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adding choleskies cythonized

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This commit is contained in:
James Hensman 2015-04-27 19:47:19 +01:00
parent c00f76d250
commit 25cebf790c
5 changed files with 6364 additions and 21 deletions
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5
GPy/kern/_src/coregionalize.py
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@ -6,6 +6,7 @@ import numpy as np
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
from ...util.config import config # for assesing whether to use weave
import coregionalize_cython
try:
from scipy import weave
@ -169,9 +170,9 @@ class Coregionalize(Kern):
dL_dK_small[j,i] = tmp1[:,index2==j].sum()
return dL_dK_small
def gradient_reduce_cython(self, dL_dK, index, index2):
def _gradient_reduce_cython(self, dL_dK, index, index2):
index, index2 = index[:,0], index2[:,0]
return coregionalize_cython.gradient_reduce(self.output_dim, dL_dK, index, index2
return coregionalize_cython.gradient_reduce(self.B.shape[0], dL_dK, index, index2)
def update_gradients_diag(self, dL_dKdiag, X):

29
GPy/kern/_src/coregionalize_cython.pyx
View file

@ -1,40 +1,33 @@
#cython: boundscheck=False
#cython: wraparound=False
import cython
import numpy as np
cimport numpy as np
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
def K_symmetric(np.ndarray[double, ndim=2] B, np.ndarray[int, ndim=1] X):
N = X.size
K = np.zeros((N, N))
cdef int N = X.size
cdef np.ndarray[np.double_t, ndim=2] K = np.empty((N, N))
for n in range(N):
for m in range(N):
K[n,m] = B[X[n],X[m]]
return K
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
def K_asymmetric(np.ndarray[double, ndim=2] B, np.ndarray[int, ndim=1] X, np.ndarray[int, ndim=1] X2):
N = X.size
M = X2.size
K = np.zeros((N, M))
cdef int N = X.size
cdef int M = X2.size
cdef np.ndarray[np.double_t, ndim=2] K = np.empty((N, M))
for n in range(N):
for m in range(M):
K[n,m] = B[X[n],X2[m]]
return K
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
def gradient_reduce(int D, np.ndarray[double, ndim=2] dL_dK, np.ndarray[int, ndim=1] index, np.ndarray[int, ndim=1] index2):
dL_dK_small = np.zeros((D, D))
N = index.size
M = index2.size
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] + D*index2[i]] += dL_dK[i+j*M];
dL_dK_small[index[j],index2[i]] += dL_dK[i,j];
return dL_dK_small