Added copyright headers, used new visualization framework in examples

Author: misberner

algorithms/features/src/main/java/de/learnlib/algorithms/features/observationtable/ObservationTable.java

@@ -129,7 +129,6 @@ public static interface Row<I,D> extends Iterable<D> {
 		public D getCellContent(@Nonnegative int index) throws IndexOutOfBoundsException;
 	}
 
-	
 	/**
 	 * Representation of an inconsistency in the observation table.
 	 * <p>

algorithms/ttt/src/main/java/de/learnlib/algorithms/ttt/base/BaseTTTLearner.java

@@ -528,6 +528,7 @@ private Splitter<I,D> findSplitter(DTNode<I,D> blockRoot) {
 	 * @return a counterexample uncovering an inconsistency, or {@code null}
 	 * if the hypothesis is consistent with the discrimination tree
 	 */
+	// TODO can be removed
 	private DefaultQuery<I, D> checkHypothesisConsistency() {
 		for(DTNode<I,D> leaf : dtree.getRoot().subtreeLeaves()) {
 			TTTState<I,D> state = leaf.state;

core/src/main/java/de/learnlib/nfa/NFALearner.java

@@ -1,3 +1,19 @@
+/* Copyright (C) 2015 TU Dortmund
+ * This file is part of LearnLib, http://www.learnlib.de/.
+ * 
+ * LearnLib is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License version 3.0 as published by the Free Software Foundation.
+ * 
+ * LearnLib is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Lesser General Public License for more details.
+ * 
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with LearnLib; if not, see
+ * http://www.gnu.de/documents/lgpl.en.html.
+ */
 package de.learnlib.nfa;
 
 import net.automatalib.automata.fsa.NFA;

core/src/main/java/de/learnlib/nfa/NFALearnerWrapper.java

@@ -1,3 +1,19 @@
+/* Copyright (C) 2015 TU Dortmund
+ * This file is part of LearnLib, http://www.learnlib.de/.
+ * 
+ * LearnLib is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License version 3.0 as published by the Free Software Foundation.
+ * 
+ * LearnLib is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Lesser General Public License for more details.
+ * 
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with LearnLib; if not, see
+ * http://www.gnu.de/documents/lgpl.en.html.
+ */
 package de.learnlib.nfa;
 
 import javax.annotation.Nonnull;

core/src/main/java/de/learnlib/statistics/SimpleProfiler.java

@@ -17,9 +17,9 @@
 
 package de.learnlib.statistics;
 
-import java.util.HashMap;
 import java.util.Map;
 import java.util.Map.Entry;
+import java.util.concurrent.ConcurrentHashMap;
 
 import javax.annotation.Nonnull;
 import javax.annotation.ParametersAreNonnullByDefault;
@@ -34,8 +34,8 @@ public class SimpleProfiler {
 
   private static boolean PROFILE = true;
 
-  private static final Map<String,Counter> cumulated = new HashMap<>();
-  private static final Map<String,Long> pending = new HashMap<>();
+  private static final Map<String,Counter> cumulated = new ConcurrentHashMap<>();
+  private static final Map<String,Long> pending = new ConcurrentHashMap<>();
 
   private static LearnLogger logger = LearnLogger.getLogger(SimpleProfiler.class.getName());
 

examples/src/main/java/de/learnlib/examples/example1/Example.java

@@ -17,8 +17,14 @@
 package de.learnlib.examples.example1;
 
 import java.io.IOException;
-import java.io.Writer;
 
+import net.automatalib.automata.fsa.DFA;
+import net.automatalib.automata.fsa.impl.compact.CompactDFA;
+import net.automatalib.util.automata.builders.AutomatonBuilders;
+import net.automatalib.util.graphs.dot.GraphDOT;
+import net.automatalib.visualization.Visualization;
+import net.automatalib.words.Alphabet;
+import net.automatalib.words.impl.Alphabets;
 import de.learnlib.algorithms.features.observationtable.OTUtils;
 import de.learnlib.algorithms.features.observationtable.writer.ObservationTableASCIIWriter;
 import de.learnlib.algorithms.lstargeneric.dfa.ExtensibleLStarDFA;
@@ -30,13 +36,6 @@
 import de.learnlib.oracles.SimulatorOracle.DFASimulatorOracle;
 import de.learnlib.statistics.SimpleProfiler;
 
-import net.automatalib.automata.fsa.DFA;
-import net.automatalib.automata.fsa.impl.compact.CompactDFA;
-import net.automatalib.commons.dotutil.DOT;
-import net.automatalib.util.graphs.dot.GraphDOT;
-import net.automatalib.words.Alphabet;
-import net.automatalib.words.impl.Alphabets;
-
 /**
  * This example shows the usage of a learning algorithm and an equivalence test
  * as part of an experiment in order to learn a simulated SUL (system under
@@ -55,22 +54,23 @@ private static CompactDFA<Character> constructSUL() {
         // input alphabet contains characters 'a'..'b'
     	Alphabet<Character> sigma = Alphabets.characters('a', 'b');
 
