--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/EDeN_train.xml	Wed Sep 04 05:10:04 2013 -0400
@@ -0,0 +1,87 @@
+<tool id="bg_eden_train" name="EDeN Train" version="0.1">
+    <description></description>
+    <requirements>
+    </requirements>
+    <command>
+        EDeN --action TRAIN
+
+        --input_data_file_name $infile
+        --file_type "SPARSE_VECTOR"
+        --binary_file_type
+
+        ## TODO: we need a tool that creates such a file, maybe from the metadata of an SDF file
+        ## target_file_name is a file with 1 or -1 one in each row, indicating the class
+        --target_file_name $target_infile
+        --model_file_name $model_outfile
+
+        --lambda $lambda ##??? notation?
+        --epochs $epoch
+        
+        --sparsification_num_iterations $sparsification_num_iterations
+        --topological_regularization_num_neighbors $topological_regularization_num_neighbors
+        --topological_regularization_decay_rate $topological_regularization_decay_rate
+
+        --num_iterations $num_iterations
+        --threshold $threshold
+        --only_positive $only_positive
+        --only_negative $only_negative
+
+        --random_seed $random_seed
+
+    </command>
+    <inputs>
+        <param format="eden_sparse_vector" name="infile" type="data" label="Input Graph" help=""/>
+        <param format="txt" name="target_infile" type="data" label="Target file" help=""/>
+
+        <param name="kernel_type" type="select" display="radio" label="Type of the Kernel">
+            <option value="NSPDK">NSPDK</option>
+            <option value="WDK">WDK</option>
+            <option value="PBK">PBK</option>
+            <option value="USPK">USPK</option>
+            <option value="DDK">DDK</option>
+            <option value="NSDDK">ANSDDK</option>
+            <option value="SK">SK [NSPDK]</option>
+        </param>
+
+        <param name="graph_type" type="select" display="radio" label="Type of Graph">
+            <option value="DIRECTED">directed</option>
+            <option value="UNDIRECTED">undirected</option>
+        </param>
+
+        <param name="epoch" type="integer" value="10" label="Epoch, Stochastic gradient descend algorithm." help="">
+            <validator type="in_range" min="1" />
+        </param>
+        <param name="lambda" type="text" value="1e-4" label="lambda, Stochastic gradient descend algorithm." help="" />
+
+    </inputs>
+    <outputs>
+        <data format="txt" name="model_outfile" label="Train Model from ${on_string}"/>
+    </outputs>
+    <tests>
+        <test>
+            <param name="infile" value="3_molceuls.sdf" />
+            <output name="outfile" file="3_molecules.gspan" />
+        </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.
+
+
+REFERENCES
+==========
+
+The code for Stochastic Gradient Descent SVM is adapted from http://leon.bottou.org/projects/sgd. Léon Bottou and Yann LeCun, ''Large Scale Online Learning'', Advances in Neural Information Processing Systems 16, Edited by Sebastian Thrun, Lawrence Saul and Bernhard Schölkopf, MIT Press, Cambridge, MA, 2004.
+
+
+
+    </help>
+</tool>