actually changing coregionalise to coregionalize

GPy/examples/regression.py

@@ -22,7 +22,7 @@ def coregionalisation_toy2(max_iters=100):
     Y = np.vstack((Y1, Y2))
 
     k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
-    k2 = GPy.kern.coregionalise(2,1)
+    k2 = GPy.kern.coregionalize(2,1)
     k = k1**k2 #k = k1.prod(k2,tensor=True)
     m = GPy.models.GPRegression(X, Y, kernel=k)
     m.constrain_fixed('.*rbf_var', 1.)
@@ -82,7 +82,7 @@ def coregionalisation_sparse(max_iters=100):
     k1 = GPy.kern.rbf(1)
 
     m = GPy.models.SparseGPMultioutputRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel_list=[k1],num_inducing=20)
-    #k2 = GPy.kern.coregionalise(2, 2)
+    #k2 = GPy.kern.coregionalize(2, 2)
     #k = k1**k2 #.prod(k2, tensor=True) # + GPy.kern.white(2,0.001)
     #m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
     m.constrain_fixed('.*rbf_var', 1.)
@@ -135,7 +135,7 @@ def epomeo_gpx(max_iters=100):
                    np.random.randint(0, 4, num_inducing)[:, None]))
 
     k1 = GPy.kern.rbf(1)
-    k2 = GPy.kern.coregionalise(output_dim=5, rank=5)
+    k2 = GPy.kern.coregionalize(output_dim=5, rank=5)
     k = k1**k2 
 
     m = GPy.models.SparseGPRegression(t, Y, kernel=k, Z=Z, normalize_Y=True)

GPy/kern/constructors.py

@@ -346,7 +346,7 @@ 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::
@@ -358,7 +358,7 @@ 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 corregionalize
     :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
@@ -369,7 +369,7 @@ def coregionalise(num_outputs,W_columns=1, W=None, kappa=None):
     :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])
 
 
@@ -448,11 +448,11 @@ 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

@@ -1,11 +1,11 @@
 import bias
 import Brownian
-import coregionalise
+import coregionalize
 import exponential
 import finite_dimensional
 import fixed
 import gibbs
-import hetero
+#import hetero #hetero.py is not commited: omitting for now. JH. 
 import hierarchical
 import independent_outputs
 import linear

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
 

GPy/testing/kernel_tests.py

@@ -4,12 +4,7 @@
 import unittest
 import numpy as np
 import GPy
-<<<<<<< HEAD
-    
 verbose = False
-=======
-
->>>>>>> 1bc93747178b0bab1b7177568388ebd4207647e0
 
 class KernelTests(unittest.TestCase):
     def test_kerneltie(self):
@@ -93,7 +88,7 @@ class KernelTests(unittest.TestCase):
         Y = np.vstack((Y1,Y2))
 
         k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
-        k2 = GPy.kern.coregionalise(2,1)
+        k2 = GPy.kern.coregionalize(2,1)
         kern = k1**k2
         self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
 

GPy/util/multioutput.py

@@ -9,8 +9,8 @@ def build_lcm(input_dim, num_outputs, CK = [], NC = [], W_columns=1,W=None,kappa
 
     :input_dim: Input dimensionality
     :num_outputs: Number of outputs
-    :param CK: List of coregionalized kernels (i.e., this will be multiplied by a coregionalise kernel).
-    :param K: List of kernels that will be added up together with CK, but won't be multiplied by a coregionalise kernel
+    :param CK: List of coregionalized kernels (i.e., this will be multiplied by a coregionalize kernel).
+    :param K: List of kernels that will be added up together with CK, but won't be multiplied by a coregionalize kernel
     :param W_columns: number tuples of the corregionalization parameters 'coregion_W'
     :type W_columns: integer
     """
@@ -25,9 +25,9 @@ def build_lcm(input_dim, num_outputs, CK = [], NC = [], W_columns=1,W=None,kappa
             k.input_dim = input_dim + 1
             warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
 
-    kernel = CK[0].prod(kern.coregionalise(num_outputs,W_columns,W,kappa),tensor=True)
+    kernel = CK[0].prod(kern.coregionalize(num_outputs,W_columns,W,kappa),tensor=True)
     for k in CK[1:]:
-        k_coreg = kern.coregionalise(num_outputs,W_columns,W,kappa)
+        k_coreg = kern.coregionalize(num_outputs,W_columns,W,kappa)
         kernel += k.prod(k_coreg,tensor=True)
     for k in NC:
         kernel += k