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Commented out weave functions for Py3 support

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Mike Croucher 2015-03-01 10:18:27 +00:00
parent a0dc90596c
commit 34103ca37c
1 changed files with 9 additions and 4 deletions
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13
GPy/kern/_src/stationary.py
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@ -8,10 +8,15 @@ from ...core.parameterization.transformations import Logexp
from ...util.linalg import tdot from ...util.linalg import tdot
from ... import util from ... import util
import numpy as np import numpy as np
from scipy import integrate, weave from scipy import integrate
from ...util.config import config # for assesing whether to use weave from ...util.config import config # for assesing whether to use weave
from ...util.caching import Cache_this from ...util.caching import Cache_this
try:
from scipy import weave
except ImportError:
config.set('weave', 'working', 'False')
class Stationary(Kern): class Stationary(Kern):
""" """
Stationary kernels (covariance functions). Stationary kernels (covariance functions).
@ -167,9 +172,9 @@ class Stationary(Kern):
except: except:
print("\n Weave compilation failed. Falling back to (slower) numpy implementation\n") print("\n Weave compilation failed. Falling back to (slower) numpy implementation\n")
config.set('weave', 'working', 'False') config.set('weave', 'working', 'False')
self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(X[:,q:q+1] - X2[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in xrange(self.input_dim)]) self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(X[:,q:q+1] - X2[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in range(self.input_dim)])
else: else:
self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(X[:,q:q+1] - X2[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in xrange(self.input_dim)]) self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(X[:,q:q+1] - X2[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in range(self.input_dim)])
else: else:
r = self._scaled_dist(X, X2) r = self._scaled_dist(X, X2)
self.lengthscale.gradient = -np.sum(dL_dr*r)/self.lengthscale self.lengthscale.gradient = -np.sum(dL_dr*r)/self.lengthscale
@ -234,7 +239,7 @@ class Stationary(Kern):
#the lower memory way with a loop #the lower memory way with a loop
ret = np.empty(X.shape, dtype=np.float64) ret = np.empty(X.shape, dtype=np.float64)
for q in xrange(self.input_dim): for q in range(self.input_dim):
np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q]) np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q])
ret /= self.lengthscale**2 ret /= self.lengthscale**2