coregionalisation changed to coregionalization

GPy/kern/constructors.py

@@ -340,7 +340,7 @@ def symmetric(k):
     k_.parts = [symmetric.Symmetric(p) for p in k.parts]
     return k_
 
-def coregionalise(num_outputs,W_columns=1, W=None, kappa=None):
+def coregionalize(num_outputs,W_columns=1, W=None, kappa=None):
     """
     Coregionlization matrix B, of the form:
     .. math::
@@ -352,18 +352,18 @@ def coregionalise(num_outputs,W_columns=1, W=None, kappa=None):
 
     it is obtainded as the tensor product between a kernel k(x,y) and B.
 
-    :param num_outputs: the number of outputs to corregionalise
+    :param num_outputs: the number of outputs to coregionalize
     :type num_outputs: int
     :param W_columns: number of columns of the W matrix (this parameter is ignored if parameter W is not None)
     :type W_colunns: int
-    :param W: a low rank matrix that determines the correlations between the different outputs, together with kappa it forms the coregionalisation matrix B
+    :param W: a low rank matrix that determines the correlations between the different outputs, together with kappa it forms the coregionalization matrix B
     :type W: numpy array of dimensionality (num_outpus, W_columns)
     :param kappa: a vector which allows the outputs to behave independently
     :type kappa: numpy array of dimensionality  (num_outputs,)
     :rtype: kernel object
 
     """
-    p = parts.coregionalise.Coregionalise(num_outputs,W_columns,W,kappa)
+    p = parts.coregionalize.Coregionalize(num_outputs,W_columns,W,kappa)
     return kern(1,[p])
 
 
@@ -442,11 +442,11 @@ def build_lcm(input_dim, num_outputs, kernel_list = [], W_columns=1,W=None,kappa
             k.input_dim = input_dim
             warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
 
-    k_coreg = coregionalise(num_outputs,W_columns,W,kappa)
+    k_coreg = coregionalize(num_outputs,W_columns,W,kappa)
     kernel = kernel_list[0]**k_coreg.copy()
 
     for k in kernel_list[1:]:
-        k_coreg = coregionalise(num_outputs,W_columns,W,kappa)
+        k_coreg = coregionalize(num_outputs,W_columns,W,kappa)
         kernel += k**k_coreg.copy()
 
     return kernel

GPy/kern/parts/__init__.py

@@ -1,6 +1,6 @@
 import bias
 import Brownian
-import coregionalise
+import coregionalize
 import exponential
 import finite_dimensional
 import fixed

GPy/kern/parts/coregionalize.py

@@ -7,7 +7,7 @@ from GPy.util.linalg import mdot, pdinv
 import pdb
 from scipy import weave
 
-class Coregionalise(Kernpart):
+class Coregionalize(Kernpart):
     """
     Kernel for intrinsic/linear coregionalization models
 
@@ -25,12 +25,12 @@ class Coregionalise(Kernpart):
     :type num_outputs: int
     :param W_columns: number of columns of the W matrix (this parameter is ignored if parameter W is not None)
     :type W_colunns: int
-    :param W: a low rank matrix that determines the correlations between the different outputs, together with kappa it forms the coregionalisation matrix B
+    :param W: a low rank matrix that determines the correlations between the different outputs, together with kappa it forms the coregionalization matrix B
     :type W: numpy array of dimensionality (num_outpus, W_columns)
     :param kappa: a vector which allows the outputs to behave independently
     :type kappa: numpy array of dimensionality  (num_outputs,)
 
-    .. Note: see coregionalisation examples in GPy.examples.regression for some usage.
+    .. Note: see coregionalization examples in GPy.examples.regression for some usage.
     """
     def __init__(self,num_outputs,W_columns=1, W=None, kappa=None):
         self.input_dim = 1

GPy/kern/parts/prod.py

@@ -18,7 +18,7 @@ class Prod(Kernpart):
     """
     def __init__(self,k1,k2,tensor=False):
         self.num_params = k1.num_params + k2.num_params
-        self.name = '['+k1.name + '(x)' + k2.name +']'
+        self.name = '['+k1.name + '**' + k2.name +']'
         self.k1 = k1
         self.k2 = k2
         if tensor: