Merged with devel

GPy/core/gp.py

@@ -27,12 +27,6 @@ class GP(GPBase):
         GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self.update_likelihood_approximation()
 
-    def getstate(self):
-        return GPBase.getstate(self)
-
-    def setstate(self, state):
-        GPBase.setstate(self, state)
-        self._set_params(self._get_params())
 
     def _set_params(self, p):
         new_kern_params = p[:self.kern.num_params_transformed()]
@@ -200,3 +194,11 @@ class GP(GPBase):
         """
         Xnew = self._add_output_index(Xnew, output)
         return self.predict(Xnew, which_parts=which_parts, full_cov=full_cov, likelihood_args=likelihood_args)
+
+    def getstate(self):
+        return GPBase.getstate(self)
+
+    def setstate(self, state):
+        GPBase.setstate(self, state)
+        self._set_params(self._get_params())
+

GPy/core/gp_base.py

@@ -9,7 +9,9 @@ from ..likelihoods import Gaussian, Gaussian_Mixed_Noise
 class GPBase(Model):
     """
     Gaussian process base model for holding shared behaviour between
-    sparse_GP and GP models.
+    sparse_GP and GP models, and potentially other models in the future.
+
+    Here we define some functions that are use
     """
     def __init__(self, X, likelihood, kernel, normalize_X=False):
         self.X = X
@@ -34,29 +36,6 @@ class GPBase(Model):
         # All leaf nodes should call self._set_params(self._get_params()) at
         # the end
 
-    def getstate(self):
-        """
-        Get the current state of the class, here we return everything that is needed to recompute the model.
-        """
-        return Model.getstate(self) + [self.X,
-                self.num_data,
-                self.input_dim,
-                self.kern,
-                self.likelihood,
-                self.output_dim,
-                self._Xoffset,
-                self._Xscale]
-
-    def setstate(self, state):
-        self._Xscale = state.pop()
-        self._Xoffset = state.pop()
-        self.output_dim = state.pop()
-        self.likelihood = state.pop()
-        self.kern = state.pop()
-        self.input_dim = state.pop()
-        self.num_data = state.pop()
-        self.X = state.pop()
-        Model.setstate(self, state)
 
     def posterior_samples_f(self,X,size=10,which_parts='all',full_cov=True):
         """
@@ -269,155 +248,33 @@ class GPBase(Model):
         else:
             raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
 
-    def plot_single_output_f(self, output=None, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
+    def getstate(self):
         """
-        For a specific output, in a multioutput model, this function works just as plot_f on single output models.
+        Get the curent state of the class. This is only used to efficiently
-
+        pickle the model. See also self.setstate
-        :param output: which output to plot (for multiple output models only)
-        :type output: integer (first output is 0)
-        :param samples: the number of a posteriori samples to plot
-        :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
-        :param which_data: which if the training data to plot (default all)
-        :type which_data: 'all' or a slice object to slice self.X, self.Y
-        :param which_parts: which of the kernel functions to plot (additively)
-        :type which_parts: 'all', or list of bools
-        :param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
-        :type resolution: int
-        :param full_cov:
-        :type full_cov: bool
-                :param fignum: figure to plot on.
-        :type fignum: figure number
-        :param ax: axes to plot on.
-        :type ax: axes handle
         """
-        assert output is not None, "An output must be specified."
+        return Model.getstate(self) + [self.X,
-        assert len(self.likelihood.noise_model_list) > output, "The model has only %s outputs." %(self.output_dim + 1)
+                self.num_data,
+                self.input_dim,
+                self.kern,
+                self.likelihood,
+                self.output_dim,
+                self._Xoffset,
+                self._Xscale]
 
