Merge branch 'devel' of github.com:SheffieldML/GPy into devel

GPy/examples/dimensionality_reduction.py

@@ -415,7 +415,6 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
 def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
     from GPy import kern
     from GPy.models import MRD
-    from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
 
     D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
     _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
@@ -429,12 +428,8 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
         inanlist.append(inan)
         Y[inan] = _np.nan
 
-    imlist = []
-    for inan in inanlist:
-        imlist.append(VarDTCMissingData(limit=1, inan=inan))
-
     m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing,
-            kernel=k, inference_method=imlist,
+            kernel=k, inference_method=None,
             initx="random", initz='permute', **kw)
 
     if optimize:

GPy/inference/latent_function_inference/var_dtc.py

@@ -167,7 +167,7 @@ class VarDTC(LatentFunctionInference):
             woodbury_vector = Cpsi1Vf # == Cpsi1V
         else:
             print 'foobar'
-            stop
+            import ipdb; ipdb.set_trace()
             psi1V = np.dot(Y.T*beta, psi1).T
             tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
             tmp, _ = dpotrs(LB, tmp, lower=1)

GPy/likelihoods/ordinal.py

@@ -1,47 +0,0 @@
-# Copyright (c) 2014 The GPy authors (see AUTHORS.txt)
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import sympy as sym
-from GPy.util.symbolic import gammaln, normcdfln, normcdf, IndMatrix, create_matrix
-import numpy as np
-import link_functions
-from symbolic import Symbolic
-from scipy import stats
-
-class Ordinal(Symbolic):
-    """
-    Ordinal
-
-    .. math::
-        p(y_{i}|\pi(f_{i})) = \left(\frac{r}{r+f_i}\right)^r \frac{\Gamma(r+y_i)}{y!\Gamma(r)}\left(\frac{f_i}{r+f_i}\right)^{y_i}
-
-    .. Note::
-        Y takes non zero integer values..
-        link function should have a positive domain, e.g. log (default).
-
-    .. See also::
-        symbolic.py, for the parent class
-    """
-    def __init__(self, categories=3, gp_link=None):
-        if gp_link is None:
-            gp_link = link_functions.Identity()
-
-        dispersion = sym.Symbol('width', positive=True, real=True)
-        y_0 = sym.Symbol('y_0', nonnegative=True, integer=True)
-        f_0 = sym.Symbol('f_0', positive=True, real=True) 
-        log_pdf = create_matrix('log_pdf', 1, categories)
-        log_pdf[0] = normcdfln(-f_0)
-        if categories>2:
-            w = create_matrix('w', 1, categories)
-            log_pdf[categories-1] = normcdfln(w.sum() + f_0)
-            for i in range(1, categories-1):
-                log_pdf[i] = sym.log(normcdf(w[0, 0:i-1].sum() + f_0) - normcdf(w[0, 0:i].sum()-f_0) )
-        else:
-            log_pdf[1] = normcdfln(f_0)
-        log_pdf.index_var = y_0
-        super(Ordinal, self).__init__(log_pdf=log_pdf, gp_link=gp_link, name='Ordinal')
-
-        # TODO: Check this.
-        self.log_concave = True
-

GPy/models/mrd.py

@@ -13,11 +13,11 @@ from ..inference.latent_function_inference import InferenceMethodList
 from ..likelihoods import Gaussian
 from ..util.initialization import initialize_latent
 from ..core.sparse_gp import SparseGP, GP
-from GPy.models.bayesian_gplvm import BayesianGPLVM
 from GPy.core.parameterization.variational import VariationalPosterior
-from GPy.core.sparse_gp_mpi import SparseGP_MPI
+from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
+from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch
 
-class MRD(BayesianGPLVM):
+class MRD(BayesianGPLVMMiniBatch):
     """
     !WARNING: This is bleeding edge code and still in development.
     Functionality may change fundamentally during development!
@@ -92,7 +92,8 @@ class MRD(BayesianGPLVM):
         else:
             fracs = [X.var(0)]*len(Ylist)
 
-        self.Z = Param('inducing inputs', self._init_Z(initz, X))
+        Z = self._init_Z(initz, X)
+        self.Z = Param('inducing inputs', Z)
         self.num_inducing = self.Z.shape[0] # ensure M==N if M>N
 
