Merge branch 'dev' of git-st.inf.tu-dresden.de:OpenLicht/eraser into dev

eraser-base/src/main/jastadd/DecisionTree.jrag

@@ -3,21 +3,21 @@ aspect DecisionTree {
   // let DecisionTreeLeaf implement Leaf
   public class DecisionTreeLeaf implements Leaf { }
 
-  //--- classify ---
-  syn DecisionTreeLeaf DecisionTreeRoot.classify() {
-    return getRootRule().classify();
+  //--- internalClassify ---
+  syn DecisionTreeLeaf DecisionTreeRoot.internalClassify() {
+    return getRootRule().internalClassify();
   }
 
-  syn DecisionTreeLeaf DecisionTreeElement.classify();
+  syn DecisionTreeLeaf DecisionTreeElement.internalClassify();
 
-  syn DecisionTreeLeaf DecisionTreeRule.classify();
+  syn DecisionTreeLeaf DecisionTreeRule.internalClassify();
 
-  syn DecisionTreeLeaf ItemStateCheckRule.classify() {
+  syn DecisionTreeLeaf ItemStateCheckRule.internalClassify() {
     boolean chooseLeft = getItemStateCheck().holds();
-    return (chooseLeft ? getLeft() : getRight()).classify();
+    return (chooseLeft ? getLeft() : getRight()).internalClassify();
   }
 
-  syn DecisionTreeLeaf DecisionTreeLeaf.classify() = this;
+  syn DecisionTreeLeaf DecisionTreeLeaf.internalClassify() = this;
 
   //--- holds ---
   syn boolean ItemStateCheck.holds() = holdsFor(getItem());
@@ -47,19 +47,19 @@ aspect DecisionTree {
   }
 
   //--- computePreferences ---
-  syn List<ItemPreference> DecisionTreeLeaf.computePreferences() {
+  syn MachineLearningResult DecisionTreeLeaf.computePreferences() {
     // iterate over preference of this leaf, and all its parents and ancestors
-    List<ItemPreference> result = new ArrayList<>();
+    MachineLearningResult result = new MachineLearningResult();
     Set<Item> seenItems = new HashSet<>();
     List<DecisionTreeElement> ancestors = ancestors();
     for (ItemPreference pref : getPreferenceList()) {
-      result.add(pref);
+      result.addItemPreference(pref);
       seenItems.add(pref.getItem());
     }
     for (DecisionTreeElement ancestor : ancestors) {
       for (ItemPreference pref : ancestor.getPreferenceList()) {
         if (!seenItems.contains(pref.getItem())) {
-          result.add(pref);
+          result.addItemPreference(pref);
           seenItems.add(pref.getItem());
         }
       }

eraser-base/src/main/jastadd/Item.jrag

@@ -401,7 +401,6 @@ aspect ItemHandling {
   //--- ItemPreference.apply ---
   public abstract void ItemPreference.apply();
   public void ItemPreferenceColor.apply() {
-    logger.debug("Apply color preference {} -> {}", getItem().getID(), getPreferredHSB());
     getItem().setStateFromColor(getPreferredHSB());
     getItem().freeze();
     for (Item controller : getItem().getControlledByList()) {
@@ -409,8 +408,8 @@ aspect ItemHandling {
     }
     getItem().unfreeze();
   }
+  //--- ItemPreference.apply ---
   public void ItemPreferenceDouble.apply() {
-    logger.debug("Apply double preference {} -> {}", getItem().getID(), getPreferredValue());
     getItem().setStateFromDouble(getPreferredValue());
     getItem().freeze();
     for (Item controller : getItem().getControlledByList()) {
@@ -419,6 +418,14 @@ aspect ItemHandling {
     getItem().unfreeze();
   }
 
