From c76f1a4d6dcc70115e0eff4520f8257f395292a9 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Tue, 11 Feb 2014 14:06:42 +0000 Subject: [PATCH 1/2] Minor reorganising --- GPy/core/parameterization/param.py | 8 ++++---- GPy/inference/latent_function_inference/laplace.py | 5 ++--- GPy/testing/likelihood_tests.py | 4 ++-- 3 files changed, 8 insertions(+), 9 deletions(-) diff --git a/GPy/core/parameterization/param.py b/GPy/core/parameterization/param.py index 583c6425..772cbd42 100644 --- a/GPy/core/parameterization/param.py +++ b/GPy/core/parameterization/param.py @@ -157,14 +157,14 @@ class Param(ObservableArray, Constrainable, Gradcheckable): #=========================================================================== def tie_to(self, param): """ - :param param: the parameter object to tie this parameter to. + :param param: the parameter object to tie this parameter to. Can be ParamConcatenation (retrieved by regexp search) - + Tie this parameter to the given parameter. Broadcasting is not allowed, but you can tie a whole dimension to one parameter: self[:,0].tie_to(other), where other is a one-value parameter. - + Note: For now only one parameter can have ties, so all of a parameter will be removed, when re-tieing! """ @@ -534,7 +534,7 @@ class ParamConcatenation(object): def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3): return self.params[0]._highest_parent_._checkgrad(self, verbose, step, tolerance) #checkgrad.__doc__ = Gradcheckable.checkgrad.__doc__ - + __lt__ = lambda self, val: self._vals() < val __le__ = lambda self, val: self._vals() <= val __eq__ = lambda self, val: self._vals() == val diff --git a/GPy/inference/latent_function_inference/laplace.py b/GPy/inference/latent_function_inference/laplace.py index bc81a86a..4edb9a1d 100644 --- a/GPy/inference/latent_function_inference/laplace.py +++ b/GPy/inference/latent_function_inference/laplace.py @@ -92,12 +92,11 @@ class LaplaceInference(object): iteration = 0 while difference > self._mode_finding_tolerance and iteration < self._mode_finding_max_iter: W = -likelihood.d2logpdf_df2(f, Y, extra_data=Y_metadata) - - W_f = W*f grad = likelihood.dlogpdf_df(f, Y, extra_data=Y_metadata) + W_f = W*f + b = W_f + grad # R+W p46 line 6. - #W12BiW12Kb, B_logdet = self._compute_B_statistics(K, W.copy(), np.dot(K, b), likelihood.log_concave) W12BiW12, _, _ = self._compute_B_statistics(K, W, likelihood.log_concave) W12BiW12Kb = np.dot(W12BiW12, np.dot(K, b)) diff --git a/GPy/testing/likelihood_tests.py b/GPy/testing/likelihood_tests.py index 7f48ac95..9920d648 100644 --- a/GPy/testing/likelihood_tests.py +++ b/GPy/testing/likelihood_tests.py @@ -1,10 +1,10 @@ import numpy as np import unittest import GPy -from GPy.models import GradientChecker +from ..models import GradientChecker import functools import inspect -from GPy.likelihoods import link_functions +from ..likelihoods import link_functions from ..core.parameterization import Param from functools import partial #np.random.seed(300) From 610dbed38a313303946696c4c54b6770b100127b Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Tue, 11 Feb 2014 15:23:00 +0000 Subject: [PATCH 2/2] Fixed copy bug of observable array --- GPy/core/parameterization/array_core.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/GPy/core/parameterization/array_core.py b/GPy/core/parameterization/array_core.py index 4b5b7700..fe2a951e 100644 --- a/GPy/core/parameterization/array_core.py +++ b/GPy/core/parameterization/array_core.py @@ -25,7 +25,7 @@ class ParamList(list): if el is other: return True return False - + pass class C(np.ndarray): __array_priority__ = 1. @@ -80,8 +80,8 @@ class ObservableArray(ListArray, Observable): def __getslice__(self, start, stop): return self.__getitem__(slice(start, stop)) def __setslice__(self, start, stop, val): - return self.__setitem__(slice(start, stop), val) + return self.__setitem__(slice(start, stop), val) def __copy__(self, *args): - return ObservableArray(self.base.base.copy(*args)) + return ObservableArray(self.view(np.ndarray).copy()) def copy(self, *args): return self.__copy__(*args)