general tidying in models

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/gp_regression.py

@@ -39,5 +39,3 @@ class GPRegression(GP):
 
     def setstate(self, state):
         return GP.setstate(self, state)
-
-    pass

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)
+
+