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1 <tool id="bg_eden_test" name="EDeN Test" version="0.1">
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2 <description></description>
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7
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3 <macros>
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4 <import>eden_macros.xml</import>
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5 </macros>
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6 <expand macro="requirements" />
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7 <command>
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8 EDeN --action TEST
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2
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9
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10 --input_data_file_name $sparse_vector_infile
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11 --file_type "SPARSE_VECTOR"
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12 --binary_file_type
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13
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14 --model_file_name $model_infile
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15
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16 --minimal_output
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17
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18 </command>
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19 <inputs>
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20 <param format="eden_sparse_vector" name="sparse_vector_infile" type="data" label="Input File" help="Sparse Vector file, created with EDeN convert." />
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21 <param format="txt" name="model_infile" type="data" label="Input Model"
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22 help="Created with EDeN Train."/>
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23
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24 <expand macro="kernel_type_options" />
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25
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26 <expand macro="graph_types" />
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27
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28 <expand macro="normalization_kernel_hash_radius_dist_vertex" />
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29
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30 </inputs>
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31 <outputs>
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32 <data format="tabular" name="prediction" from_work_dir="prediction" label="EDeN on ${on_string}: Prediction"/>
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33 <data format="tabular" name="margin" from_work_dir="margin" label="EDeN on ${on_string}: Margin"/>
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34 </outputs>
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35 <tests>
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36 <test>
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37 </test>
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38 </tests>
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39 <help>
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40
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41 .. class:: infomark
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42
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43 **What it does**
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44
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45 The linear model is induced using the accelerated stochastic gradient descent technique by Léon Bottou and Yann LeCun.
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46 When the target information is 0, a self-training algorithm is used to impute a positive or negative class to the unsupervised instances.
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47 If the target information is imbalanced a minority class resampling technique is used to rebalance the training set.
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48
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49 @references@
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50
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51 </help>
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52 </tool>
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