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

GPy/core/parameterization/lists_and_dicts.py

@@ -58,6 +58,7 @@ class ObservablesList(object):
     def __repr__(self):
         return self._poc.__repr__()
     
+
     def add(self, priority, observable, callble):
         if observable is not None:
             ins = 0
@@ -87,7 +88,6 @@ class ObservablesList(object):
     def __iter__(self):
         self.flush()
         for p, o, c in self._poc:
-            if o() is not None:
             yield p, o(), c 
 
     def __len__(self):
@@ -95,11 +95,10 @@ class ObservablesList(object):
         return self._poc.__len__()
 
     def __deepcopy__(self, memo):
-        self.flush()
         s = ObservablesList()
-        for p,o,c in self._poc:
+        for p,o,c in self:
             import copy
-            s.add(p, copy.deepcopy(o(), memo), copy.deepcopy(c, memo))
+            s.add(p, copy.deepcopy(o, memo), copy.deepcopy(c, memo))
         s.flush()
         return s
 

GPy/core/parameterization/param.py

@@ -57,9 +57,9 @@ class Param(OptimizationHandlable, ObsAr):
 
     def build_pydot(self,G):
         import pydot
-        node = pydot.Node(id(self), shape='record', label=self.name)
+        node = pydot.Node(id(self), shape='trapezium', label=self.name)#, fontcolor='white', color='white')
         G.add_node(node)
-        for o in self.observers.keys():
+        for _, o, _ in self.observers:
             label = o.name if hasattr(o, 'name') else str(o)
             observed_node = pydot.Node(id(o), label=label)
             G.add_node(observed_node)
@@ -89,6 +89,13 @@ class Param(OptimizationHandlable, ObsAr):
     def param_array(self):
         return self
 
+    @property
+    def values(self):
+        """
+        Return self as numpy array view
+        """
+        return self.view(np.ndarray)
+
     @property
     def gradient(self):
         """
@@ -99,11 +106,11 @@ class Param(OptimizationHandlable, ObsAr):
         """
         if getattr(self, '_gradient_array_', None) is None:
             self._gradient_array_ = numpy.empty(self._realshape_, dtype=numpy.float64)
-        return self._gradient_array_[self._current_slice_]
+        return self._gradient_array_#[self._current_slice_]
 
     @gradient.setter
     def gradient(self, val):
-        self._gradient_array_[self._current_slice_] = val
+        self._gradient_array_[:] = val
 
     #===========================================================================
     # Array operations -> done
@@ -114,7 +121,10 @@ class Param(OptimizationHandlable, ObsAr):
         #if not reduce(lambda a, b: a or numpy.any(b is Ellipsis), s, False) and len(s) <= self.ndim:
         #    s += (Ellipsis,)
         new_arr = super(Param, self).__getitem__(s, *args, **kwargs)
-        try: new_arr._current_slice_ = s; new_arr._original_ = self.base is new_arr.base
+        try: 
+            new_arr._current_slice_ = s
+            new_arr._gradient_array_ = self.gradient[s]
+            new_arr._original_ = self.base is new_arr.base
         except AttributeError: pass  # returning 0d array or float, double etc
         return new_arr
 
@@ -161,8 +171,29 @@ class Param(OptimizationHandlable, ObsAr):
     # parameterizable
     #===========================================================================
     def traverse(self, visit, *args, **kwargs):
+        """
+        Traverse the hierarchy performing visit(self, *args, **kwargs) at every node passed by.
+        See "visitor pattern" in literature. This is implemented in pre-order fashion.
+
+        This will function will just call visit on self, as Param are leaf nodes.
+        """
         visit(self, *args, **kwargs)
     
+    def traverse_parents(self, visit, *args, **kwargs):
+        """
+        Traverse the hierarchy upwards, visiting all parents and their children, except self.
+        See "visitor pattern" in literature. This is implemented in pre-order fashion.
+    
+        Example:
+    
+        parents = []
+        self.traverse_parents(parents.append)
+        print parents
+        """
+        if self.has_parent():
+            self.__visited = True
+            self._parent_._traverse_parents(visit, *args, **kwargs)
+            self.__visited = False
 
     #===========================================================================
     # Convenience
@@ -236,9 +267,16 @@ class Param(OptimizationHandlable, ObsAr):
                 and len(set(map(len, clean_curr_slice))) <= 1):
                 return numpy.fromiter(itertools.izip(*clean_curr_slice),
                     dtype=[('', int)] * self._realndim_, count=len(clean_curr_slice[0])).view((int, self._realndim_))
+        try:
             expanded_index = list(self._expand_index(slice_index))
-        return numpy.fromiter(itertools.product(*expanded_index),
+            indices = numpy.fromiter(itertools.product(*expanded_index),
                  dtype=[('', int)] * self._realndim_, count=reduce(lambda a, b: a * b.size, expanded_index, 1)).view((int, self._realndim_))
+        except:
+            print "Warning: extended indexing was used"
+            indices = np.indices(self._realshape_, dtype=int)
+            indices = indices[(slice(None),)+slice_index]
+            indices = np.rollaxis(indices, 0, indices.ndim)
+        return indices
     def _max_len_names(self, gen, header):
         gen = map(lambda x: " ".join(map(str, x)), gen)
         return reduce(lambda a, b:max(a, len(b)), gen, len(header))
@@ -280,7 +318,7 @@ class Param(OptimizationHandlable, ObsAr):
 class ParamConcatenation(object):
     def __init__(self, params):
         """
-        Parameter concatenation for convienience of printing regular expression matched arrays
+        Parameter concatenation for convenience of printing regular expression matched arrays
         you can index this concatenation as if it was the flattened concatenation
         of all the parameters it contains, same for setting parameters (Broadcasting enabled).
 

GPy/core/parameterization/parameter_core.py

@@ -176,24 +176,23 @@ class Pickleable(object):
         #raise NotImplementedError, "Copy is not yet implemented, TODO: Observable hierarchy"
         import copy
         memo = {}
+        # the next part makes sure that we do not include parents in any form:
         parents = []
-        self.traverse_parents(parents.append)
+        self.traverse_parents(parents.append) # collect parents
-        # remove self, which is the first arguments
-        parents = [p for p in parents if p is not self]
         for p in parents:
-            memo[id(p)] = None
+            memo[id(p)] = None # set all parents to be None, so they will not be copied
-        memo[id(self.gradient)] = None
+        memo[id(self.gradient)] = None # reset the gradient
-        memo[id(self.param_array)] = None
+        memo[id(self.param_array)] = None # and param_array
-        memo[id(self._fixes_)] = None
+        memo[id(self._fixes_)] = None # fixes have to be reset, as this is now highest parent
-        c = copy.deepcopy(self, memo)
+        c = copy.deepcopy(self, memo) # and start the copy
         c._parent_index_ = None
         return c
 
     def __deepcopy__(self, memo):
-        s = self.__new__(self.__class__)
+        s = self.__new__(self.__class__) # fresh instance
-        memo[id(self)] = s
+        memo[id(self)] = s # be sure to break all cycles --> self is already done
         import copy
-        s.__dict__.update(copy.deepcopy(self.__dict__, memo))
+        s.__dict__.update(copy.deepcopy(self.__dict__, memo)) # standard copy
         return s
 
     def __getstate__(self):
@@ -542,7 +541,7 @@ class Constrainable(Nameable, Indexable, Observable):
     def _add_to_index_operations(self, which, reconstrained, what, warning):
         """
         Helper preventing copy code.
-        This addes the given what (transformation, prior etc) to parameter index operations which.
+        This adds the given what (transformation, prior etc) to parameter index operations which.
         revonstrained are reconstrained indices.
         warn when reconstraining parameters if warning is True.
         TODO: find out which parameters have changed specifically
@@ -580,12 +579,6 @@ class OptimizationHandlable(Constrainable):
     def __init__(self, name, default_constraint=None, *a, **kw):
         super(OptimizationHandlable, self).__init__(name, default_constraint=default_constraint, *a, **kw)
 
-    def transform(self):
-        [np.put(self.param_array, ind, c.finv(self.param_array.flat[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
-
-    def untransform(self):
-        [np.put(self.param_array, ind, c.f(self.param_array.flat[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
-
     def _get_params_transformed(self):
         # transformed parameters (apply transformation rules)
         p = self.param_array.copy()
@@ -599,15 +592,15 @@ class OptimizationHandlable(Constrainable):
         return p
 
     def _set_params_transformed(self, p):
-        if p is self.param_array:
+        if not(p is self.param_array):
-            p = p.copy()
             if self.has_parent() and self.constraints[__fixed__].size != 0:
                 fixes = np.ones(self.size).astype(bool)
                 fixes[self.constraints[__fixed__]] = FIXED
                 self.param_array.flat[fixes] = p
             elif self._has_fixes(): self.param_array.flat[self._fixes_] = p
             else: self.param_array.flat = p
-        self.untransform()
+        [np.put(self.param_array, ind, c.f(self.param_array.flat[ind])) 
+         for c, ind in self.constraints.iteritems() if c != __fixed__]
         self._trigger_params_changed()
 
     def _trigger_params_changed(self, trigger_parent=True):
@@ -621,7 +614,7 @@ class OptimizationHandlable(Constrainable):
     def num_params(self):
         """
         Return the number of parameters of this parameter_handle.
-        Param objects will allways return 0.
+        Param objects will always return 0.
         """
         raise NotImplemented, "Abstract, please implement in respective classes"
 
@@ -713,7 +706,11 @@ class Parameterizable(OptimizationHandlable):
 
     @property
     def param_array(self):
-        if self._param_array_ is None:
+        """
+        Array representing the parameters of this class.
+        There is only one copy of all parameters in memory, two during optimization.
+        """
+        if self.__dict__.get('_param_array_', None) is None:
             self._param_array_ = np.empty(self.size, dtype=np.float64)
         return self._param_array_
 
@@ -723,7 +720,9 @@ class Parameterizable(OptimizationHandlable):
 
     def traverse(self, visit, *args, **kwargs):
         """
-        Traverse the hierarchy performing visit(self, *args, **kwargs) at every node passed by.
+        Traverse the hierarchy performing visit(self, *args, **kwargs) 
+        at every node passed by downwards. This function includes self!
+
         See "visitor pattern" in literature. This is implemented in pre-order fashion.
 
         Example:
@@ -738,10 +737,11 @@ class Parameterizable(OptimizationHandlable):
             self.__visited = True
             for c in self._parameters_:
                 c.traverse(visit, *args, **kwargs)
+            self.__visited = False
 
     def traverse_parents(self, visit, *args, **kwargs):
         """
-        Traverse the hierarchy upwards, visiting all parents and their children.
+        Traverse the hierarchy upwards, visiting all parents and their children except self.
         See "visitor pattern" in literature. This is implemented in pre-order fashion.
     
         Example:
@@ -750,19 +750,26 @@ class Parameterizable(OptimizationHandlable):
         self.traverse_parents(parents.append)
         print parents
         """
-        if not self.__visited:
-            visit(self, *args, **kwargs)
-            self.__visited = True
         if self.has_parent():
-                self._parent_.traverse_parents(visit, *args, **kwargs)
+            self.__visited = True
+            self._parent_._traverse_parents(visit, *args, **kwargs)
+            self.__visited = False
+        
+    def _traverse_parents(self, visit, *args, **kwargs):
+        if not self.__visited:
+            self.__visited = True
+            visit(self, *args, **kwargs)
+            if self.has_parent():
+                self._parent_._traverse_parents(visit, *args, **kwargs)
                 self._parent_.traverse(visit, *args, **kwargs)
             self.__visited = False
+
     #=========================================================================
     # Gradient handling
     #=========================================================================
     @property
     def gradient(self):
-        if not hasattr(self, '_gradient_array_'):
+        if self.__dict__.get('_gradient_array_', None) is None:
             self._gradient_array_ = np.empty(self.size, dtype=np.float64)
         return self._gradient_array_
 
@@ -823,11 +830,10 @@ class Parameterizable(OptimizationHandlable):
         #    raise HierarchyError, "parameter {} already in another model ({}), create new object (or copy) for adding".format(param._short(), param._highest_parent_._short())
         elif param not in self._parameters_:
             if param.has_parent():
-                parent = param._parent_
+                def visit(parent, self):
-                while parent is not None:
                     if parent is self:
                         raise HierarchyError, "You cannot add a parameter twice into the hierarchy"
-                    parent = parent._parent_
+                param.traverse_parents(visit, self)
                 param._parent_.remove_parameter(param)
             # make sure the size is set
             if index is None:
@@ -871,7 +877,7 @@ class Parameterizable(OptimizationHandlable):
         :param param: param object to remove from being a parameter of this parameterized object.
         """
         if not param in self._parameters_:
-            raise RuntimeError, "Parameter {} does not belong to this object, remove parameters directly from their respective parents".format(param._short())
+            raise RuntimeError, "Parameter {} does not belong to this object {}, remove parameters directly from their respective parents".format(param._short(), self.name)
 
         start = sum([p.size for p in self._parameters_[:param._parent_index_]])
         self._remove_parameter_name(param)
@@ -903,10 +909,12 @@ class Parameterizable(OptimizationHandlable):
         if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
             # no parameters for this class
             return
-        old_size = 0
+        if self.param_array.size != self.size:
             self.param_array = np.empty(self.size, dtype=np.float64)
+        if self.gradient.size != self.size:
             self._gradient_array_ = np.empty(self.size, dtype=np.float64)
 
+        old_size = 0
         self._param_slices_ = []
         for i, p in enumerate(self._parameters_):
             p._parent_ = self
@@ -921,6 +929,7 @@ class Parameterizable(OptimizationHandlable):
 
             if not p.param_array.flags['C_CONTIGUOUS']:
                 raise ValueError, "This should not happen! Please write an email to the developers with the code, which reproduces this error. All parameter arrays must be C_CONTIGUOUS"
+            
             p.param_array.data = self.param_array[pslice].data
             p.full_gradient.data = self.full_gradient[pslice].data
 

GPy/core/parameterization/parameterized.py

@@ -82,15 +82,15 @@ class Parameterized(Parameterizable):
         import pydot  # @UnresolvedImport
         iamroot = False
         if G is None:
-            G = pydot.Dot(graph_type='digraph')
+            G = pydot.Dot(graph_type='digraph', bgcolor=None)
             iamroot=True
-        node = pydot.Node(id(self), shape='record', label=self.name)
+        node = pydot.Node(id(self), shape='box', label=self.name)#, color='white')
         G.add_node(node)
         for child in self._parameters_:
             child_node = child.build_pydot(G)
-            G.add_edge(pydot.Edge(node, child_node))
+            G.add_edge(pydot.Edge(node, child_node))#, color='white'))
 
-        for o in self.observers.keys():
+        for _, o, _ in self.observers:
             label = o.name if hasattr(o, 'name') else str(o)
             observed_node = pydot.Node(id(o), label=label)
             G.add_node(observed_node)

GPy/core/parameterization/variational.py

@@ -100,6 +100,9 @@ class VariationalPosterior(Parameterized):
             n.__dict__.update(dc)
             n._parameters_[dc['mean']._parent_index_] = dc['mean']
             n._parameters_[dc['variance']._parent_index_] = dc['variance']
+            n._gradient_array_ = None
+            oversize = self.size - self.mean.size - self.variance.size
+            n.size = n.mean.size + n.variance.size + oversize
             n.ndim = n.mean.ndim
             n.shape = n.mean.shape
             n.num_data = n.mean.shape[0]

GPy/examples/dimensionality_reduction.py

@@ -408,13 +408,13 @@ def stick(kernel=None, optimize=True, verbose=True, plot=True):
     data = GPy.util.datasets.osu_run1()
     # optimize
     m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel)
-    if optimize: m.optimize(messages=verbose, max_f_eval=10000)
+    if optimize: m.optimize('bfgs', messages=verbose, max_f_eval=10000)
     if plot:
         plt.clf
         ax = m.plot_latent()
         y = m.Y[0, :]
         data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
-        vis = GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, latent_axes=ax)
+        vis = GPy.plotting.matplot_dep.visualize.lvm(m.X[:1, :].copy(), m, data_show, latent_axes=ax)
         raw_input('Press enter to finish')
 
     return m
@@ -475,24 +475,28 @@ def robot_wireless(optimize=True, verbose=True, plot=True):
 def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
     from GPy.models import BayesianGPLVM
     from matplotlib import pyplot as plt
+    import numpy as np
     import GPy
 
     data = GPy.util.datasets.osu_run1()
     Q = 6
-    kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.Bias(Q, _np.exp(-2)) + GPy.kern.White(Q, _np.exp(-2))
+    kernel = GPy.kern.RBF(Q, lengthscale=np.repeat(.5, Q), ARD=True) 
     m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
+    
+    m.data = data
+    m.likelihood.variance = 0.001
+    
     # optimize
-    m.ensure_default_constraints()
+    if optimize: m.optimize('bfgs', messages=verbose, max_iters=800, xtol=1e-300, ftol=1e-300)
-    if optimize: m.optimize('scg', messages=verbose, max_iters=200, xtol=1e-300, ftol=1e-300)
-    m._set_params(m._get_params())
     if plot:
         plt.clf, (latent_axes, sense_axes) = plt.subplots(1, 2)
         plt.sca(latent_axes)
-        m.plot_latent()
+        m.plot_latent(ax=latent_axes)
-        y = m.likelihood.Y[0, :].copy()
+        y = m.Y[:1, :].copy()
-        data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
+        data_show = GPy.plotting.matplot_dep.visualize.stick_show(y, connect=data['connect'])
-        GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X[0, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
+        GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean[:1, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
-        raw_input('Press enter to finish')
+        plt.draw()
+        #raw_input('Press enter to finish')
 
     return m
 
@@ -509,7 +513,7 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose
     if optimize: m.optimize(messages=verbose, max_f_eval=10000)
     if plot:
         ax = m.plot_latent()
-        y = m.likelihood.Y[0, :]
+        y = m.Y[0, :]
         data_show = GPy.plotting.matplot_dep.visualize.skeleton_show(y[None, :], data['skel'])
         lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
         raw_input('Press enter to finish')

GPy/gpy_config.cfg

@@ -6,6 +6,10 @@
 # some platforms, hence this option.
 openmp=False
 
+[datasets]
+# location for the local data cache
+dir=$HOME/tmp/GPy-datasets/
+
 [anaconda]
 # if you have an anaconda python installation please specify it here.
 installed = False

GPy/inference/optimization/scg.py

@@ -32,7 +32,7 @@ def print_out(len_maxiters, fnow, current_grad, beta, iteration):
     sys.stdout.flush()
 
 def exponents(fnow, current_grad):
-    exps = [np.abs(fnow), current_grad]
+    exps = [np.abs(np.float(fnow)), current_grad]
     return np.sign(exps) * np.log10(exps).astype(int)
 
 def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True, xtol=None, ftol=None, gtol=None):

GPy/kern/__init__.py

@@ -3,16 +3,18 @@ from _src.rbf import RBF
 from _src.linear import Linear, LinearFull
 from _src.static import Bias, White
 from _src.brownian import Brownian
-from _src.stationary import Exponential, Matern32, Matern52, ExpQuad, RatQuad, Cosine
+from _src.stationary import Exponential, OU, Matern32, Matern52, ExpQuad, RatQuad, Cosine
 from _src.mlp import MLP
 from _src.periodic import PeriodicExponential, PeriodicMatern32, PeriodicMatern52
 from _src.independent_outputs import IndependentOutputs, Hierarchical
 from _src.coregionalize import Coregionalize
 from _src.ssrbf import SSRBF # TODO: ZD: did you remove this?
 from _src.ODE_UY import ODE_UY
+from _src.ODE_UYC import ODE_UYC
+from _src.ODE_st import ODE_st
+from _src.ODE_t import ODE_t
 from _src.poly import Poly
-#from _src.ODE_UYC import ODE_UYC ADD THIS FILE TO THE REPO!!
+
-#from _src.ODE_st import ODE_st
 # TODO: put this in an init file somewhere
 #I'm commenting this out because the files were not added. JH. Remember to add the files before commiting
 try:

GPy/kern/_src/ODE_UYC.py

@@ -0,0 +1,290 @@
+# Copyright (c) 2013, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+from kern import Kern
+from ...core.parameterization import Param
+from ...core.parameterization.transformations import Logexp
+import numpy as np
+from independent_outputs import index_to_slices
+
+class ODE_UYC(Kern):
+    def __init__(self, input_dim, variance_U=3., variance_Y=1., lengthscale_U=1., lengthscale_Y=1., ubias =1. ,active_dims=None, name='ode_uyc'):
+        assert input_dim ==2, "only defined for 2 input dims"
+        super(ODE_UYC, self).__init__(input_dim, active_dims, name)
+
+        self.variance_Y = Param('variance_Y', variance_Y, Logexp())
+        self.variance_U = Param('variance_U', variance_U, Logexp())
+        self.lengthscale_Y = Param('lengthscale_Y', lengthscale_Y, Logexp())
+        self.lengthscale_U = Param('lengthscale_U', lengthscale_U, Logexp())
+        self.ubias = Param('ubias', ubias, Logexp())
+
+        self.add_parameters(self.variance_Y, self.variance_U, self.lengthscale_Y, self.lengthscale_U, self.ubias)
+
+    def K(self, X, X2=None):
+        # model :   a * dy/dt + b * y = U
+        #lu=sqrt(3)/theta1  ly=1/theta2  theta2= a/b :thetay   sigma2=1/(2ab) :sigmay
+
+        X,slices = X[:,:-1],index_to_slices(X[:,-1])
+        if X2 is None:
+            X2,slices2 = X,slices
+            K = np.zeros((X.shape[0], X.shape[0]))
+        else:
+            X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
+            K = np.zeros((X.shape[0], X2.shape[0]))
+
+        #stop
+        #rdist = X[:,0][:,None] - X2[:,0][:,None].T
+        rdist = X - X2.T
+        ly=1/self.lengthscale_Y
+        lu=np.sqrt(3)/self.lengthscale_U
+        #iu=self.input_lengthU  #dimention of U
+        Vu=self.variance_U
+        Vy=self.variance_Y
+        #Vy=ly/2
+        #stop
+
+
+        # kernel for kuu  matern3/2
+        kuu = lambda dist:Vu * (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist)) +self.ubias
+
+        # kernel for kyy
+        k1 = lambda dist:np.exp(-ly*np.abs(dist))*(2*lu+ly)/(lu+ly)**2
+        k2 = lambda dist:(np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
+        k3 = lambda dist:np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
+        kyy = lambda dist:Vu*Vy*(k1(dist) + k2(dist) + k3(dist))
+
+
+        # cross covariance function
+        kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly)))
+        #kyu3 = lambda dist: 0
+
+        k1cros = lambda dist:np.exp(ly*dist)/(lu-ly) * ( 1- np.exp( (lu-ly)*dist) + lu* ( dist*np.exp( (lu-ly)*dist ) + (1- np.exp( (lu-ly)*dist ) ) /(lu-ly)   )    )
+        #k1cros = lambda dist:0
+
+        k2cros = lambda dist:np.exp(ly*dist)*( 1/(lu+ly) + lu/(lu+ly)**2 )
+        #k2cros = lambda dist:0
+
+        Vyu=np.sqrt(Vy*ly*2)
+
+        # cross covariance kuy
+        kuyp = lambda dist:Vu*Vyu*(kyu3(dist))       #t>0 kuy
+        kuyn = lambda dist:Vu*Vyu*(k1cros(dist)+k2cros(dist))      #t<0 kuy
+        # cross covariance kyu
+        kyup = lambda dist:Vu*Vyu*(k1cros(-dist)+k2cros(-dist))    #t>0 kyu
+        kyun = lambda dist:Vu*Vyu*(kyu3(-dist))       #t<0 kyu
+
+
+        for i, s1 in enumerate(slices):
+            for j, s2 in enumerate(slices2):
+                for ss1 in s1:
+                    for ss2 in s2:
+                        if i==0 and j==0:
+                            K[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
+                        elif i==0 and j==1:
+                            #K[ss1,ss2]=  np.where(  rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[ss1,ss2]) )   )
+                            K[ss1,ss2]=  np.where(  rdist[ss1,ss2]>0 , kuyp(rdist[ss1,ss2]), kuyn(rdist[ss1,ss2] )   )
+                        elif i==1 and j==1:
+                            K[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
+                        else:
+                            #K[ss1,ss2]= 0
+                            #K[ss1,ss2]= np.where(  rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[ss1,ss2]) )   )
+                            K[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , kyup(rdist[ss1,ss2]), kyun(rdist[ss1,ss2] )   )
+        return K
+
+
+
+    def Kdiag(self, X):
+        """Compute the diagonal of the covariance matrix associated to X."""
+        Kdiag = np.zeros(X.shape[0])
+        ly=1/self.lengthscale_Y
+        lu=np.sqrt(3)/self.lengthscale_U
+
+        Vu = self.variance_U
+        Vy=self.variance_Y
+
+        k1 = (2*lu+ly)/(lu+ly)**2
+        k2 = (ly-2*lu + 2*lu-ly ) / (ly-lu)**2
+        k3 = 1/(lu+ly) + (lu)/(lu+ly)**2
+
+        slices = index_to_slices(X[:,-1])
+
+        for i, ss1 in enumerate(slices):
+            for s1 in ss1:
+                if i==0:
+                    Kdiag[s1]+= self.variance_U + self.ubias
+                elif i==1:
+                    Kdiag[s1]+= Vu*Vy*(k1+k2+k3)
+                else:
+                    raise ValueError, "invalid input/output index"
+        #Kdiag[slices[0][0]]+= self.variance_U   #matern32 diag
+        #Kdiag[slices[1][0]]+= self.variance_U*self.variance_Y*(k1+k2+k3)  #  diag
+        return Kdiag
+
+
+    def update_gradients_full(self, dL_dK, X, X2=None):
+        """derivative of the covariance matrix with respect to the parameters."""
+        X,slices = X[:,:-1],index_to_slices(X[:,-1])
+        if X2 is None:
+            X2,slices2 = X,slices
+        else:
+            X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
+        #rdist = X[:,0][:,None] - X2[:,0][:,None].T
+
+        rdist = X - X2.T
+        ly=1/self.lengthscale_Y
+        lu=np.sqrt(3)/self.lengthscale_U
+
+        Vu=self.variance_U
+        Vy=self.variance_Y
+        Vyu = np.sqrt(Vy*ly*2)
+        dVdly = 0.5/np.sqrt(ly)*np.sqrt(2*Vy)
+        dVdVy = 0.5/np.sqrt(Vy)*np.sqrt(2*ly)
+
+        rd=rdist.shape[0]
+        dktheta1 = np.zeros([rd,rd])
+        dktheta2 = np.zeros([rd,rd])
+        dkUdvar = np.zeros([rd,rd])
+        dkYdvar = np.zeros([rd,rd])
+
+        dkdubias = np.zeros([rd,rd])
+
+        # dk dtheta for UU
+        UUdtheta1 = lambda dist: np.exp(-lu* dist)*dist + (-dist)*np.exp(-lu* dist)*(1+lu*dist)
+        UUdtheta2 = lambda dist: 0
+        #UUdvar = lambda dist: (1 + lu*dist)*np.exp(-lu*dist)
+        UUdvar = lambda dist: (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist))
+
+        # dk dtheta for YY
+
+        dk1theta1 = lambda dist: np.exp(-ly*dist)*2*(-lu)/(lu+ly)**3
+
+        dk2theta1 = lambda dist: (1.0)*(
+            np.exp(-lu*dist)*dist*(-ly+2*lu-lu*ly*dist+dist*lu**2)*(ly-lu)**(-2) + np.exp(-lu*dist)*(-2+ly*dist-2*dist*lu)*(ly-lu)**(-2)
+            +np.exp(-dist*lu)*(ly-2*lu+ly*lu*dist-dist*lu**2)*2*(ly-lu)**(-3)
+            +np.exp(-dist*ly)*2*(ly-lu)**(-2)
+            +np.exp(-dist*ly)*2*(2*lu-ly)*(ly-lu)**(-3)
+            )
+
+        dk3theta1 = lambda dist: np.exp(-dist*lu)*(lu+ly)**(-2)*((2*lu+ly+dist*lu**2+lu*ly*dist)*(-dist-2/(lu+ly))+2+2*lu*dist+ly*dist)
+
+        #dktheta1 = lambda dist: self.variance_U*self.variance_Y*(dk1theta1+dk2theta1+dk3theta1)
+
+
+
+
+        dk1theta2 = lambda dist: np.exp(-ly*dist) * ((lu+ly)**(-2)) * (  (-dist)*(2*lu+ly)  +  1  +  (-2)*(2*lu+ly)/(lu+ly)  )
+
+        dk2theta2 =lambda dist:  1*(
+            np.exp(-dist*lu)*(ly-lu)**(-2) * ( 1+lu*dist+(-2)*(ly-2*lu+lu*ly*dist-dist*lu**2)*(ly-lu)**(-1) )
+            +np.exp(-dist*ly)*(ly-lu)**(-2) * ( (-dist)*(2*lu-ly) -1+(2*lu-ly)*(-2)*(ly-lu)**(-1) )
+            )
+
+        dk3theta2 = lambda dist: np.exp(-dist*lu) * (-3*lu-ly-dist*lu**2-lu*ly*dist)/(lu+ly)**3
+
+        #dktheta2 = lambda dist: self.variance_U*self.variance_Y*(dk1theta2 + dk2theta2 +dk3theta2)
+
+        # kyy kernel
+
+        k1 = lambda dist: np.exp(-ly*dist)*(2*lu+ly)/(lu+ly)**2
+        k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
+        k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
+        #dkdvar = k1+k2+k3
+
+
+
+        # cross covariance function
+        kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly)))
+
+        k1cros = lambda dist:np.exp(ly*dist)/(lu-ly) * ( 1- np.exp( (lu-ly)*dist) + lu* ( dist*np.exp( (lu-ly)*dist ) + (1- np.exp( (lu-ly)*dist ) ) /(lu-ly)   )    )
+
+        k2cros = lambda dist:np.exp(ly*dist)*( 1/(lu+ly) + lu/(lu+ly)**2 )
+        # cross covariance kuy
+        kuyp = lambda dist:(kyu3(dist))       #t>0 kuy
+        kuyn = lambda dist:(k1cros(dist)+k2cros(dist))      #t<0 kuy
+        # cross covariance kyu
+        kyup = lambda dist:(k1cros(-dist)+k2cros(-dist))    #t>0 kyu
+        kyun = lambda dist:(kyu3(-dist))       #t<0 kyu
+
+        # dk dtheta for UY
+
+
+        dkyu3dtheta2 = lambda dist: np.exp(-lu*dist) * ( (-1)*(lu+ly)**(-2)*(1+lu*dist+lu*(lu+ly)**(-1)) + (lu+ly)**(-1)*(-lu)*(lu+ly)**(-2) )
+        dkyu3dtheta1 = lambda dist: np.exp(-lu*dist)*(lu+ly)**(-1)* ( (-dist)*(1+dist*lu+lu*(lu+ly)**(-1)) -\
+         (lu+ly)**(-1)*(1+dist*lu+lu*(lu+ly)**(-1)) +dist+(lu+ly)**(-1)-lu*(lu+ly)**(-2) )
+
+        dkcros2dtheta1 = lambda dist: np.exp(ly*dist)* ( -(ly+lu)**(-2) + (ly+lu)**(-2) + (-2)*lu*(lu+ly)**(-3)  )
+        dkcros2dtheta2 = lambda dist: np.exp(ly*dist)*dist* ( (ly+lu)**(-1) + lu*(lu+ly)**(-2) ) + \
+                                      np.exp(ly*dist)*( -(lu+ly)**(-2) + lu*(-2)*(lu+ly)**(-3)  )
+
+        dkcros1dtheta1 = lambda dist: np.exp(ly*dist)*(     -(lu-ly)**(-2)*(  1-np.exp((lu-ly)*dist) + lu*dist*np.exp((lu-ly)*dist)+ \
+          lu*(1-np.exp((lu-ly)*dist))/(lu-ly)  )  +  (lu-ly)**(-1)*(  -np.exp( (lu-ly)*dist )*dist + dist*np.exp( (lu-ly)*dist)+\
+          lu*dist**2*np.exp((lu-ly)*dist)+(1-np.exp((lu-ly)*dist))/(lu-ly) - lu*np.exp((lu-ly)*dist)*dist/(lu-ly) -\
+          lu*(1-np.exp((lu-ly)*dist))/(lu-ly)**2  )   )
+
+        dkcros1dtheta2 = lambda t: np.exp(ly*t)*t/(lu-ly)*( 1-np.exp((lu-ly)*t) +lu*t*np.exp((lu-ly)*t)+\
+            lu*(1-np.exp((lu-ly)*t))/(lu-ly)  )+\
+            np.exp(ly*t)/(lu-ly)**2* ( 1-np.exp((lu-ly)*t) +lu*t*np.exp((lu-ly)*t) + lu*( 1-np.exp((lu-ly)*t) )/(lu-ly)  )+\
+            np.exp(ly*t)/(lu-ly)*( np.exp((lu-ly)*t)*t -lu*t*t*np.exp((lu-ly)*t) +lu*t*np.exp((lu-ly)*t)/(lu-ly)+\
+            lu*( 1-np.exp((lu-ly)*t) )/(lu-ly)**2 )
+
+        dkuypdtheta1 = lambda dist:(dkyu3dtheta1(dist))       #t>0 kuy
+        dkuyndtheta1 = lambda dist:(dkcros1dtheta1(dist)+dkcros2dtheta1(dist))      #t<0 kuy
+        # cross covariance kyu
+        dkyupdtheta1 = lambda dist:(dkcros1dtheta1(-dist)+dkcros2dtheta1(-dist))    #t>0 kyu
+        dkyundtheta1 = lambda dist:(dkyu3dtheta1(-dist))       #t<0 kyu
+
+        dkuypdtheta2 = lambda dist:(dkyu3dtheta2(dist))       #t>0 kuy
+        dkuyndtheta2 = lambda dist:(dkcros1dtheta2(dist)+dkcros2dtheta2(dist))      #t<0 kuy
+        # cross covariance kyu
+        dkyupdtheta2 = lambda dist:(dkcros1dtheta2(-dist)+dkcros2dtheta2(-dist))    #t>0 kyu
+        dkyundtheta2 = lambda dist:(dkyu3dtheta2(-dist))       #t<0 kyu
+
+
+        for i, s1 in enumerate(slices):
+            for j, s2 in enumerate(slices2):
+                for ss1 in s1:
+                    for ss2 in s2:
+                        if i==0 and j==0:
+                            #target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
+                            dktheta1[ss1,ss2] = Vu*UUdtheta1(np.abs(rdist[ss1,ss2]))
+                            dktheta2[ss1,ss2] = 0
+                            dkUdvar[ss1,ss2] = UUdvar(np.abs(rdist[ss1,ss2]))
+                            dkYdvar[ss1,ss2] = 0
+                            dkdubias[ss1,ss2] = 1
+                        elif i==0 and j==1:
+                            ########target[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) )   )
+                            #np.where(  rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) )   )
+                            #dktheta1[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , self.variance_U*self.variance_Y*dkcrtheta1(np.abs(rdist[ss1,ss2])) ,self.variance_U*self.variance_Y*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2])))    )
+                            #dktheta2[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , self.variance_U*self.variance_Y*dkcrtheta2(np.abs(rdist[ss1,ss2])) ,self.variance_U*self.variance_Y*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2])))    )
+                            dktheta1[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , Vu*Vyu*dkuypdtheta1(rdist[ss1,ss2]),Vu*Vyu*dkuyndtheta1(rdist[ss1,ss2]) )
+                            dkUdvar[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , Vyu*kuyp(rdist[ss1,ss2]), Vyu* kuyn(rdist[ss1,ss2])  )
+                            dktheta2[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , Vu*Vyu*dkuypdtheta2(rdist[ss1,ss2])+Vu*dVdly*kuyp(rdist[ss1,ss2]),Vu*Vyu*dkuyndtheta2(rdist[ss1,ss2])+Vu*dVdly*kuyn(rdist[ss1,ss2]) )
+                            dkYdvar[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , Vu*dVdVy*kuyp(rdist[ss1,ss2]), Vu*dVdVy* kuyn(rdist[ss1,ss2])  )
+                            dkdubias[ss1,ss2] = 0
+                        elif i==1 and j==1:
+                            #target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
+                            dktheta1[ss1,ss2] = self.variance_U*self.variance_Y*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))+dk3theta1(np.abs(rdist[ss1,ss2])))
+                            dktheta2[ss1,ss2] = self.variance_U*self.variance_Y*(dk1theta2(np.abs(rdist[ss1,ss2])) + dk2theta2(np.abs(rdist[ss1,ss2])) +dk3theta2(np.abs(rdist[ss1,ss2])))
+                            dkUdvar[ss1,ss2] = self.variance_Y*(k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) )
+                            dkYdvar[ss1,ss2] = self.variance_U*(k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) )
+                            dkdubias[ss1,ss2] = 0
+                        else:
+                            #######target[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) )   )
+                            #dktheta1[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 ,self.variance_U*self.variance_Y*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) , self.variance_U*self.variance_Y*dkcrtheta1(np.abs(rdist[ss1,ss2])) )
+                            #dktheta2[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 ,self.variance_U*self.variance_Y*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) , self.variance_U*self.variance_Y*dkcrtheta2(np.abs(rdist[ss1,ss2])) )
+                            dktheta1[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , Vu*Vyu*dkyupdtheta1(rdist[ss1,ss2]),Vu*Vyu*dkyundtheta1(rdist[ss1,ss2])  )
+                            dkUdvar[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , Vyu*kyup(rdist[ss1,ss2]),Vyu*kyun(rdist[ss1,ss2]))
+                            dktheta2[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , Vu*Vyu*dkyupdtheta2(rdist[ss1,ss2])+Vu*dVdly*kyup(rdist[ss1,ss2]),Vu*Vyu*dkyundtheta2(rdist[ss1,ss2])+Vu*dVdly*kyun(rdist[ss1,ss2])  )
+                            dkYdvar[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , Vu*dVdVy*kyup(rdist[ss1,ss2]), Vu*dVdVy*kyun(rdist[ss1,ss2]))
+                            dkdubias[ss1,ss2] = 0
+        #stop
+        self.variance_U.gradient = np.sum(dkUdvar * dL_dK)     # Vu
+
+        self.variance_Y.gradient = np.sum(dkYdvar * dL_dK)     # Vy
+
+        self.lengthscale_U.gradient = np.sum(dktheta1*(-np.sqrt(3)*self.lengthscale_U**(-2))* dL_dK)     #lu
+
+        self.lengthscale_Y.gradient = np.sum(dktheta2*(-self.lengthscale_Y**(-2)) * dL_dK)              #ly
+
+        self.ubias.gradient = np.sum(dkdubias * dL_dK) 
+

