New configuration for learner.

- ScenarioDefinition describes relevant items, kind of learner and initial data files
- NormalizationHelper can now be serialized to JSON
- Goal and kind of learner are now enums instead of integer constants
- Added tests for loaded variants of learner

eraser-base/src/main/java/de/tudresden/inf/st/eraser/jastadd/model/MachineLearningHandlerFactory.java

@@ -28,7 +28,7 @@ public abstract class MachineLearningHandlerFactory implements MachineLearningSe
     this.knowledgeBase = root;
   }
 
-  public abstract void initializeFor(MachineLearningHandlerFactoryTarget target, URL configUrl) throws IOException;
+  public abstract void initializeFor(MachineLearningHandlerFactoryTarget target, URL configUrl) throws IOException, ClassNotFoundException;
 
   public abstract MachineLearningEncoder createEncoder();
 

eraser-base/src/main/java/de/tudresden/inf/st/eraser/util/ParserUtils.java

@@ -3,6 +3,8 @@ package de.tudresden.inf.st.eraser.util;
 import beaver.Parser;
 import beaver.Scanner;
 import beaver.Symbol;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.fasterxml.jackson.databind.SerializationFeature;
 import de.tudresden.inf.st.eraser.jastadd.model.*;
 import de.tudresden.inf.st.eraser.jastadd.parser.EraserParser;
 import de.tudresden.inf.st.eraser.jastadd.scanner.EraserScanner;
@@ -27,6 +29,9 @@ public class ParserUtils {
   public static final String UNKNOWN_GROUP_NAME = "Unknown";
   private static boolean verboseLoading = false;
   private static final Logger logger = LogManager.getLogger(ParserUtils.class);
+  private static final ObjectMapper OBJECT_MAPPER_INSTANCE = new ObjectMapper()
+      .enable(SerializationFeature.ORDER_MAP_ENTRIES_BY_KEYS)
+      .enable(SerializationFeature.INDENT_OUTPUT);
 
   private interface ReaderProvider {
     Reader provide() throws IOException;
@@ -253,4 +258,17 @@ public class ParserUtils {
     return root.getSmartHomeEntityModel().items().get(0);
   }
 
+  public static <T> T loadFrom(URL location, Class<T> valueType) throws IOException {
+    return OBJECT_MAPPER_INSTANCE.readValue(location, valueType);
+  }
+
+  public static <T> void saveTo(T value, URL location) throws IOException {
+    if (location.getProtocol().equals("file")) {
+      // write to (local) file directly
+      OBJECT_MAPPER_INSTANCE.writeValue(new File(location.getFile()), value);
+    } else {
+      OBJECT_MAPPER_INSTANCE.writeValue(location.openConnection().getOutputStream(), value);
+    }
+  }
+
 }

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java

@@ -2,7 +2,7 @@ package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
 
 import com.fasterxml.jackson.databind.ObjectMapper;
 import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.LearnerSettings;
-import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.LearnerSettingsColumnDefinition;
+import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.SimpleColumnDefinition;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
 import org.encog.Encog;
@@ -10,10 +10,8 @@ import org.encog.ml.data.MLData;
 import org.encog.ml.data.versatile.NormalizationHelper;
 import org.encog.ml.data.versatile.VersatileMLDataSet;
 import org.encog.ml.data.versatile.columns.ColumnDefinition;
-import org.encog.ml.data.versatile.columns.ColumnType;
 import org.encog.ml.data.versatile.sources.CSVDataSource;
 import org.encog.ml.data.versatile.sources.VersatileDataSource;
-import org.encog.ml.factory.MLMethodFactory;
 import org.encog.ml.model.EncogModel;
 import org.encog.neural.networks.BasicNetwork;
 import org.encog.persist.EncogDirectoryPersistence;
@@ -48,7 +46,7 @@ public class Learner {
    * @throws IOException if an error occurs in {@link ObjectMapper#readValue(URL, Class)}
    */
   Learner(URL configURL) throws IOException {
-    this(new ObjectMapper().readValue(configURL, LearnerSettings.class));
+    this(LearnerSettings.loadFrom(configURL));
   }
 
