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+package basiclearner;
+
+import java.io.File;
+import java.io.FileNotFoundException;
+import java.io.IOException;
+import java.io.PrintWriter;
+import java.util.Calendar;
+import java.util.Collection;
+import java.util.Random;
+
+import net.automatalib.automata.transout.MealyMachine;
+import net.automatalib.commons.dotutil.DOT;
+import net.automatalib.util.graphs.dot.GraphDOT;
+import net.automatalib.words.Alphabet;
+import net.automatalib.words.Word;
+import net.automatalib.words.impl.SimpleAlphabet;
+
+import com.google.common.collect.Lists;
+
+import de.learnlib.acex.analyzers.AcexAnalyzers;
+import de.learnlib.algorithms.kv.mealy.KearnsVaziraniMealy;
+import de.learnlib.algorithms.lstargeneric.ce.ObservationTableCEXHandlers;
+import de.learnlib.algorithms.lstargeneric.closing.ClosingStrategies;
+import de.learnlib.algorithms.lstargeneric.mealy.ExtensibleLStarMealy;
+import de.learnlib.algorithms.ttt.mealy.TTTLearnerMealy;
+import de.learnlib.api.EquivalenceOracle;
+import de.learnlib.api.LearningAlgorithm;
+import de.learnlib.api.MembershipOracle.MealyMembershipOracle;
+import de.learnlib.api.SUL;
+import de.learnlib.eqtests.basic.WMethodEQOracle;
+import de.learnlib.eqtests.basic.WpMethodEQOracle;
+import de.learnlib.eqtests.basic.mealy.RandomWalkEQOracle;
+import de.learnlib.experiments.Experiment.MealyExperiment;
+import de.learnlib.oracles.DefaultQuery;
+import de.learnlib.oracles.ResetCounterSUL;
+import de.learnlib.oracles.SULOracle;
+import de.learnlib.oracles.SymbolCounterSUL;
+import de.learnlib.statistics.Counter;
+
+/**
+ * General learning testing framework. All basic settings are at the top of this file and can be configured
+ * by hard-coding or by simply changing them from your own code.
+ * 
+ * Based on the learner experiment setup of Joshua Moerman, https://gitlab.science.ru.nl/moerman/Learnlib-Experiments
+ * 
+ * @author Ramon Janssen
+ */
+public class BasicLearner {
+	//***********************************************************************************//
+ 	// Learning settings (hardcoded, simply set to a different value to change learning) //
+	//***********************************************************************************//
+	/**
+	 * name to give to the resulting .dot-file and .pdf-file (extensions are added automatically)
+	 */
+	public static String
+			FINAL_MODEL_FILENAME = "learnedModel",
+			INTERMEDIATE_HYPOTHESIS_FILENAME = "hypothesis";
+	/**
+	 * For controlled experiments only: store every hypotheses as a file. Useful for 'debugging'
+	 * if the learner does not terminate (hint: the TTT-algorithm produces many hypotheses).
+	 */
+	public static boolean saveAllHypotheses = true;
+	/**
+	 * For random walk, the chance to reset after every input
+	 */
+	public static double randomWalk_chanceOfResetting = 0.1;
+	/**
+	 * For random walk, the number of symbols that is tested in total (divided over multiple traces).
+	 */
+	public static int randomWalk_numberOfSymbols = 300;
+	/**
+	 * MaxDepth-parameter for W-method and Wp-method. Typically not larger than 3. Decrease for quicker runs.