+  //--- ItemPreference.describe ---
+  syn String ItemPreference.describe() = getItem().getID() + " -> " + getNewStateAsString();
+
+  //--- ItemPreference.getNewStateAsString ---
+  syn String ItemPreference.getNewStateAsString();
+  eq ItemPreferenceColor.getNewStateAsString() = getPreferredHSB().toString();
+  eq ItemPreferenceDouble.getNewStateAsString() = Double.toString(getPreferredValue());
+
 //  // override Item.init$Children from JastAdd's own ASTNode aspect
 //  refine ASTNode public void Item.init$Children() {
 //    refined();

eraser-base/src/main/jastadd/MachineLearning.jrag

@@ -8,10 +8,10 @@ aspect MachineLearning {
   public interface Leaf {
     String getLabel();
     int getActivityIdentifier();
-    List<ItemPreference> computePreferences();
+    MachineLearningResult computePreferences();
   }
 
-  syn Leaf InternalMachineLearningModel.classify();
+  syn Leaf InternalMachineLearningModel.internalClassify();
 
   //--- currentActivityName ---
   syn String Root.currentActivityName() = JavaUtils.ifPresentOrElseReturn(
@@ -31,8 +31,8 @@ aspect MachineLearning {
     return (int) ((ItemPreferenceDouble) preferences.get(0)).getPreferredValue();
   }
 
-  //--- currentPreferences ---
-  syn List<ItemPreference> Root.currentPreferences() = getMachineLearningRoot().getPreferenceLearning().getDecoder().classify().getPreferences();
+//  //--- currentPreferences ---
+//  syn List<ItemPreference> Root.currentPreferences() = getMachineLearningRoot().getPreferenceLearning().getDecoder().classify().getItemPreferences();
 
   //--- canSetActivity ---
   syn boolean MachineLearningModel.canSetActivity() = false;
@@ -45,7 +45,7 @@ aspect MachineLearning {
   }
 
   //--- DummyMachineLearningModel.classify ---
-  eq DummyMachineLearningModel.classify() {
+  eq DummyMachineLearningModel.internalClassify() {
     if (logger.isInfoEnabled() && getItemList().size() > 0) {
       logger.info("Dummy classification of {}, values of connected items: {}",
           mlKind(),
@@ -125,6 +125,12 @@ aspect MachineLearning {
   private MachineLearningEncoder ExternalMachineLearningModel.encoder;
   public void ExternalMachineLearningModel.setEncoder(MachineLearningEncoder encoder) {
     this.encoder = encoder;
+    for (Item relevantItem : encoder.getRelevantItems()) {
+      addRelevantItem(relevantItem);
+    }
+    for (Item targetItem : encoder.getTargets()) {
+      addTargetItem(targetItem);
+    }
   }
   private MachineLearningDecoder ExternalMachineLearningModel.decoder;
   public void ExternalMachineLearningModel.setDecoder(MachineLearningDecoder decoder) {
@@ -161,6 +167,43 @@ aspect MachineLearning {
     return this.decoder;
   }
 
+  //--- classify ---
+  public MachineLearningResult MachineLearningModel.classify() {
+    MachineLearningResult result = getDecoder().classify();
+    setLastPreference(result);
+    return result;
+  }
+
+  //--- equals ---
+  public boolean MachineLearningResult.equals(Object other) {
+    if (!(other instanceof MachineLearningResult)) {
+      return false;
+    }
+    MachineLearningResult otherResult = (MachineLearningResult) other;
+    if (getNumItemPreference() != otherResult.getNumItemPreference()) {
+      return false;
+    }
+    for (int i = 0; i < getNumItemPreference(); i++) {
+      if (!getItemPreference(i).equals(otherResult.getItemPreference(i))) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  public abstract boolean ItemPreference.equals(Object other);
+  public boolean ItemPreferenceDouble.equals(Object other) {
+    if (!(other instanceof ItemPreferenceDouble)) {
+      return false;
+    }
+    return getItem() == ((ItemPreferenceDouble) other).getItem() && getPreferredValue() == ((ItemPreferenceDouble) other).getPreferredValue();
+  }
+  public boolean ItemPreferenceColor.equals(Object other) {
+    if (!(other instanceof ItemPreferenceColor)) {
+      return false;
+    }
+    return getItem() == ((ItemPreferenceColor) other).getItem() && getPreferredHSB() == ((ItemPreferenceColor) other).getPreferredHSB();
+  }
 }
 
