pickling and caching

GPy/models/bayesian_gplvm.py

@@ -2,7 +2,6 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from gplvm import GPLVM
 from .. import kern
 from ..core import SparseGP
 from ..likelihoods import Gaussian
@@ -61,18 +60,6 @@ class BayesianGPLVM(SparseGP):
         SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, **kwargs)
         self.add_parameter(self.X, index=0)
 
-    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 set_X_gradients(self, X, X_grad):
         """Set the gradients of the posterior distribution of X in its specific form."""
         X.mean.gradient, X.variance.gradient = X_grad

GPy/models/gp_regression.py

@@ -29,8 +29,3 @@ class GPRegression(GP):
 
         super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata)
 
-    def _getstate(self):
-        return GP._getstate(self)
-
-    def _setstate(self, state):
-        return GP._setstate(self, state)

GPy/models/gplvm.py

@@ -44,12 +44,6 @@ class GPLVM(GP):
         super(GPLVM, self).parameters_changed()
         self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
 
-    def _getstate(self):
-        return GP._getstate(self)
-
-    def _setstate(self, state):
-        GP._setstate(self, state)
-
     def jacobian(self,X):
         target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
         for i in range(self.output_dim):

GPy/models/mrd.py

@@ -65,14 +65,17 @@ class MRD(Model):
             from ..kern import RBF
             self.kern = [RBF(input_dim, ARD=1, lengthscale=fracs[i], name='rbf'.format(i)) for i in range(len(Ylist))]
         elif isinstance(kernel, Kern):
-            self.kern = [kernel.copy(name='{}'.format(kernel.name, i)) for i in range(len(Ylist))]
+            self.kern = []
+            for i in range(len(Ylist)):
+                k = kernel.copy()
+                self.kern.append(k)
         else:
             assert len(kernel) == len(Ylist), "need one kernel per output"
             assert all([isinstance(k, Kern) for k in kernel]), "invalid kernel object detected!"
             self.kern = kernel
 
         if X_variance is None:
-            X_variance = np.random.uniform(0, .1, X.shape)
+            X_variance = np.random.uniform(0.1, 0.2, X.shape)
 
         self.variational_prior = NormalPrior()
         self.X = NormalPosterior(X, X_variance)
@@ -108,8 +111,8 @@ class MRD(Model):
     def parameters_changed(self):
         self._log_marginal_likelihood = 0
         self.posteriors = []
-        self.Z.gradient = 0.
-        self.X.gradient = 0.
+        self.Z.gradient[:] = 0.
+        self.X.gradient[:] = 0.
 
         for y, k, l, i in itertools.izip(self.Ylist, self.kern, self.likelihood, self.inference_method):
             posterior, lml, grad_dict = i.inference(k, self.X, self.Z, l, y)
@@ -160,6 +163,8 @@ class MRD(Model):
             X = np.random.randn(Ylist[0].shape[0], self.input_dim)
             fracs = X.var(0)
             fracs = [fracs]*self.input_dim
+        X -= X.mean()
+        X /= X.std()
         return X, fracs
 
     def _init_Z(self, init="permute", X=None):

GPy/models/sparse_gp_classification.py

@@ -46,11 +46,3 @@ class SparseGPClassification(SparseGP):
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
         self.ensure_default_constraints()
 
-    def _getstate(self):
-        return SparseGP._getstate(self)
-
-
-    def _setstate(self, state):
-        return SparseGP._setstate(self, state)
-
-    pass

GPy/models/sparse_gp_regression.py

@@ -51,14 +51,6 @@ class SparseGPRegression(SparseGP):
 
         SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC())
 
-    def _getstate(self):
-        return SparseGP._getstate(self)
-
-    def _setstate(self, state):
-        return SparseGP._setstate(self, state)
-
-
-
 class SparseGPRegressionUncertainInput(SparseGP):
     """
     Gaussian Process model for regression with Gaussian variance on the inputs (X_variance)

GPy/models/sparse_gplvm.py

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

GPy/models/svigp_regression.py

@@ -43,10 +43,3 @@ class SVIGPRegression(SVIGP):
         SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
         self.load_batch()
 
-    def _getstate(self):
-        return GPBase._getstate(self)
-
-
-    def _setstate(self, state):
-        return GPBase._setstate(self, state)
-

GPy/models/warped_gp.py

@@ -30,14 +30,6 @@ class WarpedGP(GP):
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self._set_params(self._get_params())
 
-    def _getstate(self):
-        return GP._getstate(self)
-
-
-    def _setstate(self, state):
-        return GP._setstate(self, state)
-
-
     def _scale_data(self, Y):
         self._Ymax = Y.max()
         self._Ymin = Y.min()