GPy/kern/_src/ODE_st.py

@@ -0,0 +1,267 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+from kern import Kern
+from ...core.parameterization import Param
+from ...core.parameterization.transformations import Logexp
+import numpy as np
+from independent_outputs import index_to_slices
+
+
+class ODE_st(Kern):
+    """
+    kernel resultiong from a first order ODE with OU driving GP
+
+    :param input_dim: the number of input dimension, has to be equal to one
+    :type input_dim: int
+    :param varianceU: variance of the driving GP
+    :type varianceU: float
+    :param lengthscaleU: lengthscale of the driving GP  (sqrt(3)/lengthscaleU)
+    :type lengthscaleU: float
+    :param varianceY: 'variance' of the transfer function
+    :type varianceY: float
+    :param lengthscaleY: 'lengthscale' of the transfer function (1/lengthscaleY)
+    :type lengthscaleY: float
+    :rtype: kernel object
+
+    """
+    
+    def __init__(self, input_dim, a=1.,b=1., c=1.,variance_Yx=3.,variance_Yt=1.5, lengthscale_Yx=1.5, lengthscale_Yt=1.5, active_dims=None, name='ode_st'):
+        assert input_dim ==3, "only defined for 3 input dims"
+        super(ODE_st, self).__init__(input_dim, active_dims, name)
+
+        self.variance_Yt = Param('variance_Yt', variance_Yt, Logexp())
+        self.variance_Yx = Param('variance_Yx', variance_Yx, Logexp())
+        self.lengthscale_Yt = Param('lengthscale_Yt', lengthscale_Yt, Logexp())
+        self.lengthscale_Yx = Param('lengthscale_Yx', lengthscale_Yx, Logexp())        
+
+        self.a= Param('a', a, Logexp())
+        self.b = Param('b', b, Logexp())
+        self.c = Param('c', c, Logexp())
+
+        self.add_parameters(self.a, self.b, self.c, self.variance_Yt, self.variance_Yx, self.lengthscale_Yt,self.lengthscale_Yx)
+
+
+    def K(self, X, X2=None):        
+    # model :   -a d^2y/dx^2  + b dy/dt + c * y = U
+    # kernel Kyy rbf spatiol temporal
+    # vyt Y temporal variance  vyx Y spatiol variance   lyt Y temporal lengthscale   lyx Y spatiol lengthscale
+    # kernel Kuu doper( doper(Kyy))
+    # a   b    c    lyt   lyx    vyx*vyt
+        """Compute the covariance matrix between X and X2."""        
+        X,slices = X[:,:-1],index_to_slices(X[:,-1])
+        if X2 is None:
+            X2,slices2 = X,slices
+            K = np.zeros((X.shape[0], X.shape[0]))
+        else:
+            X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
+            K = np.zeros((X.shape[0], X2.shape[0]))
+
+
+        tdist = (X[:,0][:,None] - X2[:,0][None,:])**2
+        xdist = (X[:,1][:,None] - X2[:,1][None,:])**2
+
+        ttdist = (X[:,0][:,None] - X2[:,0][None,:])
+        #rdist = [tdist,xdist]
+        #dist = np.abs(X - X2.T)
+        vyt = self.variance_Yt
+        vyx = self.variance_Yx
+        
+        lyt=1/(2*self.lengthscale_Yt)
+        lyx=1/(2*self.lengthscale_Yx)
+
+        a = self.a ## -a is used in the model, negtive diffusion
+        b = self.b
+        c = self.c
+
+        kyy = lambda tdist,xdist: np.exp(-lyt*(tdist) -lyx*(xdist))
+
+        k1 = lambda tdist: (2*lyt - 4*lyt**2 * (tdist) )
+
+        k2 = lambda xdist: ( 4*lyx**2 * (xdist)  - 2*lyx )
+
+        k3 = lambda xdist: ( 3*4*lyx**2 - 6*8*xdist*lyx**3 + 16*xdist**2*lyx**4 )
+
+        k4 = lambda ttdist: 2*lyt*(ttdist)
+
+        for i, s1 in enumerate(slices):
+            for j, s2 in enumerate(slices2):
+                for ss1 in s1:
+                    for ss2 in s2:
+                        if i==0 and j==0:
+                            K[ss1,ss2] = vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                        elif i==0 and j==1:
+                            K[ss1,ss2] = (-a*k2(xdist[ss1,ss2]) + b*k4(ttdist[ss1,ss2]) + c)*vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            #K[ss1,ss2]=  np.where(  rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[ss1,ss2]) )   )
+                            #K[ss1,ss2]=  np.where(  rdist[ss1,ss2]>0 , kuyp(rdist[ss1,ss2]), kuyn(rdist[ss1,ss2] )   )
+                        elif i==1 and j==1:
+                            K[ss1,ss2] = ( b**2*k1(tdist[ss1,ss2]) - 2*a*c*k2(xdist[ss1,ss2]) + a**2*k3(xdist[ss1,ss2]) + c**2 )* vyt*vyx* kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                        else:
+                            K[ss1,ss2] = (-a*k2(xdist[ss1,ss2]) - b*k4(ttdist[ss1,ss2]) + c)*vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            #K[ss1,ss2]= np.where(  rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[ss1,ss2]) )   )
+                            #K[ss1,ss2] = np.where(  rdist[ss1,ss2]>0 , kyup(rdist[ss1,ss2]), kyun(rdist[ss1,ss2] )   )
+        
+        #stop
+        return K
+
+    def Kdiag(self, X):
+        """Compute the diagonal of the covariance matrix associated to X."""
+        vyt = self.variance_Yt
+        vyx = self.variance_Yx
+
+        lyt = 1./(2*self.lengthscale_Yt)
+        lyx = 1./(2*self.lengthscale_Yx)
+
+        a = self.a
+        b = self.b
+        c = self.c
+
+        ## dk^2/dtdt'
+        k1 = (2*lyt )*vyt*vyx
+        ## dk^2/dx^2
+        k2 = ( - 2*lyx )*vyt*vyx
+        ## dk^4/dx^2dx'^2
+        k3 = ( 4*3*lyx**2 )*vyt*vyx
+
+
+        Kdiag = np.zeros(X.shape[0])
+        slices = index_to_slices(X[:,-1])
+
+        for i, ss1 in enumerate(slices):
+            for s1 in ss1:
+                if i==0:
+                    Kdiag[s1]+= vyt*vyx
+                elif i==1:
+                    #i=1
+                    Kdiag[s1]+= b**2*k1 - 2*a*c*k2 + a**2*k3 + c**2*vyt*vyx
+                    #Kdiag[s1]+= Vu*Vy*(k1+k2+k3)
+                else:
+                    raise ValueError, "invalid input/output index"
+
+        return Kdiag
+        
+
+    def update_gradients_full(self, dL_dK, X, X2=None):
+    #def dK_dtheta(self, dL_dK, X, X2, target):
+        """derivative of the covariance matrix with respect to the parameters."""
+        X,slices = X[:,:-1],index_to_slices(X[:,-1])
+        if X2 is None:
+            X2,slices2 = X,slices
+            K = np.zeros((X.shape[0], X.shape[0]))
+        else:
+            X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
+        
+        vyt = self.variance_Yt
+        vyx = self.variance_Yx
+
+        lyt = 1./(2*self.lengthscale_Yt)
+        lyx = 1./(2*self.lengthscale_Yx)
+
+        a = self.a
+        b = self.b
+        c = self.c
+
+        tdist = (X[:,0][:,None] - X2[:,0][None,:])**2
+        xdist = (X[:,1][:,None] - X2[:,1][None,:])**2
+        #rdist = [tdist,xdist]
+        ttdist = (X[:,0][:,None] - X2[:,0][None,:])
+        
+        rd=tdist.shape[0]
+
+        dka = np.zeros([rd,rd])
+        dkb = np.zeros([rd,rd])
+        dkc = np.zeros([rd,rd])
+        dkYdvart = np.zeros([rd,rd])
+        dkYdvarx = np.zeros([rd,rd])
+        dkYdlent = np.zeros([rd,rd])
+        dkYdlenx = np.zeros([rd,rd])
+
+
+        kyy = lambda tdist,xdist: np.exp(-lyt*(tdist) -lyx*(xdist))
+        #k1 = lambda tdist: (lyt - lyt**2 * (tdist) )
+        #k2 = lambda xdist: ( lyx**2 * (xdist)  - lyx )
+        #k3 = lambda xdist: ( 3*lyx**2 - 6*xdist*lyx**3 + xdist**2*lyx**4 )
+        #k4 = lambda tdist: -lyt*np.sqrt(tdist)
+
+        k1 = lambda tdist: (2*lyt - 4*lyt**2 * (tdist) )
+
+        k2 = lambda xdist: ( 4*lyx**2 * (xdist)  - 2*lyx )
+
+        k3 = lambda xdist: ( 3*4*lyx**2 - 6*8*xdist*lyx**3 + 16*xdist**2*lyx**4 )
+
+        k4 = lambda ttdist: 2*lyt*(ttdist)
+
+        dkyydlyx = lambda tdist,xdist: kyy(tdist,xdist)*(-xdist)
+        dkyydlyt = lambda tdist,xdist: kyy(tdist,xdist)*(-tdist)
+
+        dk1dlyt = lambda tdist: 2. - 4*2.*lyt*tdist
+        dk2dlyx = lambda xdist: (4.*2.*lyx*xdist -2.)
+        dk3dlyx = lambda xdist: (6.*4.*lyx - 18.*8*xdist*lyx**2 + 4*16*xdist**2*lyx**3)
+
+        dk4dlyt = lambda ttdist: 2*(ttdist)
+
+        for i, s1 in enumerate(slices):
+            for j, s2 in enumerate(slices2):
+                for ss1 in s1:
+                    for ss2 in s2:
+                        if i==0 and j==0:
+                            dka[ss1,ss2] = 0
+                            dkb[ss1,ss2] = 0
+                            dkc[ss1,ss2] = 0
+                            dkYdvart[ss1,ss2] = vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdvarx[ss1,ss2] = vyt*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdlenx[ss1,ss2] = vyt*vyx*dkyydlyx(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdlent[ss1,ss2] = vyt*vyx*dkyydlyt(tdist[ss1,ss2],xdist[ss1,ss2])
+                        elif i==0 and j==1:
+                            dka[ss1,ss2] = -k2(xdist[ss1,ss2])*vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkb[ss1,ss2] = k4(ttdist[ss1,ss2])*vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkc[ss1,ss2] = vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            #dkYdvart[ss1,ss2] = 0
+                            #dkYdvarx[ss1,ss2] = 0
+                            #dkYdlent[ss1,ss2] = 0
+                            #dkYdlenx[ss1,ss2] = 0
+                            dkYdvart[ss1,ss2] = (-a*k2(xdist[ss1,ss2])+b*k4(ttdist[ss1,ss2])+c)*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdvarx[ss1,ss2] = (-a*k2(xdist[ss1,ss2])+b*k4(ttdist[ss1,ss2])+c)*vyt*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdlent[ss1,ss2] = vyt*vyx*dkyydlyt(tdist[ss1,ss2],xdist[ss1,ss2])* (-a*k2(xdist[ss1,ss2])+b*k4(ttdist[ss1,ss2])+c)+\
+                            vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])*b*dk4dlyt(ttdist[ss1,ss2])
+                            dkYdlenx[ss1,ss2] = vyt*vyx*dkyydlyx(tdist[ss1,ss2],xdist[ss1,ss2])*(-a*k2(xdist[ss1,ss2])+b*k4(ttdist[ss1,ss2])+c)+\
+                            vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])*(-a*dk2dlyx(xdist[ss1,ss2]))
+                        elif i==1 and j==1:
+                            dka[ss1,ss2] = (2*a*k3(xdist[ss1,ss2]) - 2*c*k2(xdist[ss1,ss2]))*vyt*vyx* kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkb[ss1,ss2] = 2*b*k1(tdist[ss1,ss2])*vyt*vyx* kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkc[ss1,ss2] = (-2*a*k2(xdist[ss1,ss2]) + 2*c )*vyt*vyx* kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdvart[ss1,ss2] = ( b**2*k1(tdist[ss1,ss2]) - 2*a*c*k2(xdist[ss1,ss2]) + a**2*k3(xdist[ss1,ss2]) + c**2 )*vyx* kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdvarx[ss1,ss2] = ( b**2*k1(tdist[ss1,ss2]) - 2*a*c*k2(xdist[ss1,ss2]) + a**2*k3(xdist[ss1,ss2]) + c**2 )*vyt* kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdlent[ss1,ss2] = vyt*vyx*dkyydlyt(tdist[ss1,ss2],xdist[ss1,ss2])*( b**2*k1(tdist[ss1,ss2]) - 2*a*c*k2(xdist[ss1,ss2]) + a**2*k3(xdist[ss1,ss2]) + c**2 ) +\
+                            vyx*vyt*kyy(tdist[ss1,ss2],xdist[ss1,ss2])*b**2*dk1dlyt(tdist[ss1,ss2])
+                            dkYdlenx[ss1,ss2] = vyt*vyx*dkyydlyx(tdist[ss1,ss2],xdist[ss1,ss2])*( b**2*k1(tdist[ss1,ss2]) - 2*a*c*k2(xdist[ss1,ss2]) + a**2*k3(xdist[ss1,ss2]) + c**2 ) +\
+                            vyx*vyt*kyy(tdist[ss1,ss2],xdist[ss1,ss2])* (-2*a*c*dk2dlyx(xdist[ss1,ss2]) + a**2*dk3dlyx(xdist[ss1,ss2]) )
+                        else:
+                            dka[ss1,ss2] = -k2(xdist[ss1,ss2])*vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkb[ss1,ss2] = -k4(ttdist[ss1,ss2])*vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkc[ss1,ss2] = vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            #dkYdvart[ss1,ss2] = 0
+                            #dkYdvarx[ss1,ss2] = 0
+                            #dkYdlent[ss1,ss2] = 0
+                            #dkYdlenx[ss1,ss2] = 0
+                            dkYdvart[ss1,ss2] = (-a*k2(xdist[ss1,ss2])-b*k4(ttdist[ss1,ss2])+c)*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdvarx[ss1,ss2] = (-a*k2(xdist[ss1,ss2])-b*k4(ttdist[ss1,ss2])+c)*vyt*kyy(tdist[ss1,ss2],xdist[ss1,ss2])
+                            dkYdlent[ss1,ss2] = vyt*vyx*dkyydlyt(tdist[ss1,ss2],xdist[ss1,ss2])* (-a*k2(xdist[ss1,ss2])-b*k4(ttdist[ss1,ss2])+c)+\
+                            vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])*(-1)*b*dk4dlyt(ttdist[ss1,ss2])
+                            dkYdlenx[ss1,ss2] = vyt*vyx*dkyydlyx(tdist[ss1,ss2],xdist[ss1,ss2])*(-a*k2(xdist[ss1,ss2])-b*k4(ttdist[ss1,ss2])+c)+\
+                            vyt*vyx*kyy(tdist[ss1,ss2],xdist[ss1,ss2])*(-a*dk2dlyx(xdist[ss1,ss2])) 
+
+        self.a.gradient = np.sum(dka * dL_dK)  
+
+        self.b.gradient = np.sum(dkb * dL_dK) 
+
+        self.c.gradient = np.sum(dkc * dL_dK)
+
+
+        self.variance_Yt.gradient = np.sum(dkYdvart * dL_dK)  # Vy
+
+        self.variance_Yx.gradient = np.sum(dkYdvarx * dL_dK)
+
+        self.lengthscale_Yt.gradient = np.sum(dkYdlent*(-0.5*self.lengthscale_Yt**(-2)) * dL_dK)    #ly np.sum(dktheta2*(-self.lengthscale_Y**(-2)) * dL_dK) 
+
+        self.lengthscale_Yx.gradient =  np.sum(dkYdlenx*(-0.5*self.lengthscale_Yx**(-2)) * dL_dK)
+

