added fixed effect kernel

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, prod, prod_orthogonal, symmetric, coregionalise, rational_quadratic
+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, rational_quadratic, fixed
 from kern import kern

GPy/kern/constructors.py

@@ -12,6 +12,7 @@ from exponential import exponential as exponentialpart
 from Matern32 import Matern32 as Matern32part
 from Matern52 import Matern52 as Matern52part
 from bias import bias as biaspart
+from fixed import fixed as fixedpart
 from finite_dimensional import finite_dimensional as finite_dimensionalpart
 from spline import spline as splinepart
 from Brownian import Brownian as Brownianpart
@@ -296,3 +297,16 @@ def rational_quadratic(D,variance=1., lengthscale=1., power=1.):
     """
     part = rational_quadraticpart(D,variance, lengthscale, power)
     return kern(D, [part])
+
+def fixed(D, K, variance=1.):
+    """
+     Construct a fixed effect kernel.
+
+     Arguments
+     ---------
+     D (int), obligatory
+     K (np.array), obligatory
+     variance (float)
+    """
+    part = fixedpart(D, K, variance)
+    return kern(D, [part])

GPy/kern/fixed.py

@@ -0,0 +1,42 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+from kernpart import kernpart
+import numpy as np
+import hashlib
+
+class fixed(kernpart):
+    def __init__(self,D,K,variance=1.):
+        """
+        :param D: the number of input dimensions
+        :type D: int
+        :param variance: the variance of the kernel
+        :type variance: float
+        """
+        self.D = D
+        self.fixed_K = K
+        self.Nparam = 1
+        self.name = 'fixed'
+        self._set_params(np.array([variance]).flatten())
+
+    def _get_params(self):
+        return self.variance
+
+    def _set_params(self,x):
+        assert x.shape==(1,)
+        self.variance = x
+
+    def _get_param_names(self):
+        return ['variance']
+
+    def K(self,X,X2,target):
+        target += self.variance * self.fixed_K
+
+    def dK_dtheta(self,partial,X,X2,target):
+        target += (partial * self.fixed_K).sum()
+
+    def dK_dX(self, partial,X, X2, target):
+        pass
+
+    def dKdiag_dX(self,partial,X,target):
+        pass

GPy/testing/kernel_tests.py

@@ -15,6 +15,17 @@ class KernelTests(unittest.TestCase):
         m = GPy.models.GP_regression(X,Y,K)
         self.assertTrue(m.checkgrad())
 
+    def test_fixedkernel(self):
+        """
+        Fixed effect kernel test
+        """
+        X = np.random.rand(30, 4)
+        K = np.dot(X, X.T)
+        kernel = GPy.kern.fixed(4, K)
+        Y = np.ones((30,1))
+        m = GPy.models.GP_regression(X,Y,kernel=kernel)
+        self.assertTrue(m.checkgrad())
+
     def test_coregionalisation(self):
         X1 = np.random.rand(50,1)*8
         X2 = np.random.rand(30,1)*5