added robust pickling, switches to old behaviour, if get/setstate not implemented

GPy/core/__init__.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 from model import *
-from parameterised import *
+from parameterized import *
 import priors
 from gp import GP
 from sparse_gp import SparseGP

GPy/core/gp.py

@@ -31,8 +31,11 @@ class GP(GPBase):
         GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self._set_params(self._get_params())
 
-    def __setstate__(self, state):
-        GPBase.__setstate__(self, state)
+    def getstate(self):
+        return GPBase.getstate(self)
+
+    def setstate(self, state):
+        GPBase.setstate(self, state)
         self._set_params(self._get_params())
 
     def _set_params(self, p):

GPy/core/gp_base.py

@@ -33,12 +33,12 @@ class GPBase(Model):
         # All leaf nodes should call self._set_params(self._get_params()) at
         # the end
 
-    def __getstate__(self):
+    def getstate(self):
         """
         Get the current state of the class,
         here just all the indices, rest can get recomputed
         """
-        return Model.__getstate__(self) + [self.X,
+        return Model.getstate(self) + [self.X,
                 self.num_data,
                 self.input_dim,
                 self.kern,
@@ -47,7 +47,7 @@ class GPBase(Model):
                 self._Xoffset,
                 self._Xscale]
 
-    def __setstate__(self, state):
+    def setstate(self, state):
         self._Xscale = state.pop()
         self._Xoffset = state.pop()
         self.output_dim = state.pop()
@@ -56,7 +56,7 @@ class GPBase(Model):
         self.input_dim = state.pop()
         self.num_data = state.pop()
         self.X = state.pop()
-        Model.__setstate__(self, state)
+        Model.setstate(self, state)
 
     def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
         """

GPy/core/model.py

@@ -6,18 +6,18 @@ from .. import likelihoods
 from ..inference import optimization
 from ..util.linalg import jitchol
 from GPy.util.misc import opt_wrapper
-from parameterised import Parameterised
+from parameterized import Parameterized
 import multiprocessing as mp
 import numpy as np
 from GPy.core.domains import POSITIVE, REAL
 from numpy.linalg.linalg import LinAlgError
 # import numdifftools as ndt
 
-class Model(Parameterised):
+class Model(Parameterized):
     _fail_count = 0 # Count of failed optimization steps (see objective)
     _allowed_failures = 10 # number of allowed failures
     def __init__(self):
-        Parameterised.__init__(self)
+        Parameterized.__init__(self)
         self.priors = None
         self.optimization_runs = []
         self.sampling_runs = []
@@ -32,24 +32,27 @@ class Model(Parameterised):
     def _log_likelihood_gradients(self):
         raise NotImplementedError, "this needs to be implemented to use the Model class"
 
-    def __getstate__(self):
+    def getstate(self):
         """
-        Get the current state of the class,
-        here just all the indices, rest can get recomputed
+        Get the current state of the class.
+        
+        Inherited from Parameterized, so add those parameters to the state
         """
-        return Parameterised.__getstate__(self) + \
+        return Parameterized.getstate(self) + \
             [self.priors, self.optimization_runs,
              self.sampling_runs, self.preferred_optimizer]
 
-    def __setstate__(self, state):
+    def setstate(self, state):
         """
         set state from previous call to getstate
+        
+        call Parameterized with the rest of the state
         """
         self.preferred_optimizer = state.pop()
         self.sampling_runs = state.pop()
         self.optimization_runs = state.pop()
         self.priors = state.pop()
-        Parameterised.__setstate__(self, state)
+        Parameterized.setstate(self, state)
 
     def set_prior(self, regexp, what):
         """
@@ -355,7 +358,7 @@ class Model(Parameterised):
         return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld
 
     def __str__(self):
-        s = Parameterised.__str__(self).split('\n')
+        s = Parameterized.__str__(self).split('\n')
         # add priors to the string
         if self.priors is not None:
             strs = [str(p) if p is not None else '' for p in self.priors]

