Skip to main content

Browse the archive

https://github.com/GPflow/GPflow
21 November 2022, 03:07:34 UTC
Revision bb08f22e337d1487b8d9ab9944d8b9f7fff853ff authored by Vincent Dutordoir on 18 June 2018, 17:04:06 UTC, committed by Artem Artemev on 18 June 2018, 17:04:06 UTC 
Multi-output conditionals (#724)
 
* Introduction of MultiOutputFeatures (Mof) and MultiOutputKernels (Mok).
These are used to specify a particular setup of multi-output correlation.

* Multiple-dispatch for conditional. This allows GPflow to select the most efficient conditional code depending on your choice of Mof and Mok.

* Multiple-dispatch for Kuu and Kuf. Previously Kuu(.) and Kuf(.) were member functions of the feature class. This became cumbersome as the calculation of Kuu and Kuf also depends on the kernel used. In line with conditional we now also use multiple-dispatch to calculate Kuu and Kuf for a particular combination of Mok and Mof.

* The actual maths to efficiently calculate the output-correlated conditional (credits to @markvdw )

* sample_conditional function that makes sure that the most efficient code is used to get a sample from the conditional distribution.

* Minor: we updated a couple of models to use the new multi-output conditional.
1 parent 6baeb43
Raw File
Tip revision: bb08f22e337d1487b8d9ab9944d8b9f7fff853ff authored by Vincent Dutordoir on 18 June 2018, 17:04:06 UTC
Multi-output conditionals (#724)
Tip revision: bb08f22
Dockerfile 
# Copyright 2016 The GPflow authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

#USAGE:
#To run from prebuilt version use:
#docker run -it -p 8888:8888 gpflow/gpflow

#To replicate build use:
#docker build -t gpflow/gpflow .
#Assumes you are running from within cloned repo.

#Uses official Tensorflow docker for cpu only.
FROM tensorflow/tensorflow:1.0.0
COPY ./ /usr/local/GPflow/
RUN cd /usr/local/GPflow && \
    python setup.py develop && \
    rm /notebooks/*  && \
    apt-get clean  && \
    rm -rf /var/lib/apt/lists/*
COPY doc/source/notebooks/ LICENSE README.md /notebooks/
back to top