Merge with new changes from devel

.appveyor_twine_upload.bat

@@ -0,0 +1,5 @@
+IF "%APPVEYOR_REPO_BRANCH%"=="deploy" (
+  twine upload --skip-existing dist/*
+) ELSE (
+  ECHO Only deploy on deploy branch
+)

GPy/__init__.py

@@ -18,7 +18,7 @@ from .util import normalizer
 
 # backwards compatibility
 import sys
-backwards_compatibility = ['lists_and_dicts', 'observable_array', 'ties_and_remappings', 'index_operations']
+backwards_compatibility = ['lists_and_dicts', 'observable_array', 'index_operations']
 for bc in backwards_compatibility:
     sys.modules['GPy.core.parameterization.{!s}'.format(bc)] = getattr(core.parameterization, bc)
 

GPy/__version__.py

@@ -1 +1 @@
-__version__ = "1.8.0"
+__version__ = "1.8.4"

GPy/core/parameterization/__init__.py

@@ -6,4 +6,4 @@ from .parameterized import Parameterized
 from . import transformations
 
 from paramz.core import lists_and_dicts, index_operations, observable_array, observable
-from paramz import ties_and_remappings, ObsAr
+from paramz import ObsAr

GPy/core/parameterization/priors.py

@@ -5,7 +5,7 @@
 import numpy as np
 from scipy.special import gammaln, digamma
 from ...util.linalg import pdinv
-from paramz.domains import _REAL, _POSITIVE
+from paramz.domains import _REAL, _POSITIVE, _NEGATIVE
 import warnings
 import weakref
 
@@ -92,7 +92,6 @@ class Gaussian(Prior):
 #         self.constant = -0.5 * np.log(2 * np.pi * self.sigma2)
 
 class Uniform(Prior):
-    domain = _REAL
     _instances = []
 
     def __new__(cls, lower=0, upper=1):  # Singleton:
@@ -101,13 +100,24 @@ class Uniform(Prior):
             for instance in cls._instances:
                 if instance().lower == lower and instance().upper == upper:
                     return instance()
-        o = super(Prior, cls).__new__(cls, lower, upper)
+        newfunc = super(Prior, cls).__new__
+        if newfunc is object.__new__:
+            o = newfunc(cls)  
+        else:
+            o = newfunc(cls, lower, upper)     
         cls._instances.append(weakref.ref(o))
         return cls._instances[-1]()
 
     def __init__(self, lower, upper):
         self.lower = float(lower)
         self.upper = float(upper)
+        assert self.lower < self.upper, "Lower needs to be strictly smaller than upper."
+        if self.lower >= 0:
+            self.domain = _POSITIVE
+        elif self.upper <= 0:
+            self.domain = _NEGATIVE
+        else:
+            self.domain = _REAL
 
     def __str__(self):
         return "[{:.2g}, {:.2g}]".format(self.lower, self.upper)

GPy/testing/prior_tests.py

@@ -83,6 +83,35 @@ class PriorTests(unittest.TestCase):
         # should raise an assertionerror.
         self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
 
+    def test_uniform(self):
+        xmin, xmax = 1, 2.5*np.pi
+        b, C, SNR = 1, 0, 0.1
+        X = np.linspace(xmin, xmax, 500)
+        y  = b*X + C + 1*np.sin(X)
+        y += 0.05*np.random.randn(len(X))
+        X, y = X[:, None], y[:, None]
+        m = GPy.models.SparseGPRegression(X, y)
+        uniform = GPy.priors.Uniform(0, 2)
+        m.rbf.set_prior(uniform)
+        m.randomize()
+        self.assertTrue(m.checkgrad())
+        
+        m.Z.set_prior(uniform)
+        m.randomize()
+        self.assertTrue(m.checkgrad())
+        
+        m.Z.unconstrain()
+        uniform = GPy.priors.Uniform(-1, 10)
+        m.Z.set_prior(uniform)
+        m.randomize()
+        self.assertTrue(m.checkgrad())
+
+        m.Z.constrain_negative()
+        uniform = GPy.priors.Uniform(-1, 0)
+        m.Z.set_prior(uniform)
+        m.randomize()
+        self.assertTrue(m.checkgrad())
+
     def test_set_gaussian_for_reals(self):
         xmin, xmax = 1, 2.5*np.pi
         b, C, SNR = 1, 0, 0.1