GPy/core/parameterized.py

@@ -9,7 +9,7 @@ import cPickle
 import warnings
 import transformations
 
-class Parameterised(object):
+class Parameterized(object):
     def __init__(self):
         """
         This is the base class for model and kernel. Mostly just handles tieing and constraining of parameters
@@ -20,19 +20,40 @@ class Parameterised(object):
         self.constrained_indices = []
         self.constraints = []
 
-    def pickle(self, filename, protocol= -1):
-        f = file(filename, 'w')
-        cPickle.dump(self, f, protocol)
-        f.close()
+    def pickle(self, filename, protocol=None):
+        if protocol is None:
+            if self._has_get_set_state():
+                protocol = 0
+            else:
+                protocol = -1
+        with open(filename, 'w') as f:
+            cPickle.dump(self, f, protocol)
 
     def copy(self):
         """Returns a (deep) copy of the current model """
         return copy.deepcopy(self)
 
     def __getstate__(self):
+        if self._has_get_set_state():
+            return self.getstate()
+        return self.__dict__
+
+    def __setstate__(self, state):
+        if self._has_get_set_state():
+            return self.setstate(state)
+        self.__dict__ = state
+
+    def _has_get_set_state(self):
+        return 'getstate' in vars(self.__class__) and 'setstate' in vars(self.__class__)
+
+    def getstate(self):
         """
         Get the current state of the class,
         here just all the indices, rest can get recomputed
+        
+        For inheriting from Parameterized:
+        Allways append the state of the inherited object 
+        and call down to the inherited object in setstate!! 
         """
         return [self.tied_indices,
                 self.fixed_indices,
@@ -40,54 +61,13 @@ class Parameterised(object):
                 self.constrained_indices,
                 self.constraints]
 
-    def __setstate__(self, state):
+    def setstate(self, state):
         self.constraints = state.pop()
         self.constrained_indices = state.pop()
         self.fixed_values = state.pop()
         self.fixed_indices = state.pop()
         self.tied_indices = state.pop()
 
-    @property
-    def params(self):
-        """
-        Returns a **copy** of parameters in non transformed space
-
-        :see_also: :py:func:`GPy.core.Parameterised.params_transformed`
-        """
-        return self._get_params()
-
-    @params.setter
-    def params(self, params):
-        self._set_params(params)
-
-    @property
-    def params_transformed(self):
-        """
-        Returns a **copy** of parameters in transformed space
-
-        :see_also: :py:func:`GPy.core.Parameterised.params`
-        """
-        return self._get_params_transformed()
-
-    @params_transformed.setter
-    def params_transformed(self, params):
-        self._set_params_transformed(params)
-
-    _get_set_deprecation = """get and set methods wont be available at next minor release
-        in the next releases you will get and set with following syntax:
-        Assume m is a model class:
-        print m['var']          # > prints all parameters matching 'var'
-        m['var'] = 2.           # > sets all parameters matching 'var' to 2.
-        m['var'] = <array-like> # > sets parameters matching 'var' to <array-like>
-        """
-    def get(self, regexp):
-        warnings.warn(self._get_set_deprecation, FutureWarning, stacklevel=2)
-        return self[regexp]
-
-    def set(self, regexp, val):
-        warnings.warn(self._get_set_deprecation, FutureWarning, stacklevel=2)
-        self[regexp] = val
-
     def __getitem__(self, regexp, return_names=False):
         """
         Get a model parameter by name.  The name is applied as a regular
@@ -120,6 +100,9 @@ class Parameterised(object):
             raise AttributeError, "no parameter matches %s" % name
 
     def tie_params(self, regexp):
+        """
+        Tie (all!) parameters matching the regular expression `regexp`. 
+        """
         matches = self.grep_param_names(regexp)
         assert matches.size > 0, "need at least something to tie together"
         if len(self.tied_indices):

GPy/core/sparse_gp.py

@@ -50,22 +50,22 @@ class SparseGP(GPBase):
         if self.has_uncertain_inputs:
             self.X_variance /= np.square(self._Xscale)
 