         # sort out the kernels
@@ -104,6 +105,7 @@ class MRD(BayesianGPLVM):
             kernels = []
             for i in range(len(Ylist)):
                 k = kernel.copy()
+                print k is kernel, k.observers, k.constraints
                 kernels.append(k)
         else:
             assert len(kernel) == len(Ylist), "need one kernel per output"
@@ -114,7 +116,7 @@ class MRD(BayesianGPLVM):
             X_variance = np.random.uniform(0.1, 0.2, X.shape)
 
         self.variational_prior = NormalPrior()
-        self.X = NormalPosterior(X, X_variance)
+        #self.X = NormalPosterior(X, X_variance)
 
         if likelihoods is None:
             likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(len(Ylist))]
@@ -123,48 +125,33 @@ class MRD(BayesianGPLVM):
         self.logger.info("adding X and Z")
         super(MRD, self).__init__(Y, input_dim, X=X, X_variance=X_variance, num_inducing=num_inducing,
                  Z=self.Z, kernel=None, inference_method=self.inference_method, likelihood=Gaussian(),
-                 name='bayesian gplvm', mpi_comm=None, normalizer=None,
+                 name='manifold relevance determination', normalizer=None,
                  missing_data=False, stochastic=False, batchsize=1)
 
-        import GPy
         self._log_marginal_likelihood = 0
 
-        print "------------"
-        print self.size
-        print self.constraints[GPy.constraints.Logexp()][-10:]
-        print "------------"
         self.unlink_parameter(self.likelihood)
-        print self.size
-        print self.constraints[GPy.constraints.Logexp()][-10:]
-        print "------------"
         self.unlink_parameter(self.kern)
-        print self.size
+        del self.kern
-        print self.constraints[GPy.constraints.Logexp()][-10:]
+        del self.likelihood
-        print "------------"
-
-        print
-        print '================='
 
         self.num_data = Ylist[0].shape[0]
         if isinstance(batchsize, int):
             batchsize = itertools.repeat(batchsize)
 
-        print self.size
+        self.bgplvms = []
-        print self.constraints[GPy.constraints.Logexp()][-10:]
 
         for i, n, k, l, Y, im, bs in itertools.izip(itertools.count(), Ynames, kernels, likelihoods, Ylist, self.inference_method, batchsize):
             assert Y.shape[0] == self.num_data, "All datasets need to share the number of datapoints, and those have to correspond to one another"
             md = np.isnan(Y).any()
-            spgp = SparseGP(self.X, Y, self.Z, k, l, im, n, None, normalizer, md, stochastic, bs)
+            spgp = SparseGPMiniBatch(self.X, Y, Z, k, l, im, n, None, normalizer, md, stochastic, bs)
             spgp.unlink_parameter(spgp.Z)
+            del spgp.Z
+            del spgp.X
             spgp.Z = self.Z
+            spgp.X = self.X
             self.link_parameter(spgp, i+2)
-
+            self.bgplvms.append(spgp)
-        print self.constraints[GPy.constraints.Logexp()][-10:]
-        self.link_parameter(self.Z, 2)
-        print self.size
-        print self.constraints[GPy.constraints.Logexp()][-10:]
-        print "==========="
 
         self.posterior = None
         self.logger.info("init done")
@@ -173,7 +160,9 @@ class MRD(BayesianGPLVM):
         self._log_marginal_likelihood = 0
         self.Z.gradient[:] = 0.
         self.X.gradient[:] = 0.
-        for b, i in itertools.izip(self.parameters[3:], self.inference_method):
+        for b, i in itertools.izip(self.bgplvms, self.inference_method):
+            self._log_marginal_likelihood += b._log_marginal_likelihood
+
             self.logger.info('working on im <{}>'.format(hex(id(i))))
             self.Z.gradient[:] += b.full_values['Zgrad']
             grad_dict = b.grad_dict
@@ -195,6 +184,7 @@ class MRD(BayesianGPLVM):
             # update for the KL divergence
             self.variational_prior.update_gradients_KL(self.X)
             self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
+            pass
 
     def log_likelihood(self):
         return self._log_marginal_likelihood
@@ -268,7 +258,7 @@ class MRD(BayesianGPLVM):
         Prediction for data set Yindex[default=0].
         This predicts the output mean and variance for the dataset given in Ylist[Yindex]
         """
-        b = self.parameters[Yindex+2]
+        b = self.bgplvms[Yindex]
         self.posterior = b.posterior
         self.kern = b.kern
         self.likelihood = b.likelihood
@@ -317,16 +307,20 @@ class MRD(BayesianGPLVM):
         from ..plotting.matplot_dep import dim_reduction_plots
         if "Yindex" not in predict_kwargs:
             predict_kwargs['Yindex'] = 0
+
+        Yindex = predict_kwargs['Yindex']
         if ax is None:
             fig = plt.figure(num=fignum)
             ax = fig.add_subplot(111)
         else:
             fig = ax.figure
+        self.kern = self.bgplvms[Yindex].kern
+        self.likelihood = self.bgplvms[Yindex].likelihood
         plot = dim_reduction_plots.plot_latent(self, labels, which_indices,
                                         resolution, ax, marker, s,
                                         fignum, plot_inducing, legend,
                                         plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
-        ax.set_title(self.bgplvms[predict_kwargs['Yindex']].name)
+        ax.set_title(self.bgplvms[Yindex].name)
         try:
             fig.tight_layout()
         except:
@@ -336,7 +330,9 @@ class MRD(BayesianGPLVM):
 
