From 7190e0e6bb4f3e4aebcab8ce9360b2f1cbe3aa04 Mon Sep 17 00:00:00 2001 From: James Hensman Date: Thu, 24 Oct 2013 22:13:52 +0100 Subject: [PATCH] general tidying in models --- GPy/models/bayesian_gplvm.py | 25 ++++++++++--------- GPy/models/bcgplvm.py | 2 +- GPy/models/gp_regression.py | 2 -- GPy/models/gplvm.py | 16 ++++++------ GPy/models/mrd.py | 47 ++++++++++++++++++------------------ 5 files changed, 47 insertions(+), 45 deletions(-) diff --git a/GPy/models/bayesian_gplvm.py b/GPy/models/bayesian_gplvm.py index d4d29711..21b46a8a 100644 --- a/GPy/models/bayesian_gplvm.py +++ b/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 diff --git a/GPy/models/bcgplvm.py b/GPy/models/bcgplvm.py index 9f5866c3..92db6953 100644 --- a/GPy/models/bcgplvm.py +++ b/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): diff --git a/GPy/models/gp_regression.py b/GPy/models/gp_regression.py index 86e1f7de..1644b661 100644 --- a/GPy/models/gp_regression.py +++ b/GPy/models/gp_regression.py @@ -39,5 +39,3 @@ class GPRegression(GP): def setstate(self, state): return GP.setstate(self, state) - - pass diff --git a/GPy/models/gplvm.py b/GPy/models/gplvm.py index ad78d51f..795389a7 100644 --- a/GPy/models/gplvm.py +++ b/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) + + diff --git a/GPy/models/mrd.py b/GPy/models/mrd.py index 1435028f..2aaa731c 100644 --- a/GPy/models/mrd.py +++ b/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) + +