   Learner(LearnerSettings settings) {
@@ -83,12 +81,12 @@ public class Learner {
     VersatileMLDataSet data = new VersatileMLDataSet(source);
     final int inputSize = settings.inputColumns.size();
     for (int index = 0; index < inputSize; index++) {
-      LearnerSettingsColumnDefinition columnDefinition = settings.inputColumns.get(index);
+      SimpleColumnDefinition columnDefinition = settings.inputColumns.get(index);
       data.defineSourceColumn(columnDefinition.name, index, columnDefinition.type);
     }
     List<ColumnDefinition> targets = new ArrayList<>();
     for (int targetIndex = 0; targetIndex < settings.targetColumns.size(); targetIndex++) {
-      LearnerSettingsColumnDefinition columnDefinition = settings.targetColumns.get(targetIndex);
+      SimpleColumnDefinition columnDefinition = settings.targetColumns.get(targetIndex);
       targets.add(data.defineSourceColumn(columnDefinition.name, inputSize + targetIndex, columnDefinition.type));
     }
     if (targets.size() == 1) {
@@ -118,7 +116,24 @@ public class Learner {
     return result;
   }
 
+  void load(URL networkLocation, URL normalizationHelperLocation, boolean useJsonFormat)
+      throws IOException, ClassNotFoundException {
+    this.network = (BasicNetwork) EncogDirectoryPersistence.loadObject(networkLocation.openStream());
+    this.normalizationHelper = LearnerPersistenceUtils.loadNormalizationHelperFrom(
+        normalizationHelperLocation, useJsonFormat);
+  }
+
   void shutdown() {
     Encog.getInstance().shutdown();
   }
+
+  public void save() {
+    EncogDirectoryPersistence.saveObject(new File(settings.name + "_network.eg"), this.network);
+    try {
+      LearnerPersistenceUtils.saveNormalizationHelper(this.normalizationHelper,
+          new File(settings.name + "_normalizer.json").toURI().toURL(), true);
+    } catch (IOException e) {
+      logger.catching(e);
+    }
+  }
 }

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/LearnerGoal.java

+package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
+
+/**
+ * Goal of the Learner.
+ *
+ * @author rschoene - Initial contribution
+ */
+public enum LearnerGoal {
+  ACTIVITY_PHONE_AND_WATCH,
+  PREFERENCE_BRIGHTNESS_IRIS
+}

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/LearnerKind.java

+package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
+
+/**
+ * Kind of Learner.
+ *
+ * @author rschoene - Initial contribution
+ */
+public enum LearnerKind {
+  /** The standard learner, getting a CSV for initial training */
+  normal,
+  /** A learner loaded a pre-trained model, getting a BasicNetwork and a NormalizationHelper */
+  loaded
+}

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/LearnerPersistenceUtils.java