-    def __getstate__(self):
+    def getstate(self):
         """
         Get the current state of the class,
         here just all the indices, rest can get recomputed
         """
-        return GPBase.__getstate__(self) + [self.Z,
+        return GPBase.getstate(self) + [self.Z,
                 self.num_inducing,
                 self.has_uncertain_inputs,
                 self.X_variance]
 
-    def __setstate__(self, state):
+    def setstate(self, state):
         self.X_variance = state.pop()
         self.has_uncertain_inputs = state.pop()
         self.num_inducing = state.pop()
         self.Z = state.pop()
-        GPBase.__setstate__(self, state)
+        GPBase.setstate(self, state)
 
     def _compute_kernel_matrices(self):
         # kernel computations, using BGPLVM notation

GPy/core/svigp.py

@@ -91,6 +91,14 @@ class SVIGP(GPBase):
         self._param_steplength_trace = []
         self._vb_steplength_trace = []
 
+    def getstate(self):
+        return GPBase.getstate(self)
+
+
+    def setstate(self, state):
+        return GPBase.setstate(self, state)
+
+
     def _compute_kernel_matrices(self):
         # kernel computations, using BGPLVM notation
         self.Kmm = self.kern.K(self.Z)

GPy/kern/kern.py

@@ -3,12 +3,12 @@
 
 import numpy as np
 import pylab as pb
-from ..core.parameterised import Parameterised
+from ..core.parameterized import Parameterized
 from parts.kernpart import Kernpart
 import itertools
 from parts.prod import Prod as prod
 
-class kern(Parameterised):
+class kern(Parameterized):
     def __init__(self, input_dim, parts=[], input_slices=None):
         """
         This is the main kernel class for GPy. It handles multiple (additive) kernel functions, and keeps track of variaous things like which parameters live where.
@@ -41,14 +41,14 @@ class kern(Parameterised):
 
         self.compute_param_slices()
 
-        Parameterised.__init__(self)
+        Parameterized.__init__(self)
 
-    def __getstate__(self):
+    def getstate(self):
         """
         Get the current state of the class,
         here just all the indices, rest can get recomputed
         """
-        return Parameterised.__getstate__(self) + [self.parts,
+        return Parameterized.getstate(self) + [self.parts,
                 self.Nparts,
                 self.num_params,
                 self.input_dim,
@@ -56,14 +56,14 @@ class kern(Parameterised):
                 self.param_slices
                 ]
 
-    def __setstate__(self, state):
+    def setstate(self, state):
         self.param_slices = state.pop()
         self.input_slices = state.pop()
         self.input_dim = state.pop()
         self.num_params = state.pop()
         self.Nparts = state.pop()
         self.parts = state.pop()
-        Parameterised.__setstate__(self, state)
+        Parameterized.setstate(self, state)
 
 
     def plot_ARD(self, fignum=None, ax=None, title=None):

GPy/models/bayesian_gplvm.py

@@ -48,16 +48,16 @@ class BayesianGPLVM(SparseGP, GPLVM):
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
         self.ensure_default_constraints()
 
-    def __getstate__(self):
+    def getstate(self):
         """
         Get the current state of the class,
         here just all the indices, rest can get recomputed
         """
-        return SparseGP.__getstate__(self) + [self.init]
+        return SparseGP.getstate(self) + [self.init]
 
-    def __setstate__(self, state):
+    def setstate(self, state):
         self.init = state.pop()
-        SparseGP.__setstate__(self, state)
+        SparseGP.setstate(self, state)
 
     def _get_param_names(self):
         X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])

GPy/models/gp_regression.py

@@ -25,11 +25,20 @@ class GPRegression(GP):
 
     """
 
-    def __init__(self,X,Y,kernel=None,normalize_X=False,normalize_Y=False):
+    def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False):
         if kernel is None:
             kernel = kern.rbf(X.shape[1])
 
-        likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
+        likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
 