 aspect ChangeEvents {
@@ -171,19 +214,30 @@ aspect ChangeEvents {
     RecognitionEvent result = new RecognitionEvent();
     result.initChangeEvent();
     for (Item relevantItem : modelOfRecognition.getRelevantItems()) {
-      result.addChangedItem(ChangedItem.newFromItem(relevantItem));
+      result.addRelevantItem(ChangedItem.newFrom(relevantItem));
+    }
+    for (ItemPreference preference : modelOfRecognition.getLastPreference().getItemPreferences()) {
+      result.addChangedItem(ChangedItem.newFrom(preference));
     }
     return result;
   }
 
   //--- newFrom Item ---
-  public static ChangedItem ChangedItem.newFromItem(Item source) {
+  public static ChangedItem ChangedItem.newFrom(Item source) {
     ChangedItem result = new ChangedItem();
     result.setItem(source);
     result.setNewStateAsString(source.getStateAsString());
     return result;
   }
 
+  //--- newFrom ItemPreference ---
+  public static ChangedItem ChangedItem.newFrom(ItemPreference update) {
+    ChangedItem result = new ChangedItem();
+    result.setItem(update.getItem());
+    result.setNewStateAsString(update.getNewStateAsString());
+    return result;
+  }
+
   //--- initChangeEvent ---
   protected void ChangeEvent.initChangeEvent() {
     this.setCreated(Instant.now());

eraser-base/src/main/jastadd/MachineLearning.relast

@@ -10,10 +10,11 @@ rel ChangedItem.Item -> Item ;
 
 RecognitionEvent : ChangeEvent ;
 rel RecognitionEvent.Activity -> Activity ;
+rel RecognitionEvent.RelevantItem* -> ChangedItem ;
 
 ManualChangeEvent : ChangeEvent ;
 
-abstract MachineLearningModel ::= ;
+abstract MachineLearningModel ::= LastPreference:MachineLearningResult ;
 rel MachineLearningModel.RelevantItem* <-> Item.RelevantInMachineLearningModel* ;
 rel MachineLearningModel.TargetItem* <-> Item.TargetInMachineLearningModel* ;
 
@@ -21,6 +22,8 @@ ExternalMachineLearningModel : MachineLearningModel ;
 
 abstract InternalMachineLearningModel : MachineLearningModel ::= <OutputApplication:DoubleDoubleFunction> ;
 
+MachineLearningResult ::= ItemPreference* ;
+
 abstract ItemPreference ::= ;
 rel ItemPreference.Item -> Item ;
 

eraser-base/src/main/jastadd/NeuralNetwork.jrag

@@ -18,17 +18,19 @@ aspect NeuralNetwork {
     public int getActivityIdentifier() {
       return (int) number;
     }
-    public List<ItemPreference> computePreferences() {
-      return Collections.singletonList(new ItemPreferenceDouble(affectedItem, number));
+    public MachineLearningResult computePreferences() {
+      MachineLearningResult result = new MachineLearningResult();
+      result.addItemPreference(new ItemPreferenceDouble(affectedItem, number));
+      return result;
     }
   }
 
-  //--- classify ---
-  syn DoubleNumber NeuralNetworkRoot.classify() {
-    return getOutputLayer().classify();
+  //--- internalClassify ---
+  syn DoubleNumber NeuralNetworkRoot.internalClassify() {
+    return getOutputLayer().internalClassify();
   }
 