+	 */
+	public static int w_wp_methods_maxDepth = 2;
+
+	//*****************************************//
+	// Predefined learning and testing methods //
+	//*****************************************//
+	/**
+	 * The learning algorithms. LStar is the basic algorithm, TTT performs much faster
+	 * but is a bit more inaccurate and produces more intermediate hypotheses, so test well)
+	 */
+	public enum LearningMethod { LStar, RivestSchapire, TTT, KearnsVazirani }
+	/**
+	 * The testing algorithms. Random walk is the simplest, but performs badly on large models:
+	 * the chance of hitting a erroneous long trace is very small. WMethod and WpMethod are
+	 * smarter. UserQueries asks the user for which inputs to try as counter-example: have a
+	 * look at the hypothesis, and try to think of one
+	 */
+	public enum TestingMethod { RandomWalk, WMethod, WpMethod, UserQueries }
+
+	public static LearningAlgorithm<MealyMachine<?, String, ?, String>, String, Word<String>> loadLearner(
+			LearningMethod learningMethod, MealyMembershipOracle<String,String> sulOracle, Alphabet<String> alphabet) {
+		switch (learningMethod){
+			case LStar:
+				return new ExtensibleLStarMealy<String, String>(alphabet, sulOracle, Lists.<Word<String>>newArrayList(), ObservationTableCEXHandlers.CLASSIC_LSTAR, ClosingStrategies.CLOSE_SHORTEST);
+			case RivestSchapire:
+				return new ExtensibleLStarMealy<String, String>(alphabet, sulOracle, Lists.<Word<String>>newArrayList(), ObservationTableCEXHandlers.RIVEST_SCHAPIRE, ClosingStrategies.CLOSE_SHORTEST);
+			case TTT:
+				return new TTTLearnerMealy<String, String>(alphabet, sulOracle, AcexAnalyzers.LINEAR_FWD);
+			case KearnsVazirani:
+				return new KearnsVaziraniMealy<String, String>(alphabet, sulOracle, false, AcexAnalyzers.LINEAR_FWD);
+			default:
+				throw new RuntimeException("No learner selected");
+		}
+	}
+
+	public static EquivalenceOracle<MealyMachine<?, String, ?, String>, String, Word<String>> loadTester(
+			TestingMethod testMethod, SUL<String,String> sul, MealyMembershipOracle<String,String> sulOracle) {
+		switch (testMethod){
+			// simplest method, but doesn't perform well in practice, especially for large models
+			case RandomWalk:
+				return new RandomWalkEQOracle<>(randomWalk_chanceOfResetting, randomWalk_numberOfSymbols, true, new Random(123456l), sul);
+			// Other methods are somewhat smarter than random testing: state coverage, trying to distinguish states, etc.
+			case WMethod:
+				return new WMethodEQOracle.MealyWMethodEQOracle<>(w_wp_methods_maxDepth, sulOracle);
+			case WpMethod:
+				return new WpMethodEQOracle.MealyWpMethodEQOracle<>(w_wp_methods_maxDepth, sulOracle);
+			case UserQueries:
+				return new UserEQOracle(sul);
+			default:
+				throw new RuntimeException("No test oracle selected!");
+		}
+	}
+
+	/**
+	 * Simple example of running a learning experiment
+	 * @param learner Learning algorithm, wrapping the SUL
+	 * @param eqOracle Testing algorithm, wrapping the SUL
+	 * @param alphabet Input alphabet
+	 * @throws IOException if the result cannot be written
+	 */
+	public static void runSimpleExperiment(
+			LearningAlgorithm<MealyMachine<?, String, ?, String>, String, Word<String>> learner,
+			EquivalenceOracle<MealyMachine<?, String, ?, String>, String, Word<String>> eqOracle,
+			Alphabet<String> alphabet) throws IOException {
+		MealyExperiment<String, String> experiment
+				= new MealyExperiment<String, String>(learner, eqOracle, alphabet);
+		experiment.run();
+		System.out.println("Ran " + experiment.getRounds().getCount() + " rounds");
+		produceOutput(FINAL_MODEL_FILENAME, experiment.getFinalHypothesis(), alphabet, true);
+	}
+
+	/**
+	 * Simple example of running a learning experiment
+	 * @param sul Direct access to SUL
