From 34103ca37c44032fffe7ac7ce54faa7a47641769 Mon Sep 17 00:00:00 2001 From: Mike Croucher Date: Sun, 1 Mar 2015 10:18:27 +0000 Subject: [PATCH] Commented out weave functions for Py3 support --- GPy/kern/_src/stationary.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/GPy/kern/_src/stationary.py b/GPy/kern/_src/stationary.py index 5052b7b0..0465a556 100644 --- a/GPy/kern/_src/stationary.py +++ b/GPy/kern/_src/stationary.py @@ -8,10 +8,15 @@ from ...core.parameterization.transformations import Logexp from ...util.linalg import tdot from ... import util 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.caching import Cache_this +try: + from scipy import weave +except ImportError: + config.set('weave', 'working', 'False') + class Stationary(Kern): """ Stationary kernels (covariance functions). @@ -167,9 +172,9 @@ class Stationary(Kern): except: print("\n Weave compilation failed. Falling back to (slower) numpy implementation\n") 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: - 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: r = self._scaled_dist(X, X2) self.lengthscale.gradient = -np.sum(dL_dr*r)/self.lengthscale @@ -234,7 +239,7 @@ class Stationary(Kern): #the lower memory way with a loop 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]) ret /= self.lengthscale**2