From 4d00b9db039e97fd61c5951b305ea3b8e8ce3b87 Mon Sep 17 00:00:00 2001 From: James Hensman Date: Thu, 13 Mar 2014 10:23:07 +0000 Subject: [PATCH] import not relative in tests --- GPy/kern/_src/independent_outputs.py | 14 +++++++------- GPy/testing/likelihood_tests.py | 6 +++--- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/GPy/kern/_src/independent_outputs.py b/GPy/kern/_src/independent_outputs.py index 73a8c585..0cbd5be4 100644 --- a/GPy/kern/_src/independent_outputs.py +++ b/GPy/kern/_src/independent_outputs.py @@ -73,7 +73,7 @@ class IndependentOutputs(Kern): slices = index_to_slices(X[:,self.index_dim]) if X2 is None: - [[collate_grads(dL_dK[s,s], X[s], None) for s in slices_i] for slices_i in slices] + [[collate_grads(dL_dK[s,ss], X[s], X[ss]) for s,ss in itertools.product(slices_i, slices_i)] for slices_i in slices] else: slices2 = index_to_slices(X2[:,self.index_dim]) [[[collate_grads(dL_dK[s,s2],X[s],X2[s2]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)] @@ -83,10 +83,10 @@ class IndependentOutputs(Kern): target = np.zeros_like(X) slices = index_to_slices(X[:,self.index_dim]) if X2 is None: - [[np.copyto(target[s,:-1], self.kern.gradients_X(dL_dK[s,s],X[s],None)) for s in slices_i] for slices_i in slices] + [[np.copyto(target[s,self.kern.active_dims], self.kern.gradients_X(dL_dK[s,s],X[s],X[ss])) for s, ss in product(slices_i, slices_i)] for slices_i in slices] else: - X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1]) - [[[np.copyto(target[s,:-1], self.kern.gradients_X(dL_dK[s,s2], X[s], X2[s2])) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)] + X2,slices2 = X2[:,:self.index_dim],index_to_slices(X2[:,-1]) + [[[np.copyto(target[s,:self.index_dim], self.kern.gradients_X(dL_dK[s,s2], X[s], X2[s2])) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)] return target def gradients_X_diag(self, dL_dKdiag, X): @@ -95,12 +95,12 @@ class IndependentOutputs(Kern): [[np.copyto(target[s,:-1], self.kern.gradients_X_diag(dL_dKdiag[s],X[s])) for s in slices_i] for slices_i in slices] return target - def update_gradients_diag(self,dL_dKdiag,X,target): + def update_gradients_diag(self, dL_dKdiag, X): target = np.zeros(self.kern.size) def collate_grads(dL, X): self.kern.update_gradients_diag(dL,X) - self.target += self.kern.gradient - X,slices = X[:,:-1],index_to_slices(X[:,-1]) + target[:] += self.kern.gradient + slices = index_to_slices(X[:,self.index_dim]) [[collate_grads(dL_dKdiag[s], X[s,:]) for s in slices_i] for slices_i in slices] self.kern.gradient = target diff --git a/GPy/testing/likelihood_tests.py b/GPy/testing/likelihood_tests.py index c71842d8..d55b0190 100644 --- a/GPy/testing/likelihood_tests.py +++ b/GPy/testing/likelihood_tests.py @@ -1,11 +1,11 @@ import numpy as np import unittest import GPy -from ..models import GradientChecker +from GPy.models import GradientChecker import functools import inspect -from ..likelihoods import link_functions -from ..core.parameterization import Param +from GPy.likelihoods import link_functions +from GPy.core.parameterization import Param from functools import partial #np.random.seed(300) #np.random.seed(7)