     def __getstate__(self):
         state = super(MRD, self).__getstate__()
+        if state.has_key('kern'):
             del state['kern']
+        if state.has_key('likelihood'):
             del state['likelihood']
         return state
 

GPy/testing/model_tests.py

@@ -447,6 +447,7 @@ class GradientTests(np.testing.TestCase):
         m = GPy.models.GPHeteroscedasticRegression(X, Y, kern)
         self.assertTrue(m.checkgrad())
 
+
     def test_gp_kronecker_gaussian(self):
         N1, N2 = 30, 20
         X1 = np.random.randn(N1, 1)

GPy/testing/observable_tests.py

@@ -130,7 +130,6 @@ class Test(unittest.TestCase):
         self.assertEqual(self._first, self._trigger, 'priority should be second')
         self.assertEqual(self._second, self._trigger_priority, 'priority should be second')
 
-
 if __name__ == "__main__":
     #import sys;sys.argv = ['', 'Test.testName']
     unittest.main()

GPy/testing/parameterized_tests.py

@@ -221,6 +221,31 @@ class ParameterizedTest(unittest.TestCase):
         np.testing.assert_equal(t.x.constraints[Logistic(0,1)], c[Logistic(0,1)])
         np.testing.assert_equal(t.x.constraints['fixed'], c['fixed'])
 
+    def test_parameter_modify_in_init(self):
+        class TestLikelihood(Parameterized):
+            def __init__(self, param1 = 2., param2 = 3.):
+                super(TestLikelihood, self).__init__("TestLike")
+                self.p1 = Param('param1', param1)
+                self.p2 = Param('param2', param2)
+
+                self.link_parameter(self.p1)
+                self.link_parameter(self.p2)
+
+                self.p1.fix()
+                self.p1.unfix()
+                self.p2.constrain_negative()
+                self.p1.fix()
+                self.p2.constrain_positive()
+                self.p2.fix()
+                self.p2.constrain_positive()
+
+        m = TestLikelihood()
+        print m
+        val = m.p1.values.copy()
+        self.assert_(m.p1.is_fixed)
+        self.assert_(m.constraints[GPy.constraints.Logexp()].tolist(), [1])
+        m.randomize()
+        self.assertEqual(m.p1, val)
 
     def test_printing(self):
         print self.test1

GPy/testing/pickle_tests.py

@@ -141,6 +141,7 @@ class Test(ListDictTestCase):
         pcopy.optimize('bfgs')
         par.optimize('bfgs')
         np.testing.assert_allclose(pcopy.param_array, par.param_array, atol=1e-6)
+        par.randomize()
         with tempfile.TemporaryFile('w+b') as f:
             par.pickle(f)
             f.seek(0)

GPy/util/parallel.py

@@ -4,11 +4,10 @@ The module of tools for parallelization (MPI)
 
 try:
     from mpi4py import MPI
-except:
+    def get_id_within_node(comm=MPI.COMM_WORLD):
-    pass
-
-def get_id_within_node(comm=MPI.COMM_WORLD):
         rank = comm.rank
         nodename =  MPI.Get_processor_name()
         nodelist = comm.allgather(nodename)
         return len([i for i in nodelist[:rank] if i==nodename])
+except:
+    pass

README.md

@@ -3,11 +3,11 @@ GPy
 
 A Gaussian processes framework in Python.
 
+* [GPy homepage](http://sheffieldml.github.io/GPy/)
 * [User mailing list](https://lists.shef.ac.uk/sympa/subscribe/gpy-users)
 * [Online documentation](https://gpy.readthedocs.org/en/latest/)
 * [Unit tests (Travis-CI)](https://travis-ci.org/SheffieldML/GPy)
 
-
 Continuous integration status: ![CI status](https://travis-ci.org/SheffieldML/GPy.png)
 
 Citation
@@ -20,6 +20,10 @@ Citation
       year = {2012--2014}
     }
 
+Pronounciation
+==============
+We like to pronounce it 'Gee-pie'.
+
 Getting started
 ===============
 Installing with pip

doc/GPy.inference.latent_function_inference.rst

@@ -44,6 +44,14 @@ GPy.inference.latent_function_inference.fitc module
     :undoc-members:
     :show-inheritance:
 