-  syn DoubleNumber OutputLayer.classify() {
+  syn DoubleNumber OutputLayer.internalClassify() {
     double[] inputs = new double[getNumOutputNeuron()];
     for (int i = 0; i < getNumOutputNeuron(); ++i) {
       OutputNeuron n = getOutputNeuron(i);

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

@@ -48,8 +48,7 @@ public class InternalMachineLearningHandler implements MachineLearningEncoder, M
 
   @Override
   public MachineLearningResult classify() {
-    List<ItemPreference> preferences = model.classify().computePreferences();
-    return new InternalMachineLearningResult(preferences);
+    return model.internalClassify().computePreferences();
   }
 
   @Override

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

-package de.tudresden.inf.st.eraser.jastadd.model;
-
-import java.util.List;
-
-/**
- * Result of a classification returned by an internally held machine learning model.
- *
- * @author rschoene - Initial contribution
- */
-public class InternalMachineLearningResult implements MachineLearningResult {
-  private final List<ItemPreference> preferences;
-
-  InternalMachineLearningResult(List<ItemPreference> preferences) {
-    this.preferences = preferences;
-  }
-
-  @Override
-  public List<ItemPreference> getPreferences() {
-    return this.preferences;
-  }
-}

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

-package de.tudresden.inf.st.eraser.jastadd.model;
-
-import de.tudresden.inf.st.eraser.jastadd.model.ItemPreference;
-
-import java.util.List;
-
-/**
- * Representation of a classification result using a MachineLearningModel.
- *
- * @author rschoene - Initial contribution
- */
-@SuppressWarnings("unused")
-public interface MachineLearningResult {
-
-  // Object rawClass();
-
-  // double rawConfidence();
-
-  // can be used for both activity and preferences
-  /**
-   * Get the result as a list of item preferences, i.e., new states to be set for those items.
-   * @return the classification result as item preferences
-   */
-  List<ItemPreference> getPreferences();
-}

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

@@ -103,8 +103,7 @@ public class MqttReceiver implements AutoCloseable {
     mqtt.setHost(this.host);
     connection = mqtt.callbackConnection();
     AtomicReference<Throwable> error = new AtomicReference<>();
-    connection.listener(new Listener() {
-      @Override
+    connection.listener(new ExtendedListener() {
       public void onConnected() {
         logger.debug("Connected");
       }
@@ -115,12 +114,17 @@ public class MqttReceiver implements AutoCloseable {
       }
 
       @Override
-      public void onPublish(UTF8Buffer topicBuffer, Buffer body, Runnable ack) {
-        String topicString = topicBuffer.toString();
+      public void onPublish(UTF8Buffer topic, Buffer body, Callback<Callback<Void>> ack) {
+        String topicString = topic.toString();
         String message = body.ascii().toString();
 //        logger.debug("{}: {}", topicString, message);
         onMessageCallback.accept(topicString, message);
-        ack.run();  // always acknowledge message
+        ack.onSuccess(null);  // always acknowledge message
+      }
+
+      @Override
+      public void onPublish(UTF8Buffer topicBuffer, Buffer body, Runnable ack) {
+        logger.warn("onPublish should not be called");
       }
 
       @Override

eraser-base/src/test/java/de/tudresden/inf/st/eraser/DecisionTreeTest.java

@@ -25,17 +25,17 @@ public class DecisionTreeTest {
     dtroot.setRootRule(newRule(isLessThanFour, isFourOrGreater, "check item1", check));
 
     // current value is four, so return value should be "four or greater"
-    Leaf leaf = dtroot.classify();
+    Leaf leaf = dtroot.internalClassify();
     Assert.assertEquals(isFourOrGreater, leaf);
 
     // change value to 5, so return value should still be "four or greater"
     mai.item.setState(5);
-    leaf = dtroot.classify();
+    leaf = dtroot.internalClassify();
     Assert.assertEquals(isFourOrGreater, leaf);
 
     // change value to 2, so return value should now be "less than four"
     mai.item.setState(2);
-    leaf = dtroot.classify();
+    leaf = dtroot.internalClassify();
     Assert.assertEquals(isLessThanFour, leaf);
   }
 
