core updates

GPy/core/parameterization/array_core.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-__updated__ = '2014-03-21'
+__updated__ = '2014-03-24'
 
 import numpy as np
 from parameter_core import Observable
@@ -38,24 +38,9 @@ class ObsAr(np.ndarray, Observable):
         np.ndarray.__setstate__(self, state[0])
         Observable._setstate(self, state[1])
 
-    def _s_not_empty(self, s):
-        # this checks whether there is something picked by this slice.
-        return True
-        # TODO:  disarmed, for performance increase,
-        if not isinstance(s, (list,tuple,np.ndarray)):
-            return True
-        if isinstance(s, (list,tuple)):
-            return len(s)!=0
-        if isinstance(s, np.ndarray):
-            if s.dtype is bool:
-                return np.all(s)
-            else:
-                return s.size != 0
-
     def __setitem__(self, s, val):
-        if self._s_not_empty(s):
-            super(ObsAr, self).__setitem__(s, val)
-            self.notify_observers()
+        super(ObsAr, self).__setitem__(s, val)
+        self.notify_observers()
 
     def __getslice__(self, start, stop):
         return self.__getitem__(slice(start, stop))

GPy/core/parameterization/index_operations.py

@@ -183,7 +183,7 @@ class ParameterIndexOperationsView(object):
 
 
     def remove(self, prop, indices):
-        removed = self._param_index_ops.remove(prop, indices+self._offset)
+        removed = self._param_index_ops.remove(prop, numpy.array(indices)+self._offset)
         if removed.size > 0:
             return removed - self._size + 1
         return removed

GPy/models/bayesian_gplvm.py

@@ -26,7 +26,10 @@ class BayesianGPLVM(SparseGP):
                  Z=None, kernel=None, inference_method=None, likelihood=None, name='bayesian gplvm', **kwargs):
         if X == None:
             from ..util.initialization import initialize_latent
-            X = initialize_latent(init, input_dim, Y)
+            X, fracs = initialize_latent(init, input_dim, Y)
+        else:
+            fracs = np.ones(input_dim)
+
         self.init = init
 
         if X_variance is None:
@@ -38,7 +41,7 @@ class BayesianGPLVM(SparseGP):
         assert Z.shape[1] == X.shape[1]
 
         if kernel is None:
-            kernel = kern.RBF(input_dim) # + kern.white(input_dim)
+            kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=True) # + kern.white(input_dim)
 
         if likelihood is None:
             likelihood = Gaussian()