GPy/kern/_src/ODE_t.py

@@ -0,0 +1,165 @@
+from kern import Kern
+from ...core.parameterization import Param
+from ...core.parameterization.transformations import Logexp
+import numpy as np
+from independent_outputs import index_to_slices
+
+
+class ODE_t(Kern):
+
+        def __init__(self, input_dim, a=1., c=1.,variance_Yt=3., lengthscale_Yt=1.5,ubias =1., active_dims=None, name='ode_st'):
+                assert input_dim ==2, "only defined for 2 input dims"
+                super(ODE_t, self).__init__(input_dim, active_dims, name)
+
+                self.variance_Yt = Param('variance_Yt', variance_Yt, Logexp())
+                self.lengthscale_Yt = Param('lengthscale_Yt', lengthscale_Yt, Logexp())        
+
+                self.a= Param('a', a, Logexp())
+                self.c = Param('c', c, Logexp())
+                self.ubias = Param('ubias', ubias, Logexp())
+                self.add_parameters(self.a, self.c, self.variance_Yt, self.lengthscale_Yt,self.ubias)
+
+        def K(self, X, X2=None):
+                """Compute the covariance matrix between X and X2."""        
+                X,slices = X[:,:-1],index_to_slices(X[:,-1])
+                if X2 is None:
+                        X2,slices2 = X,slices
+                        K = np.zeros((X.shape[0], X.shape[0]))
+                else:
+                        X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
+                        K = np.zeros((X.shape[0], X2.shape[0]))
+
+                tdist = (X[:,0][:,None] - X2[:,0][None,:])**2
+                ttdist = (X[:,0][:,None] - X2[:,0][None,:])
+                
+                vyt = self.variance_Yt
+                
+                lyt=1/(2*self.lengthscale_Yt)
+
+                a = -self.a
+                c = self.c
+
+                kyy = lambda tdist: np.exp(-lyt*(tdist))
+
+                k1 = lambda tdist: (2*lyt - 4*lyt**2 *(tdist) )
+
+                k4 = lambda tdist: 2*lyt*(tdist)
+
+                for i, s1 in enumerate(slices):
+                        for j, s2 in enumerate(slices2):
+                                for ss1 in s1:
+                                    for ss2 in s2:
+                                        if i==0 and j==0:
+                                            K[ss1,ss2] = vyt*kyy(tdist[ss1,ss2])
+                                        elif i==0 and j==1:
+                                            K[ss1,ss2] = (k4(ttdist[ss1,ss2])+1)*vyt*kyy(tdist[ss1,ss2])
+                                            #K[ss1,ss2] = (2*lyt*(ttdist[ss1,ss2])+1)*vyt*kyy(tdist[ss1,ss2])
+                                        elif i==1 and j==1:
+                                            K[ss1,ss2] = ( k1(tdist[ss1,ss2]) + 1. )*vyt* kyy(tdist[ss1,ss2])+self.ubias
+                                        else:
+                                            K[ss1,ss2] = (-k4(ttdist[ss1,ss2])+1)*vyt*kyy(tdist[ss1,ss2])
+                                            #K[ss1,ss2] = (-2*lyt*(ttdist[ss1,ss2])+1)*vyt*kyy(tdist[ss1,ss2])
+                #stop
+                return K
+
+
+        def Kdiag(self, X):
+
+                vyt = self.variance_Yt
+                lyt = 1./(2*self.lengthscale_Yt)
+
+                a = -self.a
+                c = self.c        
+                
+                k1 = (2*lyt )*vyt
+                
+                Kdiag = np.zeros(X.shape[0])
+                slices = index_to_slices(X[:,-1])
+
+                for i, ss1 in enumerate(slices):
+                    for s1 in ss1:
+                        if i==0:
+                            Kdiag[s1]+= vyt
+                        elif i==1:
+                            #i=1
+                            Kdiag[s1]+= k1 + vyt+self.ubias
+                            #Kdiag[s1]+= Vu*Vy*(k1+k2+k3)
+                        else:
+                            raise ValueError, "invalid input/output index"
+
+                return Kdiag
+
+        def update_gradients_full(self, dL_dK, X, X2=None):
+                """derivative of the covariance matrix with respect to the parameters."""
+                X,slices = X[:,:-1],index_to_slices(X[:,-1])
+                if X2 is None:
+                    X2,slices2 = X,slices
+                    K = np.zeros((X.shape[0], X.shape[0]))
+                else:
+                    X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
+
+
+                vyt = self.variance_Yt
+
+                lyt = 1./(2*self.lengthscale_Yt)
+
+                tdist = (X[:,0][:,None] - X2[:,0][None,:])**2
+                ttdist = (X[:,0][:,None] - X2[:,0][None,:])
+                #rdist = [tdist,xdist]
+                
+                rd=tdist.shape[0]
+
+                dka = np.zeros([rd,rd])
+                dkc = np.zeros([rd,rd])
+                dkYdvart = np.zeros([rd,rd])
+                dkYdlent = np.zeros([rd,rd])
+
+                dkdubias = np.zeros([rd,rd])
+
+                kyy = lambda tdist: np.exp(-lyt*(tdist))
+                dkyydlyt = lambda tdist: kyy(tdist)*(-tdist)
+
+                k1 = lambda tdist: (2*lyt - 4*lyt**2 * (tdist) )
+
+                k4 = lambda ttdist: 2*lyt*(ttdist)
+
+                dk1dlyt = lambda tdist: 2. - 4*2.*lyt*tdist
+
+                dk4dlyt = lambda ttdist: 2*(ttdist)
+
+                for i, s1 in enumerate(slices):
+                    for j, s2 in enumerate(slices2):
+                        for ss1 in s1:
+                            for ss2 in s2:
+                                if i==0 and j==0:
+                                    dkYdvart[ss1,ss2] = kyy(tdist[ss1,ss2])
+                                    dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])
+                                    dkdubias[ss1,ss2] = 0
+                                elif i==0 and j==1:
+                                    dkYdvart[ss1,ss2] = (k4(ttdist[ss1,ss2])+1)*kyy(tdist[ss1,ss2])
+                                    #dkYdvart[ss1,ss2] = ((2*lyt*ttdist[ss1,ss2])+1)*kyy(tdist[ss1,ss2])
+                                    dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])* (k4(ttdist[ss1,ss2])+1.)+\
+                                    vyt*kyy(tdist[ss1,ss2])*(dk4dlyt(ttdist[ss1,ss2]))
+                                    #dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])* (2*lyt*(ttdist[ss1,ss2])+1.)+\
+                                    #vyt*kyy(tdist[ss1,ss2])*(2*ttdist[ss1,ss2])
+                                    dkdubias[ss1,ss2] = 0
+                                elif i==1 and j==1:
+                                    dkYdvart[ss1,ss2] = (k1(tdist[ss1,ss2]) + 1. )* kyy(tdist[ss1,ss2])
+                                    dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])*( k1(tdist[ss1,ss2]) + 1. ) +\
+                          			vyt*kyy(tdist[ss1,ss2])*dk1dlyt(tdist[ss1,ss2])
+                                    dkdubias[ss1,ss2] = 1
+                                else:
+                                    dkYdvart[ss1,ss2] = (-k4(ttdist[ss1,ss2])+1)*kyy(tdist[ss1,ss2])
+                                    #dkYdvart[ss1,ss2] = (-2*lyt*(ttdist[ss1,ss2])+1)*kyy(tdist[ss1,ss2])
+                                    dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])* (-k4(ttdist[ss1,ss2])+1.)+\
+                                    vyt*kyy(tdist[ss1,ss2])*(-dk4dlyt(ttdist[ss1,ss2]) )
+                                    dkdubias[ss1,ss2] = 0
+                                    #dkYdlent[ss1,ss2] = vyt*dkyydlyt(tdist[ss1,ss2])* (-2*lyt*(ttdist[ss1,ss2])+1.)+\
+                                    #vyt*kyy(tdist[ss1,ss2])*(-2)*(ttdist[ss1,ss2])
+   
+
+                self.variance_Yt.gradient = np.sum(dkYdvart * dL_dK)
+
+                self.lengthscale_Yt.gradient =  np.sum(dkYdlent*(-0.5*self.lengthscale_Yt**(-2)) * dL_dK)
+
+                self.ubias.gradient = np.sum(dkdubias * dL_dK) 

GPy/kern/_src/independent_outputs.py

@@ -180,6 +180,9 @@ class Hierarchical(CombinationKernel):
     def Kdiag(self,X):
         return np.diag(self.K(X))
 
+    def gradients_X(self, dL_dK, X, X2=None):
+        raise NotImplementedError
+
     def update_gradients_full(self,dL_dK,X,X2=None):
         slices = [index_to_slices(X[:,i]) for i in self.extra_dims]
         if X2 is None:

GPy/kern/_src/kern.py

@@ -55,7 +55,7 @@ class Kern(Parameterized):
         self._sliced_X = 0
         self.useGPU = self._support_GPU and useGPU
 
-    @Cache_this(limit=10)
+    @Cache_this(limit=20)
     def _slice_X(self, X):
         return X[:, self.active_dims]
 

GPy/kern/_src/linear.py

@@ -12,6 +12,7 @@ from ...core.parameterization.transformations import Logexp
 from ...util.caching import Cache_this
 from ...core.parameterization import variational
 from psi_comp import linear_psi_comp
+from ...util.config import *
 
 class Linear(Kern):
     """
@@ -224,12 +225,23 @@ class Linear(Kern):
         AZZA = ZA.T[:, None, :, None] * ZA[None, :, None, :]
         AZZA = AZZA + AZZA.swapaxes(1, 2)
         AZZA_2 = AZZA/2.
+        if config.getboolean('parallel', 'openmp'):
+            pragma_string = '#pragma omp parallel for private(m,mm,q,qq,factor,tmp)'
+            header_string = '#include <omp.h>'
+            weave_options = {'headers'           : ['<omp.h>'],
+                                'extra_compile_args': ['-fopenmp -O3'],
+                                'extra_link_args'   : ['-lgomp'],
+                                'libraries': ['gomp']}
+        else:
+            pragma_string = ''
+            header_string = ''
+            weave_options = {'extra_compile_args': ['-O3']}
 
         #Using weave, we can exploit the symmetry of this problem:
         code = """
         int n, m, mm,q,qq;
         double factor,tmp;
-        #pragma omp parallel for private(m,mm,q,qq,factor,tmp)
+        %s
         for(n=0;n<N;n++){
           for(m=0;m<num_inducing;m++){
             for(mm=0;mm<=m;mm++){
@@ -253,26 +265,36 @@ class Linear(Kern):
             }
           }
         }
-        """
+        """ % pragma_string
         support_code = """
-        #include <omp.h>
+        %s
         #include <math.h>
-        """
+        """ % header_string
-        weave_options = {'headers'           : ['<omp.h>'],
-                         'extra_compile_args': ['-fopenmp -O3'],  #-march=native'],
-                         'extra_link_args'   : ['-lgomp']}
         mu = vp.mean
         N,num_inducing,input_dim,mu = mu.shape[0],Z.shape[0],mu.shape[1],param_to_array(mu)
-        weave.inline(code, support_code=support_code, libraries=['gomp'],
+        weave.inline(code, support_code=support_code,
                      arg_names=['N','num_inducing','input_dim','mu','AZZA','AZZA_2','target_mu','target_S','dL_dpsi2'],
                      type_converters=weave.converters.blitz,**weave_options)
 
 
     def _weave_dpsi2_dZ(self, dL_dpsi2, Z, vp, target):
         AZA = self.variances*self._ZAinner(vp, Z)
+
+        if config.getboolean('parallel', 'openmp'):
+            pragma_string = '#pragma omp parallel for private(n,mm,q)'
+            header_string = '#include <omp.h>'
+            weave_options =  {'headers'           : ['<omp.h>'],
+                              'extra_compile_args': ['-fopenmp -O3'],
+                              'extra_link_args'   : ['-lgomp'],
+                              'libraries': ['gomp']}
+        else:
+            pragma_string = ''
+            header_string = ''
+            weave_options = {'extra_compile_args': ['-O3']}
+
         code="""
         int n,m,mm,q;
-        #pragma omp parallel for private(n,mm,q)
+        %s
         for(m=0;m<num_inducing;m++){
           for(q=0;q<input_dim;q++){
             for(mm=0;mm<num_inducing;mm++){
@@ -282,18 +304,15 @@ class Linear(Kern):
             }
           }
         }
-        """
+        """ % pragma_string
         support_code = """
-        #include <omp.h>
+        %s
         #include <math.h>
-        """
+        """ % header_string
-        weave_options = {'headers'           : ['<omp.h>'],
-                         'extra_compile_args': ['-fopenmp -O3'],  #-march=native'],
-                         'extra_link_args'   : ['-lgomp']}
 
         N,num_inducing,input_dim = vp.mean.shape[0],Z.shape[0],vp.mean.shape[1]
         mu = param_to_array(vp.mean)
-        weave.inline(code, support_code=support_code, libraries=['gomp'],
+        weave.inline(code, support_code=support_code,
                      arg_names=['N','num_inducing','input_dim','AZA','target','dL_dpsi2'],
                      type_converters=weave.converters.blitz,**weave_options)
 

GPy/kern/_src/rbf.py

@@ -10,6 +10,7 @@ from GPy.util.caching import Cache_this
 from ...core.parameterization import variational
 from psi_comp import ssrbf_psi_comp
 from psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF
+from ...util.config import *
 
 class RBF(Stationary):
     """
@@ -231,6 +232,16 @@ class RBF(Stationary):
 
     @Cache_this(limit=1)
     def _psi2computations(self, Z, vp):
+
+        if config.getboolean('parallel', 'openmp'):
+            pragma_string = '#pragma omp parallel for private(tmp, exponent_tmp)'
+            header_string = '#include <omp.h>'
+            libraries = ['gomp']
+        else:
+            pragma_string = ''
+            header_string = ''
+            libraries = []
+
         mu, S = vp.mean, vp.variance
 
