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tidien up coregionlize w AS

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James Hensman 2015-04-28 14:06:21 +01:00
parent a11cf422c2
commit e1c2eeb25d
6 changed files with 350 additions and 234 deletions
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72
GPy/kern/_src/coregionalize.py
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@ -5,14 +5,9 @@ from .kern import Kern
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
from ...util.config import config # for assesing whether to use cython
import coregionalize_cython
try:
from scipy import weave
except ImportError:
config.set('weave', 'working', 'False')
class Coregionalize(Kern):
"""
Covariance function for intrinsic/linear coregionalization models
@ -62,13 +57,8 @@ class Coregionalize(Kern):
self.B = np.dot(self.W, self.W.T) + np.diag(self.kappa)
def K(self, X, X2=None):
if config.getboolean('weave', 'working'):
try:
return self._K_weave(X, X2)
except:
print("\n Weave compilation failed. Falling back to (slower) numpy implementation\n")
config.set('weave', 'working', 'False')
return self._K_numpy(X, X2)
if config.getboolean('cython', 'working'):
return self._K_cython(X, X2)
else:
return self._K_numpy(X, X2)
@ -81,37 +71,6 @@ class Coregionalize(Kern):
index2 = np.asarray(X2, dtype=np.int)
return self.B[index,index2.T]
def _K_weave(self, X, X2=None):
"""compute the kernel function using scipy.weave"""
index = np.asarray(X, dtype=np.int)
if X2 is None:
target = np.empty((X.shape[0], X.shape[0]), dtype=np.float64)
code="""
for(int i=0;i<N; i++){
target[i+i*N] = B[index[i]+output_dim*index[i]];
for(int j=0; j<i; j++){
target[j+i*N] = B[index[i]+output_dim*index[j]];
target[i+j*N] = target[j+i*N];
}
}
"""
N, B, output_dim = index.size, self.B, self.output_dim
weave.inline(code, ['target', 'index', 'N', 'B', 'output_dim'])
else:
index2 = np.asarray(X2, dtype=np.int)
target = np.empty((X.shape[0], X2.shape[0]), dtype=np.float64)
code="""
for(int i=0;i<num_inducing; i++){
for(int j=0; j<N; j++){
target[i+j*num_inducing] = B[output_dim*index[j]+index2[i]];
}
}
"""
N, num_inducing, B, output_dim = index.size, index2.size, self.B, self.output_dim
weave.inline(code, ['target', 'index', 'index2', 'N', 'num_inducing', 'B', 'output_dim'])
return target
def _K_cython(self, X, X2=None):
if X2 is None:
return coregionalize_cython.K_symmetric(self.B, X[:,0])
@ -128,19 +87,13 @@ class Coregionalize(Kern):
else:
index2 = np.asarray(X2, dtype=np.int)
#attempt to use weave for a nasty double indexing loop: fall back to numpy
if config.getboolean('weave', 'working'):
try:
dL_dK_small = self._gradient_reduce_weave(dL_dK, index, index2)
except:
print("\n Weave compilation failed. Falling back to (slower) numpy implementation\n")
config.set('weave', 'working', 'False')
dL_dK_small = self._gradient_reduce_weave(dL_dK, index, index2)
#attempt to use cython for a nasty double indexing loop: fall back to numpy
if config.getboolean('cython', 'working'):
dL_dK_small = self._gradient_reduce_cython(dL_dK, index, index2)
else:
dL_dK_small = self._gradient_reduce_numpy(dL_dK, index, index2)
dkappa = np.diag(dL_dK_small)
dL_dK_small += dL_dK_small.T
dW = (self.W[:, None, :]*dL_dK_small[:, :, None]).sum(0)
@ -148,19 +101,6 @@ class Coregionalize(Kern):
self.W.gradient = dW
self.kappa.gradient = dkappa
def _gradient_reduce_weave(self, dL_dK, index, index2):
dL_dK_small = np.zeros_like(self.B)
code="""
for(int i=0; i<num_inducing; i++){
for(int j=0; j<N; j++){
dL_dK_small[index[j] + output_dim*index2[i]] += dL_dK[i+j*num_inducing];
}
}
"""
N, num_inducing, output_dim = index.size, index2.size, self.output_dim
weave.inline(code, ['N', 'num_inducing', 'output_dim', 'dL_dK', 'dL_dK_small', 'index', 'index2'])
return dL_dK_small
def _gradient_reduce_numpy(self, dL_dK, index, index2):
index, index2 = index[:,0], index2[:,0]
dL_dK_small = np.zeros_like(self.B)