view EDeN_test.xml @ 6:7d49e315cb95 draft

Uploaded

<tool id="bg_eden_test" name="EDeN Test" version="0.1">
    <description></description>
    <requirements>
    </requirements>
    <command>
        EDeN --action TEST

        --input_data_file_name $sparse_vector_infile
        --model_file_name $model_infile

        --file_type "SPARSE_VECTOR"
        --binary_file_type

    </command>
    <inputs>
        <param format="eden_sparse_vector" name="sparse_vector_infile" type="data" label="Input File" help=""/>
        <param format="txt" name="model_infile" type="data" label="Input Model" 
            help="created with the EDeN Train program"/>

    </inputs>
    <outputs>
        <data format="tabular" name="output" label="Generated from ${on_string}"/>
    </outputs>
    <tests>
        <test>
        </test>
    </tests>
    <help>

.. class:: infomark

**What it does** 

The linear model is induced using the accelerated stochastic gradient descent technique by Léon Bottou and Yann LeCun.
When the target information is 0, a self-training algorithm is used to impute a positive or negative class to the unsupervised instances.
If the target information is imbalanced a minority class resampling technique is used to rebalance the training set.

This tool is part of the EDeN (Explicit Decomposition with Neighborhoods) suite, developed by Fabrizio Costa.

    </help>
</tool>