         N, Q = mu.shape
@@ -253,8 +264,7 @@ class RBF(Stationary):
         variance_sq = float(np.square(self.variance))
         code = """
         double tmp, exponent_tmp;
-
+        %s 
-        #pragma omp parallel for private(tmp, exponent_tmp)
         for (int n=0; n<N; n++)
         {
             for (int m=0; m<M; m++)
@@ -278,20 +288,20 @@ class RBF(Stationary):
                         tmp = -Zdist_sq(m,mm,q) - tmp - half_log_denom(n,q);
                         exponent_tmp += tmp;
                     }
-                    //compute psi2 by exponontiating
+                    //compute psi2 by exponentiating
                     psi2(n,m,mm) = variance_sq * exp(exponent_tmp);
                     psi2(n,mm,m) = psi2(n,m,mm);
                 }
             }
         }
-        """
+        """ % pragma_string
 
         support_code = """
-        #include <omp.h>
+        %s
         #include <math.h>
-        """
+        """ % header_string
         mu = param_to_array(mu)
-        weave.inline(code, support_code=support_code, libraries=['gomp'],
+        weave.inline(code, support_code=support_code, libraries=libraries,
                      arg_names=['N', 'M', 'Q', 'mu', 'Zhat', 'mudist_sq', 'mudist', 'denom_l2', 'Zdist_sq', 'half_log_denom', 'psi2', 'variance_sq'],
                      type_converters=weave.converters.blitz, **self.weave_options)
 
@@ -303,12 +313,20 @@ class RBF(Stationary):
         #return 2.*np.einsum( 'ijk,ijk,ijkl,il->l', dL_dpsi2, psi2, Zdist_sq * (2.*S[:,None,None,:]/l2 + 1.) + mudist_sq + S[:, None, None, :] / l2, 1./(2.*S + l2))*self.lengthscale
 
         result = np.zeros(self.input_dim)
+        if config.getboolean('parallel', 'openmp'):
+            pragma_string = '#pragma omp parallel for reduction(+:tmp)'
+            header_string = '#include <omp.h>'
+            libraries = ['gomp']
+        else:
+            pragma_string = ''
+            header_string = ''
+            libraries = []
         code = """
         double tmp;
         for(int q=0; q<Q; q++)
         {
             tmp = 0.0;
-            #pragma omp parallel for reduction(+:tmp)
+            %s
             for(int n=0; n<N; n++)
             {
                 for(int m=0; m<M; m++)
@@ -326,16 +344,16 @@ class RBF(Stationary):
             result(q) = tmp;
         }
 
-        """
+        """ % pragma_string
         support_code = """
-        #include <omp.h>
+        %s
         #include <math.h>
-        """
+        """ % header_string
         N,Q = S.shape
         M = psi2.shape[-1]
 
         S = param_to_array(S)
-        weave.inline(code, support_code=support_code, libraries=['gomp'],
+        weave.inline(code, support_code=support_code, libraries=libraries,
                      arg_names=['psi2', 'dL_dpsi2', 'N', 'M', 'Q', 'mudist_sq', 'l2', 'Zdist_sq', 'S', 'result'],
                      type_converters=weave.converters.blitz, **self.weave_options)
 

GPy/kern/_src/stationary.py

@@ -192,6 +192,27 @@ class Exponential(Stationary):
     def dK_dr(self, r):
         return -0.5*self.K_of_r(r)
 
+
+class OU(Stationary):
+    """
+    OU kernel:
+
+    .. math::
+
+       k(r) = \\sigma^2 \exp(- r) \\ \\ \\ \\  \\text{ where  } r = \sqrt{\sum_{i=1}^input_dim \\frac{(x_i-y_i)^2}{\ell_i^2} }
+
+    """
+
+    def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='OU'):
+        super(OU, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
+
+    def K_of_r(self, r):
+        return self.variance * np.exp(-r)
+
+    def dK_dr(self,r):
+        return -1.*self.variance*np.exp(-r)
+
+
 class Matern32(Stationary):
     """
     Matern 3/2 kernel:

GPy/likelihoods/ordinal.py

@@ -0,0 +1,48 @@
+# 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
+from ..util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
+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/bayesian_gplvm.py

@@ -42,7 +42,7 @@ class BayesianGPLVM(SparseGP):
         assert Z.shape[1] == X.shape[1]
 
         if kernel is None:
-            kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=True) # + kern.white(input_dim)
+            kernel = kern.RBF(input_dim, lengthscale=1./fracs, ARD=True) # + kern.white(input_dim)
 
         if likelihood is None:
             likelihood = Gaussian()

GPy/plotting/matplot_dep/dim_reduction_plots.py

@@ -97,7 +97,7 @@ def plot_latent(model, labels=None, which_indices=None,
         elif type(ul) is np.int64:
             this_label = 'class %i' % ul
         else:
-            this_label = 'class %i' % i
+            this_label = unicode(ul)
         m = marker.next()
 
         index = np.nonzero(labels == ul)[0]

GPy/plotting/matplot_dep/models_plots.py

@@ -14,7 +14,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
         which_data_ycols='all', fixed_inputs=[],
         levels=20, samples=0, fignum=None, ax=None, resolution=None,
         plot_raw=False,
-        linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None):
+        linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx'):
     """
     Plot the posterior of the GP.
       - In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@@ -97,7 +97,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
 
         for d in which_data_ycols:
             plots['gpplot'] = gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)
-            plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
+            plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5)
 
         #optionally plot some samples
         if samples: #NOTE not tested with fixed_inputs

GPy/plotting/matplot_dep/visualize.py

@@ -103,7 +103,6 @@ class lvm(matplotlib_show):
             else:
                 vals = param_to_array(model.X)
 
-        vals = param_to_array(vals)
         matplotlib_show.__init__(self, vals, axes=latent_axes)
 
         if isinstance(latent_axes,mpl.axes.Axes):

GPy/testing/pickle_tests.py

@@ -132,6 +132,9 @@ class Test(ListDictTestCase):
         self.assertIsNot(par.full_gradient, pcopy.full_gradient)
         self.assertTrue(pcopy.checkgrad())
         self.assert_(np.any(pcopy.gradient!=0.0))
+        pcopy.optimize('bfgs')
+        par.optimize('bfgs')
+        np.testing.assert_allclose(pcopy.param_array, par.param_array, atol=.001)
         with tempfile.TemporaryFile('w+b') as f:
             par.pickle(f)
             f.seek(0)

GPy/util/caching.py

@@ -30,11 +30,11 @@ class Cacher(object):
         self.cached_outputs = {} # point from cache_ids to outputs
         self.inputs_changed = {} # point from cache_ids to bools
 
-    def combine_args_kw(self, args, kw):
+    def combine_inputs(self, args, kw):
         "Combines the args and kw in a unique way, such that ordering of kwargs does not lead to recompute"
         return args + tuple(c[1] for c in sorted(kw.items(), key=lambda x: x[0]))
 
-    def preprocess(self, combined_args_kw, ignore_args):
+    def prepare_cache_id(self, combined_args_kw, ignore_args):
         "get the cacheid (conc. string of argument ids in order) ignoring ignore_args"
         return "".join(str(id(a)) for i,a in enumerate(combined_args_kw) if i not in ignore_args)
 
@@ -81,23 +81,23 @@ class Cacher(object):
                 if k in kw and kw[k] is not None:
                     return self.operation(*args, **kw)
     
-        # 2: preprocess and get the unique id string for this call
+        # 2: prepare_cache_id and get the unique id string for this call
-        combined_args_kw = self.combine_args_kw(args, kw)
+        inputs = self.combine_inputs(args, kw)
-        cache_id = self.preprocess(combined_args_kw, self.ignore_args)
+        cache_id = self.prepare_cache_id(inputs, self.ignore_args)
     
         # 2: if anything is not cachable, we will just return the operation, without caching
-        if reduce(lambda a,b: a or (not isinstance(b, Observable)), combined_args_kw, False):
+        if reduce(lambda a,b: a or (not isinstance(b, Observable)), inputs, False):
             return self.operation(*args, **kw)
         # 3&4: check whether this cache_id has been cached, then has it changed?
         try:
             if(self.inputs_changed[cache_id]):
-                # 4: This happens, when one element has changed for this cache id
+                # 4: This happens, when elements have changed for this cache id
                 self.inputs_changed[cache_id] = False
                 self.cached_outputs[cache_id] = self.operation(*args, **kw)
         except KeyError:
             # 3: This is when we never saw this chache_id:
             self.ensure_cache_length(cache_id)
-            self.add_to_cache(cache_id, combined_args_kw, self.operation(*args, **kw))
+            self.add_to_cache(cache_id, inputs, self.operation(*args, **kw))
         except:
             self.reset()
             raise

