|
0
|
1 <tool id="bg_eden_test" name="EDeN Test" version="0.1">
|
|
|
2 <description></description>
|
|
|
3 <requirements>
|
|
|
4 </requirements>
|
|
|
5 <command>
|
|
|
6 EDeN --action TEST
|
|
2
|
7
|
|
|
8 --input_data_file_name $sparse_vector_infile
|
|
|
9 --model_file_name $model_infile
|
|
0
|
10
|
|
|
11 --file_type "SPARSE_VECTOR"
|
|
|
12 --binary_file_type
|
|
|
13
|
|
|
14 </command>
|
|
|
15 <inputs>
|
|
2
|
16 <param format="eden_sparse_vector" name="sparse_vector_infile" type="data" label="Input File" help=""/>
|
|
|
17 <param format="txt" name="model_infile" type="data" label="Input Model"
|
|
|
18 help="created with the EDeN Train program"/>
|
|
0
|
19
|
|
|
20 </inputs>
|
|
|
21 <outputs>
|
|
2
|
22 <data format="tabular" name="output" label="Generated from ${on_string}"/>
|
|
0
|
23 </outputs>
|
|
|
24 <tests>
|
|
|
25 <test>
|
|
|
26 </test>
|
|
|
27 </tests>
|
|
|
28 <help>
|
|
|
29
|
|
|
30 .. class:: infomark
|
|
|
31
|
|
|
32 **What it does**
|
|
|
33
|
|
|
34 The linear model is induced using the accelerated stochastic gradient descent technique by Léon Bottou and Yann LeCun.
|
|
|
35 When the target information is 0, a self-training algorithm is used to impute a positive or negative class to the unsupervised instances.
|
|
|
36 If the target information is imbalanced a minority class resampling technique is used to rebalance the training set.
|
|
|
37
|
|
|
38 This tool is part of the EDeN (Explicit Decomposition with Neighborhoods) suite, developed by Fabrizio Costa.
|
|
|
39
|
|
|
40 </help>
|
|
|
41 </tool>
|