+GPy.inference.latent_function_inference.inferenceX module
+---------------------------------------------------------
+
+.. automodule:: GPy.inference.latent_function_inference.inferenceX
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 GPy.inference.latent_function_inference.laplace module
 ------------------------------------------------------
 

doc/GPy.inference.mcmc.rst

@@ -0,0 +1,30 @@
+GPy.inference.mcmc package
+==========================
+
+Submodules
+----------
+
+GPy.inference.mcmc.hmc module
+-----------------------------
+
+.. automodule:: GPy.inference.mcmc.hmc
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+GPy.inference.mcmc.samplers module
+----------------------------------
+
+.. automodule:: GPy.inference.mcmc.samplers
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: GPy.inference.mcmc
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/GPy.kern.parts.rst

@@ -1,246 +0,0 @@
-GPy.kern.parts package
-======================
-
-Submodules
-----------
-
-GPy.kern.parts.Brownian module
-------------------------------
-
-.. automodule:: GPy.kern.parts.Brownian
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.Matern32 module
-------------------------------
-
-.. automodule:: GPy.kern.parts.Matern32
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.Matern52 module
-------------------------------
-
-.. automodule:: GPy.kern.parts.Matern52
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.bias module
---------------------------
-
-.. automodule:: GPy.kern.parts.bias
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.coregionalize module
------------------------------------
-
-.. automodule:: GPy.kern.parts.coregionalize
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.exponential module
----------------------------------
-
-.. automodule:: GPy.kern.parts.exponential
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.finite_dimensional module
-----------------------------------------
-
-.. automodule:: GPy.kern.parts.finite_dimensional
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.fixed module
----------------------------
-
-.. automodule:: GPy.kern.parts.fixed
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.gibbs module
----------------------------
-
-.. automodule:: GPy.kern.parts.gibbs
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.hetero module
-----------------------------
-
-.. automodule:: GPy.kern.parts.hetero
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.hierarchical module
-----------------------------------
-
-.. automodule:: GPy.kern.parts.hierarchical
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.independent_outputs module
------------------------------------------
-
-.. automodule:: GPy.kern.parts.independent_outputs
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.kernpart module
-------------------------------
-
-.. automodule:: GPy.kern.parts.kernpart
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.linear module
-----------------------------
-
-.. automodule:: GPy.kern.parts.linear
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.mlp module
--------------------------
-
-.. automodule:: GPy.kern.parts.mlp
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.periodic_Matern32 module
----------------------------------------
-
-.. automodule:: GPy.kern.parts.periodic_Matern32
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.periodic_Matern52 module
----------------------------------------
-
-.. automodule:: GPy.kern.parts.periodic_Matern52
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.periodic_exponential module
-------------------------------------------
-
-.. automodule:: GPy.kern.parts.periodic_exponential
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.poly module
---------------------------
-
-.. automodule:: GPy.kern.parts.poly
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.prod module
---------------------------
-
-.. automodule:: GPy.kern.parts.prod
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.prod_orthogonal module
--------------------------------------
-
-.. automodule:: GPy.kern.parts.prod_orthogonal
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.rational_quadratic module
-----------------------------------------
-
-.. automodule:: GPy.kern.parts.rational_quadratic
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.rbf module
--------------------------
-
-.. automodule:: GPy.kern.parts.rbf
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.rbf_inv module
------------------------------
-
-.. automodule:: GPy.kern.parts.rbf_inv
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.rbfcos module
-----------------------------
-
-.. automodule:: GPy.kern.parts.rbfcos
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.spline module
-----------------------------
-
-.. automodule:: GPy.kern.parts.spline
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.symmetric module
--------------------------------
-
-.. automodule:: GPy.kern.parts.symmetric
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.sympykern module
--------------------------------
-
-.. automodule:: GPy.kern.parts.sympykern
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.kern.parts.white module
----------------------------
-
-.. automodule:: GPy.kern.parts.white
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: GPy.kern.parts
-    :members:
-    :undoc-members:
-    :show-inheritance:

doc/GPy.likelihoods.noise_models.rst

@@ -1,70 +0,0 @@
-GPy.likelihoods.noise_models package
-====================================
-
-Submodules
-----------
-
-GPy.likelihoods.noise_models.binomial_noise module
---------------------------------------------------
-
-.. automodule:: GPy.likelihoods.noise_models.binomial_noise
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.likelihoods.noise_models.exponential_noise module
------------------------------------------------------
-
-.. automodule:: GPy.likelihoods.noise_models.exponential_noise
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.likelihoods.noise_models.gamma_noise module
------------------------------------------------
-
-.. automodule:: GPy.likelihoods.noise_models.gamma_noise
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.likelihoods.noise_models.gaussian_noise module
---------------------------------------------------
-
-.. automodule:: GPy.likelihoods.noise_models.gaussian_noise
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.likelihoods.noise_models.gp_transformations module
-------------------------------------------------------
-
-.. automodule:: GPy.likelihoods.noise_models.gp_transformations
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.likelihoods.noise_models.noise_distributions module
--------------------------------------------------------
-
-.. automodule:: GPy.likelihoods.noise_models.noise_distributions
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.likelihoods.noise_models.poisson_noise module
--------------------------------------------------
-
-.. automodule:: GPy.likelihoods.noise_models.poisson_noise
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: GPy.likelihoods.noise_models
-    :members:
-    :undoc-members:
-    :show-inheritance:

doc/GPy.models.rst

@@ -12,6 +12,14 @@ GPy.models.bayesian_gplvm module
     :undoc-members:
     :show-inheritance:
 
+GPy.models.bayesian_gplvm_minibatch module
+------------------------------------------
+
+.. automodule:: GPy.models.bayesian_gplvm_minibatch
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 GPy.models.bcgplvm module
 -------------------------
 
@@ -116,6 +124,14 @@ GPy.models.sparse_gp_coregionalized_regression module
     :undoc-members:
     :show-inheritance:
 
+GPy.models.sparse_gp_minibatch module
+-------------------------------------
+
+.. automodule:: GPy.models.sparse_gp_minibatch
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 GPy.models.sparse_gp_multioutput_regression module
 --------------------------------------------------
 

doc/GPy.testing.rst

@@ -28,6 +28,14 @@ GPy.testing.index_operations_tests module
     :undoc-members:
     :show-inheritance:
 
+GPy.testing.inference_tests module
+----------------------------------
+
+.. automodule:: GPy.testing.inference_tests
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 GPy.testing.kernel_tests module
 -------------------------------
 

doc/GPy.util.latent_space_visualizations.controllers.rst

@@ -1,30 +0,0 @@
-GPy.util.latent_space_visualizations.controllers package
-========================================================
-
-Submodules
-----------
-
-GPy.util.latent_space_visualizations.controllers.axis_event_controller module
------------------------------------------------------------------------------
-
-.. automodule:: GPy.util.latent_space_visualizations.controllers.axis_event_controller
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-GPy.util.latent_space_visualizations.controllers.imshow_controller module
--------------------------------------------------------------------------
-
-.. automodule:: GPy.util.latent_space_visualizations.controllers.imshow_controller
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: GPy.util.latent_space_visualizations.controllers
-    :members:
-    :undoc-members:
-    :show-inheritance:

doc/GPy.util.latent_space_visualizations.rst

@@ -1,17 +0,0 @@
-GPy.util.latent_space_visualizations package
-============================================
-
-Subpackages
------------
-
-.. toctree::
-
-    GPy.util.latent_space_visualizations.controllers
-
-Module contents
----------------
-
-.. automodule:: GPy.util.latent_space_visualizations
-    :members:
-    :undoc-members:
-    :show-inheritance:

doc/conf.py

@@ -11,6 +11,9 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
+autodoc_default_flags = ['members', 'show-inheritance', 'private-members', 'special-members']
+autodoc_member_order = "source"
+
 import sys
 import os
 
@@ -114,7 +117,7 @@ for mod_name in MOCK_MODULES:
 # ----------------------- READTHEDOCS ------------------
 on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
 
-on_rtd = True
+#on_rtd = True
 if on_rtd:
     sys.path.append(os.path.abspath('../GPy'))
 
@@ -126,7 +129,8 @@ if on_rtd:
     proc = subprocess.Popen("ls ../", stdout=subprocess.PIPE, shell=True)
     (out, err) = proc.communicate()
     print "program output:", out
-    proc = subprocess.Popen("sphinx-apidoc -f -o . ../GPy", stdout=subprocess.PIPE, shell=True)
+    #proc = subprocess.Popen("sphinx-apidoc -f -o . ../GPy", stdout=subprocess.PIPE, shell=True)
+    proc = subprocess.Popen("make html", stdout=subprocess.PIPE, shell=True)
     (out, err) = proc.communicate()
     print "program output:", out
     #proc = subprocess.Popen("whereis numpy", stdout=subprocess.PIPE, shell=True)
@@ -397,5 +401,3 @@ epub_copyright = u'2013, Author'
 
 # Allow duplicate toc entries.
 #epub_tocdup = True
-
-autodoc_member_order = "source"