@@ -64,17 +64,17 @@ public class DecisionTreeTest {
     dtroot.setRootRule(newRule(rule25, rule75, "50-item1", check50));
 
     // current value is 20, so return value should be "less than 25"
-    Leaf leaf = dtroot.classify();
+    Leaf leaf = dtroot.internalClassify();
     Assert.assertEquals(isLessThan25, leaf);
 
     // change value to 25, so return value should still be "25 or greater"
     mai.item.setState(25);
-    leaf = dtroot.classify();
+    leaf = dtroot.internalClassify();
     Assert.assertEquals(is25OrGreater, leaf);
 
     // change value to 100, so return value should now be "greater than 75"
     mai.item.setState(100);
-    leaf = dtroot.classify();
+    leaf = dtroot.internalClassify();
     Assert.assertEquals(is75OrGreater, leaf);
   }
 
@@ -139,7 +139,7 @@ public class DecisionTreeTest {
 
     for (TestResult result : testResults) {
       mai.item.setState(Math.round(result.value));
-      Leaf leaf = dtroot.classify();
+      Leaf leaf = dtroot.internalClassify();
       Assert.assertEquals(result.chooseLeft ? leaf : right, leaf);
     }
   }

eraser-base/src/test/java/de/tudresden/inf/st/eraser/NeuralNetworkTest.java

@@ -50,17 +50,17 @@ public class NeuralNetworkTest {
     neuralNetworkRoot.setOutputLayer(outputLayer);
 
     // Current value is 1, so return value is 1 * 4 * 0.6 = 2.4, 1 * 4 * 0.4 = 1.6, both are less than 4. so 0.
-    Leaf leaf = neuralNetworkRoot.classify();
+    Leaf leaf = neuralNetworkRoot.internalClassify();
     assertLeafEqual(0, leaf);
 
     // Current value is 2, so return value is 2 * 4 * 0.6 = 4.8, 2 * 4 * 0.4 = 3.2, first is less than 4. so 1.
     mai.item.setState(2);
-    leaf = neuralNetworkRoot.classify();
+    leaf = neuralNetworkRoot.internalClassify();
     assertLeafEqual(1, leaf);
 
     // Current value is 5, so return value is 5 * 4 * 0.6 = 12, 5 * 4 * 0.4 = 8, both are greater than 4. so 3.
     mai.item.setState(5);
-    leaf = neuralNetworkRoot.classify();
+    leaf = neuralNetworkRoot.internalClassify();
     assertLeafEqual(3, leaf);
   }
 

eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java

 package de.tudresden.inf.st.eraser.starter;
 