-        if which_data == 'all':
+    def setstate(self, state):
-            which_data = slice(None)
-
-        if ax is None:
-            fig = pb.figure(num=fignum)
-            ax = fig.add_subplot(111)
-
-        if self.X.shape[1] == 2:
-            Xu = self.X[self.X[:,-1]==output ,0:1]
-            Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
-            Xnew_indexed = self._add_output_index(Xnew,output)
-
-            m, v = self._raw_predict(Xnew_indexed, which_parts=which_parts)
-
-            if samples:
-                Ysim = self.posterior_samples_f(Xnew_indexed, samples, which_parts=which_parts, full_cov=True)
-                for yi in Ysim.T:
-                    ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
-
-            gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v), axes=ax)
-            ax.plot(Xu[which_data], self.likelihood.Y[self.likelihood.index==output][:,None], 'kx', mew=1.5)
-            ax.set_xlim(xmin, xmax)
-            ymin, ymax = min(np.append(self.likelihood.Y, m - 2 * np.sqrt(np.diag(v)[:, None]))), max(np.append(self.likelihood.Y, m + 2 * np.sqrt(np.diag(v)[:, None])))
-            ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
-            ax.set_ylim(ymin, ymax)
-
-        elif self.X.shape[1] == 3:
-            raise NotImplementedError, "Plots not implemented for multioutput models with 2D inputs...yet"
-            #if samples:
-            #    warnings.warn("Samples only implemented for 1 dimensional inputs.")
-
-        else:
-            raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
-
-
-    def plot_single_output(self, output=None, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None, fixed_inputs=[], linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue']):
         """
-        For a specific output, in a multioutput model, this function works just as plot_f on single output models.
+        Set the state of the model. Used for efficient pickling
-
-        :param output: which output to plot (for multiple output models only)
-        :type output: integer (first output is 0)
-        :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
-        :type plot_limits: np.array
-        :param which_data: which if the training data to plot (default all)
-        :type which_data: 'all' or a slice object to slice self.X, self.Y
-        :param which_parts: which of the kernel functions to plot (additively)
-        :type which_parts: 'all', or list of bools
-        :param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
-        :type resolution: int
-        :param levels: number of levels to plot in a contour plot.
-        :type levels: int
-        :param samples: the number of a posteriori samples to plot
-        :type samples: int
-        :param fignum: figure to plot on.
-        :type fignum: figure number
-        :param ax: axes to plot on.
-        :type ax: axes handle
-        :type output: integer (first output is 0)
-        :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
-        :type fixed_inputs: a list of tuples
-        :param linecol: color of line to plot.
-        :type linecol:
-        :param fillcol: color of fill
-        :param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
         """
-        assert output is not None, "An output must be specified."
+        self._Xscale = state.pop()
-        assert len(self.likelihood.noise_model_list) > output, "The model has only %s outputs." %(self.output_dim + 1)
+        self._Xoffset = state.pop()
-        if which_data == 'all':
+        self.output_dim = state.pop()
-            which_data = slice(None)
+        self.likelihood = state.pop()
-
+        self.kern = state.pop()
-        if ax is None:
+        self.input_dim = state.pop()
-            fig = pb.figure(num=fignum)
+        self.num_data = state.pop()
-            ax = fig.add_subplot(111)
+        self.X = state.pop()
-
+        Model.setstate(self, state)
-        if self.X.shape[1] == 2:
-            resolution = resolution or 200
-
-            Xu = self.X[self.X[:,-1]==output,:] #keep the output of interest
-            Xu = self.X * self._Xscale + self._Xoffset
-            Xu = self.X[self.X[:,-1]==output ,0:1] #get rid of the index column
-
-            Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
-            Xnew_indexed = self._add_output_index(Xnew,output)
-
-
-            m, v, lower, upper = self.predict(Xnew_indexed, which_parts=which_parts,noise_model=output)
-
-            if samples: #NOTE not tested with fixed_inputs
-                Ysim = self.posterior_samples(Xnew_indexed, samples, which_parts=which_parts, full_cov=True,noise_model=output)
-                for yi in Ysim.T:
-                    ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
-
-            for d in range(m.shape[1]):
-                gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
-                ax.plot(Xu[which_data], self.likelihood.noise_model_list[output].data, 'kx', mew=1.5)
-            ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper))
-            ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
-            ax.set_xlim(xmin, xmax)
-            ax.set_ylim(ymin, ymax)
-
-        elif self.X.shape[1] == 3:
-            raise NotImplementedError, "Plots not implemented for multioutput models with 2D inputs...yet"
-            #if samples:
-            #    warnings.warn("Samples only implemented for 1 dimensional inputs.")
-
-        else:
-            raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
-
-
-    def _add_output_index(self,X,output):
-        """
-        In a multioutput model, appends an index column to X to specify the output it is related to.
-
-        :param X: Input data
-        :type X: np.ndarray, N x self.input_dim
-        :param output: output X is related to
-        :type output: integer in {0,..., output_dim-1}
-
-        .. Note:: For multiple non-independent outputs models only.
-        """
-
-        assert hasattr(self,'multioutput'), 'This function is for multiple output models only.'
-
-        index = np.ones((X.shape[0],1))*output
-        return np.hstack((X,index))
 
     def log_predictive_density(self, x_test, y_test):
         """