GPy/util/data_resources.json

@@ -1,23 +1,21 @@
 {
-    "olivetti_glasses": {
+    "ankur_pose_data": {
+        "citation": "3D Human Pose from Silhouettes by Relevance Vector Regression (In CVPR'04). A. Agarwal and B. Triggs.",
+        "details": "Artificially generated data of silhouettes given poses. Note that the data does not display a left/right ambiguity because across the entire data set one of the arms sticks out more the the other, disambiguating the pose as to which way the individual is facing.",
         "files": [
             [
-                "has_glasses.np"
+                "ankurDataPoseSilhouette.mat"
-            ],
-            [
-                "olivettifaces.mat"
             ]
         ],
         "license": null,
-        "citation": "Information recorded in olivetti_faces entry. Should be used from there.",
+        "size": 1,
-        "details": "Information recorded in olivetti_faces entry. Should be used from there.",
         "urls": [
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/olivetti_faces/",
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/ankur_pose_data/"
-            "http://www.cs.nyu.edu/~roweis/data/"
+        ]
-        ],
-        "size": 4261047
     },
     "boston_housing": {
+        "citation": "Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.",
+        "details": "The Boston Housing data relates house values in Boston to a range of input variables.",
         "files": [
             [
                 "Index",
@@ -26,221 +24,70 @@
             ]
         ],
         "license": null,
-        "citation": "Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.",
+        "size": 51276,
-        "details": "The Boston Housing data relates house values in Boston to a range of input variables.",
         "urls": [
             "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/"
-        ],
+        ]
-        "size": 51276
     },
-    "google_trends": {
+    "boxjenkins_airline": {
-        "files": [
+        "citation": "Box & Jenkins (1976), in file: data/airpass, Description: International airline passengers: monthly totals in thousands. Jan 49 \\u2013 Dec 60",
-            []
+        "details": "International airline passengers, monthly totals from January 1949 to December 1960.",
-        ],
-        "license": null,
-        "citation": "",
-        "details": "Google trends results.",
-        "urls": [
-            "http://www.google.com/trends/"
-        ],
-        "size": 0
-    },
-    "mauna_loa": {
         "files": [
             [
-                "co2_mm_mlo.txt"
+                "boxjenkins_airline.csv"
             ]
         ],
-        "license": "-------------------------------------------------------------------- USE OF NOAA ESRL DATA\n\n  These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data.  NOAA relies on the ethics and integrity of the user to insure that ESRL receives fair credit for their work.  If the data  are obtained for potential use in a publication or presentation,  ESRL should be informed at the outset of the nature of this work.   If the ESRL data are essential to the work, or if an important  result or conclusion depends on the ESRL data, co-authorship may be appropriate.  This should be discussed at an early stage in the work.  Manuscripts using the ESRL data should be sent to ESRL for review before they are submitted for publication so we can insure that the quality and limitations of the data are accurately represented.\n\n  Contact:   Pieter Tans (303 497 6678; pieter.tans@noaa.gov)\n\n  RECIPROCITY  Use of these data implies an agreement to reciprocate. Laboratories making similar measurements agree to make their own data available to the general public and to the scientific community in an equally complete and easily accessible form. Modelers are encouraged to make available to the community, upon request, their own tools used in the interpretation of the ESRL data, namely well documented model code, transport fields, and additional information necessary for other scientists to repeat the work and to run modified versions. Model availability includes collaborative support for new users of the models.\n --------------------------------------------------------------------\n\n     See www.esrl.noaa.gov/gmd/ccgg/trends/ for additional details.",
+        "license": "You may copy and redistribute the data. You may make derivative works from the data. You may use the data for commercial purposes. You may not sublicence the data when redistributing it. You may not redistribute the data under a different license. Source attribution on any use of this data: Must refer source.",
-        "citation": "Mauna Loa Data. Dr. Pieter Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/).",
+        "size": 46779,
-        "details": "The 'average' column contains the monthly mean CO2 mole fraction determined from daily averages.  The mole fraction of CO2, expressed as parts per million (ppm) is the number of molecules of CO2 in every one million molecules of dried air (water vapor removed).  If there are missing days concentrated either early or late in the month, the monthly mean is corrected to the middle of the month using the average seasonal cycle.  Missing months are denoted by -99.99. The 'interpolated' column includes average values from the preceding column and interpolated values where data are missing.  Interpolated values are computed in two steps.  First, we compute for each month the average seasonal cycle in a 7-year window around each monthly value.  In this way the seasonal cycle is allowed to change slowly over time.  We then determine the 'trend' value for each month by removing the seasonal cycle; this result is shown in the 'trend' column.  Trend values are linearly interpolated for missing months. The interpolated monthly mean is then the sum of the average seasonal cycle value and the trend value for the missing month.\n\nNOTE: In general, the data presented for the last year are subject to change, depending on recalibration of the reference gas mixtures used, and other quality control procedures. Occasionally, earlier years may also be changed for the same reasons.  Usually these changes are minor.\n\nCO2 expressed as a mole fraction in dry air, micromol/mol, abbreviated as ppm \n\n (-99.99 missing data;  -1 no data for daily means in month)",
         "urls": [
-            "ftp://aftp.cmdl.noaa.gov/products/trends/co2/"
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/boxjenkins_airline/"
-        ],
-        "size": 46779
-    },
-    "osu_run1": {
-        "files": [
-            [
-                "run1TXT.ZIP"
-            ],
-            [
-                "connections.txt"
         ]
-        ],
-        "license": "Data is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/).",
-        "citation": "The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.",
-        "details": "Motion capture data of a stick man running from the Open Motion Data Project at Ohio State University.",
-        "urls": [
-            "http://accad.osu.edu/research/mocap/data/",
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/stick/"
-        ],
-        "size": 338103
     },
-    "swiss_roll": {
+    "brendan_faces": {
+        "citation": "Frey, B. J., Colmenarez, A and Huang, T. S. Mixtures of Local Linear Subspaces for Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1998, 32-37, June 1998. Computer Society Press, Los Alamitos, CA.",
+        "details": "A video of Brendan Frey's face popularized as a benchmark for visualization by the Locally Linear Embedding.",
         "files": [
             [
-                "swiss_roll_data.mat"
+                "frey_rawface.mat"
             ]
         ],
         "license": null,
-        "citation": "A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000",
+        "size": 1100584,
-        "details": "Swiss roll data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.",
         "urls": [
-            "http://isomap.stanford.edu/"
-        ],
-        "size": 800256
-    },
-    "ripley_prnn_data": {
-        "files": [
-            [
-                "Cushings.dat",
-                "README",
-                "crabs.dat",
-                "fglass.dat",
-                "fglass.grp",
-                "pima.te",
-                "pima.tr",
-                "pima.tr2",
-                "synth.te",
-                "synth.tr",
-                "viruses.dat",
-                "virus3.dat"
-            ]
-        ],
-        "license": null,
-        "citation": "Pattern Recognition and Neural Networks by B.D. Ripley (1996) Cambridge University Press ISBN 0 521 46986 7",
-        "details": "Data sets from Brian Ripley's Pattern Recognition and Neural Networks",
-        "urls": [
-            "http://www.stats.ox.ac.uk/pub/PRNN/"
-        ],
-        "size": 93565
-    },
-    "rogers_girolami_data": {
-        "files": [
-            [
-                "firstcoursemldata.tar.gz"
-            ]
-        ],
-        "license": null,
-        "citation": "A First Course in Machine Learning. Simon Rogers and Mark Girolami: Chapman & Hall/CRC, ISBN-13: 978-1439824146",
-        "details": "Data from the textbook 'A First Course in Machine Learning'. Available from http://www.dcs.gla.ac.uk/~srogers/firstcourseml/.",
-        "urls": [
-            "https://www.dropbox.com/sh/7p6tu1t29idgliq/_XqlH_3nt9/"
-        ],
-        "suffices": [
-            [
-                "?dl=1"
-            ]
-        ],
-        "size": 21949154
-    },
-    "singlecell": {
-        "files": [
-            [
-                "singlecell.csv"
-            ]
-        ],
-        "license": "ScienceDirect: http://www.elsevier.com/locate/termsandconditions?utm_source=sciencedirect&utm_medium=link&utm_campaign=terms",
-        "citation": "Guoji Guo, Mikael Huss, Guo Qing Tong, Chaoyang Wang, Li Li Sun, Neil D. Clarke, Paul Robson, Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst, Developmental Cell, Volume 18, Issue 4, 20 April 2010, Pages 675-685, ISSN 1534-5807, http://dx.doi.org/10.1016/j.devcel.2010.02.012. (http://www.sciencedirect.com/science/article/pii/S1534580710001103) Keywords: DEVBIO",
-        "details": "qPCR Singlecell experiment in Mouse, measuring 48 gene expressions in 1-64 cell states. The labels have been created as in Guo et al. [2010]",
-        "urls": [
-            "http://staffwww.dcs.sheffield.ac.uk/people/M.Zwiessele/data/singlecell/"
-        ],
-        "size": 233.1
-    },
-    "della_gatta": {
-        "files": [
-            [
-                "DellaGattadata.mat"
-            ]
-        ],
-        "license": null,
-        "citation": "Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Giusy Della Gatta, Mukesh Bansal, Alberto Ambesi-Impiombato, Dario Antonini, Caterina Missero, and Diego di Bernardo, Genome Research 2008",
-        "details": "The full gene expression data set from della Gatta et al (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2413161/) processed by RMA.",
-        "urls": [
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/della_gatta/"
-        ],
-        "size": 3729650
-    },
-    "creep_rupture": {
-        "files": [
-            [
-                "creeprupt.tar"
-            ]
-        ],
-        "license": null,
-        "citation": "Materials Algorithms Project Data Library: MAP_DATA_CREEP_RUPTURE. F. Brun and T. Yoshida.",
-        "details": "Provides 2066 creep rupture test results of steels (mainly of two kinds of steels: 2.25Cr and 9-12 wt% Cr ferritic steels). See http://www.msm.cam.ac.uk/map/data/materials/creeprupt-b.html.",
-        "urls": [
-            "http://www.msm.cam.ac.uk/map/data/tar/"
-        ],
-        "size": 602797
-    },
-    "olivetti_faces": {
-        "files": [
-            [
-                "att_faces.zip"
-            ],
-            [
-                "olivettifaces.mat"
-            ]
-        ],
-        "license": null,
-        "citation": "Ferdinando Samaria and Andy Harter, Parameterisation of a Stochastic Model for Human Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994",
-        "details": "Olivetti Research Labs Face data base, acquired between December 1992 and December 1994 in the Olivetti Research Lab, Cambridge (which later became AT&T Laboratories, Cambridge). When using these images please give credit to AT&T Laboratories, Cambridge. ",
-        "urls": [
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/olivetti_faces/",
             "http://www.cs.nyu.edu/~roweis/data/"
-        ],
-        "size": 8561331
-    },
-    "robot_wireless": {
-        "files": [
-            [
-                "uw-floor.txt"
         ]
-        ],
-        "license": null,
-        "citation": "WiFi-SLAM using Gaussian Process Latent Variable Models by Brian Ferris, Dieter Fox and Neil Lawrence in IJCAI'07 Proceedings pages 2480-2485. Data used in A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models by Neil D. Lawrence, JMLR 13 pg 1609--1638, 2012.",
-        "details": "Data created by Brian Ferris and Dieter Fox. Consists of WiFi access point strengths taken during a circuit of the Paul Allen building at the University of Washington.",
-        "urls": [
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/robot_wireless/"
-        ],
-        "size": 284390
     },
     "cmu_mocap_full": {
+        "citation": "Please include this in your acknowledgements: The data used in this project was obtained from mocap.cs.cmu.edu.\\nThe database was created with funding from NSF EIA-0196217.",
+        "details": "CMU Motion Capture data base. Captured by a Vicon motion capture system consisting of 12 infrared MX-40 cameras, each of which is capable of recording at 120 Hz with images of 4 megapixel resolution. Motions are captured in a working volume of approximately 3m x 8m. The capture subject wears 41 markers and a stylish black garment.",
         "files": [
             [
                 "allasfamc.zip"
             ]
         ],
         "license": "From http://mocap.cs.cmu.edu. This data is free for use in research projects. You may include this data in commercially-sold products, but you may not resell this data directly, even in converted form. If you publish results obtained using this data, we would appreciate it if you would send the citation to your published paper to jkh+mocap@cs.cmu.edu, and also would add this text to your acknowledgments section: The data used in this project was obtained from mocap.cs.cmu.edu. The database was created with funding from NSF EIA-0196217.",
-        "citation": "Please include this in your acknowledgements: The data used in this project was obtained from mocap.cs.cmu.edu.\nThe database was created with funding from NSF EIA-0196217.",
+        "size": null,
-        "details": "CMU Motion Capture data base. Captured by a Vicon motion capture system consisting of 12 infrared MX-40 cameras, each of which is capable of recording at 120 Hz with images of 4 megapixel resolution. Motions are captured in a working volume of approximately 3m x 8m. The capture subject wears 41 markers and a stylish black garment.",
         "urls": [
             "http://mocap.cs.cmu.edu/subjects"
-        ],
+        ]
-        "size": null
     },
-    "football_data": {
+    "creep_rupture": {
+        "citation": "Materials Algorithms Project Data Library: MAP_DATA_CREEP_RUPTURE. F. Brun and T. Yoshida.",
+        "details": "Provides 2066 creep rupture test results of steels (mainly of two kinds of steels: 2.25Cr and 9-12 wt% Cr ferritic steels). See http://www.msm.cam.ac.uk/map/data/materials/creeprupt-b.html.",
         "files": [
             [
-                "E0.csv",
+                "creeprupt.tar"
-                "E1.csv",
-                "E2.csv",
-                "E3.csv"
             ]
         ],
         "license": null,
-        "citation": "",
+        "size": 602797,
-        "details": "Results of English football matches since 1993/94 season.",
         "urls": [
-            "http://www.football-data.co.uk/mmz4281/"
+            "http://www.msm.cam.ac.uk/map/data/tar/"
-        ],
+        ]
-        "size": 1
     },
     "decampos_characters": {
+        "citation": "T. de Campos, B. R. Babu, and M. Varma. Character recognition in natural images. VISAPP 2009.",
+        "details": "Examples of hand written digits taken from the de Campos et al paper on Character Recognition in Natural Images.",
         "files": [
             [
                 "characters.npy",
@@ -248,47 +95,42 @@
             ]
         ],
         "license": null,
-        "citation": "T. de Campos, B. R. Babu, and M. Varma. Character recognition in natural images. VISAPP 2009.",
+        "size": 2031872,
-        "details": "Examples of hand written digits taken from the de Campos et al paper on Character Recognition in Natural Images.",
         "urls": [
             "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/decampos_digits/"
-        ],
+        ]
-        "size": 2031872
     },
-    "three_phase_oil_flow": {
+    "della_gatta": {
+        "citation": "Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Giusy Della Gatta, Mukesh Bansal, Alberto Ambesi-Impiombato, Dario Antonini, Caterina Missero, and Diego di Bernardo, Genome Research 2008",
+        "details": "The full gene expression data set from della Gatta et al (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2413161/) processed by RMA.",
         "files": [
             [
-                "DataTrnLbls.txt",
+                "DellaGattadata.mat"
-                "DataTrn.txt",
-                "DataTst.txt",
-                "DataTstLbls.txt",
-                "DataVdn.txt",
-                "DataVdnLbls.txt"
             ]
         ],
         "license": null,
-        "citation": "Bishop, C. M. and G. D. James (1993). Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research A327, 580-593",
+        "size": 3729650,
-        "details": "The three phase oil data used initially for demonstrating the Generative Topographic mapping.",
         "urls": [
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/three_phase_oil_flow/"
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/della_gatta/"
-        ],
+        ]
-        "size": 712796
     },
-    "pumadyn-32nm": {
+    "drosophila_protein": {
+        "citation": "Becker K, Balsa-Canto E, Cicin-Sain D, Hoermann A, Janssens H, et al. (2013) Reverse-Engineering Post-Transcriptional Regulation of Gap Genes in Drosophila melanogaster. PLoS Comput Biol 9(10): e1003281. doi:10.1371/journal.pcbi.1003281",
+        "details": "Expression of the gap genes Krüppel, knirps, and giant in Drosophila melanogaster. Data includes quantitative datasets of gap gene mRNA and protein expression to solve and fit a model of post-transcriptional regulation, and establish its structural and practical identifiability",
         "files": [
             [
-                "pumadyn-32nm.tar.gz"
+                "becker_et_al.csv"
             ]
         ],
-        "license": "Data is made available by the Delve system at the University of Toronto",
+        "license": null,
-        "citation": "Created by Zoubin Ghahramani using the Matlab Robotics Toolbox of Peter Corke. Corke, P. I. (1996). A Robotics Toolbox for MATLAB. IEEE Robotics and Automation Magazine, 3 (1): 24-32.",
+        "size": 0,
-        "details": "Pumadyn non linear 32 input data set with moderate noise. See http://www.cs.utoronto.ca/~delve/data/pumadyn/desc.html for details.",
         "urls": [
-            "ftp://ftp.cs.toronto.edu/pub/neuron/delve/data/tarfiles/pumadyn-family/"
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/drosophila_protein/"
-        ],
+        ]
-        "size": 5861646
     },
     "epomeo_gpx": {
+        "citation": "",
+        "details": "Five different GPS traces of the same run up Mount Epomeo in Ischia. The traces are from different sources. endomondo_1 and endomondo_2 are traces from the mobile phone app Endomondo, with a split in the middle. garmin_watch_via_endomondo is the trace from a Garmin watch, with a segment missing about 4 kilometers in. viewranger_phone and viewranger_tablet are traces from a phone and a tablet through the viewranger app. The viewranger_phone data comes from the same mobile phone as the Endomondo data (i.e. there are 3 GPS devices, but one device recorded two traces).",
         "files": [
             [
                 "endomondo_1.gpx",
@@ -299,70 +141,120 @@
             ]
         ],
         "license": null,
-        "citation": "",
+        "size": 2031872,
-        "details": "Five different GPS traces of the same run up Mount Epomeo in Ischia. The traces are from different sources. endomondo_1 and endomondo_2 are traces from the mobile phone app Endomondo, with a split in the middle. garmin_watch_via_endomondo is the trace from a Garmin watch, with a segment missing about 4 kilometers in. viewranger_phone and viewranger_tablet are traces from a phone and a tablet through the viewranger app. The viewranger_phone data comes from the same mobile phone as the Endomondo data (i.e. there are 3 GPS devices, but one device recorded two traces).",
         "urls": [
             "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/epomeo_gpx/"
-        ],
+        ]
-        "size": 2031872
     },
-    "ankur_pose_data": {
+    "football_data": {
+        "citation": "",
+        "details": "Results of English football matches since 1993/94 season.",
         "files": [
             [
-                "ankurDataPoseSilhouette.mat"
+                "E0.csv",
+                "E1.csv",
+                "E2.csv",
+                "E3.csv"
             ]
         ],
         "license": null,
-        "citation": "3D Human Pose from Silhouettes by Relevance Vector Regression (In CVPR'04). A. Agarwal and B. Triggs.",
+        "size": 1,
-        "details": "Artificially generated data of silhouettes given poses. Note that the data does not display a left/right ambiguity because across the entire data set one of the arms sticks out more the the other, disambiguating the pose as to which way the individual is facing.",
         "urls": [
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/ankur_pose_data/"
+            "http://www.football-data.co.uk/mmz4281/"
-        ],
+        ]
-        "size": 1
     },
-    "isomap_face_data": {
+    "fruitfly_tomancak": {
+        "citation": "",
+        "details": "",
         "files": [
             [
-                "face_data.mat"
+                "tomancak_exprs.csv",
+                "tomancak_se.csv",
+                "tomancak_prctile5.csv",
+                "tomancak_prctile25.csv",
+                "tomancak_prctile50.csv",
+                "tomancak_prctile75.csv",
+                "tomancak_prctile95.csv"
             ]
         ],
         "license": null,
-        "citation": "A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000",
+        "size": 59000000,
-        "details": "Face data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.",
         "urls": [
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/isomap_face_data/"