 import beaver.Parser;
+import com.fasterxml.jackson.databind.DeserializationFeature;
 import com.fasterxml.jackson.databind.ObjectMapper;
 import com.fasterxml.jackson.dataformat.yaml.YAMLFactory;
 import de.tudresden.inf.st.eraser.feedbackloop.analyze.AnalyzeImpl;
@@ -68,6 +69,7 @@ public class EraserStarter {
       System.exit(1);
     }
     ObjectMapper mapper = new ObjectMapper(new YAMLFactory());
+    mapper.disable(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES);
     File settingsFile = new File(commandLineOptions.configFile);
     Setting settings;
     try {
@@ -127,52 +129,6 @@ public class EraserStarter {
       machineLearningModel.setEncoder(handler);
       machineLearningModel.setDecoder(handler);
       root.getMachineLearningRoot().setActivityRecognition(machineLearningModel);
-      /**
-      //Begin the Integration
-      Item item1=model.resolveItem("m_accel_x").get();
-      Item item2=model.resolveItem("m_accel_y").get();
-      Item item3=model.resolveItem("m_accel_z").get();
-      Item item4=model.resolveItem("m_rotation_x").get();
-      Item item5=model.resolveItem("m_rotation_y").get();
-      Item item6=model.resolveItem("m_rotation_z").get();
-      Item item7=model.resolveItem("w_accel_x").get();
-      Item item8=model.resolveItem("w_accel_y").get();
-      Item item9=model.resolveItem("w_accel_z").get();
-      Item item10=model.resolveItem("w_rotation_x").get();
-      Item item11=model.resolveItem("w_rotation_y").get();
-      Item item12=model.resolveItem("w_rotation_z").get();
-      //0.2717419,8.698134,4.471172,0.043741,0.515962,0.854318,1.8818425,4.9320555,8.145074,0.2374878,-0.032836914,0.3381958,working
-      item1.setStateFromString("0.2717419");
-      item2.setStateFromString("8.698134");
-      item3.setStateFromString("4.471172");
-      item4.setStateFromString("0.043741");
-      item5.setStateFromString("0.515962");
-      item6.setStateFromString("0.854318");
-      item7.setStateFromString("1.8818425");
-      item8.setStateFromString("4.9320555");
-      item9.setStateFromString("8.145074");
-      item10.setStateFromString("0.2374878");
-      item11.setStateFromString("-0.032836914");
-      item12.setStateFromString("0.3381958");
-
-      ArrayList<Item> newData=new ArrayList<>();
-      newData.add(item1);
-      newData.add(item2);
-      newData.add(item3);
-      newData.add(item4);
-      newData.add(item5);
-      newData.add(item6);
-      newData.add(item7);
-      newData.add(item8);
-      newData.add(item9);
-      newData.add(item10);
-      newData.add(item11);
-      newData.add(item12);
-      handler.newData(newData);*/
-      List<ItemPreference> preference=handler.classify().getPreferences();
-      for(ItemPreference preference1 : preference){
-        preference1.apply();
-      }
     }
 
     // initialize preference learning
@@ -188,23 +144,9 @@ public class EraserStarter {
       machineLearningModel.setEncoder(handler);
       machineLearningModel.setDecoder(handler);
       root.getMachineLearningRoot().setPreferenceLearning(machineLearningModel);
-      //working,medium,240,70
-      /**Item activity_item = model.resolveItem("activity").get();
-      String activity=activity_item.getStateAsString();
-      activity_item.setStateFromString(activity);
-      Item brightness_item = model.resolveItem("w_brightness").get();
-      brightness_item.setStateFromString("medium");
-      ArrayList<Item> newData1 = new ArrayList<>();
-      newData1.add(activity_item);
-      newData1.add(brightness_item);
-      handler.newData(newData1);*/
-      List<ItemPreference> preference=handler.classify().getPreferences();
-      for(ItemPreference preference1 : preference){
-        preference1.apply();
-      }
     }
 
-    machineLearningRoot.getPreferenceLearning().connectItems(settings.preference.items);
+//    machineLearningRoot.getPreferenceLearning().connectItems(settings.preference.items);
     if (!machineLearningRoot.getActivityRecognition().check()) {
       logger.fatal("Invalid activity recognition!");
       System.exit(1);

eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/Setting.java

@@ -6,7 +6,6 @@ import org.apache.logging.log4j.Logger;
 import java.net.MalformedURLException;
 import java.net.URL;
 import java.nio.file.Paths;
-import java.util.List;
 
 /**
  * Setting bean.
@@ -47,10 +46,10 @@ class Setting {
     public boolean dummy = false;
     /** Model id. Default: 1.*/
     public int id = 1;
-    /** Items to connect to inputs */
-    public List<String> items;
-    /** Item to change with classification result */
-    public String affectedItem;
+//    /** Items to connect to inputs */
+//    public List<String> items;
+//    /** Item to change with classification result */
+//    public String affectedItem;
   }
   public class OpenHabContainer {
     /** The URL from which to import and at which openHAB is running */

eraser.starter/starter-setting.yaml

@@ -31,8 +31,8 @@ activity:
   dummy: false
   # Model id. Default: 1.
   id: 1
-  # Item to change with classification result
-  affectedItem: activity
+#  # Item to change with classification result
+#  affectedItem: activity
 
