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Commit 36df4a48 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Updated gitlab and readme

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# run the test suite
tests:
image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/multiview-machine-learning-omis/ubuntu:18.04
image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/summit/ubuntu:18.04
tags:
- docker
script:
......@@ -9,7 +9,7 @@ tests:
# generate the documentation
doc:
image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/multiview-machine-learning-omis/ubuntu:18.04
image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/summit/ubuntu:18.04
tags:
- docker
only:
......@@ -30,7 +30,7 @@ doc:
# project public
pages:
image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/multiview-machine-learning-omis/ubuntu:18.04
image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/summit/ubuntu:18.04
tags:
- docker
only:
......
......@@ -71,6 +71,7 @@ For further information about classifier-specific arguments, see the [documentat
In order to start a benchmark on your own dataset, you need to format it so SuMMIT can use it.
##### If you already have an HDF5 dataset file it must be formatted as :
* One dataset for each view called `ViewI` with `I` being the view index with 2 attribures :
* `attrs["name"]` a string for the name of the view
* `attrs["sparse"]` a boolean specifying whether the view is sparse or not (WIP)
......@@ -79,6 +80,7 @@ In order to start a benchmark on your own dataset, you need to format it so SuMM
* One dataset for the labels called `Labels` with one attribute :
* `attrs["names"]` a list of strings encoded in utf-8 naming the labels in the right order
* One group for the additional data called `Metadata` containing at least 1 dataset :
* `"example_ids"`, a numpy array of type `S100`, with the ids of the examples in the right order
* And three attributes :
......
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