kernel slices allowed

GPy/kern/_src/independent_outputs.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-from kern import Kern
+from kern import Kern, CombinationKernel
 import numpy as np
 import itertools
 
@@ -32,7 +32,7 @@ def index_to_slices(index):
     [ret[ind_i].append(slice(*indexes_i)) for ind_i,indexes_i in zip(ind[switchpoints[:-1]],zip(switchpoints,switchpoints[1:]))]
     return ret
 
-class IndependentOutputs(Kern):
+class IndependentOutputs(CombinationKernel):
     """
     A kernel which can represent several independent functions.
     this kernel 'switches off' parts of the matrix where the output indexes are different.
@@ -41,12 +41,12 @@ class IndependentOutputs(Kern):
     the rest of the columns of X are passed to the underlying kernel for computation (in blocks).
 
     """
-    def __init__(self, index_dim, kern, name='independ'):
+    def __init__(self, kern, index_dim=-1, name='independ'):
         assert isinstance(index_dim, int), "IndependentOutputs kernel is only defined with one input dimension being the indeces"
-        super(IndependentOutputs, self).__init__(np.r_[0:max(max(kern.active_dims)+1, index_dim+1)], name)
+        super(IndependentOutputs, self).__init__(kernels=[kern], extra_dims=[index_dim], name=name)
         self.index_dim = index_dim
         self.kern = kern
-        self.add_parameters(self.kern)
+        #self.add_parameters(self.kern)
 
     def K(self,X ,X2=None):
         slices = index_to_slices(X[:,self.index_dim])

GPy/kern/_src/kern.py

@@ -27,10 +27,18 @@ class Kern(Parameterized):
         Do not instantiate.
         """
         super(Kern, self).__init__(name=name, *a, **kw)
-        self.active_dims = active_dims or slice(0, input_dim)
+        self.active_dims = active_dims if active_dims is not None else slice(0, input_dim)
         self.input_dim = input_dim
         assert isinstance(self.active_dims, (slice, list, tuple, np.ndarray)), 'active_dims needs to be an array-like or slice object over dimensions, {} given'.format(self.active_dims.__class__)
-        assert self.active_dims.size == self.input_dim, "input_dim {} does not match len(active_dim) {}".format(self.input_dim, self.active_dims.size)
+        if isinstance(self.active_dims, slice):
+            self.active_dims = slice(self.active_dims.start or 0, self.active_dims.stop or self.input_dim, self.active_dims.step or 1)
+            active_dim_size = int(np.round((self.active_dims.stop-self.active_dims.start)/self.active_dims.step))
+        elif isinstance(self.active_dims, np.ndarray):
+            assert self.active_dims.ndim == 1, 'only flat indices allowed, given active_dims.shape={}, provide only indexes to the dimensions of the input'.format(self.active_dims.shape)
+            active_dim_size = self.active_dims.size
+        else:
+            active_dim_size = len(self.active_dims)
+        assert active_dim_size == self.input_dim, "input_dim={} does not match len(active_dim)={}, active_dims={}".format(self.input_dim, active_dim_size, self.active_dims)
         self._sliced_X = 0
 
     @Cache_this(limit=10)
@@ -207,10 +215,12 @@ class CombinationKernel(Kern):
         assert all([isinstance(k, Kern) for k in kernels])
         import itertools
         # make sure the active dimensions of all underlying kernels are covered:
-        ma = reduce(lambda a,b: max(a, b.stop if isinstance(b, slice) else max(b)), itertools.chain((x.active_dims for x in kernels), [extra_dims]), 0)
-        input_dim = np.r_[0:ma+1]
+        #ma = reduce(lambda a,b: max(a, b.stop if isinstance(b, slice) else max(b)), itertools.chain((x.active_dims for x in kernels)), 0)
+        active_dims = reduce(np.union1d, (np.r_[x.active_dims] for x in kernels), np.array([], dtype=int))
+        input_dim = active_dims.max()+1 + len(extra_dims)
+        active_dims = slice(active_dims.max()+1+len(extra_dims))
         # initialize the kernel with the full input_dim
-        super(CombinationKernel, self).__init__(input_dim, name)
+        super(CombinationKernel, self).__init__(input_dim, active_dims, name)
         self.extra_dims = extra_dims
         self.add_parameters(*kernels)
 

GPy/testing/kernel_tests.py

@@ -228,12 +228,12 @@ class KernelGradientTestsContinuous(unittest.TestCase):
         self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
 
     def test_Prod(self):
-        k = GPy.kern.Matern32([2,3]) * GPy.kern.RBF([0,4]) + GPy.kern.Linear(self.D)
+        k = GPy.kern.Matern32(2, active_dims=[2,3]) * GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)
         k.randomize()
         self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
 
     def test_Add(self):
-        k = GPy.kern.Matern32([2,3]) + GPy.kern.RBF([0,4]) + GPy.kern.Linear(self.D)
+        k = GPy.kern.Matern32(2, active_dims=[2,3]) + GPy.kern.RBF(2, active_dims=[0,4]) + GPy.kern.Linear(self.D)
         k.randomize()
         self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
 
@@ -283,15 +283,16 @@ class KernelTestsMiscellaneous(unittest.TestCase):
     def setUp(self):
         N, D = 100, 10
         self.X = np.linspace(-np.pi, +np.pi, N)[:,None] * np.ones(D)
-        self.rbf = GPy.kern.RBF(range(2))
-        self.linear = GPy.kern.Linear((3,6))
-        self.matern = GPy.kern.Matern32(np.array([2,4,7]))
+        self.rbf = GPy.kern.RBF(2, active_dims=slice(0,4,2))
+        self.linear = GPy.kern.Linear(2, active_dims=(3,9))
+        self.matern = GPy.kern.Matern32(3, active_dims=np.array([2,4,9]))
         self.sumkern = self.rbf + self.linear
         self.sumkern += self.matern
         self.sumkern.randomize()
 
     def test_active_dims(self):
-        self.assertListEqual(self.sumkern.active_dims.tolist(), range(8))
+        self.assertEqual(self.sumkern.input_dim, 9)
+        self.assertEqual(self.sumkern.active_dims, slice(9))
 
     def test_which_parts(self):
         self.assertTrue(np.allclose(self.sumkern.K(self.X, which_parts=[self.linear, self.matern]), self.linear.K(self.X)+self.matern.K(self.X)))
@@ -312,9 +313,9 @@ class KernelTestsNonContinuous(unittest.TestCase):
         self.X_block[0:N, -1] = 1
         self.X_block[N:N+1, -1] = 2
 
-    def test_IndependantOutputs(self):
+    def test_IndependentOutputs(self):
         k = GPy.kern.RBF(self.D)
-        kern = GPy.kern.IndependentOutputs(self.D+self.D,k)
+        kern = GPy.kern.IndependentOutputs(k, -1)
         self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose))
 
 if __name__ == "__main__":