appveyor.yml

@@ -3,7 +3,7 @@ environment:
     secure: 8/ZjXFwtd1S7ixd7PJOpptupKKEDhm2da/q3unabJ00=
   COVERALLS_REPO_TOKEN:
     secure: d3Luic/ESkGaWnZrvWZTKrzO+xaVwJWaRCEP0F+K/9DQGPSRZsJ/Du5g3s4XF+tS
-  gpy_version: 1.8.0
+  gpy_version: 1.8.4
   matrix:
     - PYTHON_VERSION: 2.7
       MINICONDA: C:\Miniconda-x64
@@ -71,16 +71,7 @@ deploy_script:
 - echo repository = https://testpypi.python.org/pypi >> %USERPROFILE%\\.pypirc
 - echo username = maxz >> %USERPROFILE%\\.pypirc
 - echo password = %pip_access% >> %USERPROFILE%\\.pypirc
-- ps: >-
-    If ($env:APPVEYOR_REPO_BRANCH -eq 'devel') { 
-        echo not deploying on devel # twine upload --skip-existing -r test dist/*
-    }
-    ElseIf ($env:APPVEYOR_REPO_BRANCH -eq 'deploy') {  
-        twine upload --skip-existing dist/*
-    }
-    Else {
-        echo not deploying on other branches
-    }
+- .appveyor_twine_upload.bat
  
 # deploy:
 #   - provider: GitHub
@@ -91,4 +82,4 @@ deploy_script:
 #     prerelease: false
 #     on:
 #         branch: deploy                 # release from deploy branch only
-#         appveyor_repo_tag: true        # deploy on tag push only
+#         appveyor_repo_tag: true        # deploy on tag push only

doc/source/tuto_creating_new_kernels.rst

@@ -41,8 +41,8 @@ For all Kerns the first parameter ``input_dim`` corresponds to the
 dimension of the input space, and the following parameters stand for
 the parameterization of the kernel.
 
-You have to call ``super(<class_name>, self).__init__(input_dim,
-name)`` to make sure the input dimension and name of the kernel are
+You have to call ``super(<class_name>, self).__init__(input_dim, active_dims, 
+name)`` to make sure the input dimension (and possible dimension restrictions using active_dims) and name of the kernel are
 stored in the right place. These attributes are available as
 ``self.input_dim`` and ``self.name`` at runtime.  Parameterization is
 done by adding :py:class:`~GPy.core.parameterization.param.Param`
@@ -53,8 +53,8 @@ your code. The parameters have to be added by calling
 :py:class:`~GPy.core.parameterization.param.Param` objects as
 arguments::
 
-    def __init__(self,input_dim,variance=1.,lengthscale=1.,power=1.):
-        super(RationalQuadratic, self).__init__(input_dim, 'rat_quad')
+    def __init__(self,input_dim,variance=1.,lengthscale=1.,power=1.,active_dims=None):
+        super(RationalQuadratic, self).__init__(input_dim, active_dims, 'rat_quad')
 	assert input_dim == 1, "For this kernel we assume input_dim=1"
         self.variance = Param('variance', variance)
         self.lengthscale = Param('lengtscale', lengthscale)

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.8.0
+current_version = 1.8.4
 tag = True
 commit = True
 

setup.py

@@ -150,7 +150,7 @@ setup(name = 'GPy',
       py_modules = ['GPy.__init__'],
       test_suite = 'GPy.testing',
       setup_requires = ['numpy>=1.7'],
-      install_requires = ['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.7.4'],
+      install_requires = ['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.8.5'],
       extras_require = {'docs':['sphinx'],
                         'optional':['mpi4py',
                                     'ipython>=4.0.0',