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/fruitfly_tomancak/"
-        ],
+        ]
-        "size": 24229368
     },
-    "brendan_faces": {
+    "fruitfly_tomancak_cel_files": {
+        "citation": "'Systematic determination of patterns of gene expression during Drosophila embryogenesis' Pavel Tomancak, Amy Beaton, Richard Weiszmann, Elaine Kwan, ShengQiang Shu, Suzanna E Lewis, Stephen Richards, Michael Ashburner, Volker Hartenstein, Susan E Celniker, and Gerald M Rubin",
+        "details": "Gene expression results from blastoderm development in Drosophila Melanogaster.",
         "files": [ 
             [
-                "frey_rawface.mat"
+                "embryo_tc_4_1.CEL",
+                "embryo_tc_4_2.CEL",
+                "embryo_tc_4_3.CEL",
+                "embryo_tc_4_4.CEL",
+                "embryo_tc_4_5.CEL",
+                "embryo_tc_4_6.CEL",
+                "embryo_tc_4_7.CEL",
+                "embryo_tc_4_8.CEL",
+                "embryo_tc_4_9.CEL",
+                "embryo_tc_4_10.CEL",
+                "embryo_tc_4_11.CEL",
+                "embryo_tc_4_12.CEL",
+                "embryo_tc_6_1.CEL",
+                "embryo_tc_6_2.CEL",
+                "embryo_tc_6_3.CEL",
+                "embryo_tc_6_4.CEL",
+                "embryo_tc_6_5.CEL",
+                "embryo_tc_6_6.CEL",
+                "embryo_tc_6_7.CEL",
+                "embryo_tc_6_8.CEL",
+                "embryo_tc_6_9.CEL",
+                "embryo_tc_6_10.CEL",
+                "embryo_tc_6_11.CEL",
+                "embryo_tc_6_12.CEL",
+                "embryo_tc_8_1.CEL",
+                "embryo_tc_8_2.CEL",
+                "embryo_tc_8_3.CEL",
+                "embryo_tc_8_4.CEL",
+                "embryo_tc_8_5.CEL",
+                "embryo_tc_8_6.CEL",
+                "embryo_tc_8_7.CEL",
+                "embryo_tc_8_8.CEL",
+                "embryo_tc_8_9.CEL",
+                "embryo_tc_8_10.CEL",
+                "embryo_tc_8_11.CEL",
+                "embryo_tc_8_12.CEL",
+                "CG_AffyOligo_Gadfly3_01_13_03",
+                "embryo_tc_rma_release2.txt",
+                "embryo_tc_rma_release3.txt",
+                "na_affy_oligo.dros",
+                "README.TXT"
             ]
         ],
         "license": null,
-        "citation": "Frey, B. J., Colmenarez, A and Huang, T. S. Mixtures of Local Linear Subspaces for Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1998, 32-37, June 1998. Computer Society Press, Los Alamitos, CA.",
+        "size": 389000000,
-        "details": "A video of Brendan Frey's face popularized as a benchmark for visualization by the Locally Linear Embedding.",
         "urls": [
-            "http://www.cs.nyu.edu/~roweis/data/"
+            "ftp://ftp.fruitfly.org/pub/embryo_tc_array_data/"
-        ],
+        ]
-        "size": 1100584
     },
-    "olympic_marathon_men": {
+    "google_trends": {
+        "citation": "",
+        "details": "Google trends results.",
         "files": [
             [
-                "olympicMarathonTimes.csv"
+                
             ]
         ],
         "license": null,
-        "citation": null,
+        "size": 0,
-        "details": "Olympic mens' marathon gold medal winning times from 1896 to 2012. Time given in pace (minutes per kilometer). Data is originally downloaded and collated from Wikipedia, we are not responsible for errors in the data",
         "urls": [
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/olympic_marathon_men/"
+            "http://www.google.com/trends/"
-        ],
+        ]
-        "size": 584
     },
+    
     "hapmap3": {
+        "citation": "Gibbs, Richard A., et al. 'The international HapMap project.' Nature 426.6968 (2003): 789-796.",
+        "details": "HapMap Project: Single Nucleotide Polymorphism sequenced in all human populations. \n        The HapMap phase three SNP dataset - 1184 samples out of 11 populations.\n        See http://www.nature.com/nature/journal/v426/n6968/abs/nature02168.html for details.\n\n        SNP_matrix (A) encoding [see Paschou et all. 2007 (PCA-Correlated SNPs...)]:\n        Let (B1,B2) be the alphabetically sorted bases, which occur in the j-th SNP, then\n\n              /  1, iff SNPij==(B1,B1)\n        Aij = |  0, iff SNPij==(B1,B2)\n              \\\\ -1, iff SNPij==(B2,B2)\n\n        The SNP data and the meta information (such as iid, sex and phenotype) are\n        stored in the dataframe datadf, index is the Individual ID, \n        with following columns for metainfo:\n\n            * family_id   -> Family ID\n            * paternal_id -> Paternal ID\n            * maternal_id -> Maternal ID\n            * sex         -> Sex (1=male; 2=female; other=unknown)\n            * phenotype   -> Phenotype (-9, or 0 for unknown)\n            * population  -> Population string (e.g. 'ASW' - 'YRI')\n            * rest are SNP rs (ids)\n\n        More information is given in infodf:\n\n            * Chromosome:\n                - autosomal chromosemes                -> 1-22\n                - X    X chromosome                    -> 23\n                - Y    Y chromosome                    -> 24\n                - XY   Pseudo-autosomal region of X    -> 25\n                - MT   Mitochondrial                   -> 26\n            * Relative Positon (to Chromosome) [base pairs]\n\n        ",
         "files": [
             [
                 "hapmap3_r2_b36_fwd.consensus.qc.poly.map.bz2",
@@ -371,28 +263,92 @@
             ]
         ],
         "license": "International HapMap Project Public Access License (http://hapmap.ncbi.nlm.nih.gov/cgi-perl/registration#licence)",
-        "citation": "Gibbs, Richard A., et al. \"The international HapMap project.\" Nature 426.6968 (2003): 789-796.",
+        "size": 3458246739,
-        "details": "\n        HapMap Project: Single Nucleotide Polymorphism sequenced in all human populations. \n        The HapMap phase three SNP dataset - 1184 samples out of 11 populations.\n        See http://www.nature.com/nature/journal/v426/n6968/abs/nature02168.html for details.\n\n        SNP_matrix (A) encoding [see Paschou et all. 2007 (PCA-Correlated SNPs...)]:\n        Let (B1,B2) be the alphabetically sorted bases, which occur in the j-th SNP, then\n\n              /  1, iff SNPij==(B1,B1)\n        Aij = |  0, iff SNPij==(B1,B2)\n              \\ -1, iff SNPij==(B2,B2)\n\n        The SNP data and the meta information (such as iid, sex and phenotype) are\n        stored in the dataframe datadf, index is the Individual ID, \n        with following columns for metainfo:\n\n            * family_id   -> Family ID\n            * paternal_id -> Paternal ID\n            * maternal_id -> Maternal ID\n            * sex         -> Sex (1=male; 2=female; other=unknown)\n            * phenotype   -> Phenotype (-9, or 0 for unknown)\n            * population  -> Population string (e.g. 'ASW' - 'YRI')\n            * rest are SNP rs (ids)\n\n        More information is given in infodf:\n\n            * Chromosome:\n                - autosomal chromosemes                -> 1-22\n                - X    X chromosome                    -> 23\n                - Y    Y chromosome                    -> 24\n                - XY   Pseudo-autosomal region of X    -> 25\n                - MT   Mitochondrial                   -> 26\n            * Relative Positon (to Chromosome) [base pairs]\n\n        ",
         "urls": [
             "http://hapmap.ncbi.nlm.nih.gov/downloads/genotypes/latest_phaseIII_ncbi_b36/plink_format/"
-        ],
+        ]
-        "size": 3458246739
     },
-    "boxjenkins_airline": {
+    "isomap_face_data": {
+        "citation": "A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000",
+        "details": "Face data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.",
         "files": [
             [
-                "boxjenkins_airline.csv"
+                "face_data.mat"
             ]
         ],
-        "license": "You may copy and redistribute the data. You may make derivative works from the data. You may use the data for commercial purposes. You may not sublicence the data when redistributing it. You may not redistribute the data under a different license. Source attribution on any use of this data: Must refer source.",
+        "license": null,
-        "citation": "Box & Jenkins (1976), in file: data/airpass, Description: International airline passengers: monthly totals in thousands. Jan 49 – Dec 60",
+        "size": 24229368,
-        "details": "International airline passengers, monthly totals from January 1949 to December 1960.",
         "urls": [
-            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/boxjenkins_airline/"
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/isomap_face_data/"
+        ]
+    },
+    "mauna_loa": {
+        "citation": "Mauna Loa Data. Dr. Pieter Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/).",
+        "details": "The 'average' column contains the monthly mean CO2 mole fraction determined from daily averages.  The mole fraction of CO2, expressed as parts per million (ppm) is the number of molecules of CO2 in every one million molecules of dried air (water vapor removed).  If there are missing days concentrated either early or late in the month, the monthly mean is corrected to the middle of the month using the average seasonal cycle.  Missing months are denoted by -99.99. The 'interpolated' column includes average values from the preceding column and interpolated values where data are missing.  Interpolated values are computed in two steps.  First, we compute for each month the average seasonal cycle in a 7-year window around each monthly value.  In this way the seasonal cycle is allowed to change slowly over time.  We then determine the 'trend' value for each month by removing the seasonal cycle; this result is shown in the 'trend' column.  Trend values are linearly interpolated for missing months. The interpolated monthly mean is then the sum of the average seasonal cycle value and the trend value for the missing month.\n\nNOTE: In general, the data presented for the last year are subject to change, depending on recalibration of the reference gas mixtures used, and other quality control procedures. Occasionally, earlier years may also be changed for the same reasons.  Usually these changes are minor.\n\nCO2 expressed as a mole fraction in dry air, micromol/mol, abbreviated as ppm \n\n (-99.99 missing data;  -1 no data for daily means in month)",
+        "files": [
+            [
+                "co2_mm_mlo.txt"
+            ]
         ],
-        "size": 46779
+        "license": "-------------------------------------------------------------------- USE OF NOAA ESRL DATA\n\n  These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data.  NOAA relies on the ethics and integrity of the user to insure that ESRL receives fair credit for their work.  If the data  are obtained for potential use in a publication or presentation,  ESRL should be informed at the outset of the nature of this work.   If the ESRL data are essential to the work, or if an important  result or conclusion depends on the ESRL data, co-authorship may be appropriate.  This should be discussed at an early stage in the work.  Manuscripts using the ESRL data should be sent to ESRL for review before they are submitted for publication so we can insure that the quality and limitations of the data are accurately represented.\n\n  Contact:   Pieter Tans (303 497 6678; pieter.tans@noaa.gov)\n\n  RECIPROCITY  Use of these data implies an agreement to reciprocate. Laboratories making similar measurements agree to make their own data available to the general public and to the scientific community in an equally complete and easily accessible form. Modelers are encouraged to make available to the community, upon request, their own tools used in the interpretation of the ESRL data, namely well documented model code, transport fields, and additional information necessary for other scientists to repeat the work and to run modified versions. Model availability includes collaborative support for new users of the models.\n --------------------------------------------------------------------\n\n     See www.esrl.noaa.gov/gmd/ccgg/trends/ for additional details.",
+        "size": 46779,
+        "urls": [
+            "ftp://aftp.cmdl.noaa.gov/products/trends/co2/"
+        ]
+    },
+    "olivetti_faces": {
+        "citation": "Ferdinando Samaria and Andy Harter, Parameterisation of a Stochastic Model for Human Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994",
+        "details": "Olivetti Research Labs Face data base, acquired between December 1992 and December 1994 in the Olivetti Research Lab, Cambridge (which later became AT&T Laboratories, Cambridge). When using these images please give credit to AT&T Laboratories, Cambridge. ",
+        "files": [
+            [
+                "att_faces.zip"
+            ],
+            [
+                "olivettifaces.mat"
+            ]
+        ],
+        "license": null,
+        "size": 8561331,
+        "urls": [
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/olivetti_faces/",
+            "http://www.cs.nyu.edu/~roweis/data/"
+        ]
+    },
+    "olivetti_glasses": {
+        "citation": "Information recorded in olivetti_faces entry. Should be used from there.",
+        "details": "Information recorded in olivetti_faces entry. Should be used from there.",
+        "files": [
+            [
+                "has_glasses.np"
+            ],
+            [
+                "olivettifaces.mat"
+            ]
+        ],
+        "license": null,
+        "size": 4261047,
+        "urls": [
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/olivetti_faces/",
+            "http://www.cs.nyu.edu/~roweis/data/"
+        ]
+    },
+    "olympic_marathon_men": {
+        "citation": null,
+        "details": "Olympic mens' marathon gold medal winning times from 1896 to 2012. Time given in pace (minutes per kilometer). Data is originally downloaded and collated from Wikipedia, we are not responsible for errors in the data",
+        "files": [
+            [
+                "olympicMarathonTimes.csv"
+            ]
+        ],
+        "license": null,
+        "size": 584,
+        "urls": [
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/olympic_marathon_men/"
+        ]
     },
     "osu_accad": {
+        "citation": "The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.",
+        "details": "Motion capture data of different motions from the Open Motion Data Project at Ohio State University.",
         "files": [
             [
                 "swagger1TXT.ZIP",
@@ -415,26 +371,176 @@
             ]
         ],
         "license": "Data is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/).",
-        "citation": "The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.",
+        "size": 15922790,
-        "details": "Motion capture data of different motions from the Open Motion Data Project at Ohio State University.",
         "urls": [
             "http://accad.osu.edu/research/mocap/data/",
             "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/stick/"
+        ]
+    },
+    "osu_run1": {
+        "citation": "The Open Motion Data Project by The Ohio State University Advanced Computing Center for the Arts and Design, http://accad.osu.edu/research/mocap/mocap_data.htm.",
+        "details": "Motion capture data of a stick man running from the Open Motion Data Project at Ohio State University.",
+        "files": [
+            [
+                "run1TXT.ZIP"
             ],
-        "size": 15922790
+            [
+                "connections.txt"
+            ]
+        ],
+        "license": "Data is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/).",
+        "size": 338103,
+        "urls": [
+            "http://accad.osu.edu/research/mocap/data/",
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/stick/"
+        ]
+    },
+    "pumadyn-32nm": {
+        "citation": "Created by Zoubin Ghahramani using the Matlab Robotics Toolbox of Peter Corke. Corke, P. I. (1996). A Robotics Toolbox for MATLAB. IEEE Robotics and Automation Magazine, 3 (1): 24-32.",
+        "details": "Pumadyn non linear 32 input data set with moderate noise. See http://www.cs.utoronto.ca/~delve/data/pumadyn/desc.html for details.",
+        "files": [
+            [
+                "pumadyn-32nm.tar.gz"
+            ]
+        ],
+        "license": "Data is made available by the Delve system at the University of Toronto",
+        "size": 5861646,
+        "urls": [
+            "ftp://ftp.cs.toronto.edu/pub/neuron/delve/data/tarfiles/pumadyn-family/"
+        ]
+    },
+    "ripley_prnn_data": {
+        "citation": "Pattern Recognition and Neural Networks by B.D. Ripley (1996) Cambridge University Press ISBN 0 521 46986 7",
+        "details": "Data sets from Brian Ripley's Pattern Recognition and Neural Networks",
+        "files": [
+            [
+                "Cushings.dat",
+                "README",
+                "crabs.dat",
+                "fglass.dat",
+                "fglass.grp",
+                "pima.te",
+                "pima.tr",
+                "pima.tr2",
+                "synth.te",
+                "synth.tr",
+                "viruses.dat",
+                "virus3.dat"
+            ]
+        ],
+        "license": null,
+        "size": 93565,
+        "urls": [
+            "http://www.stats.ox.ac.uk/pub/PRNN/"
+        ]
+    },
+    "robot_wireless": {
+        "citation": "WiFi-SLAM using Gaussian Process Latent Variable Models by Brian Ferris, Dieter Fox and Neil Lawrence in IJCAI'07 Proceedings pages 2480-2485. Data used in A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models by Neil D. Lawrence, JMLR 13 pg 1609--1638, 2012.",
+        "details": "Data created by Brian Ferris and Dieter Fox. Consists of WiFi access point strengths taken during a circuit of the Paul Allen building at the University of Washington.",
+        "files": [
+            [
+                "uw-floor.txt"
+            ]
+        ],
+        "license": null,
+        "size": 284390,
+        "urls": [
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/robot_wireless/"
+        ]
+    },
+    "rogers_girolami_data": {
+        "citation": "A First Course in Machine Learning. Simon Rogers and Mark Girolami: Chapman & Hall/CRC, ISBN-13: 978-1439824146",
+        "details": "Data from the textbook 'A First Course in Machine Learning'. Available from http://www.dcs.gla.ac.uk/~srogers/firstcourseml/.",
+        "files": [
+            [
+                "firstcoursemldata.tar.gz"
+            ]
+        ],
+        "license": null,
+        "size": 21949154,
+        "suffices": [
+            [
+                "?dl=1"
+            ]
+        ],
+        "urls": [
+            "https://www.dropbox.com/sh/7p6tu1t29idgliq/_XqlH_3nt9/"
+        ]
+    },
+    "singlecell": {
+        "citation": "Guoji Guo, Mikael Huss, Guo Qing Tong, Chaoyang Wang, Li Li Sun, Neil D. Clarke, Paul Robson, Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst, Developmental Cell, Volume 18, Issue 4, 20 April 2010, Pages 675-685, ISSN 1534-5807, http://dx.doi.org/10.1016/j.devcel.2010.02.012. (http://www.sciencedirect.com/science/article/pii/S1534580710001103) Keywords: DEVBIO",
+        "details": "qPCR TaqMan array single cell experiment in mouse. The data is taken from the early stages of development when the Blastocyst is forming. At the 32 cell stage the data is already separated into the trophectoderm (TE) which goes onto form the placenta and the inner cellular mass (ICM). The ICM further differentiates into the epiblast (EPI)---which gives rise to the endoderm, mesoderm and ectoderm---and the primitive endoderm (PE) which develops into the amniotic sack. Guo et al selected 48 genes for expression measurement. They labelled the resulting cells and their labels are included as an aide to visualization.",
+        "files": [
+            [
+                "singlecell.csv"
+            ]
+        ],
+        "license": "ScienceDirect: http://www.elsevier.com/locate/termsandconditions?utm_source=sciencedirect&utm_medium=link&utm_campaign=terms",
+        "size": 233.1,
+        "urls": [
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/singlecell/"
+        ]
+    },
+    "sod1_mouse": {
+        "citation": "Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis' Nardo G1, Iennaco R, Fusi N, Heath PR, Marino M, Trolese MC, Ferraiuolo L, Lawrence N, Shaw PJ, Bendotti C Brain. 2013 Nov;136(Pt 11):3305-32. doi: 10.1093/brain/awt250. Epub 2013 Sep 24.",
+        "details": "Gene expression data from two separate strains of mice: C57 and 129Sv in wild type and SOD1 mutant strains.",
+        "files": [
+            [
+                "sod1_C57_129_exprs.csv",
+                "sod1_C57_129_se.csv"
+            ]
+        ],
+        "license": null,
+        "size": 0,
+        "urls": [
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/sod1_mouse/"
+        ]
+    },
+    "swiss_roll": {
+        "citation": "A Global Geometric Framework for Nonlinear Dimensionality Reduction, J. B. Tenenbaum, V. de Silva and J. C. Langford, Science 290 (5500): 2319-2323, 22 December 2000",
+        "details": "Swiss roll data made available by Tenenbaum, de Silva and Langford to demonstrate isomap, available from http://isomap.stanford.edu/datasets.html.",
+        "files": [
+            [
+                "swiss_roll_data.mat"
+            ]
+        ],
+        "license": null,
+        "size": 800256,
+        "urls": [
+            "http://isomap.stanford.edu/"
+        ]
+    },
+    "three_phase_oil_flow": {
+        "citation": "Bishop, C. M. and G. D. James (1993). Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research A327, 580-593",
+        "details": "The three phase oil data used initially for demonstrating the Generative Topographic mapping.",
+        "files": [
+            [
+                "DataTrnLbls.txt",
+                "DataTrn.txt",
+                "DataTst.txt",
+                "DataTstLbls.txt",
+                "DataVdn.txt",
+                "DataVdnLbls.txt"
+            ]
+        ],
+        "license": null,
+        "size": 712796,
+        "urls": [
+            "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/three_phase_oil_flow/"
+        ]
     },
     "xw_pen": {
+        "citation": "Michael E. Tipping and Neil D. Lawrence. Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing, 69:123--141, 2005",
+        "details": "Accelerometer pen data used for robust regression by Tipping and Lawrence.",
         "files": [
             [
                 "xw_pen_15.csv"
             ]
         ],
         "license": null,
-        "citation": "Michael E. Tipping and Neil D. Lawrence. Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing, 69:123--141, 2005",
+        "size": 3410,
-        "details": "Accelerometer pen data used for robust regression by Tipping and Lawrence.",
         "urls": [
             "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/xw_pen/"
-        ],
+        ]
-        "size": 3410
     }
 }