+package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
+
+import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.NormalizationHelperData;
+import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.SimpleColumnDefinition;
+import de.tudresden.inf.st.eraser.util.ParserUtils;
+import org.apache.logging.log4j.LogManager;
+import org.apache.logging.log4j.Logger;
+import org.encog.EncogError;
+import org.encog.ml.data.versatile.NormalizationHelper;
+import org.encog.ml.data.versatile.columns.ColumnDefinition;
+import org.encog.ml.data.versatile.columns.ColumnType;
+import org.encog.ml.data.versatile.missing.MeanMissingHandler;
+import org.encog.ml.data.versatile.normalizers.Normalizer;
+import org.encog.ml.data.versatile.normalizers.RangeNormalizer;
+import org.encog.ml.data.versatile.normalizers.strategies.BasicNormalizationStrategy;
+import org.encog.ml.data.versatile.normalizers.strategies.NormalizationStrategy;
+
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.ObjectInputStream;
+import java.io.ObjectOutputStream;
+import java.net.URL;
+import java.net.URLConnection;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Optional;
+import java.util.stream.Collectors;
+
+/**
+ * Handles objects storing to and loading from files.
+ *
+ * @author rschoene - Initial contribution
+ */
+public class LearnerPersistenceUtils {
+
+  private static final Logger logger = LogManager.getLogger(LearnerPersistenceUtils.class);
+
+  static NormalizationHelper loadNormalizationHelperFrom(URL location, boolean useJsonFormat) throws IOException, ClassNotFoundException {
+    NormalizationHelper result;
+    if (useJsonFormat) {
+      NormalizationHelperData data = NormalizationHelperData.loadFrom(location);
+      result = new NormalizationHelper();
+      result.setSourceColumns(mapSimpleColumnDefs(data.sourceColumns));
+      result.setInputColumns(mapSimpleColumnDefs(data.inputColumns));
+      result.setOutputColumns(mapSimpleColumnDefs(data.outputColumns));
+      result.setNormStrategy(new BasicNormalizationStrategy(
+          data.inputLow, data.inputHigh, data.outputLow, data.outputHigh));
+      data.unknownValues.forEach(result::defineUnknownValue);
+      data.missingToMean.forEach((name, mean) -> findColumnDef(name, result).ifPresent(
+          columnDefinition -> result.defineMissingHandler(columnDefinition, new MeanMissingHandler())));
+    } else {
+      URLConnection connection = location.openConnection();
+      ObjectInputStream in = new ObjectInputStream(connection.getInputStream());
+      result = (NormalizationHelper) in.readObject();
+    }
+    return result;
+  }
+
+  private static Optional<ColumnDefinition> findColumnDef(String name, NormalizationHelper result) {
+    List<ColumnDefinition> allDefs = new ArrayList<>();
+    allDefs.addAll(result.getSourceColumns());
+    allDefs.addAll(result.getInputColumns());
+    allDefs.addAll(result.getOutputColumns());
+    for(ColumnDefinition colDef : allDefs) {
+      if (colDef.getName().equals(name)) {
+        return Optional.of(colDef);
+      }
+    }
+    logger.warn("Could not find column with name '{}'", name);
+    return Optional.empty();
+  }
+
+  private static List<ColumnDefinition> mapSimpleColumnDefs(List<SimpleColumnDefinition> columnDefinitions) {
+    return columnDefinitions.stream()
+        .map(colDef -> {
+          ColumnDefinition result = new ColumnDefinition(colDef.name, colDef.type);
+          result.setLow(colDef.low);
+          result.setHigh(colDef.high);
+          result.setSd(colDef.sd);
+          result.setMean(colDef.mean);
+          result.setCount(colDef.count);
+          result.setIndex(colDef.index);
+          colDef.classes.forEach(result::defineClass);
+          return result;
+        })
+        .collect(Collectors.toList());
+  }
+
+  private static List<SimpleColumnDefinition> mapColumnDefs(List<ColumnDefinition> columnDefinitions) {
+    return columnDefinitions.stream()
+        .map(colDef -> {
+          SimpleColumnDefinition result = new SimpleColumnDefinition();
+          result.name = colDef.getName();
+          result.type = colDef.getDataType();
+          result.low = colDef.getLow();
+          result.high = colDef.getHigh();