GPy/core/sparse_gp.py

@@ -52,23 +52,6 @@ class SparseGP(GPBase):
 
         self._const_jitter = None
 
-    def getstate(self):
-        """
-        Get the current state of the class,
-        here just all the indices, rest can get recomputed
-        """
-        return GPBase.getstate(self) + [self.Z,
-                self.num_inducing,
-                self.has_uncertain_inputs,
-                self.X_variance]
-
-    def setstate(self, state):
-        self.X_variance = state.pop()
-        self.has_uncertain_inputs = state.pop()
-        self.num_inducing = state.pop()
-        self.Z = state.pop()
-        GPBase.setstate(self, state)
-
     def _compute_kernel_matrices(self):
         # kernel computations, using BGPLVM notation
         self.Kmm = self.kern.K(self.Z)
@@ -87,7 +70,6 @@ class SparseGP(GPBase):
 
         # factor Kmm
         self._Lm = jitchol(self.Kmm + self._const_jitter)
-        # TODO: no white kernel needed anymore, all noise in likelihood --------
 
         # The rather complex computations of self._A
         if self.has_uncertain_inputs:
@@ -421,145 +403,21 @@ class SparseGP(GPBase):
         else:
             raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
 
-    def predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False):
+    def getstate(self):
         """
-        For a specific output, predict the function at the new point(s) Xnew.
+        Get the current state of the class,
-
+        here just all the indices, rest can get recomputed
-        :param Xnew: The points at which to make a prediction
-        :type Xnew: np.ndarray, Nnew x self.input_dim
-        :param output: output to predict
-        :type output: integer in {0,..., num_outputs-1}
-        :param which_parts:  specifies which outputs kernel(s) to use in prediction
-        :type which_parts: ('all', list of bools)
-        :param full_cov: whether to return the full covariance matrix, or just the diagonal
-        :type full_cov: bool
-        :rtype: posterior mean,  a Numpy array, Nnew x self.input_dim
-        :rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
-        :rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays,  Nnew x self.input_dim
-
-        .. Note:: For multiple output models only
         """
+        return GPBase.getstate(self) + [self.Z,
+                self.num_inducing,
+                self.has_uncertain_inputs,
+                self.X_variance]
 
-        assert hasattr(self,'multioutput')
+    def setstate(self, state):
-        index = np.ones_like(Xnew)*output
+        self.X_variance = state.pop()
-        Xnew = np.hstack((Xnew,index))
+        self.has_uncertain_inputs = state.pop()
-
+        self.num_inducing = state.pop()
-        # normalize X values
+        self.Z = state.pop()
-        Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
+        GPBase.setstate(self, state)
-        mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
-
-        # now push through likelihood
-        mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
-        return mean, var, _025pm, _975pm
-
-    def _raw_predict_single_output(self, _Xnew, output=0, X_variance_new=None, which_parts='all', full_cov=False,stop=False):
-        """
-        Internal helper function for making predictions for a specific output,
-        does not account for normalization or likelihood
-        ---------
-
-        :param Xnew: The points at which to make a prediction
-        :type Xnew: np.ndarray, Nnew x self.input_dim
-        :param output: output to predict
-        :type output: integer in {0,..., num_outputs-1}
-        :param which_parts:  specifies which outputs kernel(s) to use in prediction
-        :type which_parts: ('all', list of bools)
-        :param full_cov: whether to return the full covariance matrix, or just the diagonal
-
-        .. Note:: For multiple output models only
-        """
-        Bi, _ = dpotri(self.LB, lower=0)  # WTH? this lower switch should be 1, but that doesn't work!
-        symmetrify(Bi)
-        Kmmi_LmiBLmi = backsub_both_sides(self._Lm, np.eye(self.num_inducing) - Bi)
-
-        if self.Cpsi1V is None:
-            psi1V = np.dot(self.psi1.T,self.likelihood.V)
-            tmp, _ = dtrtrs(self._Lm, np.asfortranarray(psi1V), lower=1, trans=0)
-            tmp, _ = dpotrs(self.LB, tmp, lower=1)
-            self.Cpsi1V, _ = dtrtrs(self._Lm, tmp, lower=1, trans=1)
-
-        assert hasattr(self,'multioutput')
-        index = np.ones_like(_Xnew)*output
-        _Xnew = np.hstack((_Xnew,index))
-
-        if X_variance_new is None:
-            Kx = self.kern.K(self.Z, _Xnew, which_parts=which_parts)
-            mu = np.dot(Kx.T, self.Cpsi1V)
-            if full_cov:
-                Kxx = self.kern.K(_Xnew, which_parts=which_parts)
-                var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting
-            else:
-                Kxx = self.kern.Kdiag(_Xnew, which_parts=which_parts)
-                var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0)
-        else:
-            Kx = self.kern.psi1(self.Z, _Xnew, X_variance_new)
-            mu = np.dot(Kx, self.Cpsi1V)
-            if full_cov:
-                raise NotImplementedError, "TODO"
-            else:
-                Kxx = self.kern.psi0(self.Z, _Xnew, X_variance_new)
-                psi2 = self.kern.psi2(self.Z, _Xnew, X_variance_new)
-                var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
-
-        return mu, var[:, None]
 
