--- a/EDeN_test.xml	Thu Sep 05 12:52:45 2013 -0400
+++ b/EDeN_test.xml	Tue Oct 29 11:07:49 2013 -0400
@@ -1,15 +1,31 @@
 <tool id="bg_eden_test" name="EDeN Test" version="0.1">
     <description></description>
-    <requirements>
-    </requirements>
+    <macros>
+        <import>eden_macros.xml</import>
+    </macros>
+    <expand macro="requirements" />
     <command>
+        tmp_dir=`mktemp -d -u`;
         EDeN --action TEST
 
-        --input_data_file_name $sparse_vector_infile
+        --input_data_file_name  $sparse_vector_infile
+        --file_type "SPARSE_VECTOR"
+        --binary_file_type
+
         --model_file_name $model_infile
 
-        --file_type "SPARSE_VECTOR"
-        --binary_file_type
+        @kernel_type_options@
+
+        --graph_type $graph_type
+
+        @normalization_kernel_hash_radius_dist_vertex@
+
+        --output_directory_path \$tmp_dir
+        --minimal_output 
+
+        ;
+        cp \$tmp_dir/prediction $output;
+        rm \$tmp_dir -rf
 
     </command>
     <inputs>
@@ -17,6 +33,12 @@
         <param format="txt" name="model_infile" type="data" label="Input Model" 
             help="created with the EDeN Train program"/>
 
+        <expand macro="kernel_type_options" />
+
+        <expand macro="graph_types" />
+
+        <expand macro="normalization_kernel_hash_radius_dist_vertex" />
+
     </inputs>
     <outputs>
         <data format="tabular" name="output" label="Generated from ${on_string}"/>
@@ -35,7 +57,7 @@
 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@
 
     </help>
 </tool>