+	 * @param learningMethod One of the default learning methods from this class
+	 * @param testingMethod One of the default testing methods from this class
+	 * @param alphabet Input alphabet
+	 * @throws IOException if the result cannot be written
+	 */
+	public static void runSimpleExperiment (
+			SUL<String,String> sul,
+			LearningMethod learningMethod,
+			TestingMethod testingMethod,
+			Collection<String> alphabet
+			) throws IOException {
+		Alphabet<String> learlibAlphabet = new SimpleAlphabet<String>(alphabet);
+		LearningSetup learningSetup = new LearningSetup(sul, learningMethod, testingMethod, learlibAlphabet);
+		runSimpleExperiment(learningSetup.learner, learningSetup.eqOracle, learlibAlphabet);
+	}
+	
+	/**
+	 * More detailed example of running a learning experiment. Starts learning, and then loops testing,
+	 * and if counterexamples are found, refining again. Also prints some statistics about the experiment
+	 * @param learner learner Learning algorithm, wrapping the SUL
+	 * @param eqOracle Testing algorithm, wrapping the SUL
+	 * @param nrSymbols A counter for the number of symbols that have been sent to the SUL (for statistics)
+	 * @param nrResets A counter for the number of resets that have been sent to the SUL (for statistics)
+	 * @param alphabet Input alphabet
+	 * @throws IOException
+	 */
+	public static void runControlledExperiment(
+			LearningAlgorithm<MealyMachine<?, String, ?, String>, String, Word<String>> learner,
+			EquivalenceOracle<MealyMachine<?, String, ?, String>, String, Word<String>> eqOracle,
+			Counter nrSymbols, Counter nrResets,
+			Alphabet<String> alphabet) throws IOException {
+		try {
+			// prepare some counters for printing statistics
+			int stage = 0;
+			long lastNrResetsValue = 0, lastNrSymbolsValue = 0;
+			
+			// start the actual learning
+			learner.startLearning();
+			
+			while(true) {
+				// store hypothesis as file
+				if(saveAllHypotheses) {
+					String outputFilename = INTERMEDIATE_HYPOTHESIS_FILENAME + stage;
+					produceOutput(outputFilename, learner.getHypothesisModel(), alphabet, false);
+					System.out.println("model size " + learner.getHypothesisModel().getStates().size());
+				}
+	
+				// Print statistics
+				System.out.println(stage + ": " + Calendar.getInstance().getTime());
+				// Log number of queries/symbols
+				System.out.println("Hypothesis size: " + learner.getHypothesisModel().size() + " states");
+				long roundResets = nrResets.getCount() - lastNrResetsValue, roundSymbols = nrSymbols.getCount() - lastNrSymbolsValue;
+				System.out.println("learning queries/symbols: " + nrResets.getCount() + "/" + nrSymbols.getCount()
+						+ "(" + roundResets + "/" + roundSymbols + " this learning round)");
+				lastNrResetsValue = nrResets.getCount();
+				lastNrSymbolsValue = nrSymbols.getCount();
+				
+				// Search for CE
+				DefaultQuery<String, Word<String>> ce = eqOracle.findCounterExample(learner.getHypothesisModel(), alphabet);
+				
+				// Log number of queries/symbols
+				roundResets = nrResets.getCount() - lastNrResetsValue;
+				roundSymbols = nrSymbols.getCount() - lastNrSymbolsValue;
+				System.out.println("testing queries/symbols: " + nrResets.getCount() + "/" + nrSymbols.getCount()
+						+ "(" + roundResets + "/" + roundSymbols + " this testing round)");
+				lastNrResetsValue = nrResets.getCount();
+				lastNrSymbolsValue = nrSymbols.getCount();
+				
+				if(ce == null) {
+					// No counterexample found, stop learning
+					System.out.println("\nFinished learning!");
+					produceOutput(FINAL_MODEL_FILENAME, learner.getHypothesisModel(), alphabet, true);
+					break;
+				} else {
+					// Counterexample found, rinse and repeat
+					System.out.println();
+					stage++;
+					learner.refineHypothesis(ce);
+				}
+			}
+		} catch (Exception e) {