 
-    def plot_single_output_f(self, output=None, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
-
-        if ax is None:
-            fig = pb.figure(num=fignum)
-            ax = fig.add_subplot(111)
-        if fignum is None and ax is None:
-                fignum = fig.num
-        if which_data is 'all':
-            which_data = slice(None)
-
-        GPBase.plot_single_output_f(self, output=output, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, full_cov=full_cov, fignum=fignum, ax=ax)
-
-        if self.X.shape[1] == 2:
-            if self.has_uncertain_inputs:
-                Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
-                ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
-                            xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
-                            ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
-            Zu = self.Z * self._Xscale + self._Xoffset
-            Zu = Zu[Zu[:,1]==output,0:1]
-            ax.plot(Zu[:,0], np.zeros_like(Zu[:,0]) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
-
-        elif self.X.shape[1] == 2:
-            Zu = self.Z * self._Xscale + self._Xoffset
-            Zu = Zu[Zu[:,1]==output,0:2]
-            ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
-
-
-        else:
-            raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
-
-    def plot_single_output(self, output=None, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=None):
-        if ax is None:
-            fig = pb.figure(num=fignum)
-            ax = fig.add_subplot(111)
-        if fignum is None and ax is None:
-                fignum = fig.num
-        if which_data is 'all':
-            which_data = slice(None)
-
-        GPBase.plot_single_output(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, levels=20, fignum=fignum, ax=ax, output=output)
-
-        if self.X.shape[1] == 2:
-            if self.has_uncertain_inputs:
-                Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
-                ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
-                            xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
-                            ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
-            Zu = self.Z * self._Xscale + self._Xoffset
-            Zu = Zu[Zu[:,1]==output,0:1]
-            ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
-
-        elif self.X.shape[1] == 3:
-            Zu = self.Z * self._Xscale + self._Xoffset
-            Zu = Zu[Zu[:,1]==output,0:1]
-            ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
-
-        else:
-            raise NotImplementedError, "Cannot define a frame with more than two input dimensions"

GPy/core/svigp.py

@@ -18,30 +18,16 @@ class SVIGP(GPBase):
     Stochastic Variational inference in a Gaussian Process
 
     :param X: inputs
-    :type X: np.ndarray (N x Q)
+    :type X: np.ndarray (num_data x num_inputs)
     :param Y: observed data
-    :type Y: np.ndarray of observations (N x D)
+    :type Y: np.ndarray of observations (num_data x output_dim)
-    :param batchsize: the size of a h
+    :param batchsize: the size of a minibatch
-
-    Additional kwargs are used as for a sparse GP. They include:
-
     :param q_u: canonical parameters of the distribution squasehd into a 1D array
     :type q_u: np.ndarray
-    :param M: Number of inducing points (optional, default 10. Ignored if Z is not None)
-    :type M: int
     :param kernel: the kernel/covariance function. See link kernels
     :type kernel: a GPy kernel
-    :param Z: inducing inputs (optional, see note)
+    :param Z: inducing inputs
-    :type Z: np.ndarray (M x Q) | None
+    :type Z: np.ndarray (num_inducing x num_inputs)
-    :param X_uncertainty: The uncertainty in the measurements of X (Gaussian variance)
-    :type X_uncertainty: np.ndarray (N x Q) | None
-    :param Zslices: slices for the inducing inputs (see slicing TODO: link)
-    :param M: Number of inducing points (optional, default 10. Ignored if Z is not None)
-    :type M: int
-    :param beta: noise precision. TODO: ignore beta if doing EP
-    :type beta: float
-    :param normalize_(X|Y): whether to normalize the data before computing (predictions will be in original scales)
-    :type normalize_(X|Y): bool
 
     """
 

GPy/examples/regression.py

@@ -57,8 +57,8 @@ def coregionalization_toy(max_iters=100):
     m.optimize(max_iters=max_iters)
 
     fig, axes = pb.subplots(2,1)
-    m.plot_single_output(output=0,ax=axes[0])
+    m.plot(fixed_inputs=[(1,0)],ax=axes[0])
-    m.plot_single_output(output=1,ax=axes[1])
+    m.plot(fixed_inputs=[(1,1)],ax=axes[1])
     axes[0].set_title('Output 0')
     axes[1].set_title('Output 1')
     return m

