annotate NSPDK_sparseVect.xml @ 3:d10410cfd17a draft

planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/branchForIterations/tools/GraphClust/NSPDK commit 5a545fc913b3a07e1452e67f66b2ca593660abdf
author rnateam
date Mon, 27 Feb 2017 12:03:12 -0500
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1 <tool id="nspdk_sparse" name="NSPDK_sparseVect" version="9.2">
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2 <requirements>
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3 <requirement type="package" version="0.1.9">graphclust-wrappers</requirement>
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4 <requirement type="package" version="9.2">nspdk</requirement>
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5 </requirements>
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6 <stdio>
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7 <exit_code range="1:" />
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8 </stdio>
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9 <command>
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10 <![CDATA[
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165fe96228be planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/branchForIterations/tools/GraphClust/NSPDK commit 21aaee40723b5341b4236edeb0e72995c2054053
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13 'NSPDK_sparseVect.pl' '$data_fasta' '$gspan_file' $max_rad $max_dist_relations
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15 ]]>
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16 </command>
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17 <inputs>
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18 <param type="data" name="gspan_file" format="searchgui_archive" />
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19 <param type="data" name="data_fasta" format="fasta" />
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20 <param name="max_rad" type="integer" value="3" size="5" label="maximum radius " help="-R"/>
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21 <param name="max_dist_relations" type="integer" value="3" size="5" label="maximum distance relations" help="-D"/>
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22 </inputs>
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23 <outputs>
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24 <data name="data_svector" format="zip" from_work_dir="SVECTOR/data.svector" label="data_svector"/>
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25 </outputs>
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26 <tests>
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27 <test>
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28 <param name="data_fasta" value="data.fasta"/>
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29 <param name="gspan_file" value="1.group.gspan.bz2" ftype="searchgui_archive"/>
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30 <param name="max_rad" value="3"/>
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31 <param name="max_dist_relations" value="3"/>
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32 <output name="data_svector" file="SVECTOR/data.svector" ftype="zip" />
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33 </test>
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34 </tests>
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35 <help>
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36 <![CDATA[
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38 **What it does**
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39
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40 Produces an explicit sparse feature encoding.
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41 Integer code for the invariant graph encoding is used as a feature indicator. In this way,
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42 the integer associated to each feature (i.e. each pair or neighborhood subgraphs of radius r whose
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43 roots are at distance d) can be interpreted as the feature key and the (normalized) count of occurrences as its value.
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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).
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46 **Parameters**
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47
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48 + **-R** <max radius> (default: 1)
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49 + **-D** <max distance relations> (default: 4)
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50
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51
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52 ]]>
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53 </help>
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54 <citations>
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55 <citation type="doi">10.1093/bioinformatics/bts224</citation>
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56 <citation type="bibtex">@inproceedings{costa2010fast,
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57 title={Fast neighborhood subgraph pairwise distance kernel},
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58 author={Costa, Fabrizio and De Grave, Kurt},
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59 booktitle={Proceedings of the 26th International Conference on Machine Learning},
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60 pages={255--262},
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61 year={2010},
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62 organization={Omnipress}
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63 }
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64 </citation>
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65 </citations>
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66 </tool>