Update ExperimentsScaleAlphabet

ExperimentsScaleAlphabet → ExperimentsScaleAlphabet.java

-import de.learnlib.acex.AcexAnalyzer;
 import de.learnlib.algorithm.kv.dfa.KearnsVaziraniDFA;
 import de.learnlib.algorithm.kv.dfa.KearnsVaziraniDFABuilder;
 import de.learnlib.algorithm.lstar.dfa.ClassicLStarDFABuilder;
 import de.learnlib.algorithm.observationpack.dfa.OPLearnerDFA;
 import de.learnlib.algorithm.observationpack.dfa.OPLearnerDFABuilder;
 import de.learnlib.algorithm.rivestschapire.RivestSchapireDFA;
-import de.learnlib.algorithm.ttt.base.AbstractTTTLearner;
 import de.learnlib.algorithm.ttt.dfa.TTTLearnerDFA;
 import de.learnlib.algorithm.ttt.dfa.TTTLearnerDFABuilder;
 import de.learnlib.filter.statistic.oracle.DFACounterOracle;
 import de.learnlib.oracle.EquivalenceOracle;
 import de.learnlib.oracle.MembershipOracle;
 import de.learnlib.oracle.equivalence.DFASimulatorEQOracle;
-import de.learnlib.oracle.equivalence.WMethodEQOracle;
 import de.learnlib.oracle.membership.DFASimulatorOracle;
 import de.learnlib.query.DefaultQuery;
 import net.automatalib.alphabet.Alphabet;
@@ -20,14 +17,14 @@ import net.automatalib.alphabet.Alphabets;
 import net.automatalib.automaton.fsa.DFA;
 import de.learnlib.algorithm.lstar.dfa.ClassicLStarDFA;
 import de.learnlib.algorithm.rivestschapire.RivestSchapireDFABuilder;
-import net.automatalib.word.Word;
 import org.knowm.xchart.QuickChart;
 import org.knowm.xchart.SwingWrapper;
 import org.knowm.xchart.XYChart;
+import org.knowm.xchart.XYSeries;
 
-import java.util.ArrayList;
 import java.util.List;
 import java.util.Random;
+import java.util.stream.Collectors;
 
 public class ExperimentsScaleAlphabet {
 
@@ -35,7 +32,7 @@ public class ExperimentsScaleAlphabet {
         // Define configurations
         double[] yValues = {0.01};
         int[] zValues = { 100 };
-        int[] xValues = {10,20,30,40,50,60,70,80,90,100};
+        int[] xValues = {100,200,300};
 
         // Declare arrays to store the average membership queries and average equivalence queries
         double[] avgMemQueriesLstar = new double[xValues.length];
@@ -198,6 +195,10 @@ public class ExperimentsScaleAlphabet {
 // Create the second XY chart (equivalence queries) with extended series
                 XYChart chart2 = QuickChart.getChart("Average Equivalence Queries, n=100, Acceptence Ratio = 0.01", "Alphabet Length", "Queries", extendedSeriesNames2, xValuesDouble, new double[][]{avgEqQueriesLstar, avgEqQueriesRS,avgEqQueriesKV, avgEqQueriesOP, avgEqQueriesTTT, upperboundeqValues});
                 new SwingWrapper(chart2).displayChart();
+
+                printXYChartPairs(chart1);
+                printXYChartPairs(chart2);
+                printXYChartPairs(chartExcludingLS);
             }
 
 
@@ -302,4 +303,32 @@ public class ExperimentsScaleAlphabet {
         return n;
     }
 
+
+
+    public static void printXYChartPairs(XYChart chart) {
+        System.out.println("Chart: " + chart.getTitle());
+        // Get all series in the chart
+        List<XYSeries> seriesList = chart.getSeriesMap().values().stream().collect(Collectors.toList());
+
+        // Iterate over each series
+        for (XYSeries series : seriesList) {
+            System.out.println("Series: " + series.getName());
+
+            // Get x and y data
+            double[] xData = series.getXData();
+            double[] yData = series.getYData();
+
+            // Ensure x and y data are of the same size
+            if (xData.length != yData.length) {
+                System.out.println("Mismatch in data size for series: " + series.getName());
+                continue;
+            }
+
+            // Print (x, y) pairs
+            for (int i = 0; i < xData.length; i++) {
+                System.out.println("(" + xData[i] + ", " + yData[i] + ")");
+            }
+        }
+    }
+
 }