From 7871af8dec93070e0e9db542b35cc499e3a3defa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Masha=20Naslidnyk=20=F0=9F=A6=89?= Date: Fri, 10 Jan 2020 12:42:03 +0000 Subject: [PATCH] self.num_data and self.input_dim are set dynamically in class GP() after the shape of X. In MRD, the user-specific values are passed around until X is defined. --- GPy/core/gp.py | 11 ++++++++--- GPy/models/mrd.py | 24 ++++++++++-------------- 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/GPy/core/gp.py b/GPy/core/gp.py index d8f1e758..0f6b2a01 100644 --- a/GPy/core/gp.py +++ b/GPy/core/gp.py @@ -43,8 +43,6 @@ class GP(Model): self.X = X.copy() else: self.X = ObsAr(X) - self.num_data, self.input_dim = self.X.shape - assert Y.ndim == 2 logger.info("initializing Y") @@ -199,6 +197,14 @@ class GP(Model): def _predictive_variable(self): return self.X + @property + def num_data(self): + return self.X.shape[0] + + @property + def input_dim(self): + return self.X.shape[1] + def set_XY(self, X=None, Y=None): """ Set the input / output data of the model @@ -236,7 +242,6 @@ class GP(Model): else: self.X = ObsAr(X) - self.num_data, self.input_dim = self.X.shape self.update_model(True) def set_X(self,X): diff --git a/GPy/models/mrd.py b/GPy/models/mrd.py index 4239f7a9..3c8cd4d1 100644 --- a/GPy/models/mrd.py +++ b/GPy/models/mrd.py @@ -63,7 +63,6 @@ class MRD(BayesianGPLVMMiniBatch): Ynames=None, normalizer=False, stochastic=False, batchsize=10): self.logger = logging.getLogger(self.__class__.__name__) - self.input_dim = input_dim self.num_inducing = num_inducing if isinstance(Ylist, dict): @@ -87,11 +86,11 @@ class MRD(BayesianGPLVMMiniBatch): self.inference_method = inference_method if X is None: - X, fracs = self._init_X(initx, Ylist) + X, fracs = self._init_X(input_dim, initx, Ylist) else: fracs = [X.var(0)]*len(Ylist) - Z = self._init_Z(initz, X) + Z = self._init_Z(initz, X, input_dim) self.Z = Param('inducing inputs', Z) self.num_inducing = self.Z.shape[0] # ensure M==N if M>N @@ -128,7 +127,6 @@ class MRD(BayesianGPLVMMiniBatch): self.unlink_parameter(self.likelihood) self.unlink_parameter(self.kern) - self.num_data = Ylist[0].shape[0] if isinstance(batchsize, int): batchsize = itertools.repeat(batchsize) @@ -187,32 +185,32 @@ class MRD(BayesianGPLVMMiniBatch): def log_likelihood(self): return self._log_marginal_likelihood - def _init_X(self, init='PCA', Ylist=None): + def _init_X(self, input_dim, init='PCA', Ylist=None): if Ylist is None: Ylist = self.Ylist if init in "PCA_concat": - X, fracs = initialize_latent('PCA', self.input_dim, np.hstack(Ylist)) + X, fracs = initialize_latent('PCA', input_dim, np.hstack(Ylist)) fracs = [fracs]*len(Ylist) elif init in "PCA_single": - X = np.zeros((Ylist[0].shape[0], self.input_dim)) - fracs = np.empty((len(Ylist), self.input_dim)) - for qs, Y in zip(np.array_split(np.arange(self.input_dim), len(Ylist)), Ylist): + X = np.zeros((Ylist[0].shape[0], input_dim)) + fracs = np.empty((len(Ylist), input_dim)) + for qs, Y in zip(np.array_split(np.arange(input_dim), len(Ylist)), Ylist): x, frcs = initialize_latent('PCA', len(qs), Y) X[:, qs] = x fracs[:, qs] = frcs else: # init == 'random': - X = np.random.randn(Ylist[0].shape[0], self.input_dim) + X = np.random.randn(Ylist[0].shape[0], input_dim) fracs = X.var(0) fracs = [fracs]*len(Ylist) X -= X.mean() X /= X.std() return X, fracs - def _init_Z(self, init, X): + def _init_Z(self, init, X, input_dim): if init in "permute": Z = np.random.permutation(X.copy())[:self.num_inducing] elif init in "random": - Z = np.random.randn(self.num_inducing, self.input_dim) * X.var() + Z = np.random.randn(self.num_inducing, input_dim) * X.var() return Z def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None, Yindex=0): @@ -350,5 +348,3 @@ class MRD(BayesianGPLVMMiniBatch): print('# Private dimensions model ' + str(i) + ':' + str(privateDims[i])) return sharedDims, privateDims - -