+          result.sd = colDef.getSd();
+          result.mean = colDef.getMean();
+          result.count = colDef.getCount();
+          result.index = colDef.getIndex();
+          result.classes = colDef.getClasses();
+          return result;
+        })
+        .collect(Collectors.toList());
+  }
+
+  static void saveNormalizationHelper(NormalizationHelper normalizationHelper, URL targetLocation, boolean useJsonFormat) throws IOException {
+    ObjectOutputStream out;
+    if (targetLocation.getProtocol().equals("file")) {
+      // we want to write to a file, thus can not use URL directly (FileURLConnection does not support output)
+      out = new ObjectOutputStream(new FileOutputStream(targetLocation.getFile()));
+    } else {
+      URLConnection connection = targetLocation.openConnection();
+      connection.setDoOutput(true);
+
+      out = new ObjectOutputStream(connection.getOutputStream());
+    }
+    if (useJsonFormat) {
+      NormalizationHelperData data = new NormalizationHelperData();
+      data.sourceColumns = mapColumnDefs(normalizationHelper.getSourceColumns());
+      data.inputColumns = mapColumnDefs(normalizationHelper.getInputColumns());
+      data.outputColumns = mapColumnDefs(normalizationHelper.getOutputColumns());
+      NormalizationStrategy normStrategy = normalizationHelper.getNormStrategy();
+      if (normStrategy instanceof BasicNormalizationStrategy) {
+        BasicNormalizationStrategy basicNormStrategy = (BasicNormalizationStrategy) normStrategy;
+        double[] lowAndHigh = extractLowAndHigh(basicNormStrategy.getInputNormalizers().get(ColumnType.continuous));
+        data.inputLow = lowAndHigh[0];
+        data.inputHigh = lowAndHigh[1];
+        lowAndHigh = extractLowAndHigh(basicNormStrategy.getOutputNormalizers().get(ColumnType.continuous));
+        data.outputLow = lowAndHigh[0];
+        data.outputHigh = lowAndHigh[1];
+      } else {
+        logger.warn("Unknown normalization strategy, can not serialize {}", normStrategy);
+      }
+      data.unknownValues = normalizationHelper.getUnknownValues();
+      testForMissingHandlers(normalizationHelper, data);
+      ParserUtils.saveTo(data, targetLocation);
+    } else {
+      out.writeObject(normalizationHelper);
+    }
+    out.close();
+  }
+
+  private static void testForMissingHandlers(NormalizationHelper normalizationHelper, NormalizationHelperData data) {
+    List<ColumnDefinition> allDefs = new ArrayList<>();
+    allDefs.addAll(normalizationHelper.getSourceColumns());
+    allDefs.addAll(normalizationHelper.getInputColumns());
+    allDefs.addAll(normalizationHelper.getOutputColumns());
+    String firstUnknown;
+    if (normalizationHelper.getUnknownValues().isEmpty()) {
+      normalizationHelper.defineUnknownValue(null);
+      firstUnknown = null;
+    } else {
+      firstUnknown = normalizationHelper.getUnknownValues().get(0);
+    }
+    double[] buffer = new double[1];
+    for (ColumnDefinition colDef : allDefs) {
+      try {
+        normalizationHelper.normalizeToVector(colDef, 0, buffer, false, firstUnknown);
+        // there was a missing handler defined
+        data.missingToMean.put(colDef.getName(), colDef.getMean());
+      } catch (EncogError e) {
+        // missing handler is not defined, so move on
+      }
+    }
+  }
+
+  private static double[] extractLowAndHigh(Normalizer norm) {
+    if (norm instanceof RangeNormalizer) {
+      /* reconstruct low and high from the normalizer, as we know the normalization formula is
+          result = ((value - col.low) / (col.high - col.low)) * (normHigh - normLow) + normLow
+         if the first part of the product is zero, we get normLow
+         if the first part of the product is one, we get normHigh
+       */
+      double[] buffer = new double[2];
+      ColumnDefinition colDef = new ColumnDefinition("dummy", ColumnType.continuous);
+      colDef.setLow(0);
+      colDef.setHigh(1);
+      // now we have (value / 1) as first part of the product
+      norm.normalizeColumn(colDef, 0, buffer, 0);
+      norm.normalizeColumn(colDef, 1, buffer, 1);
+      return buffer;
+    } else {
+      logger.error("Unknown normalizer, can not serialize {}", norm);
+      return new double[2];
+    }
+
+  }
+
+}

