform(0.5, 1.5, input_dim)
     else:
         var = Xr.var(0)