Added xw_pen data.

GPy/util/datasets.py

@@ -145,6 +145,12 @@ The database was created with funding from NSF EIA-0196217.""",
                                         'citation' : 'A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000',
                                         'license' : None,
                                         'size' : 24229368},
+                  'xw_pen' : {'urls' : [neil_url + 'xw_pen/'],
+                                        'files' : [['xw_pen_15.csv']],
+                                        'details' : """Accelerometer pen data used for robust regression by Tipping and Lawrence.""",
+                                        'citation' : 'Michael E. Tipping and Neil D. Lawrence. Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing, 69:123--141, 2005',
+                                        'license' : None,
+                                        'size' : 3410}
                   }
 
 
@@ -609,6 +615,14 @@ def olivetti_faces(data_set='olivetti_faces'):
     lbls = np.asarray(lbls)[:, None]
     return data_details_return({'Y': Y, 'lbls' : lbls, 'info': "ORL Faces processed to 64x64 images."}, data_set)
 
+def xw_pen(data_set='xw_pen'):
+    if not data_available(data_set):
+        download_data(data_set)
+    Y = np.loadtxt(os.path.join(data_path, data_set, 'xw_pen_15.csv'), delimiter=',')
+    X = np.arange(485)[:, None]
+    return data_details_return({'Y': Y, 'X': X, 'info': "Tilt data from a personalized digital assistant pen."}, data_set)
+
+    
 def download_rogers_girolami_data():
     if not data_available('rogers_girolami_data'):
         download_data(data_set)

GPy/util/symbolic.py

@@ -28,13 +28,25 @@ class sim_h(Function):
     @classmethod
     def eval(cls, t, tprime, d_i, d_j, l):
         # putting in the is_Number stuff forces it to look for a fdiff method for derivative.
-        return (exp((d_j/2*l)**2)/(d_i+d_j)
+        if (t.is_Number
-                *(exp(-d_j*(tprime - t))
+            and tprime.is_Number
-                  *(erf((tprime-t)/l - d_j/2*l)
+            and d_i.is_Number
-                    + erf(t/l + d_j/2*l))
+            and d_j.is_Number
-                  - exp(-(d_j*tprime + d_i))
+            and l.is_Number):
-                  *(erf(tprime/l - d_j/2*l)
+            if (t is S.NaN
-                    + erf(d_j/2*l))))
+                or tprime is S.NaN
+                or d_i is S.NaN
+                or d_j is S.NaN
+                or l is S.NaN):
+                return S.NaN
+            else:
+                return (exp((d_j/2*l)**2)/(d_i+d_j)
+                        *(exp(-d_j*(tprime - t))
+                          *(erf((tprime-t)/l - d_j/2*l)
+                            + erf(t/l + d_j/2*l))
+                          - exp(-(d_j*tprime + d_i))
+                          *(erf(tprime/l - d_j/2*l)
+                            + erf(d_j/2*l))))
 
 class erfc(Function):
     nargs = 1