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningHandlerFactoryImpl.java

@@ -7,6 +7,9 @@ import de.tudresden.inf.st.eraser.jastadd.model.MachineLearningHandlerFactory;
 import java.io.IOException;
 import java.net.URL;
 
+import static de.tudresden.inf.st.eraser.feedbackloop.learner_backup.LearnerGoal.ACTIVITY_PHONE_AND_WATCH;
+import static de.tudresden.inf.st.eraser.feedbackloop.learner_backup.LearnerGoal.PREFERENCE_BRIGHTNESS_IRIS;
+
 /**
  * Factory to create handlers of type {@link MachineLearningHandlerFactoryImpl}.
  *
@@ -17,14 +20,14 @@ public class MachineLearningHandlerFactoryImpl extends MachineLearningHandlerFac
   private MachineLearningImpl handler;
 
   @Override
-  public void initializeFor(MachineLearningHandlerFactoryTarget target, URL configUrl) throws IOException {
+  public void initializeFor(MachineLearningHandlerFactoryTarget target, URL configUrl) throws IOException, ClassNotFoundException {
     switch (target) {
       case ACTIVITY_RECOGNITION:
-        handler = new MachineLearningImpl(MachineLearningImpl.GOAL_ACTIVITY_PHONE_AND_WATCH, configUrl);
+        handler = new MachineLearningImpl(ACTIVITY_PHONE_AND_WATCH, configUrl);
         handler.setKnowledgeBaseRoot(knowledgeBase);
         break;
       case PREFERENCE_LEARNING:
-        handler = new MachineLearningImpl(MachineLearningImpl.GOAL_PREFERENCE_BRIGHTNESS_IRIS, configUrl);
+        handler = new MachineLearningImpl(PREFERENCE_BRIGHTNESS_IRIS, configUrl);
         handler.setKnowledgeBaseRoot(knowledgeBase);
         break;
       default:
@@ -43,9 +46,10 @@ public class MachineLearningHandlerFactoryImpl extends MachineLearningHandlerFac
     return handler;
   }
 
+  @Override
   public void shutdown() {
     if (handler != null) {
-      handler.getLearner().shutdown();
+      handler.shutdown();
     }
   }
 }

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningImpl.java

 package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
 
+import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.LearnerScenarioDefinition;
 import de.tudresden.inf.st.eraser.jastadd.model.*;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
 
+import java.io.File;
 import java.io.IOException;
 import java.net.MalformedURLException;
 import java.net.URL;
@@ -13,12 +15,13 @@ import java.util.stream.Collectors;
 
 public class MachineLearningImpl implements MachineLearningDecoder, MachineLearningEncoder {
 
-  public static final int GOAL_ACTIVITY_PHONE_AND_WATCH = 1;
-  public static final int GOAL_PREFERENCE_BRIGHTNESS_IRIS = 2;
+//  public static final int GOAL_ACTIVITY_PHONE_AND_WATCH = 1;
+//  public static final int GOAL_PREFERENCE_BRIGHTNESS_IRIS = 2;
 
   private final Logger logger = LogManager.getLogger(MachineLearningImpl.class);
   private final Learner learner;
-  private final int goal;
+  private final LearnerGoal goal;
+  private final LearnerScenarioDefinition scenarioDefinition;
 
   private List<Item> target_item_list;
   private List<Item> relevant_item_list;
@@ -34,8 +37,16 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
   private String activity_result;
   private Instant lastModelUpdate;
 
-  public MachineLearningImpl(int goal, URL configURL) throws IOException {
-    this.learner = new Learner(configURL);
+  /**
+   * Create a new MachineLearning handler.
+   * @param goal      The goal of the learner
+   * @param configURL The URL for the {@link LearnerScenarioDefinition}
+   * @throws IOException If an error occurs during initialization of the Learner
+   */
+  public MachineLearningImpl(LearnerGoal goal, URL configURL) throws IOException {
+    scenarioDefinition = LearnerScenarioDefinition.loadFrom(configURL);
+    URL learnerSettingsURL = filenameToURL(scenarioDefinition.definitionFile);
+    this.learner = new Learner(learnerSettingsURL);
     this.goal = goal;
   }
 
@@ -45,29 +56,10 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
     SmartHomeEntityModel model = this.root.getSmartHomeEntityModel();
     List<String> targetItemNames, relevantItemNames;
     switch (this.goal) {
-      case GOAL_ACTIVITY_PHONE_AND_WATCH:
-        targetItemNames = Collections.singletonList("activity");
-        relevantItemNames = Arrays.asList(
-            "m_accel_x",
-            "m_accel_y",
-            "m_accel_z",
-            "m_rotation_x",
-            "m_rotation_y",
-            "m_rotation_z",
-            "w_accel_x",
-            "w_accel_y",
-            "w_accel_z",
-            "w_rotation_x",
-            "w_rotation_y",
-            "w_rotation_z"
-        );
-        break;
-      case GOAL_PREFERENCE_BRIGHTNESS_IRIS:
-        targetItemNames = Collections.singletonList("iris1_item");
-        relevantItemNames = Arrays.asList(
-            "activity",
-            "w_brightness"
-        );
+      case ACTIVITY_PHONE_AND_WATCH:
+      case PREFERENCE_BRIGHTNESS_IRIS:
+        targetItemNames = scenarioDefinition.targetItemNames;
+        relevantItemNames = scenarioDefinition.relevantItemNames;
         break;
       default:
         logger.error("Unknown goal value ({}) set", this.goal);
@@ -99,11 +91,10 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
     /* FIXME either save state of unchanged items here (if only changed items are reported) <- pull model
              or let knowledge base pass all relevant items <- push model
     */
-    if (this.goal == GOAL_ACTIVITY_PHONE_AND_WATCH) {
-
+    switch (this.goal) {
+      case ACTIVITY_PHONE_AND_WATCH:
         for (Item item : changedItems) {
-        if(item.getID().equals("m_accel_x"))
-        {
+          if (item.getID().equals("m_accel_x")) {
             a_length = 0;
           }
           int i = 0;
@@ -128,8 +119,8 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
           a_length = 0;
           Arrays.fill(this.a_new_data, null);
         }
-
-    } else if (this.goal == GOAL_PREFERENCE_BRIGHTNESS_IRIS) {
+        break;
+      case PREFERENCE_BRIGHTNESS_IRIS:
         for (Item item : changedItems) {
           if (root.getSmartHomeEntityModel().getActivityItem().equals(item)) {
             String test = item.getStateAsString();
@@ -141,6 +132,9 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
           }
         }
         this.preference_result = learner.predictor(this.p_new_data);
+        break;
+      default:
+        logger.error("Unknown goal value ({}) set in newData", this.goal);
     }
   }
 
@@ -168,23 +162,15 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
   public MachineLearningResult classify() {
     MachineLearningResult result = new MachineLearningResult();
     switch (this.goal) {
-      case GOAL_ACTIVITY_PHONE_AND_WATCH:
-        String activityStringValue = activity_result;
-        if (activityStringValue == null) {
+      case ACTIVITY_PHONE_AND_WATCH:
+        if (activity_result == null) {
           return result;
         }
         Item activityItem = resolve(this.root.getSmartHomeEntityModel(), "activity");
-        ItemUpdate classifiedActivity = null;
-        for (Activity activity : this.root.getMachineLearningRoot().getActivityList()) {
-          if (activity.getLabel().equals(activityStringValue)) {
-            classifiedActivity = new ItemUpdateDouble(activityItem, activity.getIdentifier());
-          }
-        }
-        if (classifiedActivity != null) {
-          result.addItemUpdate(classifiedActivity);
-        }
-        return result;
-      case GOAL_PREFERENCE_BRIGHTNESS_IRIS:
+        this.root.resolveActivity(activity_result).ifPresent(
+            activity -> result.addItemUpdate(new ItemUpdateDouble(activityItem, activity.getIdentifier())));
+        break;
+      case PREFERENCE_BRIGHTNESS_IRIS:
         Item iris1 = resolve(this.root.getSmartHomeEntityModel(), "iris1_item");
         int color = 0;
         int brightness = 0;
@@ -193,19 +179,33 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
           brightness = Math.round(Float.parseFloat(preference_result[1]));
         }
         result.addItemUpdate(new ItemUpdateColor(iris1, TupleHSB.of(color, 100, brightness)));
-        return result;
+        break;
       default:
         logger.error("Unknown goal value ({}) set in classify", this.goal);
-        return result;
     }
+    return result;
   }
 
