[licensing] replaced licensing with BSD, and erfcx

GPy/core/parameterization/transformations.py

@@ -488,7 +488,7 @@ class Logistic(Transformation):
                     return instance()
         newfunc = super(Transformation, cls).__new__
         if newfunc is object.__new__:
-            o = newfunc(cls)  
+            o = newfunc(cls)
         else:
             o = newfunc(cls, lower, upper, *args, **kwargs)
         cls._instances.append(weakref.ref(o))

GPy/core/svgp.py

@@ -1,5 +1,5 @@
 # Copyright (c) 2014, James Hensman, Alex Matthews
-# Distributed under the terms of the GNU General public License, see LICENSE.txt
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
 from ..util import choleskies

GPy/models/gp_kronecker_gaussian_regression.py

@@ -1,5 +1,5 @@
 # Copyright (c) 2014, James Hensman, Alan Saul
-# Distributed under the terms of the GNU General public License, see LICENSE.txt
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
 from ..core.model import Model

GPy/models/gp_var_gauss.py

@@ -1,5 +1,5 @@
 # Copyright (c) 2014, James Hensman, Alan Saul
-# Distributed under the terms of the GNU General public License, see LICENSE.txt
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
 from ..core import GP

GPy/testing/run_coverage.sh

@@ -1 +1 @@
-nosetests . --with-coverage --logging-level=INFO --cover-html --cover-html-dir=coverage --cover-package=GPy --cover-erase --cover-omit=GPy.examples
+nosetests . --with-coverage --logging-level=INFO --cover-html --cover-html-dir=coverage --cover-package=GPy --cover-erase

GPy/util/choleskies.py

@@ -1,5 +1,5 @@
 # Copyright James Hensman and Max Zwiessele 2014, 2015
-# Licensed under the GNU GPL version 3.0
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
 from . import linalg

GPy/util/erfcx.py

@@ -1,63 +0,0 @@
-## Copyright (C) 2010 Soren Hauberg
-##
-## Copyright James Hensman 2011
-## 
-## This program is free software; you can redistribute it and/or modify it
-## under the terms of the GNU General Public License as published by
-## the Free Software Foundation; either version 3 of the License, or (at
-## your option) any later version.
-##
-## This program is distributed in the hope that it will be useful, but
-## WITHOUT ANY WARRANTY; without even the implied warranty of
-## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-## General Public License for more details.
-##
-## You should have received a copy of the GNU General Public License
-## along with this program; see the file COPYING.  If not, see
-## <http://www.gnu.org/licenses/>.
-
-import numpy as np
-
-def erfcx (arg):
-	arg = np.atleast_1d(arg)
-	assert(np.all(np.isreal(arg)),"erfcx: input must be real")
-
-	## Get precision dependent thresholds -- or not :p
-	xneg = -26.628;
-	xmax = 2.53e+307;
-
-	## Allocate output
-	result = np.zeros (arg.shape)
-
-	## Find values where erfcx can be evaluated
-	idx_neg = (arg < xneg);
-	idx_max = (arg > xmax);
-	idx = ~(idx_neg | idx_max);
-
-	arg = arg [idx];
-
-	## Perform the actual computation
-	t = 3.97886080735226 / (np.abs (arg) + 3.97886080735226);
-	u = t - 0.5;
-	y = (((((((((u * 0.00127109764952614092 + 1.19314022838340944e-4) * u \
-	    - 0.003963850973605135)   * u - 8.70779635317295828e-4) * u +     \
-	      0.00773672528313526668) * u + 0.00383335126264887303) * u -     \
-	      0.0127223813782122755)  * u - 0.0133823644533460069)  * u +     \
-	      0.0161315329733252248)  * u + 0.0390976845588484035)  * u +     \
-	      0.00249367200053503304;
-	y = ((((((((((((y * u - 0.0838864557023001992) * u -           \
-	      0.119463959964325415) * u + 0.0166207924969367356) * u + \
-	      0.357524274449531043) * u + 0.805276408752910567)  * u + \
-	      1.18902982909273333)  * u + 1.37040217682338167)   * u + \
-	      1.31314653831023098)  * u + 1.07925515155856677)   * u + \
-	      0.774368199119538609) * u + 0.490165080585318424)  * u + \
-	      0.275374741597376782) * t;
-
-	y [arg < 0] = 2 * np.exp (arg [arg < 0]**2) - y [arg < 0];
-
-	## Put the results back into something with the same size is the original input
-	result [idx] = y;
-	result [idx_neg] = np.inf;
-	## result (idx_max) = 0; # not needed as we initialise with zeros
-	return(result)
-

GPy/util/ln_diff_erfs.py

@@ -2,12 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 #Only works for scipy 0.12+
-try:
+from scipy.special import erfcx, erf
-    from scipy.special import erfcx, erf
-except ImportError:
-    from scipy.special import erf
-    from .erfcx import erfcx
-
 import numpy as np
 
 def ln_diff_erfs(x1, x2, return_sign=False):
@@ -17,7 +12,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
     :param x2 : argument of the negative erf
     :type x2: ndarray
     :return: tuple containing (log(abs(erf(x1) - erf(x2))), sign(erf(x1) - erf(x2)))
-    
+
     Based on MATLAB code that was written by Antti Honkela and modified by David Luengo and originally derived from code by Neil Lawrence.
     """
     x1 = np.require(x1).real
@@ -26,7 +21,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
         x1 = np.reshape(x1, (1, 1))
     if x2.size==1:
         x2 = np.reshape(x2, (1, 1))
-        
+
     if x1.shape==x2.shape:
         v = np.zeros_like(x1)
     else:
@@ -36,7 +31,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
             v = np.zeros(x1.shape)
         else:
             raise ValueError("This function does not broadcast unless provided with a scalar.")
-    
+
     if x1.size == 1:
         x1 = np.tile(x1, x2.shape)
 
@@ -92,7 +87,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
                     v[flags] = np.log(erfcx(-_x1)
                                    -erfcx(-_x2)*np.exp(_x1**2
                                                           -_x2**2))-_x1**2
-        
+
     # TODO: switch back on log of zero warnings.
 
     if return_sign: