added GPy.tests(), removed some useless tests

GPy/__init__.py

@@ -9,3 +9,8 @@ import util
 import examples
 from core import priors
 import likelihoods
+import testing
+from numpy.testing import Tester
+
+def tests():
+    Tester(testing).test(verbose=10)

GPy/testing/bgplvm_tests.py

@@ -12,6 +12,7 @@ class BGPLVMTests(unittest.TestCase):
         k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        Y -= Y.mean(axis=0)
         k = GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
         m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
         m.constrain_positive('(rbf|bias|noise|white|S)')
@@ -24,6 +25,7 @@ class BGPLVMTests(unittest.TestCase):
         k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        Y -= Y.mean(axis=0)
         k = GPy.kern.linear(Q) + GPy.kern.white(Q, 0.00001)
         m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
         m.constrain_positive('(linear|bias|noise|white|S)')
@@ -36,6 +38,7 @@ class BGPLVMTests(unittest.TestCase):
         k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
         K = k.K(X)
         Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+        Y -= Y.mean(axis=0)
         k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
         m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
         m.constrain_positive('(rbf|bias|noise|white|S)')

GPy/testing/kernel_tests.py

@@ -13,7 +13,6 @@ class KernelTests(unittest.TestCase):
         X = np.random.rand(5,5)
         Y = np.ones((5,1))
         m = GPy.models.GP_regression(X,Y,K)
-        print m
         self.assertTrue(m.checkgrad())
 
     def test_coregionalisation(self):

GPy/testing/unit_tests.py

@@ -177,17 +177,6 @@ class GradientTests(unittest.TestCase):
         m.approximate_likelihood()
         self.assertTrue(m.checkgrad())
 
-    def test_warped_GP(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.warpedGP(X, y, warping_terms = 3)
-        m.constrain_positive('(tanh_a|tanh_b|rbf|white|bias)')
-        self.assertTrue(m.checkgrad())
-
 
 if __name__ == "__main__":
     print "Running unit tests, please be (very) patient..."

setup.py

@@ -3,8 +3,6 @@
 
 import os
 from setuptools import setup
-#from numpy.distutils.core import Extension, setup
-#from sphinx.setup_command import BuildDoc
 
 # Version number
 version = '0.1.3'
@@ -14,12 +12,12 @@ def read(fname):
 
 setup(name = 'GPy',
       version = version,
-      author = 'James Hensman, Nicolo Fusi, Ricardo Andrade, Nicolas Durrande, Alan Saul, Neil D. Lawrence',
+      author = read('AUTHORS.txt'),
       author_email = "james.hensman@gmail.com",
       description = ("The Gaussian Process Toolbox"),
       license = "BSD 3-clause",
       keywords = "machine-learning gaussian-processes kernels",
-      url = "http://ml.sheffield.ac.uk/GPy/",
+      url = "http://sheffieldml.github.com/GPy/",
       packages = ['GPy', 'GPy.core', 'GPy.kern', 'GPy.util', 'GPy.models', 'GPy.inference', 'GPy.examples', 'GPy.likelihoods'],
       package_dir={'GPy': 'GPy'},
       package_data = {'GPy': ['GPy/examples']},