-  void startTraining() throws MalformedURLException {
-    learner.train();
+  void startTraining() throws IOException, ClassNotFoundException {
+    switch (this.scenarioDefinition.kind) {
+      case normal:
+        learner.train(filenameToURL(this.scenarioDefinition.dataFiles.get(0)));
+        break;
+      case loaded:
+        learner.load(filenameToURL(this.scenarioDefinition.dataFiles.get(0)),
+            filenameToURL(this.scenarioDefinition.dataFiles.get(1)), true);
+    }
     this.lastModelUpdate = Instant.now();
   }
 
-  Learner getLearner() {
-    return learner;
+  void shutdown() {
+    if (this.scenarioDefinition.saveModels) {
+      learner.save();
+    }
+    learner.shutdown();
+  }
+
+  private static URL filenameToURL(String filename) throws MalformedURLException {
+    return new File(filename).toURI().toURL();
   }
 }

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Main.java

@@ -4,16 +4,18 @@ import com.fasterxml.jackson.databind.ObjectMapper;
 import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.LearnerSettings;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
+import org.encog.ml.data.versatile.NormalizationHelper;
 import org.encog.util.csv.CSVFormat;
 import org.encog.util.csv.ReadCSV;
 
 import java.io.File;
 import java.io.IOException;
-import java.net.MalformedURLException;
+import java.net.URL;
 import java.nio.file.Paths;
 import java.util.Arrays;
 import java.util.stream.Collectors;
 
+@SuppressWarnings("unused")
 public class Main {
 
   private static final Logger logger = LogManager.getLogger(Main.class);
@@ -25,7 +27,18 @@ public class Main {
 //    learner.preference_train("../datasets/backup/preference_data.csv");
 //    learner.train("datasets/backup/activity_data.csv","datasets/backup/preference_data.csv");
 //    activity_validation_learner();
-    testSettings();
+//    testSettings();
+    testLoadNormalizationHelper();
+  }
+
+  private static void testLoadNormalizationHelper() {
+    try {
+      URL binaryURL = Paths.get("src", "test", "resources", "activity_normalizer.bin").toUri().toURL();
+      NormalizationHelper normalizer = LearnerPersistenceUtils.loadNormalizationHelperFrom(binaryURL, false);
+      System.out.println("loaded");
+    } catch (IOException | ClassNotFoundException e) {
+      logger.catching(e);
+    }
   }
 
   private static void testSettings() {

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/ReaderCSV.java

 package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
 
-import java.awt.*;
-import java.io.FileReader;
-import java.io.*;
-import java.util.Arrays;
-
 import com.opencsv.CSVReader;
-import org.apache.http.client.fluent.Request;
 import org.apache.http.HttpResponse;
+import org.apache.http.client.fluent.Request;
 import org.apache.http.entity.ContentType;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
-import java.util.concurrent.locks.Lock;
-import java.util.concurrent.locks.Condition;
-import java.util.concurrent.locks.ReentrantLock;
+
+import java.io.File;
+import java.io.FileReader;
+import java.util.Arrays;
 
 public class ReaderCSV{
 

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner_backup/data/LearnerScenarioDefinition.java

+package de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data;
+
+import com.fasterxml.jackson.annotation.JsonInclude;
+import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.LearnerKind;
+import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.MachineLearningImpl;
+import de.tudresden.inf.st.eraser.util.ParserUtils;
+
+import java.io.IOException;
+import java.net.URL;
+import java.util.List;
+
+/**
+ * Settings to initialize {@link MachineLearningImpl}.
+ *
+ * @author rschoene - Initial contribution
+ */
+@JsonInclude(JsonInclude.Include.NON_DEFAULT)
+public class LearnerScenarioDefinition {
+  public List<String> relevantItemNames;
+  public List<String> targetItemNames;
+  public String definitionFile;
+  public List<String> dataFiles;
+  public LearnerKind kind = LearnerKind.normal;
+  /** Save models at shutdown */
+  public boolean saveModels = false;
+
+  public static LearnerScenarioDefinition loadFrom(URL location) throws IOException {
+    return ParserUtils.loadFrom(location, LearnerScenarioDefinition.class);
+  }
+}

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner_backup/data/LearnerSettings.java

