From 786feded414e7ad00a562890eec930655c65633f Mon Sep 17 00:00:00 2001
From: Mike Croucher <michael.p.croucher@googlemail.com>
Date: Fri, 27 Feb 2015 17:35:26 +0000
Subject: [PATCH] Import fix for Py3

---
 .../latent_function_inference/__init__.py     | 16 ++--
 GPy/util/univariate_Gaussian.py               | 73 ++++++++++---------
 2 files changed, 45 insertions(+), 44 deletions(-)

diff --git a/GPy/inference/latent_function_inference/__init__.py b/GPy/inference/latent_function_inference/__init__.py
index 67f57638..2d52369f 100644
--- a/GPy/inference/latent_function_inference/__init__.py
+++ b/GPy/inference/latent_function_inference/__init__.py
@@ -61,15 +61,15 @@ class InferenceMethodList(LatentFunctionInference, list):
         for inf in state:
             self.append(inf)
 
-from exact_gaussian_inference import ExactGaussianInference
-from laplace import Laplace
+from .exact_gaussian_inference import ExactGaussianInference
+from .laplace import Laplace
 from GPy.inference.latent_function_inference.var_dtc import VarDTC
-from expectation_propagation import EP
-from expectation_propagation_dtc import EPDTC
-from dtc import DTC
-from fitc import FITC
-from var_dtc_parallel import VarDTC_minibatch
-from svgp import SVGP
+from .expectation_propagation import EP
+from .expectation_propagation_dtc import EPDTC
+from .dtc import DTC
+from .fitc import FITC
+from .var_dtc_parallel import VarDTC_minibatch
+from .svgp import SVGP
 
 # class FullLatentFunctionData(object):
 #
diff --git a/GPy/util/univariate_Gaussian.py b/GPy/util/univariate_Gaussian.py
index 09b2e99c..977eb461 100644
--- a/GPy/util/univariate_Gaussian.py
+++ b/GPy/util/univariate_Gaussian.py
@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from scipy import weave
+#from scipy import weave
 
 def std_norm_pdf(x):
     """Standard Gaussian density function"""
@@ -37,41 +37,42 @@ def std_norm_cdf(x):
         cdf_x = cdf_x.reshape(x_shape)
     return cdf_x
 
-def std_norm_cdf_weave(x):
-    """
-    Cumulative standard Gaussian distribution
-    Based on Abramowitz, M. and Stegun, I. (1970)
-
-    A weave implementation of std_norm_cdf, which is faster. this is unused,
-    because of the difficulties of a weave dependency. (see github issue #94)
-
-    """
-    #Generalize for many x
-    x = np.asarray(x).copy()
-    cdf_x = np.zeros_like(x)
-    N = x.size
-    support_code = "#include <math.h>"
-    code = """
-
-    double sign, t, erf;
-    for (int i=0; i<N; i++){
-        sign = 1.0;
-        if (x[i] < 0.0){
-            sign = -1.0;
-            x[i] = -x[i];
-        }
-        x[i] = x[i]/sqrt(2.0);
-
-        t = 1.0/(1.0 +  0.3275911*x[i]);
-
-        erf = 1. - exp(-x[i]*x[i])*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429)))));
-
-        //return_val = 0.5*(1.0 + sign*erf);
-        cdf_x[i] = 0.5*(1.0 + sign*erf);
-    }
-    """
-    weave.inline(code, arg_names=['x', 'cdf_x', 'N'], support_code=support_code)
-    return cdf_x
+#Commented out since this isn't used...and since it breaks Py3 compatibility
+#def std_norm_cdf_weave(x):
+#    """
+#    Cumulative standard Gaussian distribution
+#    Based on Abramowitz, M. and Stegun, I. (1970)
+#
+#    A weave implementation of std_norm_cdf, which is faster. this is unused,
+#    because of the difficulties of a weave dependency. (see github issue #94)
+#
+#    """
+#    #Generalize for many x
+#    x = np.asarray(x).copy()
+#    cdf_x = np.zeros_like(x)
+#    N = x.size
+#    support_code = "#include <math.h>"
+#    code = """
+#
+#    double sign, t, erf;
+#    for (int i=0; i<N; i++){
+#        sign = 1.0;
+#        if (x[i] < 0.0){
+#            sign = -1.0;
+#            x[i] = -x[i];
+#        }
+#        x[i] = x[i]/sqrt(2.0);
+#
+#       t = 1.0/(1.0 +  0.3275911*x[i]);
+#
+#        erf = 1. - exp(-x[i]*x[i])*t*(0.254829592 + t*(-0.284496736 + t*(1.421413741 + t*(-1.453152027 + t*(1.061405429)))));
+#
+#        //return_val = 0.5*(1.0 + sign*erf);
+#        cdf_x[i] = 0.5*(1.0 + sign*erf);
+#    }
+#    """
+#    weave.inline(code, arg_names=['x', 'cdf_x', 'N'], support_code=support_code)
+#    return cdf_x
 
 def inv_std_norm_cdf(x):
     """