GPy/util/datasets/COPYRIGHT

@@ -1 +0,0 @@
-These datasets are reproduced for educational purposes only. No copyright infringement intended!

GPy/util/datasets/column_2C

@@ -1,310 +0,0 @@
-63.03 22.55 39.61 40.48 98.67 -0.25 1
-39.06 10.06 25.02 29 114.41 4.56 1
-68.83 22.22 50.09 46.61 105.99 -3.53 1
-69.3 24.65 44.31 44.64 101.87 11.21 1
-49.71 9.65 28.32 40.06 108.17 7.92 1
-40.25 13.92 25.12 26.33 130.33 2.23 1
-53.43 15.86 37.17 37.57 120.57 5.99 1
-45.37 10.76 29.04 34.61 117.27 -10.68 1
-43.79 13.53 42.69 30.26 125 13.29 1
-36.69 5.01 41.95 31.68 84.24 0.66 1
-49.71 13.04 31.33 36.67 108.65 -7.83 1
-31.23 17.72 15.5 13.52 120.06 0.5 1
-48.92 19.96 40.26 28.95 119.32 8.03 1
-53.57 20.46 33.1 33.11 110.97 7.04 1
-57.3 24.19 47 33.11 116.81 5.77 1
-44.32 12.54 36.1 31.78 124.12 5.42 1
-63.83 20.36 54.55 43.47 112.31 -0.62 1
-31.28 3.14 32.56 28.13 129.01 3.62 1
-38.7 13.44 31 25.25 123.16 1.43 1
-41.73 12.25 30.12 29.48 116.59 -1.24 1
-43.92 14.18 37.83 29.74 134.46 6.45 1
-54.92 21.06 42.2 33.86 125.21 2.43 1
-63.07 24.41 54 38.66 106.42 15.78 1
-45.54 13.07 30.3 32.47 117.98 -4.99 1
-36.13 22.76 29 13.37 115.58 -3.24 1
-54.12 26.65 35.33 27.47 121.45 1.57 1
-26.15 10.76 14 15.39 125.2 -10.09 1
-43.58 16.51 47 27.07 109.27 8.99 1
-44.55 21.93 26.79 22.62 111.07 2.65 1
-66.88 24.89 49.28 41.99 113.48 -2.01 1
-50.82 15.4 42.53 35.42 112.19 10.87 1
-46.39 11.08 32.14 35.31 98.77 6.39 1
-44.94 17.44 27.78 27.49 117.98 5.57 1
-38.66 12.99 40 25.68 124.91 2.7 1
-59.6 32 46.56 27.6 119.33 1.47 1
-31.48 7.83 24.28 23.66 113.83 4.39 1
-32.09 6.99 36 25.1 132.26 6.41 1
-35.7 19.44 20.7 16.26 137.54 -0.26 1
-55.84 28.85 47.69 27 123.31 2.81 1
-52.42 19.01 35.87 33.41 116.56 1.69 1
-35.49 11.7 15.59 23.79 106.94 -3.46 1
-46.44 8.4 29.04 38.05 115.48 2.05 1
-53.85 19.23 32.78 34.62 121.67 5.33 1
-66.29 26.33 47.5 39.96 121.22 -0.8 1
-56.03 16.3 62.28 39.73 114.02 -2.33 1
-50.91 23.02 47 27.9 117.42 -2.53 1
-48.33 22.23 36.18 26.1 117.38 6.48 1
-41.35 16.58 30.71 24.78 113.27 -4.5 1
-40.56 17.98 34 22.58 121.05 -1.54 1
-41.77 17.9 20.03 23.87 118.36 2.06 1
-55.29 20.44 34 34.85 115.88 3.56 1
-74.43 41.56 27.7 32.88 107.95 5 1
-50.21 29.76 36.1 20.45 128.29 5.74 1
-30.15 11.92 34 18.23 112.68 11.46 1
-41.17 17.32 33.47 23.85 116.38 -9.57 1
-47.66 13.28 36.68 34.38 98.25 6.27 1
-43.35 7.47 28.07 35.88 112.78 5.75 1
-46.86 15.35 38 31.5 116.25 1.66 1
-43.2 19.66 35 23.54 124.85 -2.92 1
-48.11 14.93 35.56 33.18 124.06 7.95 1
-74.38 32.05 78.77 42.32 143.56 56.13 1
-89.68 32.7 83.13 56.98 129.96 92.03 1
-44.53 9.43 52 35.1 134.71 29.11 1
-77.69 21.38 64.43 56.31 114.82 26.93 1
-76.15 21.94 82.96 54.21 123.93 10.43 1
-83.93 41.29 62 42.65 115.01 26.59 1
-78.49 22.18 60 56.31 118.53 27.38 1
-75.65 19.34 64.15 56.31 95.9 69.55 1
-72.08 18.95 51 53.13 114.21 1.01 1
-58.6 -0.26 51.5 58.86 102.04 28.06 1
-72.56 17.39 52 55.18 119.19 32.11 1
-86.9 32.93 47.79 53.97 135.08 101.72 1
-84.97 33.02 60.86 51.95 125.66 74.33 1
-55.51 20.1 44 35.42 122.65 34.55 1
-72.22 23.08 91 49.14 137.74 56.8 1
-70.22 39.82 68.12 30.4 148.53 145.38 1
-86.75 36.04 69.22 50.71 139.41 110.86 1
-58.78 7.67 53.34 51.12 98.5 51.58 1
-67.41 17.44 60.14 49.97 111.12 33.16 1
-47.74 12.09 39 35.66 117.51 21.68 1
-77.11 30.47 69.48 46.64 112.15 70.76 1
-74.01 21.12 57.38 52.88 120.21 74.56 1
-88.62 29.09 47.56 59.53 121.76 51.81 1
-81.1 24.79 77.89 56.31 151.84 65.21 1
-76.33 42.4 57.2 33.93 124.27 50.13 1
-45.44 9.91 45 35.54 163.07 20.32 1
-59.79 17.88 59.21 41.91 119.32 22.12 1
-44.91 10.22 44.63 34.7 130.08 37.36 1
-56.61 16.8 42 39.81 127.29 24.02 1
-71.19 23.9 43.7 47.29 119.86 27.28 1
-81.66 28.75 58.23 52.91 114.77 30.61 1
-70.95 20.16 62.86 50.79 116.18 32.52 1
-85.35 15.84 71.67 69.51 124.42 76.02 1
-58.1 14.84 79.65 43.26 113.59 50.24 1
-94.17 15.38 67.71 78.79 114.89 53.26 1
-57.52 33.65 50.91 23.88 140.98 148.75 1
-96.66 19.46 90.21 77.2 120.67 64.08 1
-74.72 19.76 82.74 54.96 109.36 33.31 1
-77.66 22.43 93.89 55.22 123.06 61.21 1
-58.52 13.92 41.47 44.6 115.51 30.39 1
-84.59 30.36 65.48 54.22 108.01 25.12 1
-79.94 18.77 63.31 61.16 114.79 38.54 1
-70.4 13.47 61.2 56.93 102.34 25.54 1
-49.78 6.47 53 43.32 110.86 25.34 1
-77.41 29.4 63.23 48.01 118.45 93.56 1
-65.01 27.6 50.95 37.41 116.58 7.02 1
-65.01 9.84 57.74 55.18 94.74 49.7 1
-78.43 33.43 76.28 45 138.55 77.16 1
-63.17 6.33 63 56.84 110.64 42.61 1
-68.61 15.08 63.01 53.53 123.43 39.5 1
-63.9 13.71 62.12 50.19 114.13 41.42 1
-85 29.61 83.35 55.39 126.91 71.32 1
-42.02 -6.55 67.9 48.58 111.59 27.34 1
-69.76 19.28 48.5 50.48 96.49 51.17 1
-80.99 36.84 86.96 44.14 141.09 85.87 1
-129.83 8.4 48.38 121.43 107.69 418.54 1
-70.48 12.49 62.42 57.99 114.19 56.9 1
-86.04 38.75 47.87 47.29 122.09 61.99 1
-65.54 24.16 45.78 41.38 136.44 16.38 1
-60.75 15.75 43.2 45 113.05 31.69 1
-54.74 12.1 41 42.65 117.64 40.38 1
-83.88 23.08 87.14 60.8 124.65 80.56 1
-80.07 48.07 52.4 32.01 110.71 67.73 1
-65.67 10.54 56.49 55.12 109.16 53.93 1
-74.72 14.32 32.5 60.4 107.18 37.02 1
-48.06 5.69 57.06 42.37 95.44 32.84 1
-70.68 21.7 59.18 48.97 103.01 27.81 1
-80.43 17 66.54 63.43 116.44 57.78 1
-90.51 28.27 69.81 62.24 100.89 58.82 1
-77.24 16.74 49.78 60.5 110.69 39.79 1
-50.07 9.12 32.17 40.95 99.71 26.77 1
-69.78 13.78 58 56 118.93 17.91 1
-69.63 21.12 52.77 48.5 116.8 54.82 1
-81.75 20.12 70.56 61.63 119.43 55.51 1
-52.2 17.21 78.09 34.99 136.97 54.94 1
-77.12 30.35 77.48 46.77 110.61 82.09 1
-88.02 39.84 81.77 48.18 116.6 56.77 1
-83.4 34.31 78.42 49.09 110.47 49.67 1
-72.05 24.7 79.87 47.35 107.17 56.43 1
-85.1 21.07 91.73 64.03 109.06 38.03 1
-69.56 15.4 74.44 54.16 105.07 29.7 1
-89.5 48.9 72 40.6 134.63 118.35 1
-85.29 18.28 100.74 67.01 110.66 58.88 1
-60.63 20.6 64.54 40.03 117.23 104.86 1
-60.04 14.31 58.04 45.73 105.13 30.41 1
-85.64 42.69 78.75 42.95 105.14 42.89 1
-85.58 30.46 78.23 55.12 114.87 68.38 1
-55.08 -3.76 56 58.84 109.92 31.77 1
-65.76 9.83 50.82 55.92 104.39 39.31 1
-79.25 23.94 40.8 55.3 98.62 36.71 1
-81.11 20.69 60.69 60.42 94.02 40.51 1
-48.03 3.97 58.34 44.06 125.35 35 1
-63.4 14.12 48.14 49.29 111.92 31.78 1
-57.29 15.15 64 42.14 116.74 30.34 1
-41.19 5.79 42.87 35.39 103.35 27.66 1
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-61.41 25.38 39.1 36.03 103.4 21.84 1
-56.56 8.96 52.58 47.6 98.78 50.7 1
-67.03 13.28 66.15 53.75 100.72 33.99 1
-80.82 19.24 61.64 61.58 89.47 44.17 1
-80.65 26.34 60.9 54.31 120.1 52.47 1
-68.72 49.43 68.06 19.29 125.02 54.69 1
-37.9 4.48 24.71 33.42 157.85 33.61 1
-64.62 15.23 67.63 49.4 90.3 31.33 1
-75.44 31.54 89.6 43.9 106.83 54.97 1
-71 37.52 84.54 33.49 125.16 67.77 1
-81.06 20.8 91.78 60.26 125.43 38.18 1
-91.47 24.51 84.62 66.96 117.31 52.62 1
-81.08 21.26 78.77 59.83 90.07 49.16 1
-60.42 5.27 59.81 55.15 109.03 30.27 1
-85.68 38.65 82.68 47.03 120.84 61.96 1
-82.41 29.28 77.05 53.13 117.04 62.77 1
-43.72 9.81 52 33.91 88.43 40.88 1
-86.47 40.3 61.14 46.17 97.4 55.75 1
-74.47 33.28 66.94 41.19 146.47 124.98 1
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-72.64 18.93 68 53.71 116.96 25.38 1
-71.24 5.27 86 65.97 110.7 38.26 1
-63.77 12.76 65.36 51.01 89.82 56 1
-58.83 37.58 125.74 21.25 135.63 117.31 1
-74.85 13.91 62.69 60.95 115.21 33.17 1
-75.3 16.67 61.3 58.63 118.88 31.58 1
-63.36 20.02 67.5 43.34 131 37.56 1
-67.51 33.28 96.28 34.24 145.6 88.3 1
-76.31 41.93 93.28 34.38 132.27 101.22 1
-73.64 9.71 63 63.92 98.73 26.98 1
-56.54 14.38 44.99 42.16 101.72 25.77 1
-80.11 33.94 85.1 46.17 125.59 100.29 1
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-74.09 18.82 76.03 55.27 128.41 73.39 1
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-56.1 13.11 62.64 43 116.23 31.17 -1
-69.4 18.9 75.97 50.5 103.58 -0.44 -1
-89.83 22.64 90.56 67.2 100.5 3.04 -1
-59.73 7.72 55.34 52 125.17 3.24 -1
-63.96 16.06 63.12 47.9 142.36 6.3 -1
-61.54 19.68 52.89 41.86 118.69 4.82 -1
-38.05 8.3 26.24 29.74 123.8 3.89 -1
-43.44 10.1 36.03 33.34 137.44 -3.11 -1
-65.61 23.14 62.58 42.47 124.13 -4.08 -1
-53.91 12.94 39 40.97 118.19 5.07 -1
-43.12 13.82 40.35 29.3 128.52 0.97 -1
-40.68 9.15 31.02 31.53 139.12 -2.51 -1
-37.73 9.39 42 28.35 135.74 13.68 -1
-63.93 19.97 40.18 43.96 113.07 -11.06 -1
-61.82 13.6 64 48.22 121.78 1.3 -1
-62.14 13.96 58 48.18 133.28 4.96 -1
-69 13.29 55.57 55.71 126.61 10.83 -1
-56.45 19.44 43.58 37 139.19 -1.86 -1
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-47.32 8.57 35.56 38.75 120.58 1.63 -1
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-67.29 16.72 51 50.57 137.59 4.96 -1
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-65.76 13.21 44 52.55 129.39 -1.98 -1
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-48.8 18.02 52 30.78 139.15 10.44 -1
-50.09 13.43 34.46 36.66 119.13 3.09 -1
-64.26 14.5 43.9 49.76 115.39 5.95 -1
-53.68 13.45 41.58 40.24 113.91 2.74 -1
-49 13.11 51.87 35.88 126.4 0.54 -1
-59.17 14.56 43.2 44.6 121.04 2.83 -1
-67.8 16.55 43.26 51.25 119.69 4.87 -1
-61.73 17.11 46.9 44.62 120.92 3.09 -1
-33.04 -0.32 19.07 33.37 120.39 9.35 -1
-74.57 15.72 58.62 58.84 105.42 0.6 -1
-44.43 14.17 32.24 30.26 131.72 -3.6 -1
-36.42 13.88 20.24 22.54 126.08 0.18 -1
-51.08 14.21 35.95 36.87 115.8 6.91 -1
-34.76 2.63 29.5 32.12 127.14 -0.46 -1
-48.9 5.59 55.5 43.32 137.11 19.85 -1
-46.24 10.06 37 36.17 128.06 -5.1 -1
-46.43 6.62 48.1 39.81 130.35 2.45 -1
-39.66 16.21 36.67 23.45 131.92 -4.97 -1
-45.58 18.76 33.77 26.82 116.8 3.13 -1
-66.51 20.9 31.73 45.61 128.9 1.52 -1
-82.91 29.89 58.25 53.01 110.71 6.08 -1
-50.68 6.46 35 44.22 116.59 -0.21 -1
-89.01 26.08 69.02 62.94 111.48 6.06 -1
-54.6 21.49 29.36 33.11 118.34 -1.47 -1
-34.38 2.06 32.39 32.32 128.3 -3.37 -1
-45.08 12.31 44.58 32.77 147.89 -8.94 -1
-47.9 13.62 36 34.29 117.45 -4.25 -1
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-61.45 22.69 46.17 38.75 125.67 -2.71 -1
-45.25 8.69 41.58 36.56 118.55 0.21 -1
-33.84 5.07 36.64 28.77 123.95 -0.2 -1

GPy/util/datasets/connections.txt

@@ -1,22 +0,0 @@
-LFHD, RFHD
-RFHD, RBHD
-RBHD, LBHD
-LBHD, LFHD
-LELB, LWRB
-LWRB, LFIN
-LELB, LSHO
-LSHO, RSHO
-RSHO, STRN
-LSHO, STRN
-RSHO, RELB
-RELB, RWRB
-RWRB, RFIN
-LSHO, LFWT
-RSHO, RFWT
-LFWT, RFWT
-LFWT, LKNE
-RFWT, RKNE
-LKNE, LHEE
-RKNE, RHEE
-RMT5, RHEE
-LMT5, LHEE

GPy/util/datasets/diabetes.txt

@@ -1,769 +0,0 @@
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GPy/util/datasets/mocap/cmu/README.txt

@@ -1 +0,0 @@
-this otherwise empty directory is for storing mnocap data, which we don;t distribute

GPy/util/datasets/sonar_data

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-3,120,1.9,0.7,18.5,24.0
-3,119,0.8,0.7,56.4,21.6
-3,123,5.6,1.1,13.7,56.3
-3,115,6.3,1.2,4.7,14.4
-3,126,0.5,0.2,12.2,8.8
-3,121,4.7,1.8,11.2,53.0
-3,131,2.7,0.8,9.9,4.7
-3,134,2.0,0.5,12.2,2.2
-3,141,2.5,1.3,8.5,7.5
-3,113,5.1,0.7,5.8,19.6
-3,136,1.4,0.3,32.6,8.4
-3,120,3.4,1.8,7.5,21.5
-3,125,3.7,1.1,8.5,25.9
-3,123,1.9,0.3,22.8,22.2
-3,112,2.6,0.7,41.0,19.0
-3,134,1.9,0.6,18.4,8.2
-3,119,5.1,1.1,7.0,40.8
-3,118,6.5,1.3,1.7,11.5
-3,139,4.2,0.7,4.3,6.3
-3,103,5.1,1.4,1.2,5.0
-3,97,4.7,1.1,2.1,12.6
-3,102,5.3,1.4,1.3,6.7
-

GPy/util/initialization.py

@@ -13,7 +13,11 @@ def initialize_latent(init, input_dim, Y):
         p = pca(Y)
         PC = p.project(Y, min(input_dim, Y.shape[1]))
         Xr[:PC.shape[0], :PC.shape[1]] = PC
+        var = p.fracs[:input_dim]
     else:
         var = Xr.var(0)
+    
+    Xr -= Xr.mean(0)
+    Xr /= Xr.var(0)
+    
     return Xr, var/var.max()
-    return Xr, p.fracs[:input_dim]

GPy/util/misc.py

@@ -130,14 +130,14 @@ def fast_array_equal(A, B):
     """ % pragma_string
 
     if config.getboolean('parallel', 'openmp'):
-        pragma_string = '#include <omp.h>'
+        header_string = '#include <omp.h>'
     else:
-        pragma_string = ''
+        header_string = ''
 
     support_code = """
     %s
     #include <math.h>
-    """ % pragma_string
+    """ % header_string
 
 
     weave_options_openmp = {'headers'           : ['<omp.h>'],