@@ -2,10 +2,12 @@ package de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data;
 
 import com.fasterxml.jackson.annotation.JsonInclude;
 import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.Learner;
-import org.encog.ml.data.versatile.columns.ColumnDefinition;
+import de.tudresden.inf.st.eraser.util.ParserUtils;
 import org.encog.ml.factory.MLMethodFactory;
 import org.encog.ml.model.EncogModel;
 
+import java.io.IOException;
+import java.net.URL;
 import java.util.List;
 
 /**
@@ -18,9 +20,9 @@ public class LearnerSettings {
   /** Description what this learner is about to learn */
   public String name;
   /** Input columns (of the CSV used for training) */
-  public List<LearnerSettingsColumnDefinition> inputColumns;
+  public List<SimpleColumnDefinition> inputColumns;
   /** Target columns (of the CSV used for training) */
-  public List<LearnerSettingsColumnDefinition> targetColumns;
+  public List<SimpleColumnDefinition> targetColumns;
   /** Training method */
   public String trainingMethod = MLMethodFactory.TYPE_FEEDFORWARD;
   /** Training parameter. Used in {@link EncogModel#holdBackValidation(double, boolean, int)}  */
@@ -34,4 +36,9 @@ public class LearnerSettings {
 
   /** Filename to load initial data from. Should be in another settings file! */
   public String initialDataFile = null;
+
+  public static LearnerSettings loadFrom(URL location) throws IOException {
+    return ParserUtils.loadFrom(location, LearnerSettings.class);
+  }
+
 }

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner_backup/data/NormalizationHelperData.java

+package de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data;
+
+import com.fasterxml.jackson.annotation.JsonInclude;
+import com.fasterxml.jackson.databind.ObjectMapper;
+
+import java.io.IOException;
+import java.net.URL;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * Settings to initialize {@link org.encog.ml.data.versatile.NormalizationHelper}.
+ *
+ * @author rschoene - Initial contribution
+ */
+@JsonInclude(JsonInclude.Include.NON_DEFAULT)
+public class NormalizationHelperData {
+  public List<SimpleColumnDefinition> sourceColumns;
+  public List<SimpleColumnDefinition> inputColumns;
+  public List<SimpleColumnDefinition> outputColumns;
+  public double inputLow;
+  public double inputHigh;
+  public double outputLow;
+  public double outputHigh;
+  public List<String> unknownValues = new ArrayList<>();
+  public Map<String, Double> missingToMean = new HashMap<>();
+
+  public static NormalizationHelperData loadFrom(URL location) throws IOException {
+    return new ObjectMapper().readValue(location, NormalizationHelperData.class);
+  }
+}

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner_backup/data/LearnerSettingsColumnDefinition.java → feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner_backup/data/SimpleColumnDefinition.java

@@ -3,13 +3,23 @@ package de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data;
 import com.fasterxml.jackson.annotation.JsonInclude;
 import org.encog.ml.data.versatile.columns.ColumnType;
 
+import java.util.ArrayList;
+import java.util.List;
+
 /**
- * Simple representation of a {@link org.encog.ml.data.versatile.columns.ColumnDefinition}.
+ * Simple, serializable representation of a {@link org.encog.ml.data.versatile.columns.ColumnDefinition}.
  *
  * @author rschoene - Initial contribution
  */
 @JsonInclude(JsonInclude.Include.NON_DEFAULT)
-public class LearnerSettingsColumnDefinition {
+public class SimpleColumnDefinition {
   public String name;
   public ColumnType type;
+  public int count = -1;
+  public int index = -1;
+  public double low = Double.NaN;
+  public double high = Double.NaN;
+  public double mean = Double.NaN;
+  public double sd = Double.NaN;
+  public List<String> classes = new ArrayList<>();
 }

feedbackloop.learner_backup/src/main/resources/activity_data.csv

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feedbackloop.learner_backup/src/main/resources/activity_network.eg

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\ No newline at end of file

feedbackloop.learner_backup/src/main/resources/learner_activity_phone_and_watch.json

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+}

feedbackloop.learner_backup/src/main/resources/learner_preferences_brightness_iris.json

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+}

feedbackloop.learner_backup/src/main/resources/loaded_learner_activity_phone_and_watch.json

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