ObservableArray -> ObsAr, because of pickling and ndarray printing

GPy/core/gp.py

@@ -7,7 +7,7 @@ import warnings
 from .. import kern
 from ..util.linalg import dtrtrs
 from model import Model
-from parameterization import ObservableArray
+from parameterization import ObsAr
 from .. import likelihoods
 from ..likelihoods.gaussian import Gaussian
 from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation
@@ -31,19 +31,19 @@ class GP(Model):
         super(GP, self).__init__(name)
 
         assert X.ndim == 2
-        if isinstance(X, (ObservableArray, VariationalPosterior)):
+        if isinstance(X, (ObsAr, VariationalPosterior)):
             self.X = X
-        else: self.X = ObservableArray(X)
+        else: self.X = ObsAr(X)
 
         self.num_data, self.input_dim = self.X.shape
 
         assert Y.ndim == 2
-        self.Y = ObservableArray(Y)
+        self.Y = ObsAr(Y)
         assert Y.shape[0] == self.num_data
         _, self.output_dim = self.Y.shape
 
         if Y_metadata is None:
-            Y_metadata = {}
+            self.Y_metadata = {}
         else:
             self.Y_metadata = Y_metadata
 

GPy/core/parameterization/__init__.py

@@ -1,5 +1,5 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-from param import Param, ObservableArray
+from param import Param, ObsAr
 from parameterized import Parameterized

GPy/core/parameterization/array_core.py

@@ -6,20 +6,20 @@ __updated__ = '2014-03-17'
 import numpy as np
 from parameter_core import Observable
 
-class ObservableArray(np.ndarray, Observable):
+class ObsAr(np.ndarray, Observable):
     """
     An ndarray which reports changes to its observers.
     The observers can add themselves with a callable, which
     will be called every time this array changes. The callable
     takes exactly one argument, which is this array itself.
     """
-    __array_priority__ = -1 # Never give back ObservableArray
+    __array_priority__ = -1 # Never give back ObsAr
     def __new__(cls, input_array, *a, **kw):
-        if not isinstance(input_array, ObservableArray):
+        if not isinstance(input_array, ObsAr):
             obj = np.atleast_1d(np.require(input_array, dtype=np.float64, requirements=['W', 'C'])).view(cls)
         else: obj = input_array
-        cls.__name__ = "ObsAr" # because of fixed printing of `array` in np printing
-        super(ObservableArray, obj).__init__(*a, **kw)
+        #cls.__name__ = "ObsAr" # because of fixed printing of `array` in np printing
+        super(ObsAr, obj).__init__(*a, **kw)
         return obj
 
     def __array_finalize__(self, obj):
@@ -54,7 +54,7 @@ class ObservableArray(np.ndarray, Observable):
 
     def __setitem__(self, s, val):
         if self._s_not_empty(s):
-            super(ObservableArray, self).__setitem__(s, val)
+            super(ObsAr, self).__setitem__(s, val)
             self.notify_observers(self[s])
 
     def __getslice__(self, start, stop):
@@ -64,7 +64,7 @@ class ObservableArray(np.ndarray, Observable):
         return self.__setitem__(slice(start, stop), val)
 
     def __copy__(self, *args):
-        return ObservableArray(self.view(np.ndarray).copy())
+        return ObsAr(self.view(np.ndarray).copy())
 
     def copy(self, *args):
         return self.__copy__(*args)

GPy/core/parameterization/param.py

@@ -4,7 +4,7 @@
 import itertools
 import numpy
 from parameter_core import OptimizationHandlable, adjust_name_for_printing
-from array_core import ObservableArray
+from array_core import ObsAr
 
 ###### printing
 __constraints_name__ = "Constraint"
@@ -15,7 +15,7 @@ __precision__ = numpy.get_printoptions()['precision'] # numpy printing precision
 __print_threshold__ = 5
 ######
 
-class Param(OptimizationHandlable, ObservableArray):
+class Param(OptimizationHandlable, ObsAr):
     """
     Parameter object for GPy models.
 

GPy/likelihoods/gaussian.py

@@ -50,6 +50,9 @@ class Gaussian(Likelihood):
         if isinstance(gp_link, link_functions.Identity):
             self.log_concave = True
 
+    def gaussian_variance(self):
+        return self.variance
+
     def covariance_matrix(self, Y, Y_metadata=None):
         return np.eye(Y.shape[0]) * self.variance
 

GPy/models/gp_regression.py

@@ -20,14 +20,14 @@ class GPRegression(GP):
 
     """
 
-    def __init__(self, X, Y, kernel=None):
+    def __init__(self, X, Y, kernel=None, Y_metadata=None):
 
         if kernel is None:
             kernel = kern.RBF(X.shape[1])
 
         likelihood = likelihoods.Gaussian()
 
-        super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression')
+        super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata)
 
     def _getstate(self):
         return GP._getstate(self)

GPy/testing/parameterized_tests.py

@@ -7,16 +7,16 @@ import unittest
 import GPy
 import numpy as np
 from GPy.core.parameterization.parameter_core import HierarchyError
-from GPy.core.parameterization.array_core import ObservableArray
+from GPy.core.parameterization.array_core import ObsAr
 
 class ArrayCoreTest(unittest.TestCase):
     def setUp(self):
         self.X = np.random.normal(1,1, size=(100,10))
-        self.obsX = ObservableArray(self.X)
+        self.obsX = ObsAr(self.X)
 
     def test_init(self):
-        X = ObservableArray(self.X)
-        X2 = ObservableArray(X)
+        X = ObsAr(self.X)
+        X2 = ObsAr(X)
         self.assertIs(X, X2, "no new Observable array, when Observable is given")
 
     def test_slice(self):