 # Model for preference learning. If dummy is true, then the file parameter is ignored.
 preference:
@@ -43,12 +43,12 @@ preference:
   dummy: false
   # Model id. Default: 1.
   id: 1
-  # Items to connect to inputs
-  items:
-    - activity
-    - w_brightness
-  # Item to change with classification result
-  affectedItem: iris1_item
+#  # Items to connect to inputs
+#  items:
+#    - activity
+#    - w_brightness
+#  # Item to change with classification result
+#  affectedItem: iris1_item
 
 # Initialize the knowledge base by importing data from openHAB.
 openhab:

feedbackloop.analyze/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/analyze/AnalyzeImpl.java

@@ -3,6 +3,8 @@ package de.tudresden.inf.st.eraser.feedbackloop.analyze;
 import de.tudresden.inf.st.eraser.feedbackloop.api.Analyze;
 import de.tudresden.inf.st.eraser.feedbackloop.api.Plan;
 import de.tudresden.inf.st.eraser.jastadd.model.Activity;
+import de.tudresden.inf.st.eraser.jastadd.model.ItemPreference;
+import de.tudresden.inf.st.eraser.jastadd.model.MachineLearningResult;
 import de.tudresden.inf.st.eraser.jastadd.model.Root;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
@@ -17,10 +19,12 @@ public class AnalyzeImpl implements Analyze {
   private Root knowledgeBase;
   private Plan plan;
   private Activity mostRecentActivity;
+  private MachineLearningResult mostRecentPreferences;
   private Logger logger = LogManager.getLogger(AnalyzeImpl.class);
 
   public AnalyzeImpl() {
     this.mostRecentActivity = null;
+    this.mostRecentPreferences = null;
   }
 
   @Override
@@ -40,17 +44,32 @@ public class AnalyzeImpl implements Analyze {
 
   @Override
   public void analyzeLatestChanges() {
+    MachineLearningResult recognitionResult = knowledgeBase.getMachineLearningRoot().getActivityRecognition().getDecoder().classify();
+    recognitionResult.getItemPreferences().forEach(ItemPreference::apply);
     knowledgeBase.currentActivity().ifPresent(activity -> {
+      MachineLearningResult newMLResult = knowledgeBase.getMachineLearningRoot().getPreferenceLearning().classify();
+      // check if activity has changed
       if (!activity.equals(mostRecentActivity)) {
         // new! inform plan!
         logger.info("Found new activity '{}'", activity.getLabel());
-        mostRecentActivity = activity;
+        try {
+          informPlan(activity);
+        } catch (Exception e) {
+          logger.catching(e);
+        }
+      } else {
+        // if no change, also check, if preferences have changed
+        if (!newMLResult.equals(mostRecentPreferences)) {
+          logger.info("Preferences have changed for same activity '{}'", activity.getLabel());
           try {
             informPlan(activity);
           } catch (Exception e) {
             logger.catching(e);
           }
         }
+      }
+      mostRecentActivity = activity;
+      mostRecentPreferences = newMLResult;
     });
   }
 }

feedbackloop.api/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/api/Execute.java

@@ -22,7 +22,7 @@ public interface Execute {
   void setKnowledgeBase(Root knowledgeBase);
 
   /**
-   * <b>Deprecated</b>: Use {@link #updateItems(List)} instead.
+   * <b>Deprecated</b>: Use {@link #updateItems(Iterable)} instead.
    * @param brightnessAndRgbForItems Map, keys are item names, values are RGB and brightness values
    */
   @Deprecated
@@ -32,5 +32,5 @@ public interface Execute {
    * Updates items according to given preferences
    * @param preferences tuples containing item and its new HSB value
    */
-  void updateItems(List<ItemPreference> preferences);
+  void updateItems(Iterable<ItemPreference> preferences);
 }

feedbackloop.api/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/api/Plan.java