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self.ensure_default_constraints()
+
+    def getstate(self):
+        return GP.getstate(self)
+
+
+    def setstate(self, state):
+        return GP.setstate(self, state)
+
+    pass

GPy/models/mrd.py

@@ -84,8 +84,8 @@ class MRD(Model):
         Model.__init__(self)
         self.ensure_default_constraints()
 
-    def __getstate__(self):
-        return Model.__getstate__(self) + [self.names,
+    def getstate(self):
+        return Model.getstate(self) + [self.names,
                 self.bgplvms,
                 self.gref,
                 self.nparams,
@@ -95,7 +95,7 @@ class MRD(Model):
                 self.NQ,
                 self.MQ]
 
-    def __setstate__(self, state):
+    def setstate(self, state):
         self.MQ = state.pop()
         self.NQ = state.pop()
         self.num_data = state.pop()
@@ -105,7 +105,7 @@ class MRD(Model):
         self.gref = state.pop()
         self.bgplvms = state.pop()
         self.names = state.pop()
-        Model.__setstate__(self, state)
+        Model.setstate(self, state)
 
     @property
     def X(self):

GPy/models/sparse_gp_classification.py

@@ -28,7 +28,7 @@ class SparseGPClassification(SparseGP):
 
     def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
         if kernel is None:
-            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
+            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1], 1e-3)
 
         if likelihood is None:
             distribution = likelihoods.likelihood_functions.Binomial()
@@ -41,7 +41,16 @@ class SparseGPClassification(SparseGP):
             i = np.random.permutation(X.shape[0])[:num_inducing]
             Z = X[i].copy()
         else:
-            assert Z.shape[1]==X.shape[1]
+            assert Z.shape[1] == X.shape[1]
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
         self.ensure_default_constraints()
+
+    def getstate(self):
+        return SparseGP.getstate(self)
+
+
+    def setstate(self, state):
+        return SparseGP.setstate(self, state)
+
+    pass

GPy/models/sparse_gp_regression.py

@@ -43,3 +43,13 @@ class SparseGPRegression(SparseGP):
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
         self.ensure_default_constraints()
+        pass
+
+    def getstate(self):
+        return SparseGP.getstate(self)
+
+
+    def setstate(self, state):
+        return SparseGP.setstate(self, state)
+
+    pass

GPy/models/sparse_gplvm.py

@@ -28,6 +28,14 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
         SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
         self.ensure_default_constraints()
 
+    def getstate(self):
+        return SparseGPRegression.getstate(self)
+
+
+    def setstate(self, state):
+        return SparseGPRegression.setstate(self, state)
+
+
     def _get_param_names(self):
         return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
                 + SparseGPRegression._get_param_names(self))

GPy/models/svigp_regression.py

@@ -42,3 +42,11 @@ class SVIGPRegression(SVIGP):
 
         SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
         self.load_batch()
+
+    def getstate(self):
+        return GPBase.getstate(self)
+
+
+    def setstate(self, state):
+        return GPBase.setstate(self, state)
+

GPy/models/warped_gp.py

@@ -28,6 +28,14 @@ class WarpedGP(GP):
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self._set_params(self._get_params())
 
+    def getstate(self):
+        return GP.getstate(self)
+
+
+    def setstate(self, state):
+        return GP.setstate(self, state)
+
+
     def _scale_data(self, Y):
         self._Ymax = Y.max()
         self._Ymin = Y.min()

doc/GPy.core.rst

@@ -9,6 +9,38 @@ core Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`domains` Module
+---------------------
+
+.. automodule:: GPy.core.domains
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`fitc` Module
+------------------
+
+.. automodule:: GPy.core.fitc
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`gp` Module
+----------------
+
+.. automodule:: GPy.core.gp
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`gp_base` Module
+---------------------
+
+.. automodule:: GPy.core.gp_base
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`model` Module
 -------------------
 
@@ -17,10 +49,10 @@ core Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`parameterised` Module
+:mod:`parameterized` Module
 ---------------------------
 
