[newest patch updates, cleaned interfaces and mean_function addidtions]

GPy/__init__.py

@@ -34,18 +34,24 @@ except:
     def tests(verbose=10):
         Tester(testing).test(verbose=verbose)
 
-def load(file_path):
+def load(file_or_path):
     """
     Load a previously pickled model, using `m.pickle('path/to/file.pickle)'
 
     :param file_name: path/to/file.pickle
     """
-    import cPickle as pickle
     try:
-        with open(file_path, 'rb') as f:
-            m = pickle.load(f)
+        import cPickle as pickle
+        if isinstance(file_or_path, basestring): 
+            with open(file_or_path, 'rb') as f:
+                m = pickle.load(f)
+        else:
+            m = pickle.load(file_or_path)
     except:
-        import pickle as pickle
-        with open(file_path, 'rb') as f:
-            m = pickle.load(f)
+        import pickle
+        if isinstance(file_or_path, basestring): 
+            with open(file_or_path, 'rb') as f:
+                m = pickle.load(f)
+        else:
+            m = pickle.load(file_or_path)
     return m

GPy/core/parameterization/parameter_core.py

@@ -227,8 +227,8 @@ class Nameable(Gradcheckable):
     Make an object nameable inside the hierarchy.
     """
     def __init__(self, name, *a, **kw):
-        super(Nameable, self).__init__(*a, **kw)
         self._name = name or self.__class__.__name__
+        super(Nameable, self).__init__(*a, **kw)
 
     @property
     def name(self):

GPy/core/sparse_gp.py

@@ -43,12 +43,10 @@ class SparseGP(GP):
     def __init__(self, X, Y, Z, kernel, likelihood, mean_function=None, X_variance=None, inference_method=None,
                  name='sparse gp', Y_metadata=None, normalizer=False):
 
-        self.missing_data = np.isnan(Y).any()
-        
         #pick a sensible inference method
         if inference_method is None:
             if isinstance(likelihood, likelihoods.Gaussian):
-                inference_method = var_dtc.VarDTC(limit=1 if not self.missing_data else Y.shape[1])
+                inference_method = var_dtc.VarDTC(limit=1)
             else:
                 #inference_method = ??
                 raise NotImplementedError("what to do what to do?")

GPy/mappings/linear.py

@@ -25,7 +25,7 @@ class Linear(Mapping):
     """
 
     def __init__(self, input_dim, output_dim, name='linmap'):
-        Mapping.__init__(self, input_dim=input_dim, output_dim=output_dim, name=name)
+        super(Linear, self).__init__(input_dim=input_dim, output_dim=output_dim, name=name)
         self.A = Param('A', np.random.randn(self.input_dim, self.output_dim))
         self.link_parameter(self.A)
 

GPy/models/gp_classification.py

@@ -20,10 +20,10 @@ class GPClassification(GP):
 
     """
 
-    def __init__(self, X, Y, kernel=None,Y_metadata=None):
+    def __init__(self, X, Y, kernel=None,Y_metadata=None, mean_function=None):
         if kernel is None:
             kernel = kern.RBF(X.shape[1])
 
         likelihood = likelihoods.Bernoulli()
 
-        GP.__init__(self, X=X, Y=Y,  kernel=kernel, likelihood=likelihood, inference_method=EP(), name='gp_classification')
+        GP.__init__(self, X=X, Y=Y,  kernel=kernel, likelihood=likelihood, inference_method=EP(), mean_function=mean_function, name='gp_classification')

GPy/plotting/matplot_dep/models_plots.py

@@ -230,10 +230,16 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
                             ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
 
         #set the limits of the plot to some sensible values
-        ymin, ymax = min(np.append(Y[which_data_rows, which_data_ycols].flatten(), lower)), max(np.append(Y[which_data_rows, which_data_ycols].flatten(), upper))
-        ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
-        ax.set_xlim(xmin, xmax)
-        ax.set_ylim(ymin, ymax)
+        try:
+            ymin, ymax = min(np.append(Y[which_data_rows, which_data_ycols].flatten(), lower)), max(np.append(Y[which_data_rows, which_data_ycols].flatten(), upper))
+            if ymin != ymax:
+                ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
+                ax.set_xlim(xmin, xmax)
+                ax.set_ylim(ymin, ymax)
+        except:
+            # do nothing
+            # No training data on model
+            pass
 
         #add inducing inputs (if a sparse model is used)
         if hasattr(model,"Z"):