-        // create states
-        CompactDFA<Character> dfa = new CompactDFA<>(sigma);
-        int q0 = dfa.addInitialState(true);
-        int q1 = dfa.addState(false);
-        int q2 = dfa.addState(false);
-        int q3 = dfa.addState(false);
-
-        // create transitions
-        dfa.addTransition(q0, 'a', q1);
-        dfa.addTransition(q0, 'b', q2);
-        dfa.addTransition(q1, 'a', q0);
-        dfa.addTransition(q1, 'b', q3);
-        dfa.addTransition(q2, 'a', q3);
-        dfa.addTransition(q2, 'b', q0);
-        dfa.addTransition(q3, 'a', q2);
-        dfa.addTransition(q3, 'b', q1);
+    	// create automaton
+    	CompactDFA<Character> dfa = AutomatonBuilders.newDFA(sigma)
+    			.from("q0")
+    				.on('a').to("q1")
+    				.on('b').to("q2")
+    			.from("q1")
+    				.on('a').to("q0")
+    				.on('b').to("q3")
+    			.from("q2")
+    				.on('a').to("q3")
+    				.on('b').to("q0")
+    			.from("q3")
+    				.on('a').to("q2")
+    				.on('b').to("q1")
+    			.withInitial("q0")
+    			.withAccepting("q0")
+    			.create();
 
         return dfa;
     }
@@ -142,9 +142,7 @@ public static void main(String[] args) throws IOException {
         System.out.println("Model: ");
         GraphDOT.write(result, inputs, System.out); // may throw IOException!
 
-        Writer w = DOT.createDotWriter(true);
-        GraphDOT.write(result, inputs, w);
-        w.close();
+        Visualization.visualizeAutomaton(result, inputs, true);
 
         System.out.println("-------------------------------------------------------");
 

test-support/learner-it-support/src/main/java/de/learnlib/testsupport/it/learner/LearnerVariant.java

@@ -29,6 +29,17 @@ public LearnerVariant(String name, LearningAlgorithm<? extends M,I,D> learner, i
 		this.learner = learner;
 		this.maxRounds = maxRounds;
 	}
+	
+	public String getLearnerName() {
+		String learnerName = learner.toString();
+		int atPos = learnerName.lastIndexOf('@');
+		if (atPos != -1) {
+			int simpleNameStart = learnerName.lastIndexOf('.', atPos - 1) + 1;
+			learnerName = learnerName.substring(simpleNameStart, atPos);
+		}
+		
+		return learnerName;
+	}
 
 	public String getName() {
 		return name;

test-support/learner-it-support/src/main/java/de/learnlib/testsupport/it/learner/LearnerVariantListImpl.java

@@ -22,7 +22,6 @@
 import net.automatalib.automata.fsa.DFA;
 import net.automatalib.automata.transout.MealyMachine;
 import net.automatalib.words.Word;
-
 import de.learnlib.api.LearningAlgorithm;
 import de.learnlib.mealy.MealyUtil;
 

test-support/learner-it-support/src/main/java/de/learnlib/testsupport/it/learner/SingleLearnerVariantITSubCase.java

@@ -44,12 +44,14 @@ public SingleLearnerVariantITSubCase(LearnerVariant<? extends M,I,D> variant, Le
 
 	@Override
 	public String getTestName() {
-		return variant.getLearner().getClass().getSimpleName() + "[" + variant.getName() + "]/" + example.getClass().getSimpleName();
+		return variant.getLearnerName() + "[" + variant.getName() + "]/" + example.getClass().getSimpleName();
 	}
 
 	@Test
 	public void testLearning() {
-		System.out.println("Running learner integration test " + getTestName());
+		System.out.print("Running learner integration test " + getTestName() + " ... ");
+		System.out.flush();
+		
 		LearningAlgorithm<? extends M,I,D> learner
 			= variant.getLearner();
 
@@ -63,6 +65,8 @@ public void testLearning() {
 		EquivalenceOracle<? super M, I, D> eqOracle
 			= new SimulatorEQOracle<I,D>(example.getReferenceAutomaton());
 
+		long start = System.nanoTime();
+		
 		learner.startLearning();
 
 		int roundCounter = 0;
@@ -81,6 +85,10 @@ public void testLearning() {
 		Assert.assertNull(
 				Automata.findSeparatingWord(example.getReferenceAutomaton(), learner.getHypothesisModel(), alphabet),
 				"Final hypothesis does not match reference automaton");
+		
+		long duration = (System.nanoTime() - start)/1000000L;
+		System.out.printf("ok [%d.%03ds]", duration/1000L, duration%1000L);
+		System.out.println();
 	}