-.. automodule:: GPy.core.parameterised
+.. automodule:: GPy.core.parameterized
     :members:
     :undoc-members:
     :show-inheritance:
@@ -33,3 +65,27 @@ core Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`sparse_gp` Module
+-----------------------
+
+.. automodule:: GPy.core.sparse_gp
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`svigp` Module
+-------------------
+
+.. automodule:: GPy.core.svigp
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`transformations` Module
+-----------------------------
+
+.. automodule:: GPy.core.transformations
+    :members:
+    :undoc-members:
+    :show-inheritance:
+

doc/GPy.examples.rst

@@ -25,14 +25,6 @@ examples Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`non_gaussian` Module
---------------------------
-
-.. automodule:: GPy.examples.non_gaussian
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
 :mod:`regression` Module
 ------------------------
 
@@ -41,6 +33,14 @@ examples Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`stochastic` Module
+------------------------
+
+.. automodule:: GPy.examples.stochastic
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`tutorials` Module
 -----------------------
 

doc/GPy.inference.rst

@@ -1,10 +1,18 @@
 inference Package
 =================
 
-:mod:`SGD` Module
------------------
+:mod:`conjugate_gradient_descent` Module
+----------------------------------------
 
-.. automodule:: GPy.inference.SGD
+.. automodule:: GPy.inference.conjugate_gradient_descent
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`gradient_descent_update_rules` Module
+-------------------------------------------
+
+.. automodule:: GPy.inference.gradient_descent_update_rules
     :members:
     :undoc-members:
     :show-inheritance:
@@ -25,3 +33,19 @@ inference Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`scg` Module
+-----------------
+
+.. automodule:: GPy.inference.scg
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`sgd` Module
+-----------------
+
+.. automodule:: GPy.inference.sgd
+    :members:
+    :undoc-members:
+    :show-inheritance:
+

doc/GPy.kern.rst

@@ -9,38 +9,6 @@ kern Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`Brownian` Module
-----------------------
-
-.. automodule:: GPy.kern.Brownian
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`Matern32` Module
-----------------------
-
-.. automodule:: GPy.kern.Matern32
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`Matern52` Module
-----------------------
-
-.. automodule:: GPy.kern.Matern52
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`bias` Module
-------------------
-
-.. automodule:: GPy.kern.bias
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
 :mod:`constructors` Module
 --------------------------
 
@@ -49,30 +17,6 @@ kern Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`coregionalise` Module
----------------------------
-
-.. automodule:: GPy.kern.coregionalise
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`exponential` Module
--------------------------
-
-.. automodule:: GPy.kern.exponential
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`finite_dimensional` Module
---------------------------------
-
-.. automodule:: GPy.kern.finite_dimensional
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
 :mod:`kern` Module
 ------------------
 
@@ -81,107 +25,10 @@ kern Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`kernpart` Module
-----------------------
+Subpackages
+-----------
 
-.. automodule:: GPy.kern.kernpart
-    :members:
-    :undoc-members:
-    :show-inheritance:
+.. toctree::
 
-:mod:`linear` Module
---------------------
-
-.. automodule:: GPy.kern.linear
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`periodic_Matern32` Module
--------------------------------
-
-.. automodule:: GPy.kern.periodic_Matern32
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`periodic_Matern52` Module
--------------------------------
-
-.. automodule:: GPy.kern.periodic_Matern52
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`periodic_exponential` Module
-----------------------------------
-
-.. automodule:: GPy.kern.periodic_exponential
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`prod` Module
-------------------
-
-.. automodule:: GPy.kern.prod
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`prod_orthogonal` Module
------------------------------
-
-.. automodule:: GPy.kern.prod_orthogonal
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`rational_quadratic` Module
---------------------------------
-
-.. automodule:: GPy.kern.rational_quadratic
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`rbf` Module
------------------
-
-.. automodule:: GPy.kern.rbf
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`spline` Module
---------------------
-
-.. automodule:: GPy.kern.spline
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`symmetric` Module
------------------------
-
-.. automodule:: GPy.kern.symmetric
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`sympykern` Module
------------------------
-
-.. automodule:: GPy.kern.sympykern
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-:mod:`white` Module
--------------------
-
-.. automodule:: GPy.kern.white
-    :members:
-    :undoc-members:
-    :show-inheritance:
+    GPy.kern.parts
 

doc/GPy.likelihoods.rst

@@ -9,7 +9,7 @@ likelihoods Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`EP` Module
+:mod:`ep` Module
 ----------------
 