+			String errorHypName = "hyp.before.crash.dot";
+			produceOutput(errorHypName, learner.getHypothesisModel(), alphabet, true);
+			throw e;
+		}
+	}
+
+	/**
+	 * More detailed example of running a learning experiment. Starts learning, and then loops testing,
+	 * and if counterexamples are found, refining again. Also prints some statistics about the experiment
+	 * @param sul Direct access to SUL
+	 * @param learningMethod One of the default learning methods from this class
+	 * @param testingMethod One of the default testing methods from this class
+	 * @param alphabet Input alphabet
+	 * @param alphabet Input alphabet
+	 * @throws IOException
+	 */
+	public static void runControlledExperiment(
+			SUL<String,String> sul,
+			LearningMethod learningMethod,
+			TestingMethod testingMethod,
+			Collection<String> alphabet
+		) throws IOException {
+		Alphabet<String> learnlibAlphabet = new SimpleAlphabet<String>(alphabet);
+		LearningSetup learningSetup = new LearningSetup(sul, learningMethod, testingMethod, learnlibAlphabet);
+		runControlledExperiment(learningSetup.learner, learningSetup.eqOracle, learningSetup.nrSymbols, learningSetup.nrResets, learnlibAlphabet);
+	}
+
+	/**
+	 * Produces a dot-file and a PDF (if graphviz is installed)
+	 * @param fileName filename without extension - will be used for the .dot and .pdf
+	 * @param model
+	 * @param alphabet
+	 * @param verboseError whether to print an error explaing that you need graphviz
+	 * @throws FileNotFoundException
+	 * @throws IOException
+	 */
+	public static void produceOutput(String fileName, MealyMachine<?,String,?,String> model, Alphabet<String> alphabet, boolean verboseError) throws FileNotFoundException, IOException {
+		PrintWriter dotWriter = new PrintWriter(fileName + ".dot");
+		GraphDOT.write(model, alphabet, dotWriter);
+		try {
+			DOT.runDOT(new File(fileName + ".dot"), "pdf", new File(fileName + ".pdf"));
+		} catch (Exception e) {
+			if (verboseError) {
+				System.err.println("Warning: Install graphviz to convert dot-files to PDF");
+				System.err.println(e.getMessage());
+			}
+		}
+		dotWriter.close();
+	}
+
+	/**
+	 * Helper class to configure a learning and equivalence oracle. Tell it which learning and testing method you
+	 * want, and it produces the corresponding oracles (and counters for statistics) as attributes.
+	 */
+	public static class LearningSetup {
+		public final EquivalenceOracle<MealyMachine<?, String, ?, String>, String, Word<String>> eqOracle;
+		public final LearningAlgorithm<MealyMachine<?, String, ?, String>, String, Word<String>> learner;
+		public final Counter nrSymbols, nrResets;
+
+		public LearningSetup(SUL<String,String> sul, LearningMethod learningMethod, TestingMethod testingMethod, Alphabet<String> alphabet) {
+			// Wrap the SUL in a detector for non-determinism
+			SUL<String,String> nonDetSul = new NonDeterminismCheckingSUL<String,String>(sul);
+			// Wrap the SUL in counters for symbols/resets, so that we can record some statistics
+			SymbolCounterSUL<String, String> symbolCounterSul = new SymbolCounterSUL<>("symbol counter", nonDetSul);
+			ResetCounterSUL<String, String> resetCounterSul = new ResetCounterSUL<>("reset counter", symbolCounterSul);
+			nrSymbols = symbolCounterSul.getStatisticalData();
+			nrResets = resetCounterSul.getStatisticalData();
+			// we should use the sul only through those wrappers
+			sul = resetCounterSul;
+			// Most testing/learning-algorithms want a membership-oracle instead of a SUL directly
+			MealyMembershipOracle<String,String> sulOracle = new SULOracle<>(sul);
+
+			// Choosing an equivalence oracle
+			eqOracle = loadTester(testingMethod, sul, sulOracle);
+
+			// Choosing a learner
+			learner = loadLearner(learningMethod, sulOracle, alphabet);
+		}
+	}
+}