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Posted to commits@opennlp.apache.org by jo...@apache.org on 2014/01/02 16:13:27 UTC

svn commit: r1554826 - /opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java

Author: joern
Date: Thu Jan  2 15:13:27 2014
New Revision: 1554826

URL: http://svn.apache.org/r1554826
Log:
OPENNLP-624 Removed test code

Modified:
    opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java

Modified: opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java
URL: http://svn.apache.org/viewvc/opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java?rev=1554826&r1=1554825&r2=1554826&view=diff
==============================================================================
--- opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java (original)
+++ opennlp/addons/liblinear-addon/src/main/java/LiblinearTrainer.java Thu Jan  2 15:13:27 2014
@@ -194,40 +194,4 @@ public class LiblinearTrainer extends Ab
   public boolean isSortAndMerge() {
     return true;
   }
-
-  public static void main(String[] args) throws Exception {
-
-    File file = File.createTempFile("svm", "test");
-    file.deleteOnExit();
-
-    Collection<String> lines = new ArrayList<String>();
-    lines.add("1 1:1 3:1 4:1 6:1");
-    lines.add("2 2:1 3:1 5:1 7:1");
-    lines.add("1 3:1 5:1");
-    lines.add("1 1:1 4:1 7:1");
-    lines.add("2 4:1 5:1 7:1");
-    lines.add("1 1:1 4:1 7:1");
-    lines.add("2 4:1 5:1 7:1");
-
-    BufferedWriter writer = new BufferedWriter(new FileWriter(file));
-    try {
-      for (String line : lines)
-        writer.append(line).append("\n");
-    } finally {
-      writer.close();
-    }
-
-    Train train = new Train();
-
-    Problem problem = train.readProblem(file, 0d);
-
-    Model model = Linear.train(problem, new Parameter(SolverType.L1R_LR, 10d,
-        0.02d));
-    
-    double result = Linear.predict(model, new Feature[]{new FeatureNode(4, 1d), new FeatureNode(1, 1d)});
-    double outcomes[] = new double[2];
-    double result2 = Linear.predictProbability(model, new Feature[]{new FeatureNode(4, 1d), new FeatureNode(1, 1d)}, outcomes);
-
-    System.out.println(result);
-  }
 }