 .. automodule:: GPy.likelihoods.ep
@@ -17,7 +17,7 @@ likelihoods Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`Gaussian` Module
+:mod:`gaussian` Module
 ----------------------
 
 .. automodule:: GPy.likelihoods.gaussian
@@ -41,3 +41,11 @@ likelihoods Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`link_functions` Module
+----------------------------
+
+.. automodule:: GPy.likelihoods.link_functions
+    :members:
+    :undoc-members:
+    :show-inheritance:
+

doc/GPy.models.rst

@@ -9,7 +9,7 @@ models Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`Bayesian_GPLVM` Module
+:mod:`bayesian_gplvm` Module
 ----------------------------
 
 .. automodule:: GPy.models.bayesian_gplvm
@@ -17,18 +17,18 @@ models Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`gp` Module
-----------------
+:mod:`fitc_classification` Module
+---------------------------------
 
-.. automodule:: GPy.models.gp
+.. automodule:: GPy.models.fitc_classification
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`gplvm` Module
--------------------
+:mod:`gp_classification` Module
+-------------------------------
 
-.. automodule:: GPy.models.gplvm
+.. automodule:: GPy.models.gp_classification
     :members:
     :undoc-members:
     :show-inheritance:
@@ -41,18 +41,26 @@ models Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`sparse_gp` Module
------------------------
+:mod:`gplvm` Module
+-------------------
 
-.. automodule:: GPy.models.sparse_gp
+.. automodule:: GPy.models.gplvm
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`SparseGPLVM` Module
---------------------------
+:mod:`mrd` Module
+-----------------
 
-.. automodule:: GPy.models.sparse_gplvm
+.. automodule:: GPy.models.mrd
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`sparse_gp_classification` Module
+--------------------------------------
+
+.. automodule:: GPy.models.sparse_gp_classification
     :members:
     :undoc-members:
     :show-inheritance:
@@ -65,13 +73,21 @@ models Package
     :undoc-members:
     :show-inheritance:
 
-.. :mod:`uncollapsed_sparse_GP` Module
-.. -----------------------------------
+:mod:`sparse_gplvm` Module
+--------------------------
 
-.. .. automodule:: GPy.models.uncollapsed_sparse_GP
-..     :members:
-..     :undoc-members:
-..     :show-inheritance:
+.. automodule:: GPy.models.sparse_gplvm
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`svigp_regression` Module
+------------------------------
+
+.. automodule:: GPy.models.svigp_regression
+    :members:
+    :undoc-members:
+    :show-inheritance:
 
 :mod:`warped_gp` Module
 -----------------------

doc/GPy.testing.rst

@@ -1,6 +1,14 @@
 testing Package
 ===============
 
+:mod:`testing` Package
+----------------------
+
+.. automodule:: GPy.testing
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`bgplvm_tests` Module
 --------------------------
 
@@ -9,6 +17,22 @@ testing Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`cgd_tests` Module
+-----------------------
+
+.. automodule:: GPy.testing.cgd_tests
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`checkgrad` Module
+-----------------------
+
+.. automodule:: GPy.testing.checkgrad
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`examples_tests` Module
 ----------------------------
 
@@ -33,6 +57,14 @@ testing Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`mrd_tests` Module
+-----------------------
+
+.. automodule:: GPy.testing.mrd_tests
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`prior_tests` Module
 -------------------------
 