@@ -21,7 +21,7 @@ public interface Plan {
 
   void planToMatchPreferences(Activity activity);
 
-  default void informExecute(List<ItemPreference> preferences) {
+  default void informExecute(Iterable<ItemPreference> preferences) {
     getExecute().updateItems(preferences);
   }
 }

feedbackloop.execute/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/execute/ExecuteImpl.java

@@ -54,7 +54,7 @@ public class ExecuteImpl implements Execute {
   }
 
   @Override
-  public void updateItems(List<ItemPreference> preferences) {
+  public void updateItems(Iterable<ItemPreference> preferences) {
     for (ItemPreference preference : preferences) {
       preference.apply();
     }

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

@@ -226,7 +226,7 @@ public class Main {
 
 		List<String> output = new ArrayList<>();
 		Function<DoubleNumber, String> leafToString = classification -> Double.toString(classification.number);
-		Function<NeuralNetworkRoot, DoubleNumber> classify = NeuralNetworkRoot::classify;
+		Function<NeuralNetworkRoot, DoubleNumber> classify = NeuralNetworkRoot::internalClassify;
 		DoubleNumber classification = classify.apply(nn);
 		output.add(leafToString.apply(classification));
 		System.out.println(output);

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

@@ -160,21 +160,27 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
 
   @Override
   public MachineLearningResult classify() {
+    MachineLearningResult result = new MachineLearningResult();
     switch (this.goal) {
       case GOAL_ACTIVITY_PHONE_AND_WATCH:
         String activityStringValue = activity_result;
+        if (activityStringValue == null) {
+          return result;
+        }
         Item activityItem = resolve(this.root.getSmartHomeEntityModel(), "activity");
         //activityItem.setStateFromString(activityStringValue);
         // FIXME how to translate activityStringValue to a number? or should activity item state better be a String?
-        for (int i=0; i< activites.length;i++){
-          if(activites[i].equals(activityStringValue)){
-            activityItem.setStateFromString(String.valueOf(i));
-            ItemPreference classifiedActivity = new ItemPreferenceDouble(activityItem,i);
-            return new MachineLearningResultImpl(classifiedActivity);
+        ItemPreference classifiedActivity = null;
+        for (Activity activity : this.root.getMachineLearningRoot().getActivityList()) {
+          if (activity.getLabel().equals(activityStringValue)) {
+            classifiedActivity = new ItemPreferenceDouble(activityItem, activity.getIdentifier());
           }
           logger.debug("Classify would return activity: {}", activityStringValue);
         }
-
+        if (classifiedActivity != null) {
+          result.addItemPreference(classifiedActivity);
+        }
+        return result;
       case GOAL_PREFERENCE_BRIGHTNESS_IRIS:
 //        String[] preference = {result[1], result[2]};
         // FIXME what is the meaning of result[1] and result[2]
@@ -185,11 +191,11 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
           color = Math.round(Float.valueOf(preference_result[0]));
           brightness = Math.round(Float.valueOf(preference_result[1]));
         }
-        ItemPreference classifiedPreference = new ItemPreferenceColor(iris1, TupleHSB.of(color, 100, brightness));
-        return new MachineLearningResultImpl(classifiedPreference);
+        result.addItemPreference(new ItemPreferenceColor(iris1, TupleHSB.of(color, 100, brightness)));
+        return result;
       default:
         logger.error("Unknown goal value ({}) set in classify", this.goal);
-        return new EmptyMachineLearningResult();
+        return result;
     }
   }
 
@@ -202,12 +208,4 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
     learner.preference_train(filenameOfCsv);
   }
 
-  class EmptyMachineLearningResult implements MachineLearningResult {
-
-    @Override
-    public List<ItemPreference> getPreferences() {
-      return Collections.emptyList();
-    }
-  }
-
 }