Mercurial > repos > rnateam > graphclust_nspdk
diff NSPDK_sparseVect.xml @ 0:165fe96228be draft
planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/branchForIterations/tools/GraphClust/NSPDK commit 21aaee40723b5341b4236edeb0e72995c2054053
author | rnateam |
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date | Fri, 16 Dec 2016 07:36:07 -0500 |
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children | 90a4a2e7d876 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/NSPDK_sparseVect.xml Fri Dec 16 07:36:07 2016 -0500 @@ -0,0 +1,66 @@ +<tool id="nspdk_sparse" name="NSPDK_sparseVect" version="9.2"> + <requirements> + <requirement type="package" version="0.1">graphclust-wrappers</requirement> + <requirement type="package" version="9.2">nspdk</requirement> + </requirements> + <stdio> + <exit_code range="1:" /> + </stdio> + <command> + <![CDATA[ + + + 'NSPDK_sparseVect.pl' '$data_fasta' '$gspan_file' $max_rad $max_dist_relations + +]]> + </command> + <inputs> + <param type="data" name="gspan_file" format="searchgui_archive" /> + <param type="data" name="data_fasta" format="fasta" /> + <param name="max_rad" type="integer" value="3" size="5" label="maximum radius " help="-R"/> + <param name="max_dist_relations" type="integer" value="3" size="5" label="maximum distance relations" help="-D"/> + </inputs> + <outputs> + <data name="data_svector" format="zip" from_work_dir="SVECTOR/data.svector" label="data_svector"/> + </outputs> + <tests> + <test> + <param name="data_fasta" value="data.fasta"/> + <param name="gspan_file" value="1.group.gspan.bz2" ftype="searchgui_archive"/> + <param name="max_rad" value="3"/> + <param name="max_dist_relations" value="3"/> + <output name="data_svector" file="SVECTOR/data.svector" ftype="zip" /> + </test> + </tests> + <help> + <![CDATA[ + +**What it does** + +Produces an explicit sparse feature encoding. +Integer code for the invariant graph encoding is used as a feature indicator. In this way, +the integer associated to each feature (i.e. each pair or neighborhood subgraphs of radius r whose +roots are at distance d) can be interpreted as the feature key and the (normalized) count of occurrences as its value. +This allows to obtain an explicit feature encoding for a given graph G as a sparse vector in ℝ^m (with a very high dimension m). + +**Parameters** + ++ **-R** <max radius> (default: 1) ++ **-D** <max distance relations> (default: 4) + + + ]]> + </help> + <citations> + <citation type="doi">10.1093/bioinformatics/bts224</citation> + <citation type="bibtex">@inproceedings{costa2010fast, + title={Fast neighborhood subgraph pairwise distance kernel}, + author={Costa, Fabrizio and De Grave, Kurt}, + booktitle={Proceedings of the 26th International Conference on Machine Learning}, + pages={255--262}, + year={2010}, + organization={Omnipress} + } + </citation> + </citations> +</tool>