@@ -41,6 +73,22 @@ testing Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`psi_stat_expactation_tests` Module
+----------------------------------------
+
+.. automodule:: GPy.testing.psi_stat_expactation_tests
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`psi_stat_gradient_tests` Module
+-------------------------------------
+
+.. automodule:: GPy.testing.psi_stat_gradient_tests
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`sparse_gplvm_tests` Module
 --------------------------------
 

doc/GPy.util.rst

@@ -17,6 +17,14 @@ util Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`classification` Module
+----------------------------
+
+.. automodule:: GPy.util.classification
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`datasets` Module
 ----------------------
 
@@ -25,6 +33,14 @@ util Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`decorators` Module
+------------------------
+
+.. automodule:: GPy.util.decorators
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`linalg` Module
 --------------------
 
@@ -41,6 +57,22 @@ util Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`mocap` Module
+-------------------
+
+.. automodule:: GPy.util.mocap
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`pca` Module
+-----------------
+
+.. automodule:: GPy.util.pca
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`plot` Module
 ------------------
 
@@ -49,6 +81,14 @@ util Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`plot_latent` Module
+-------------------------
+
+.. automodule:: GPy.util.plot_latent
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`squashers` Module
 -----------------------
 
@@ -57,6 +97,22 @@ util Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`univariate_Gaussian` Module
+---------------------------------
+
+.. automodule:: GPy.util.univariate_Gaussian
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`visualize` Module
+-----------------------
+
+.. automodule:: GPy.util.visualize
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`warping_functions` Module
 -------------------------------
 

doc/conf.py

@@ -103,8 +103,10 @@ class Mock(object):
 #import mock
 
 print "Mocking"
-MOCK_MODULES = ['pylab', 'sympy', 'sympy.utilities', 'sympy.utilities.codegen', 'sympy.core.cache', 'sympy.core', 'sympy.parsing', 'sympy.parsing.sympy_parser', 'matplotlib']
-#'matplotlib', 'matplotlib.color', 'matplotlib.pyplot', 'pylab' ]
+MOCK_MODULES = ['sympy', 
+    'sympy.utilities', 'sympy.utilities.codegen', 'sympy.core.cache', 
+    'sympy.core', 'sympy.parsing', 'sympy.parsing.sympy_parser', 
+    ]
 for mod_name in MOCK_MODULES:
     sys.modules[mod_name] = Mock()
 
@@ -288,7 +290,7 @@ latex_elements = {
     #'pointsize': '10pt',
 
     # Additional stuff for the LaTeX preamble.
-    #'preamble': '',
+    'preamble': '\\usepackage{MnSymbol}',
 }
 
 # Grouping the document tree into LaTeX files. List of tuples

doc/index.rst

@@ -11,6 +11,7 @@ For a quick start, you can have a look at one of the tutorials:
 * `Interacting with models <tuto_interacting_with_models.html>`_
 * `A kernel overview <tuto_kernel_overview.html>`_ 
 * `Writing new kernels <tuto_creating_new_kernels.html>`_
+* `Writing new models <tuto_creating_new_models.html>`_
 
 You may also be interested by some examples in the GPy/examples folder.
 

doc/tuto_interacting_with_models.rst

@@ -1,3 +1,5 @@
+.. _interacting_with_models:
+
 *************************************
 Interacting with models
 *************************************
@@ -210,6 +212,6 @@ white_variance and noise_variance are tied together.::
 
 Further Reading
 ===============
-All of the mechansiams for dealing with parameters are baked right into GPy.core.model, from which all of the classes in GPy.models inherrit. To learn how to construct your own model, you might want to read ??link?? creating_new_models. 
+All of the mechansiams for dealing with parameters are baked right into GPy.core.model, from which all of the classes in GPy.models inherrit. To learn how to construct your own model, you might want to read :ref:`creating_new_models`. 
 
-By deafult, GPy uses the tnc optimizer (from scipy.optimize.tnc). To use other optimisers, and to control the setting of those optimisers, as well as other funky features like automated restarts and diagnostics, you can read the optimization tutorial ??link??.
+By deafult, GPy uses the scg optimizer. To use other optimisers, and to control the setting of those optimisers, as well as other funky features like automated restarts and diagnostics, you can read the optimization tutorial ??link??.