GPy/gpy_config.cfg

@@ -4,4 +4,4 @@
 # Enable openmp support. This speeds up some computations, depending on the number
 # of cores available. Setting up a compiler with openmp support can be difficult on 
 # some platforms, hence this option.
-openmp=True
+openmp=False

GPy/models/bayesian_gplvm.py

@@ -49,18 +49,6 @@ class BayesianGPLVM(SparseGP, GPLVM):
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
         self.ensure_default_constraints()
 
-    def getstate(self):
-        """
-        Get the current state of the class,
-        here just all the indices, rest can get recomputed
-        """
-        return SparseGP.getstate(self) + [self.init]
-
-    def setstate(self, state):
-        self._const_jitter = None
-        self.init = state.pop()
-        SparseGP.setstate(self, state)
-
     def _get_param_names(self):
         X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
         S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
@@ -285,6 +273,19 @@ class BayesianGPLVM(SparseGP, GPLVM):
         fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
         return fig
 
+    def getstate(self):
+        """
+        Get the current state of the class,
+        here just all the indices, rest can get recomputed
+        """
+        return SparseGP.getstate(self) + [self.init]
+
+    def setstate(self, state):
+        self._const_jitter = None
+        self.init = state.pop()
+        SparseGP.setstate(self, state)
+
+
 def latent_cost_and_grad(mu_S, kern, Z, dL_dpsi0, dL_dpsi1, dL_dpsi2):
     """
     objective function for fitting the latent variables for test points

GPy/models/bcgplvm.py

@@ -7,7 +7,7 @@ import pylab as pb
 import sys, pdb
 from ..core import GP
 from ..models import GPLVM
-from ..mappings import *
+from ..mappings import Kernel
 
 
 class BCGPLVM(GPLVM):

GPy/models/gplvm.py

@@ -44,12 +44,6 @@ class GPLVM(GP):
             Xr[:PC.shape[0], :PC.shape[1]] = PC
         return Xr
 
-    def getstate(self):
-        return GP.getstate(self)
-
-    def setstate(self, state):
-        GP.setstate(self, state)
-
     def _get_param_names(self):
         return sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + GP._get_param_names(self)
 
@@ -68,7 +62,7 @@ class GPLVM(GP):
     def jacobian(self,X):
         target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
         for i in range(self.output_dim):
-        	target[:,:,i]=self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
+        	target[:,:,i] = self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
         return target
    
     def magnification(self,X):
@@ -91,3 +85,11 @@ class GPLVM(GP):
 
     def plot_magnification(self, *args, **kwargs):
         return util.plot_latent.plot_magnification(self, *args, **kwargs)
+
+    def getstate(self):
+        return GP.getstate(self)
+
+    def setstate(self, state):
+        GP.setstate(self, state)
+
+

GPy/models/mrd.py

@@ -81,29 +81,6 @@ class MRD(Model):
         Model.__init__(self)
         self.ensure_default_constraints()
 
-    def getstate(self):
-        return Model.getstate(self) + [self.names,
-                self.bgplvms,
-                self.gref,
-                self.nparams,
-                self.input_dim,
-                self.num_inducing,
-                self.num_data,
-                self.NQ,
-                self.MQ]
-
-    def setstate(self, state):
-        self.MQ = state.pop()
-        self.NQ = state.pop()
-        self.num_data = state.pop()
-        self.num_inducing = state.pop()
-        self.input_dim = state.pop()
-        self.nparams = state.pop()
-        self.gref = state.pop()
-        self.bgplvms = state.pop()
-        self.names = state.pop()
-        Model.setstate(self, state)
-
     @property
     def X(self):
         return self.gref.X
@@ -371,4 +348,28 @@ class MRD(Model):
         pylab.draw()
         fig.tight_layout()
 
+    def getstate(self):
+        return Model.getstate(self) + [self.names,
+                self.bgplvms,
+                self.gref,
+                self.nparams,
+                self.input_dim,
+                self.num_inducing,
+                self.num_data,
+                self.NQ,
+                self.MQ]
+
+    def setstate(self, state):
+        self.MQ = state.pop()
+        self.NQ = state.pop()
+        self.num_data = state.pop()
+        self.num_inducing = state.pop()
+        self.input_dim = state.pop()
+        self.nparams = state.pop()
+        self.gref = state.pop()
+        self.bgplvms = state.pop()
+        self.names = state.pop()
+        Model.setstate(self, state)
+
+