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fixed coregionalize cython

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This commit is contained in:
James Hensman 2015-04-28 15:57:27 +01:00
parent 9e4c33910f
commit 1ebbc49fdd
5 changed files with 437 additions and 232 deletions
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4
GPy/kern/_src/coregionalize.py
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@ -73,8 +73,8 @@ class Coregionalize(Kern):
def _K_cython(self, X, X2=None):
if X2 is None:
return coregionalize_cython.K_symmetric(self.B, X[:,0])
return coregionalize_cython.K_asymmetric(self.B, X[:,0], X2[:,0])
return coregionalize_cython.K_symmetric(self.B, np.asarray(X, dtype=np.int64)[:,0])
return coregionalize_cython.K_asymmetric(self.B, np.asarray(X, dtype=np.int64)[:,0], np.asarray(X2, dtype=np.int64)[:,0])
def Kdiag(self, X):

560
GPy/kern/_src/coregionalize_cython.c
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File diff suppressed because it is too large Load diff

10
GPy/kern/_src/coregionalize_cython.pyx
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@ -1,5 +1,5 @@
#cython: boundscheck=False
#cython: wraparound=False
#cython: boundscheck=True
#cython: wraparound=True
import cython
import numpy as np
cimport numpy as np
@ -25,9 +25,9 @@ def gradient_reduce(int D, np.ndarray[double, ndim=2] dL_dK, np.ndarray[np.int64
cdef np.ndarray[np.double_t, ndim=2] dL_dK_small = np.zeros((D, D))
cdef int N = index.size
cdef int M = index2.size
for i in range(M):
for j in range(N):
dL_dK_small[index[j],index2[i]] += dL_dK[i,j];
for i in range(N):
for j in range(M):
dL_dK_small[index2[j],index[i]] += dL_dK[i,j];
return dL_dK_small

57
GPy/testing/cython_tests.py Normal file
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@ -0,0 +1,57 @@
import numpy as np
import scipy as sp
from GPy.util import choleskies
import GPy
"""
These tests make sure that the opure python and cython codes work the same
"""
class CythonTestChols(np.testing.TestCase):
def setUp(self):
self.flat = np.random.randn(45, 5)
self.triang = np.dstack([np.eye(20)[:,:,None] for i in range(3)])
def test_flat_to_triang(self):
L1 = choleskies._flat_to_triang_pure(self.flat)
L2 = choleskies._flat_to_triang_cython(self.flat)
np.testing.assert_allclose(L1, L2)
def test_triang_to_flat(self):
A1 = choleskies._triang_to_flat_pure(self.triang)
A2 = choleskies._triang_to_flat_cython(self.triang)
np.testing.assert_allclose(A1, A2)
class test_stationary(np.testing.TestCase):
def setUp(self):
self.k = GPy.kern.RBF(10)
self.X = np.random.randn(300,10)
self.Z = np.random.randn(20,10)
self.dKxx = np.random.randn(300,300)
self.dKzz = np.random.randn(20,20)
self.dKxz = np.random.randn(300,20)
def test_square_gradX(self):
g1 = self.k._gradients_X_cython(self.dKxx, self.X)
g2 = self.k._gradients_X_pure(self.dKxx, self.X)
np.testing.assert_allclose(g1, g2)
def test_rect_gradx(self):
g1 = self.k._gradients_X_cython(self.dKxz, self.X, self.Z)
g2 = self.k._gradients_X_pure(self.dKxz, self.X, self.Z)
np.testing.assert_allclose(g1, g2)
def test_square_lengthscales(self):
g1 = self.k._lengthscale_grads_pure(self.dKxx, self.X, self.X)
g2 = self.k._lengthscale_grads_cython(self.dKxx, self.X, self.X)
np.testing.assert_allclose(g1, g2)
def test_rect_lengthscales(self):
g1 = self.k._lengthscale_grads_pure(self.dKxz, self.X, self.Z)
g2 = self.k._lengthscale_grads_cython(self.dKxz, self.X, self.Z)
np.testing.assert_allclose(g1, g2)

38
GPy/testing/kernel_tests.py
View file

@ -366,9 +366,9 @@ class KernelTestsNonContinuous(unittest.TestCase):
X2 = self.X2[self.X2[:,-1]!=2]
self.assertTrue(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
class Coregionalize_weave_test(unittest.TestCase):
class Coregionalize_cython_test(unittest.TestCase):
"""
Make sure that the coregionalize kernel work with and without weave enabled
Make sure that the coregionalize kernel work with and without cython enabled
"""
def setUp(self):
self.k = GPy.kern.Coregionalize(1, output_dim=12)
@ -378,36 +378,42 @@ class Coregionalize_weave_test(unittest.TestCase):
def test_sym(self):
dL_dK = np.random.randn(self.N1, self.N1)
GPy.util.config.config.set('weave', 'working', 'True')
K_weave = self.k.K(self.X)
GPy.util.config.config.set('cython', 'working', 'True')
K_cython = self.k.K(self.X)
self.k.update_gradients_full(dL_dK, self.X)
grads_weave = self.k.gradient.copy()
grads_cython = self.k.gradient.copy()
GPy.util.config.config.set('weave', 'working', 'False')
GPy.util.config.config.set('cython', 'working', 'False')
K_numpy = self.k.K(self.X)
self.k.update_gradients_full(dL_dK, self.X)
grads_numpy = self.k.gradient.copy()
self.assertTrue(np.allclose(K_numpy, K_weave))
self.assertTrue(np.allclose(grads_numpy, grads_weave))
self.assertTrue(np.allclose(K_numpy, K_cython))
self.assertTrue(np.allclose(grads_numpy, grads_cython))
#reset the cython state for any other tests
GPy.util.config.config.set('cython', 'working', 'true')
def test_nonsym(self):
dL_dK = np.random.randn(self.N1, self.N2)
GPy.util.config.config.set('weave', 'working', 'True')
K_weave = self.k.K(self.X, self.X2)
GPy.util.config.config.set('cython', 'working', 'True')
K_cython = self.k.K(self.X, self.X2)
self.k.gradient = 0.
self.k.update_gradients_full(dL_dK, self.X, self.X2)
grads_weave = self.k.gradient.copy()
grads_cython = self.k.gradient.copy()
GPy.util.config.config.set('weave', 'working', 'False')
GPy.util.config.config.set('cython', 'working', 'False')
K_numpy = self.k.K(self.X, self.X2)
self.k.gradient = 0.
self.k.update_gradients_full(dL_dK, self.X, self.X2)
grads_numpy = self.k.gradient.copy()
self.assertTrue(np.allclose(K_numpy, K_weave))
self.assertTrue(np.allclose(grads_numpy, grads_weave))
self.assertTrue(np.allclose(K_numpy, K_cython))
self.assertTrue(np.allclose(grads_numpy, grads_cython))
#reset the cython state for any other tests
GPy.util.config.config.set('cython', 'working', 'true')
#reset the weave state for any other tests
GPy.util.config.config.set('weave', 'working', 'False')
class KernelTestsProductWithZeroValues(unittest.TestCase):