all the product_orthogonal have been changed to prod_orthogonal for consistency

GPy/kern/__init__.py

@@ -2,5 +2,5 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, rbf_sympy, sympykern, periodic_exponential, periodic_Matern32, periodic_Matern52, product, product_orthogonal, symmetric, coregionalise
+from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, rbf_sympy, sympykern, periodic_exponential, periodic_Matern32, periodic_Matern52, prod, prod_orthogonal, symmetric, coregionalise
 from kern import kern

GPy/kern/constructors.py

@@ -18,8 +18,8 @@ from Brownian import Brownian as Brownianpart
 from periodic_exponential import periodic_exponential as periodic_exponentialpart
 from periodic_Matern32 import periodic_Matern32 as periodic_Matern32part
 from periodic_Matern52 import periodic_Matern52 as periodic_Matern52part
-from product import product as productpart
-from product_orthogonal import product_orthogonal as product_orthogonalpart
+from prod import prod as prodpart
+from prod_orthogonal import prod_orthogonal as prod_orthogonalpart
 from symmetric import symmetric as symmetric_part
 from coregionalise import coregionalise as coregionalise_part
 #TODO these s=constructors are not as clean as we'd like. Tidy the code up
@@ -245,7 +245,7 @@ def periodic_Matern52(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
     part = periodic_Matern52part(D,variance, lengthscale, period, n_freq, lower, upper)
     return kern(D, [part])
 
-def product(k1,k2):
+def prod(k1,k2):
     """
      Construct a product kernel over D from two kernels over D
 
@@ -253,10 +253,10 @@ def product(k1,k2):
     :type k1, k2: kernpart
     :rtype: kernel object
     """
-    part = productpart(k1,k2)
+    part = prodpart(k1,k2)
     return kern(k1.D, [part])
 
-def product_orthogonal(k1,k2):
+def prod_orthogonal(k1,k2):
     """
      Construct a product kernel over D1 x D2 from a kernel over D1 and another over D2.
 
@@ -264,7 +264,7 @@ def product_orthogonal(k1,k2):
     :type k1, k2: kernpart
     :rtype: kernel object
     """
-    part = product_orthogonalpart(k1,k2)
+    part = prod_orthogonalpart(k1,k2)
     return kern(k1.D+k2.D, [part])
 
 def symmetric(k):

GPy/kern/kern.py

@@ -7,8 +7,8 @@ import pylab as pb
 from ..core.parameterised import parameterised
 from kernpart import kernpart
 import itertools
-from product_orthogonal import product_orthogonal
-from product import product
+from prod_orthogonal import prod_orthogonal
+from prod import prod
 
 class kern(parameterised):
     def __init__(self,D,parts=[], input_slices=None):
@@ -161,7 +161,7 @@ class kern(parameterised):
         K1 = self.copy()
         K2 = other.copy()
 
-        newkernparts = [product(k1,k2) for k1, k2 in itertools.product(K1.parts,K2.parts)]
+        newkernparts = [prod(k1,k2) for k1, k2 in itertools.product(K1.parts,K2.parts)]
 
         slices = []
         for sl1, sl2 in itertools.product(K1.input_slices,K2.input_slices):
@@ -183,7 +183,7 @@ class kern(parameterised):
         K1 = self.copy()
         K2 = other.copy()
 
-        newkernparts = [product_orthogonal(k1,k2) for k1, k2 in itertools.product(K1.parts,K2.parts)]
+        newkernparts = [prod_orthogonal(k1,k2) for k1, k2 in itertools.product(K1.parts,K2.parts)]
 
         slices = []
         for sl1, sl2 in itertools.product(K1.input_slices,K2.input_slices):

GPy/kern/prod.py

@@ -6,7 +6,7 @@ import numpy as np
 import hashlib
 #from scipy import integrate # This may not be necessary (Nicolas, 20th Feb)
 
-class product(kernpart):
+class prod(kernpart):
     """
     Computes the product of 2 kernels that are defined on the same space
 

GPy/kern/prod_orthogonal.py

@@ -6,7 +6,7 @@ import numpy as np
 import hashlib
 #from scipy import integrate # This may not be necessary (Nicolas, 20th Feb)
 
-class product_orthogonal(kernpart):
+class prod_orthogonal(kernpart):
     """
     Computes the product of 2 kernels