From d26c02e1b1869df6fa507dda3f46a2de9708390d Mon Sep 17 00:00:00 2001 From: Zhenwen Dai Date: Thu, 23 Nov 2017 10:55:43 +0000 Subject: [PATCH] remove non-ascii characters --- GPy/models/gp_multiout_regression.py | 2 +- GPy/models/gp_multiout_regression_md.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/GPy/models/gp_multiout_regression.py b/GPy/models/gp_multiout_regression.py index aa1cf965..1660297d 100644 --- a/GPy/models/gp_multiout_regression.py +++ b/GPy/models/gp_multiout_regression.py @@ -17,7 +17,7 @@ class GPMultioutRegression(SparseGP): This is an implementation of Latent Variable Multiple Output Gaussian Processes (LVMOGP) in [Dai et al. 2017]. - Zhenwen Dai, Mauricio A. Álvarez and Neil D. Lawrence. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. In NIPS, 2017. + Zhenwen Dai, Mauricio A. Alvarez and Neil D. Lawrence. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. In NIPS, 2017. :param X: input observations. Numpy.ndarray :param Y: output observations, each column corresponding to an output dimension. Numpy.ndarray diff --git a/GPy/models/gp_multiout_regression_md.py b/GPy/models/gp_multiout_regression_md.py index 5fa53038..ba28ff6f 100644 --- a/GPy/models/gp_multiout_regression_md.py +++ b/GPy/models/gp_multiout_regression_md.py @@ -18,7 +18,7 @@ class GPMultioutRegressionMD(SparseGP): This is an implementation of Latent Variable Multiple Output Gaussian Processes (LVMOGP) in [Dai et al. 2017]. This model targets at the use case, in which each output dimension is observed at a different set of inputs. The model takes a different data format: the inputs and outputs observations of all the output dimensions are stacked together correspondingly into two matrices. An extra array is used to indicate the index of output dimension for each data point. The output dimensions are indexed using integers from 0 to D-1 assuming there are D output dimensions. - Zhenwen Dai, Mauricio A. Álvarez and Neil D. Lawrence. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. In NIPS, 2017. + Zhenwen Dai, Mauricio A. Alvarez and Neil D. Lawrence. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. In NIPS, 2017. :param X: input observations. Numpy.ndarray :param Y: output observations, each column corresponding to an output dimension. Numpy.ndarray