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comparison NSPDK_sparseVect.xml @ 2:062be03c293f draft

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planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/branchForIterations/tools/GraphClust/NSPDK commit f971832d2b34a182314e5201ea6895dd207c5923
author rnateam
date Mon, 13 Mar 2017 17:56:58 -0400
parents e772ceb48396
children 2a7f99a14b5f
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1:e772ceb48396 2:062be03c293f
1 <tool id="nspdk_sparse" name="NSPDK_sparseVect" version="9.2"> 1 <tool id="nspdk_sparse" name="NSPDK_sparseVect" version="9.2">
2 <requirements> 2 <requirements>
3 <requirement type="package" version="0.1.7">graphclust-wrappers</requirement> 3 <requirement type="package" version="0.1.10">graphclust-wrappers</requirement>
4 <requirement type="package" version="9.2">nspdk</requirement> 4 <requirement type="package" version="9.2">nspdk</requirement>
5 </requirements> 5 </requirements>
6 <stdio> 6 <stdio>
7 <exit_code range="1:" /> 7 <exit_code range="1:" />
8 </stdio> 8 </stdio>
9 <command> 9 <command>
10 <![CDATA[ 10 <![CDATA[
11 11
12 12
13 'NSPDK_sparseVect.pl' '$data_fasta' '$gspan_file' $max_rad $max_dist_relations 13 NSPDK_sparseVect.pl
14 '$data_fasta'
15 '$gspan_file'
16 $max_rad
17 $max_dist_relations
14 18
15 ]]> 19 ]]>
16 </command> 20 </command>
17 <inputs> 21 <inputs>
18 <param type="data" name="gspan_file" format="searchgui_archive" /> 22 <param type="data" name="gspan_file" />
19 <param type="data" name="data_fasta" format="fasta" /> 23 <param type="data" name="data_fasta" format="fasta" />
20 <param name="max_rad" type="integer" value="3" size="5" label="maximum radius " help="-R"/> 24 <param name="max_rad" type="integer" value="3" label="maximum radius " help="-R"/>
21 <param name="max_dist_relations" type="integer" value="3" size="5" label="maximum distance relations" help="-D"/> 25 <param name="max_dist_relations" type="integer" value="3" label="maximum distance relations" help="-D"/>
22 </inputs> 26 </inputs>
23 <outputs> 27 <outputs>
24 <data name="data_svector" format="zip" from_work_dir="SVECTOR/data.svector" label="data_svector"/> 28 <data name="data_svector" format="zip" from_work_dir="SVECTOR/data.svector" label="data_svector"/>
25 </outputs> 29 </outputs>
26 <tests> 30 <tests>
27 <test> 31 <test>
28 <param name="data_fasta" value="data.fasta"/> 32 <param name="data_fasta" value="data.fasta"/>
29 <param name="gspan_file" value="1.group.gspan.bz2" ftype="searchgui_archive"/> 33 <param name="gspan_file" value="1.group.gspan.bz2" ftype="searchgui_archive"/>
30 <param name="max_rad" value="3"/> 34 <param name="max_rad" value="3"/>
31 <param name="max_dist_relations" value="3"/> 35 <param name="max_dist_relations" value="3"/>
32 <output name="data_svector" file="SVECTOR/data.svector" ftype="zip" /> 36 <output name="data_svector" file="SVECTOR/data.svector" ftype="zip" />
33 </test> 37 </test>
34 </tests> 38 </tests>
35 <help> 39 <help>
36 <![CDATA[ 40 <![CDATA[
37 41
38 **What it does** 42 **What it does**
39 43
40 Produces an explicit sparse feature encoding. 44 Produces an explicit sparse feature encoding.
41 Integer code for the invariant graph encoding is used as a feature indicator. In this way, 45 Integer code for the invariant graph encoding is used as a feature indicator. In this way,
43 roots are at distance d) can be interpreted as the feature key and the (normalized) count of occurrences as its value. 47 roots are at distance d) can be interpreted as the feature key and the (normalized) count of occurrences as its value.
44 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). 48 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).
45 49
46 **Parameters** 50 **Parameters**
47 51
48 + **-R** <max radius> (default: 1) 52 + **-R** <max radius> (default: 1)
49 + **-D** <max distance relations> (default: 4) 53 + **-D** <max distance relations> (default: 4)
50 54
51 55
52 ]]> 56 ]]>
53 </help> 57 </help>
54 <citations> 58 <citations>
55 <citation type="doi">10.1093/bioinformatics/bts224</citation> 59 <citation type="doi">10.1093/bioinformatics/bts224</citation>
56 <citation type="bibtex">@inproceedings{costa2010fast, 60 <citation type="bibtex">@inproceedings{costa2010fast,
57 title={Fast neighborhood subgraph pairwise distance kernel}, 61 title={Fast neighborhood subgraph pairwise distance kernel},
58 author={Costa, Fabrizio and De Grave, Kurt}, 62 author={Costa, Fabrizio and De Grave, Kurt},
59 booktitle={Proceedings of the 26th International Conference on Machine Learning}, 63 booktitle={Proceedings of the 26th International Conference on Machine Learning},
60 pages={255--262}, 64 pages={255--262},
61 year={2010}, 65 year={2010},
62 organization={Omnipress} 66 organization={Omnipress}
63 } 67 }
64 </citation> 68 </citation>
65 </citations> 69 </citations>
66 </tool> 70 </tool>